essay on examination system

Essay on Examination System

Students are often asked to write an essay on Examination System in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

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100 Words Essay on Examination System

Introduction.

Examinations are a crucial part of our education system. They evaluate students’ understanding of subjects, shaping their future.

Role of Examinations

Exams test students’ knowledge and skills. They promote competition, encouraging students to work harder.

Types of Examination

Exams are of many types: oral, written, practical. Each has its own significance in assessing a student’s capabilities.

Drawbacks of Examinations

Exams can cause stress and anxiety among students. They often promote rote learning over understanding.

While exams have their drawbacks, they are still an essential tool for measuring student’s academic performance.

Also check:

Advantages and Disadvantages of Examination System

250 Words Essay on Examination System

The examination system is an integral part of the education system, serving as a yardstick to gauge a learner’s understanding of the subjects taught. It is a time-tested method that has been used for centuries to measure students’ intellectual capabilities.

The Purpose of Examinations

Examinations fulfill several purposes. They provide a standard measure to assess students’ knowledge, skills, and understanding of various subjects. Examinations also promote a competitive spirit among students, encouraging them to work harder and perform better.

Criticism of the Examination System

Despite its significance, the examination system has been criticized for its rigid structure and its inability to assess a student’s overall development. Many argue that it encourages rote learning rather than critical thinking and problem-solving skills. Moreover, the high-stakes nature of examinations can cause extreme stress and anxiety among students.

Reforming the Examination System

To address these issues, there is a growing call for reforms in the examination system. These reforms could include a shift from summative to formative assessments, which focus on continuous evaluation and feedback rather than a single high-stakes exam. Incorporating project-based assessments, group work, and presentations could also help evaluate a student’s creativity, teamwork, and communication skills.

In conclusion, while the examination system has its drawbacks, it is an essential tool for assessing students’ academic progress. However, it is imperative to continually refine and adapt this system to ensure it accurately reflects a student’s overall abilities and potential.

500 Words Essay on Examination System

The examination system: a critical evaluation.

The examination system, a cornerstone of conventional education, has been a subject of intense debate among educators, students, and policymakers. This essay aims to critically evaluate the examination system, its merits, demerits, and possible alternatives.

Examinations are designed to assess a student’s understanding of a subject within a specific timeframe. They serve as a barometer for gauging academic proficiency, helping institutions to standardize their evaluation process. Examinations often determine a student’s progression through the education system, with high-stakes exams influencing university admissions and future career prospects.

Merits of the Examination System

The examination system has several advantages. Firstly, it offers an objective measure of a student’s grasp of a subject, reducing bias in evaluation. Secondly, it fosters a competitive environment, encouraging students to strive for excellence. Thirdly, examinations can instill discipline and time management skills, as students must organize their study schedules effectively to cover the syllabus.

Drawbacks of the Examination System

Despite its merits, the examination system has significant drawbacks. It often promotes rote learning, where students memorize facts without understanding the context or application. This approach can stifle creativity and critical thinking, essential skills in the modern world. Additionally, examinations can induce stress and anxiety, impacting students’ mental health. High-stakes exams may also lead to an inequitable education system, where students with access to resources and tutoring have an unfair advantage.

Alternatives to the Traditional Examination System

Given these criticisms, many educators advocate for alternatives to traditional examinations. Continuous assessment is one such method, where students are evaluated throughout the academic year based on assignments, projects, and participation. This approach promotes continuous learning and reduces the pressure associated with end-of-term exams.

Another alternative is the portfolio-based assessment, where students compile a portfolio of their work throughout the course. This method allows for a more holistic evaluation, considering creativity, problem-solving, and practical application of knowledge.

Conclusion: A Call for Reform

In conclusion, while the examination system has its merits, it is not without its flaws. The emphasis on rote learning, the stress associated with high-stakes exams, and the potential for inequity necessitate a reevaluation of this system. Alternatives such as continuous assessment and portfolio-based assessment offer promising avenues for a more holistic, equitable, and low-stress evaluation system. As we move forward, it is crucial to adapt our education system to cater to the diverse needs of students, fostering creativity, critical thinking, and a lifelong love for learning.

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Essay on Examination with Outline Quotations and tips

Best essay on examination system in pakistan for matric/inter in 1000-1500 words.

In this post, you will find an essay discussing the examination system in Pakistan, accompanied by quotations. It is intended for students studying in Matric, FFSC, and the 2nd year. Class 12 students can utilize this essay as a practice tool for their annual exams. Similarly, FSC students can write a similar essay titled “Essay on Examination with Outline Quotations and tips” or “Best Essay on Examination System in Pakistan.” If you require additional essays for the 2nd year, please visit our collection of Essays for FSC.

Essay on Examination Outline:

1. Introduction. Examinations are an integral part of our system of education but they are extremely faulty. 2. They fail to give an accurate assessment of a student. They do not test a student’s intelligence or ability. They test only his cramming power. Judging by the examination results, many of the great scientists were quite dull. 3. The element of chance in the examinations. The making of scripts is never uniform. Some examiners may be strict, others very lenient. 4. Examinations make the work round the year uneven. Students just study near the examinations and idle away their time during the rest of the year. This makes their work round the year very uneven. 5. This system adversely affects class teaching. A good teacher feels hampered by the limits imposed by the examination system. 6. Examinations are a necessary evil. If there were no examinations, students would not study at all. And it must be said to their credit that, in spite of all the drawbacks in them, a good student has not usually failed nor a third-rater topped. 7. The need for reforms. Instead of completely doing away with the system, we should try to introduce certain reforms: (a) Semester systems can make the work round the year more even. (b) Marking scripts should be made more accurate and uniform. (c) Examinations may be accompanied with viva voce, particularly in the case of marginal students. (d) Unfair means should be checked.

Examinations Essay for Matric/Inter in 1000-1500 Words

Introduction:.

Examinations have come to stay as a part of our education system. They are considered to be a big nuisance and both the teachers and the students detest them. It is really possible to discover a large number of defects in them; still in the absence of any other satisfactory system of evaluation, it is impracticable to abolish them. They are perhaps evil, yet they are indispensable.

Examinations are often condemned on account of the very prominent role of chance involved in them. The marking of the scripts can never be uniform. Even if we grant that all examiners are sincere and earnest – in fact, many of them are whimsical and willful – we can still not affirm that examinations are scientifically impartial to all examinees. The possibility of the personal prejudices of an examiner beclouding his better judgment cannot be excluded. If an average script follows three brilliant scripts, it will be awarded poor marks; if it follows two exceptionally poor ones, it will earn a better reward than it deserves. Mr. Shahid might be too strict. He will bewail the poor standards and make fascinating crisscross patterns on the scripts. Mrs. Nadia might be a bit too lenient. She would like to give every student a pass on humanitarian grounds. How far can the awards are given by these two examiners be accepted as a fair index of the relative ability of their examinees?

Under the prevailing system of examinations, the students enjoy a ten-month holiday and have a two-month working session. They merrily skip around and flirt their time away for the first ten months. Then, as the examinations approach, one can sniff a chill of seriousness in the air. The atmosphere starts getting heavy, the infection is gradually rife and the students start pouring over their books. They skim through their syllabi, just to get the hang of what they are about, manage to stuff their brains with some ill-digested facts temporarily, then forget all about them once the examinations are over. But these two months play havoc with their physique. The whole period is spent in extreme nervous tension. Shave and hair-cut, cosmetics, and coiffures are all forgotten. Chemists are pestered to procure pills causing sleeplessness. The erstwhile lotus-eater suddenly becomes a Ulysses. But to his utter dismay, he often discovers that he is no match for the giant that examination is and collapses with acute nervous exhaustion.

This system exercises an adverse effect on the class teaching in two ways. First, a good teacher always finds himself hampered by the limitations imposed by the examination system. He does not teach, he prepares the students for the examination. Secondly, a number of students, by virtue of having a good memory, get into a class where they do not deserve to be. Their lessons being beyond their comprehension, they feel bored in the class and create mischief. It is these students who pollute the atmosphere in the class and are responsible for the widespread indiscipline found in colleges. But even the devil must be given its due. It must be acknowledged that the examinations do compel students to study a little. Or they would not study even this much. Secondly, despite all the tricks played by chance, it is never noticed that a good student has failed or a third-rater has topped. Thus examinations may not be scientifically accurate or impartial, still, they do substantial justice.

Examiners are appointed en masse and they often do a bad job of the work entrusted to them. But then there is no better substitute for this system. We cannot abolish these examinations. All we can do is to improve upon them so that they cease to be a lottery indiscreetly doling out a few lacs with innumerable blanks. Related topics. The uses and abuses of examinations. Examinations are a necessary evil.

Essay on Examination Quotations

“The difference between a good and a poor student is result”. (ETC Wanyanwu)
“Prepare well! Take two inks; you may never know when one pen will stop writing!” (Ernest Agyemang Yeboah)
“Examinations were a great trial to me.”
“The roots of education are bitter, but the fruit is sweet”. (Aristotle)

Tips for Writing an Examination Essay:

Comprehend the topic: Begin by thoroughly understanding the essay prompt. Ensure a clear understanding of what the topic requires you to discuss.
Create an essay plan: Develop an outline or structure for your essay. This will help organize your thoughts and ensure a logical flow of ideas.
Engaging introduction: Start with an attention-grabbing introduction that provides background information on examinations and presents your thesis statement.
Develop strong arguments: Use body paragraphs to elaborate on your ideas and arguments. Each paragraph should focus on a specific point, supported by evidence and examples.
Utilize relevant examples: Incorporate real-life examples or personal experiences to illustrate your points and make your essay more relatable and persuasive.
Maintain focus: Stay on-topic and avoid straying from the main theme of examinations. Ensure each paragraph directly relates to the central subject.
Be concise and clear: Use clear and concise language to express your ideas. Avoid excessive jargon or complex vocabulary that may confuse the reader.
Summarize in the conclusion: Provide a summary of your main points and restate your thesis in the conclusion. Leave the reader with a sense of closure.
Proofread and revise: Take time to review your essay for grammar, spelling, and punctuation errors. Make necessary revisions to improve clarity and coherence.
Manage time effectively: During an examination, allocate sufficient time for planning, writing, and proofreading to ensure a well-structured and polished essay.

Conclusion for Essay on Examination

examinations play a significant role in assessing students’ knowledge and progress. While they can be challenging, it is important to approach them as opportunities for growth. However, it is crucial to remember that exams should not be the sole determinant of a student’s abilities or potential. A balanced assessment approach, incorporating various evaluation methods throughout the year, provides a more comprehensive understanding of students’ skills. Additionally, it is essential to prioritize a holistic education that promotes critical thinking and problem-solving. By fostering a love for learning and focusing on overall development, examinations can serve as a means to equip students with essential skills for their future endeavors. Ultimately, the aim should be to create an educational environment that encourages continuous improvement and prepares students to become well-rounded individuals capable of making meaningful contributions to society.

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Essay on Our Examination System in Pakistan with Quotations

This post contains a sample of an Essay on Our Examination system in Pakistan with Quotations for the students of FSC, 2nd year. Students of Class 12 can prepare this essay only as practice for annual exams. Our examination system essay has been taken from Sunshine English (Comprehensive-II) and I have added some appropriate quotations in it. Students of FSC can write the same essay under the title, Essay on Our Examination System with Quotes, Examination System of Pakistan essay with quotations and Examination System in Pakistan essay. If you need more essays for 2nd year you can visit Essays for FSC .

Examination System of Pakistan Essay with Quotations for FSC, 2nd Year Students

Education makes life worth living. A country without a proper, system of education can make no progress. Only the educated and skilled people can pave the way to progress. For good education, an adequate system of examination is necessary. Unfortunately, our system of examination is not satisfactory. It is replete with faults. The current system of examination is such as tests the memory of the students. It is not a fair test of knowledge, understanding and comprehension. The questions asked in the examination are not such as may promote the inborn faculties of students. The usual practice among the students is to learn everything by heart without a complete comprehension of it. This is practised in the present system of examination. though the students pass the examination in this way, they cannot develop their awareness greatly.

Education is a responsibility on every Muslim, male or female. (Hazrat Muhammad صلی اللہ علیہ وسلم)

Furthermore, the method of evaluation of scripts is faulty. Every examiner has his particular outlook, temperament and a specified way of observation. Even the atmosphere affects the mood of examiners. As a result, the assessment is somewhat personal and therefore inaccurate. Moreover, the questions asked in the examination are subjective; they cannot be awarded exact marks.

In our country, the climate is of the extreme kind. The examinations take place usually in the hot summer. The scorching heat leads to several troubles. The students cannot concentrate fully on their studies. They have to go to the examination hall in the scorching heat of summer. Moreover, our academic terms are short because of extreme weather conditions. Consequently, students cannot read their course fully. This also adds to the difficulties of the students in the examination.

“The roots of education are bitter, but the fruit is sweet” . (Aristotle)

I have some suitable suggestions for the improvement of our examination system.

First, the examinations should be held in the spring season so that the students may work in the pleasant weather conditions. This will enable the students to focus their attention on their studies. This would also increase the length of the academic year.

“The difference between a good and a poor student is result”. ( ETC Wanyanwu)

Second, the questions papers should be set in such a way as they should be the real test of the student’s knowledge and insight.

Third, the declaration of result should be prompt and quick. This will save the time of the students. Even the length of the academic term would be increased.

Fourth, the students securing outstanding positions should be awarded prizes. They should be honoured with scholarship. This will increase the passion for study among the students. I hope that the government would take prompt steps in this regard.

“Prepare well! Take two inks; you may never know when one pen will stop writing!” ( Ernest Agyemang Yeboah)

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Essay on Our Examination System in Pakistan

January 17, 2024

Kainat Shakeel

Examinations, a foundation of the educational system, play a vital part in shaping the academic trip of students. In Pakistan, as in multitudinous countries, the examination system has endured significant changeovers over time. claw into the nuances of the Pakistani examination system, exploring its elaboration, challenges faced by students, and proposals for reform. The examination system in Pakistan holds a vital position in determining the academic prowess of students. It serves as a mark for assessing their understanding of the class, critical thinking capacities, and overall academic performance. Examinations, in various forms, have been an integral part of the educational environment in Pakistan.

Evolution of the Examination System

Taking a historical perspective, examinations in Pakistan have evolved significantly. From traditional oral assessments to the current written and formalized tests, the system has acclimated to the changing educational requirements. The shift towards a more structured examination format reflects the dynamic nature of education in Pakistan.

Structure of the Current Examination System

In the contemporary script, the examination system in Pakistan comprises colorful types, including board examinations and university assessments. The grading system, with its nuances and evaluation criteria, adds another subcaste of complexity. Understanding the complications of these structures is essential to grasp the difficulties encountered by pupils.

” In the Pakistani examination system, success is frequently equated with grades, overshadowing the significance of holistic literacy and practical operation of knowledge.”

Challenges faced by students.

During the examination period, students encounter various difficulties and obstacles that they must overcome. The stress and pressure associated with performing well, coupled with the competitive nature of examinations, produce an environment that can be inviting. Its pivotal to fix these challenges to address them effectively.

” Our examination system should be a tool for assessing skills and fostering creativity, but too frequently it becomes a source of stress and anxiety for scholars in Pakistan.”

Impact on learning.

While examinations serve as a tool for assessing academic progress, their impact on actual learning is a subject of debate. On one hand, examinations can motivate students to study and perform better. On the other hand, critics argue that the current system may prioritize rote memorization over deep understanding.

“Examinations are redoubtable indeed to the stylish set, for the topmost fool may ask further than the wisest man can answer. Charles Caleb Colton”

Reforms and proposals.

Feting the defects in the current system, ongoing reforms are aiming to make examinations more student-friendly and reflective of actual learning. Suggestions for enhancement include changes in question formats, emphasis on practical operations, and a shift towards nonstop assessment.

Technological Integration in Examinations

The integration of technology in the examination process has been a recent development. Digital examinations bring effectiveness and availability, but enterprises with security and access to technology pose challenges.

The Role of Teachers and Parents

Creating a probative environment during examinations requires collaboration between teachers and parents. Emotional and academic support from these crucial numbers can significantly impact a students capability to manage the challenges posed by examinations.

Alternatives to Traditional Examinations

Exploring indispensable assessment styles, similar to design-grounded evaluations, practical demonstrations, and nonstop assessment, offers a holistic approach to assessing students. This shift from the conventional test-centric model aims to give a further comprehensive understanding of a students capabilities.

Cultural Influences on the Examination System

Cultural stations towards examinations in Pakistan contribute to the dynamics of the system. Understanding these influences and comparing them with examination systems in other countries can give precious perceptivity to the strengths and sins of the Pakistani model.

Managing strategies for students

Admitting the stress associated with examinations, and enforcing managing strategies becomes essential. Balancing internal health with academic performance is a skill that students need to cultivate. This section explores effective managing mechanisms to navigate the grueling test period.

” Reforming the examination system in Pakistan is pivotal for producing graduates who are just academically complete but also equipped with the skills demanded for the complications of the ultramodern world.”

Government programs and impact.

Government programs play a vital part in shaping the examination of geography. An analysis of these programs and their impact on students and the education system as a total is pivotal for understanding the broader environment of examinations in Pakistan. Considering the current state of the examination system, prognostications for the future are essential. Anticipated changes and developments, both in terms of technology integration and pedagogical approaches, shape the unborn geography of examinations in Pakistan.

In conclusion, the examination system in Pakistan is a multifaceted reality with strengths and challenges. While it serves as a pivotal metric for academic performance, there a nonstop need for reform and adaptation. Striking a balance between tradition and invention, addressing the well-being of students, and fostering a positive learning environment are crucial rudiments in the ongoing converse on examinations.

Kainat Shakeel is a versatile Content Writer Head and Digital Marketer with a keen understanding of tech news, digital market trends, fashion, technology, laws, and regulations. As a storyteller in the digital realm, she weaves narratives that bridge the gap between technology and human experiences. With a passion for staying at the forefront of industry trends, her blog is a curated space where the worlds of fashion, tech, and legal landscapes converge.

Examination System In Pakistan Essay

With the help of this article we are going to explain examination system In Pakistan essay. Every country has its special system for conducting the exams at any level of education. Similarly in Pakistan there is also an education system is followed that is called as the easy way of conducting the exams at school, college and university level. So the examination system is different at each level according to eh level of studies. This system is explained below that will make you familiar by reading this article. Ministry of Education Government of Pakistan is collaborating with the boards of director to upgrade the study system and that is being followed by schools and colleges under which a student find it easier to pass in a class, such as the minimum passing marks are 33% that is the lowest aggregate as compare to any examination system in world. So to get further details and examination system in Pakistan essay, keep on reading this article.

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Educational system Pakistan is divided into five levels such as:

Primary level
Middle level
Secondary level
Intermediate level
University level

All the schools, colleges and universities in Pakistan have been set in three categories namely:

Government schools
Private schools

Examination System in Pakistan:

If we give a look over the educational and examination system of Pakistan then majority of them are found to be in the poor condition just because of the lack of attention and shortage of funds. All the teachers are not offered with the best and adequate salary. All the private schools in Pakistan are found to be doing some better jobs as they are offered with the best pays all along with the necessary training for teaching. But one of the biggest drawbacks of these private schools is that as they are giving with the excellent services then at the same time their fee charges are not affordable by each single person.

Some of the educational system that are presently working in the Pakistan they are actually producing no synergy as they are creating conflicts and division among people. In Pakistan there are English medium schools, Urdu medium schools and madaras.

All the students who are coming out from the English educational institutions they are not much aware of Islamic teachings and a student who are coming out of Urdu medium school they don’t get excellent jobs. Its better solution is that the hierarchy of schooling systems should be abolished soon. There is one of the greatest needs to improve and update the curriculum and pedagogy.

Maximum attention should be given on the subjects of mathematics and Language so that the students would be better able to enhance their skills in the creative writing. Some educational trips should be arranged for the students that will going to help improving their knowledge about the history.

Well we hope that with the help of this article all the readers would have get some idea about the examination system in Pakistan essay.

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An English Essay on Our Examination system for B.A. and F.A students

Our Examination System

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I am a published author of dozens of books in Pakistan. I have been Editor of The Guide in National University of Modern Languages Islamabad (NUML). I am MPhil in Applied Languistics from University of the Lahore. Being an M.Ed I mostly spend my time training the teachers.

Essay on Examination

Examination is a test of a person’s capacity, knowledge, and ability. It proves what standard of learning a person has acquired during a specific period of time in a specific syllabus. It is the most hated and most shunned things for some students who never like to indulge in it with pleasure until they have a charm of acquiring a degree. Otherwise, they compare it with a nightmare.

Yet examinations are not totally devoid of good. There is a saying about it.

Trials are a veritable curse but they have their use. (adsbygoogle = window.adsbygoogle || []).push({});

Education System and Exam

The system of education of mostly examination ridden which aims at the test of achievement and success. The examination is the center of studies and hard work. It is a motivating force to work.

Its importance and efficacy have been called in question. The most important point is that examinations are not the real test of knowledge and understanding. They are the test of ignorance or cramming. Still, we can say that examinations are necessary evil which cannot be avoided.

Uses of Examination

Difference between genius and dunce.

Examinations have many uses. They help us find the most efficient individual among many. we can distinguish between the scholar and the dullard, the genius and the dunce. In this way, they help us discriminate between the genuine gold and the sparkling brass.

Compel to work hard

Secondly, the examinations compel us to work hard. the careless students become serious near the examinations. They buy books they had no intention to buy and gird up their loins.

It is a fact that many students read for the sake of examinations. Thus, examinations are a very effective way of goading students to read.

Fitness for promotion to a higher grade/class

Thirdly, examinations are proof and guarantee of man’s efficiency. They provide us a proof of the fitness of the student for promotion to a higher grade/class. An employer can safely entrust a job to the degree holder. Without a degree, no one will higher his services. The factories, industries or mills cannot allow the person to perform a technical task without a specific degree/course.

Way to attain degrees / diplomas

Similarly, we do not ask everyone to prescribe medicine for us. Only the person holding a degree enjoys the right to operate upon our body. Hence, if we abolish examinations, we shall have to abolish degrees or diplomas.

Abuses of examinations

Examinations have certain abuses as well. Many students consider it a curse. They consider them to be a game of chance. The students are never sure of their success. There are always doubts in their minds. Success does not depend upon preparation. Even a student with selected studies may pass and the student with thorough preparation may fail.

Uncertainty of success

Some students keep studying the whole session but fail. On the other hand, many others who buy help books and cheap notes near the examinations and cram a few questions, pass. Such examinations are a curse for the shining students.

Test of memory

The examinations are a test of nerves. All examinations have a limit of time and place. A student is tested at a bad place and in a bad manner. The question arises how a student’s hard work and worth for a semester or full one year is judged in a short time. They are never a foolproof test of one’s ability. They are the test of one’s memory and writing/typing speed.

Use of unfair means

Some students try to use unfair means to pass out the examinations. The innocent, hardworking and intelligent remain in the background.

Final words

But in spite of all this, we cannot say that there should be no examinations. There must be some proper way of judging the real worth of the students. So proper changes are required to avoid the abuses and increase the usefulness of the examinations. The assessment criteria of the examinations must be improved in such a way that all the students can show their abilities and can pass them without any fear.

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Han through Tang (206 bce –905)
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Ming through Qing (1368–1911)
Taiping (1850–1864)
Primary Sources
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Terminological Issues
Data for Specific Examinations
Collections of Source Materials
Modern Historiography
Examination Tiers
Examination Administration
Examination Curriculum
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Impact on Literature
Examination Life
Impact on Society
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The Examination System by Rui Magone LAST REVIEWED: 12 April 2019 LAST MODIFIED: 28 April 2014 DOI: 10.1093/obo/9780199920082-0078

The examination system, also known as “civil service examinations” or “imperial examinations”—and, in Chinese, as keju 科舉, keju zhidu 科舉制度, gongju 貢舉, xuanju 選舉 or zhiju 制舉—was the imperial Chinese bureaucracy’s central institution for recruiting its officials. Following both real and idealized models from previous times, the system was established at the beginning of the 7th century CE evolving over several dynasties into a complex institution that prevailed for 1,300 years before its abolition in 1905. One of the system’s most salient features, especially in the late imperial period (1400–1900), was its meritocratic structure (at least in principle, if not necessarily in practice): almost anyone from among the empire’s male population could sit for the examinations. Moreover, candidates were selected based on their performance rather than their pedigree. In order to be accessible to candidates anywhere in the empire, the system’s infrastructure spanned the entire territory. In a long sequence of triennial qualifying examinations at the local, provincial, metropolitan, and palace levels candidates were mainly required to write rhetorically complicated essays elucidating passages from the Confucian canon. Most candidates failed at each level, and only a couple of hundred out of a million or often more examinees attained final examination success at the metropolitan and palace levels. Due to its accessibility and ubiquity, the examination system had a decisive impact on the intellectual and social landscapes of imperial China. This impact was reinforced by the rule that candidates were allowed to retake examinations as often as they needed to in order to reach the next level. It was therefore not uncommon for individuals in imperial China to spend the great part of their lives, occasionally even until their last breath, sitting for the competitions. Indeed the extant sources reveal, by their sheer quantity alone, that large parts of the population, not only aspiring candidates, were in fact obsessed with the civil service examinations in the same way that modern societies are fascinated by sports leagues. To a great extent, it was this obsession, along with the system’s centripetal force constantly pulling the population in the different regions toward the political center in the capital, which may have held the large territory of imperial China together, providing it with both coherence and cohesion. Modern Historiography has tended to have a negative view of the examination system, singling it out, and specifically its predominantly literary curriculum, as the major cause for traditional Chinese society’s failure to develop into a modern nation with a strong scientific and technological tradition of its own. In the late 20th and early 21st century, this paradigm has become gradually more nuanced as historians have begun to develop new ways of approaching the extant sources, in particular the large number of examination essays and aids.

This section addresses readers who have little or no knowledge of the examination system and need both readable and reliable introductions to the subject. These works tend to highlight and describe extensively the Qing civil examinations during the 19th century, thus often creating among readers the impression that the system worked more or less the same in previous periods. While this was clearly not so, it is undeniable that no period in the long history of the civil examinations happens to be as well documented as the 19th century. Readers who desire to obtain a historically more nuanced sense of the system are referred to the sections General Overviews and Overviews by Period . Another problem with introductory works concerns the ideal balance between information and narration. Miyazaki 1981 and especially Jackson and Hugus 1999 are focused on telling a good story rather than providing copious evidence in dense footnotes. By contrast, Wilkinson 2012 and Zi 1894 are overtly technical, requiring a slow reading pace. The best way to strike a balance is to combine both approaches by, ideally, pairing Jackson and Hugus 1999 and Wilkinson 2012 . A problem that concerns Wang 1988 , Qi 2006 , and Li 2010 , all introductory works written by Chinese scholars in Chinese, is that they often quote passages from original Primary Sources in classical Chinese without providing a modern Chinese translation. One way to access these passages linguistically is to work directly with the literature cited under Terminological Issues . Finally, even though often neglected, the examination system also included a military branch, of which Zi 1896 provides the most readable account. Compared to their civil counterparts, the military competitions were of minimal significance, but they often served as a platform to obliquely move up the civil examination ladder.

Jackson, Beverley, and David Hugus. Ladder to the Clouds: Intrigue and Tradition in Chinese Rank . Berkeley, CA: Ten Speed, 1999.

Follows the trajectory of a late Qing examination candidate from his birth to his official position. Even though often leaning toward the fictitious, it is definitely a good read and one of the best illustrated books about the late imperial examination system and officialdom.

Li Bing李兵. Qiannian keju (千年科举). Changsha, China: Yuelu shushe, 2010.

Written in a rather colloquial and therefore accessible style, this well-illustrated book by a renowned expert of the examination system gives answers to questions most frequently asked about this topic, such as whether women were allowed to sit for the examinations. Has a good and sizable list of further readings.

Miyazaki, Ichisada. China’s Examination Hell: The Civil Service Examinations of Imperial China . Translated by Conrad Shirokauer. New Haven, CT: Yale University Press, 1981.

Originally published in 1963 in a longer and more academic version, this is a popular work by one of the most prominent Japanese scholars of the examination system. Packed with vivid anecdotes, this brief and captivating text describes all examination tiers. It is focused on the circumstances of the late Qing period, albeit not always explicitly.

Qi Rushan 齐如山. Zhongguo de kemin g (中国的科名). Shenyang, China: Liaoning Jiaoyu chubanshe, 2006.

Originally published in Taipei in 1956, this is a very accessible introduction arranged according to key terms used at the examinations.

Wang Daocheng 王道成. Keju shihua (科举史话). Beijing: Zhonghua shuju, 1988.

This is a short, easy to read, yet very informative introduction to the topic by a leading expert. While mainly focused on describing the Qing period, it also devotes a chapter to the system’s history. Has a very valuable appendix containing samples of all Qing examination genres. There are several books with an identical title, so make sure to use the one authored by Wang Daocheng.

Wilkinson, Endymion. Chinese History: A New Manual . Cambridge, MA: Harvard University Asia Center, 2012.

Chapter 22 “Education and Examinations” (pp. 292–304) of this monumental work contains a systematic introduction to the structure and curriculum of the late imperial examination system. Has also a section on primary and secondary sources. There are several editions of this manual; the 2012 version is the one you should use.

Zi, Étienne. Pratique des examens littéraires en Chine . Shanghai: Imprimerie de la Mission Catholique, 1894.

This is the most thorough and reliable description of the late Qing examination system. Has a copious amount of high-quality illustrations, which have been recycled in many other publications. Even though this book is now available online, try to use the original edition if you want to consult or reproduce the illustrative material, in particular the large-scale map of the Jiangnan examination compound. Also available in a 1971 reprint (Taipei: Chengwen, 1971).

Zi, Étienne. Pratique des examens militaires en Chine . Shanghai: Imprimerie de la Mission Catholique, 1896.

This is the best account of the late Qing military examination system available in any language. Describes all tiers and provides samples of examination topics. Richly illustrated, it also includes images of the weaponry used for testing the military candidates. Like the previous text, available in a 1971 reprint (Taipei: Chengwen, 1971).

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Essay on “Examination System” Complete Essay for Class 10, Class 12 and Graduation and other classes.

Examination System

Examination are a necessary evil. It is quite understandable that whenever we put in hard work to make successful and venture, we wait for some time to see or guess the results that might’s have been achieved or might possibly be achieved. It is in this context that examinations become unavoidable. Though methods and yardsticks employed may differ and that even widely.

A student studies the whole year and then needs to be examined. It is even in the interest of the students himself or herself to know where he or she stands and how far his or her efforts have borne fruit.

However, the examination system as we have today becomes a farce in essence. It is because of many reasons and factors. The most distressing among these factors is the menace of copying. The students who may be dullards but can manage to indulge in large-scale copying get high marks, whereas the really meritorious students who have worked hard get low marks.

Even otherwise , the prevalent examination system encourages cramming. Those who have a good memory or can indulge. In cramming, steal a march over others who cannot do this. Then, it is extremely painful to all lovers of transparency that sometimes even the question papers are sold in the market a day or so before an examination.

Some efforts have been made to bring reforms in the examination such as the introduction of gradation system, the setting of a number of different question papers. Objective questions , etc. but much still remains to be desired and done.

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An automated essay scoring systems: a systematic literature review

Dadi ramesh.

1 School of Computer Science and Artificial Intelligence, SR University, Warangal, TS India

2 Research Scholar, JNTU, Hyderabad, India

Suresh Kumar Sanampudi

3 Department of Information Technology, JNTUH College of Engineering, Nachupally, Kondagattu, Jagtial, TS India

Associated Data

Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-021-10068-2.

Introduction

Due to COVID 19 outbreak, an online educational system has become inevitable. In the present scenario, almost all the educational institutions ranging from schools to colleges adapt the online education system. The assessment plays a significant role in measuring the learning ability of the student. Most automated evaluation is available for multiple-choice questions, but assessing short and essay answers remain a challenge. The education system is changing its shift to online-mode, like conducting computer-based exams and automatic evaluation. It is a crucial application related to the education domain, which uses natural language processing (NLP) and Machine Learning techniques. The evaluation of essays is impossible with simple programming languages and simple techniques like pattern matching and language processing. Here the problem is for a single question, we will get more responses from students with a different explanation. So, we need to evaluate all the answers concerning the question.

Automated essay scoring (AES) is a computer-based assessment system that automatically scores or grades the student responses by considering appropriate features. The AES research started in 1966 with the Project Essay Grader (PEG) by Ajay et al. ( 1973 ). PEG evaluates the writing characteristics such as grammar, diction, construction, etc., to grade the essay. A modified version of the PEG by Shermis et al. ( 2001 ) was released, which focuses on grammar checking with a correlation between human evaluators and the system. Foltz et al. ( 1999 ) introduced an Intelligent Essay Assessor (IEA) by evaluating content using latent semantic analysis to produce an overall score. Powers et al. ( 2002 ) proposed E-rater and Intellimetric by Rudner et al. ( 2006 ) and Bayesian Essay Test Scoring System (BESTY) by Rudner and Liang ( 2002 ), these systems use natural language processing (NLP) techniques that focus on style and content to obtain the score of an essay. The vast majority of the essay scoring systems in the 1990s followed traditional approaches like pattern matching and a statistical-based approach. Since the last decade, the essay grading systems started using regression-based and natural language processing techniques. AES systems like Dong et al. ( 2017 ) and others developed from 2014 used deep learning techniques, inducing syntactic and semantic features resulting in better results than earlier systems.

Ohio, Utah, and most US states are using AES systems in school education, like Utah compose tool, Ohio standardized test (an updated version of PEG), evaluating millions of student's responses every year. These systems work for both formative, summative assessments and give feedback to students on the essay. Utah provided basic essay evaluation rubrics (six characteristics of essay writing): Development of ideas, organization, style, word choice, sentence fluency, conventions. Educational Testing Service (ETS) has been conducting significant research on AES for more than a decade and designed an algorithm to evaluate essays on different domains and providing an opportunity for test-takers to improve their writing skills. In addition, they are current research content-based evaluation.

The evaluation of essay and short answer scoring should consider the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge. Proper assessment of the parameters mentioned above defines the accuracy of the evaluation system. But all these parameters cannot play an equal role in essay scoring and short answer scoring. In a short answer evaluation, domain knowledge is required, like the meaning of "cell" in physics and biology is different. And while evaluating essays, the implementation of ideas with respect to prompt is required. The system should also assess the completeness of the responses and provide feedback.

Several studies examined AES systems, from the initial to the latest AES systems. In which the following studies on AES systems are Blood ( 2011 ) provided a literature review from PEG 1984–2010. Which has covered only generalized parts of AES systems like ethical aspects, the performance of the systems. Still, they have not covered the implementation part, and it’s not a comparative study and has not discussed the actual challenges of AES systems.

Burrows et al. ( 2015 ) Reviewed AES systems on six dimensions like dataset, NLP techniques, model building, grading models, evaluation, and effectiveness of the model. They have not covered feature extraction techniques and challenges in features extractions. Covered only Machine Learning models but not in detail. This system not covered the comparative analysis of AES systems like feature extraction, model building, and level of relevance, cohesion, and coherence not covered in this review.

Ke et al. ( 2019 ) provided a state of the art of AES system but covered very few papers and not listed all challenges, and no comparative study of the AES model. On the other hand, Hussein et al. in ( 2019 ) studied two categories of AES systems, four papers from handcrafted features for AES systems, and four papers from the neural networks approach, discussed few challenges, and did not cover feature extraction techniques, the performance of AES models in detail.

Klebanov et al. ( 2020 ). Reviewed 50 years of AES systems, listed and categorized all essential features that need to be extracted from essays. But not provided a comparative analysis of all work and not discussed the challenges.

This paper aims to provide a systematic literature review (SLR) on automated essay grading systems. An SLR is an Evidence-based systematic review to summarize the existing research. It critically evaluates and integrates all relevant studies' findings and addresses the research domain's specific research questions. Our research methodology uses guidelines given by Kitchenham et al. ( 2009 ) for conducting the review process; provide a well-defined approach to identify gaps in current research and to suggest further investigation.

We addressed our research method, research questions, and the selection process in Sect. 2 , and the results of the research questions have discussed in Sect. 3 . And the synthesis of all the research questions addressed in Sect. 4 . Conclusion and possible future work discussed in Sect. 5 .

Research method

We framed the research questions with PICOC criteria.

Population (P) Student essays and answers evaluation systems.

Intervention (I) evaluation techniques, data sets, features extraction methods.

Comparison (C) Comparison of various approaches and results.

Outcomes (O) Estimate the accuracy of AES systems,

Context (C) NA.

Research questions

To collect and provide research evidence from the available studies in the domain of automated essay grading, we framed the following research questions (RQ):

RQ1 what are the datasets available for research on automated essay grading?

The answer to the question can provide a list of the available datasets, their domain, and access to the datasets. It also provides a number of essays and corresponding prompts.

RQ2 what are the features extracted for the assessment of essays?

The answer to the question can provide an insight into various features so far extracted, and the libraries used to extract those features.

RQ3, which are the evaluation metrics available for measuring the accuracy of algorithms?

The answer will provide different evaluation metrics for accurate measurement of each Machine Learning approach and commonly used measurement technique.

RQ4 What are the Machine Learning techniques used for automatic essay grading, and how are they implemented?

It can provide insights into various Machine Learning techniques like regression models, classification models, and neural networks for implementing essay grading systems. The response to the question can give us different assessment approaches for automated essay grading systems.

RQ5 What are the challenges/limitations in the current research?

The answer to the question provides limitations of existing research approaches like cohesion, coherence, completeness, and feedback.

Search process

We conducted an automated search on well-known computer science repositories like ACL, ACM, IEEE Explore, Springer, and Science Direct for an SLR. We referred to papers published from 2010 to 2020 as much of the work during these years focused on advanced technologies like deep learning and natural language processing for automated essay grading systems. Also, the availability of free data sets like Kaggle (2012), Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) by Yannakoudakis et al. ( 2011 ) led to research this domain.

Search Strings : We used search strings like “Automated essay grading” OR “Automated essay scoring” OR “short answer scoring systems” OR “essay scoring systems” OR “automatic essay evaluation” and searched on metadata.

Selection criteria

After collecting all relevant documents from the repositories, we prepared selection criteria for inclusion and exclusion of documents. With the inclusion and exclusion criteria, it becomes more feasible for the research to be accurate and specific.

Inclusion criteria 1 Our approach is to work with datasets comprise of essays written in English. We excluded the essays written in other languages.

Inclusion criteria 2 We included the papers implemented on the AI approach and excluded the traditional methods for the review.

Inclusion criteria 3 The study is on essay scoring systems, so we exclusively included the research carried out on only text data sets rather than other datasets like image or speech.

Exclusion criteria We removed the papers in the form of review papers, survey papers, and state of the art papers.

Quality assessment

In addition to the inclusion and exclusion criteria, we assessed each paper by quality assessment questions to ensure the article's quality. We included the documents that have clearly explained the approach they used, the result analysis and validation.

The quality checklist questions are framed based on the guidelines from Kitchenham et al. ( 2009 ). Each quality assessment question was graded as either 1 or 0. The final score of the study range from 0 to 3. A cut off score for excluding a study from the review is 2 points. Since the papers scored 2 or 3 points are included in the final evaluation. We framed the following quality assessment questions for the final study.

Quality Assessment 1: Internal validity.

Quality Assessment 2: External validity.

Quality Assessment 3: Bias.

The two reviewers review each paper to select the final list of documents. We used the Quadratic Weighted Kappa score to measure the final agreement between the two reviewers. The average resulted from the kappa score is 0.6942, a substantial agreement between the reviewers. The result of evolution criteria shown in Table Table1. 1 . After Quality Assessment, the final list of papers for review is shown in Table Table2. 2 . The complete selection process is shown in Fig. Fig.1. 1 . The total number of selected papers in year wise as shown in Fig. Fig.2. 2 .

Quality assessment analysis

Final list of papers

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Selection process

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Year wise publications

What are the datasets available for research on automated essay grading?

To work with problem statement especially in Machine Learning and deep learning domain, we require considerable amount of data to train the models. To answer this question, we listed all the data sets used for training and testing for automated essay grading systems. The Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) Yannakoudakis et al. ( 2011 ) developed corpora that contain 1244 essays and ten prompts. This corpus evaluates whether a student can write the relevant English sentences without any grammatical and spelling mistakes. This type of corpus helps to test the models built for GRE and TOFEL type of exams. It gives scores between 1 and 40.

Bailey and Meurers ( 2008 ), Created a dataset (CREE reading comprehension) for language learners and automated short answer scoring systems. The corpus consists of 566 responses from intermediate students. Mohler and Mihalcea ( 2009 ). Created a dataset for the computer science domain consists of 630 responses for data structure assignment questions. The scores are range from 0 to 5 given by two human raters.

Dzikovska et al. ( 2012 ) created a Student Response Analysis (SRA) corpus. It consists of two sub-groups: the BEETLE corpus consists of 56 questions and approximately 3000 responses from students in the electrical and electronics domain. The second one is the SCIENTSBANK(SemEval-2013) (Dzikovska et al. 2013a ; b ) corpus consists of 10,000 responses on 197 prompts on various science domains. The student responses ladled with "correct, partially correct incomplete, Contradictory, Irrelevant, Non-domain."

In the Kaggle (2012) competition, released total 3 types of corpuses on an Automated Student Assessment Prize (ASAP1) (“ https://www.kaggle.com/c/asap-sas/ ” ) essays and short answers. It has nearly 17,450 essays, out of which it provides up to 3000 essays for each prompt. It has eight prompts that test 7th to 10th grade US students. It gives scores between the [0–3] and [0–60] range. The limitations of these corpora are: (1) it has a different score range for other prompts. (2) It uses statistical features such as named entities extraction and lexical features of words to evaluate essays. ASAP + + is one more dataset from Kaggle. It is with six prompts, and each prompt has more than 1000 responses total of 10,696 from 8th-grade students. Another corpus contains ten prompts from science, English domains and a total of 17,207 responses. Two human graders evaluated all these responses.

Correnti et al. ( 2013 ) created a Response-to-Text Assessment (RTA) dataset used to check student writing skills in all directions like style, mechanism, and organization. 4–8 grade students give the responses to RTA. Basu et al. ( 2013 ) created a power grading dataset with 700 responses for ten different prompts from US immigration exams. It contains all short answers for assessment.

The TOEFL11 corpus Blanchard et al. ( 2013 ) contains 1100 essays evenly distributed over eight prompts. It is used to test the English language skills of a candidate attending the TOFEL exam. It scores the language proficiency of a candidate as low, medium, and high.

International Corpus of Learner English (ICLE) Granger et al. ( 2009 ) built a corpus of 3663 essays covering different dimensions. It has 12 prompts with 1003 essays that test the organizational skill of essay writing, and13 prompts, each with 830 essays that examine the thesis clarity and prompt adherence.

Argument Annotated Essays (AAE) Stab and Gurevych ( 2014 ) developed a corpus that contains 102 essays with 101 prompts taken from the essayforum2 site. It tests the persuasive nature of the student essay. The SCIENTSBANK corpus used by Sakaguchi et al. ( 2015 ) available in git-hub, containing 9804 answers to 197 questions in 15 science domains. Table Table3 3 illustrates all datasets related to AES systems.

ALL types Datasets used in Automatic scoring systems

Features play a major role in the neural network and other supervised Machine Learning approaches. The automatic essay grading systems scores student essays based on different types of features, which play a prominent role in training the models. Based on their syntax and semantics and they are categorized into three groups. 1. statistical-based features Contreras et al. ( 2018 ); Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ) 2. Style-based (Syntax) features Cummins et al. ( 2016 ); Darwish and Mohamed ( 2020 ); Ke et al. ( 2019 ). 3. Content-based features Dong et al. ( 2017 ). A good set of features appropriate models evolved better AES systems. The vast majority of the researchers are using regression models if features are statistical-based. For Neural Networks models, researches are using both style-based and content-based features. The following table shows the list of various features used in existing AES Systems. Table Table4 4 represents all set of features used for essay grading.

Types of features

We studied all the feature extracting NLP libraries as shown in Fig. Fig.3. that 3 . that are used in the papers. The NLTK is an NLP tool used to retrieve statistical features like POS, word count, sentence count, etc. With NLTK, we can miss the essay's semantic features. To find semantic features Word2Vec Mikolov et al. ( 2013 ), GloVe Jeffrey Pennington et al. ( 2014 ) is the most used libraries to retrieve the semantic text from the essays. And in some systems, they directly trained the model with word embeddings to find the score. From Fig. Fig.4 4 as observed that non-content-based feature extraction is higher than content-based.

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Usages of tools

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Number of papers on content based features

RQ3 which are the evaluation metrics available for measuring the accuracy of algorithms?

The majority of the AES systems are using three evaluation metrics. They are (1) quadrated weighted kappa (QWK) (2) Mean Absolute Error (MAE) (3) Pearson Correlation Coefficient (PCC) Shehab et al. ( 2016 ). The quadratic weighted kappa will find agreement between human evaluation score and system evaluation score and produces value ranging from 0 to 1. And the Mean Absolute Error is the actual difference between human-rated score to system-generated score. The mean square error (MSE) measures the average squares of the errors, i.e., the average squared difference between the human-rated and the system-generated scores. MSE will always give positive numbers only. Pearson's Correlation Coefficient (PCC) finds the correlation coefficient between two variables. It will provide three values (0, 1, − 1). "0" represents human-rated and system scores that are not related. "1" represents an increase in the two scores. "− 1" illustrates a negative relationship between the two scores.

RQ4 what are the Machine Learning techniques being used for automatic essay grading, and how are they implemented?

After scrutinizing all documents, we categorize the techniques used in automated essay grading systems into four baskets. 1. Regression techniques. 2. Classification model. 3. Neural networks. 4. Ontology-based approach.

All the existing AES systems developed in the last ten years employ supervised learning techniques. Researchers using supervised methods viewed the AES system as either regression or classification task. The goal of the regression task is to predict the score of an essay. The classification task is to classify the essays belonging to (low, medium, or highly) relevant to the question's topic. Since the last three years, most AES systems developed made use of the concept of the neural network.

Regression based models

Mohler and Mihalcea ( 2009 ). proposed text-to-text semantic similarity to assign a score to the student essays. There are two text similarity measures like Knowledge-based measures, corpus-based measures. There eight knowledge-based tests with all eight models. They found the similarity. The shortest path similarity determines based on the length, which shortest path between two contexts. Leacock & Chodorow find the similarity based on the shortest path's length between two concepts using node-counting. The Lesk similarity finds the overlap between the corresponding definitions, and Wu & Palmer algorithm finds similarities based on the depth of two given concepts in the wordnet taxonomy. Resnik, Lin, Jiang&Conrath, Hirst& St-Onge find the similarity based on different parameters like the concept, probability, normalization factor, lexical chains. In corpus-based likeness, there LSA BNC, LSA Wikipedia, and ESA Wikipedia, latent semantic analysis is trained on Wikipedia and has excellent domain knowledge. Among all similarity scores, correlation scores LSA Wikipedia scoring accuracy is more. But these similarity measure algorithms are not using NLP concepts. These models are before 2010 and basic concept models to continue the research automated essay grading with updated algorithms on neural networks with content-based features.

Adamson et al. ( 2014 ) proposed an automatic essay grading system which is a statistical-based approach in this they retrieved features like POS, Character count, Word count, Sentence count, Miss spelled words, n-gram representation of words to prepare essay vector. They formed a matrix with these all vectors in that they applied LSA to give a score to each essay. It is a statistical approach that doesn’t consider the semantics of the essay. The accuracy they got when compared to the human rater score with the system is 0.532.

Cummins et al. ( 2016 ). Proposed Timed Aggregate Perceptron vector model to give ranking to all the essays, and later they converted the rank algorithm to predict the score of the essay. The model trained with features like Word unigrams, bigrams, POS, Essay length, grammatical relation, Max word length, sentence length. It is multi-task learning, gives ranking to the essays, and predicts the score for the essay. The performance evaluated through QWK is 0.69, a substantial agreement between the human rater and the system.

Sultan et al. ( 2016 ). Proposed a Ridge regression model to find short answer scoring with Question Demoting. Question Demoting is the new concept included in the essay's final assessment to eliminate duplicate words from the essay. The extracted features are Text Similarity, which is the similarity between the student response and reference answer. Question Demoting is the number of repeats in a student response. With inverse document frequency, they assigned term weight. The sentence length Ratio is the number of words in the student response, is another feature. With these features, the Ridge regression model was used, and the accuracy they got 0.887.

Contreras et al. ( 2018 ). Proposed Ontology based on text mining in this model has given a score for essays in phases. In phase-I, they generated ontologies with ontoGen and SVM to find the concept and similarity in the essay. In phase II from ontologies, they retrieved features like essay length, word counts, correctness, vocabulary, and types of word used, domain information. After retrieving statistical data, they used a linear regression model to find the score of the essay. The accuracy score is the average of 0.5.

Darwish and Mohamed ( 2020 ) proposed the fusion of fuzzy Ontology with LSA. They retrieve two types of features, like syntax features and semantic features. In syntax features, they found Lexical Analysis with tokens, and they construct a parse tree. If the parse tree is broken, the essay is inconsistent—a separate grade assigned to the essay concerning syntax features. The semantic features are like similarity analysis, Spatial Data Analysis. Similarity analysis is to find duplicate sentences—Spatial Data Analysis for finding Euclid distance between the center and part. Later they combine syntax features and morphological features score for the final score. The accuracy they achieved with the multiple linear regression model is 0.77, mostly on statistical features.

Süzen Neslihan et al. ( 2020 ) proposed a text mining approach for short answer grading. First, their comparing model answers with student response by calculating the distance between two sentences. By comparing the model answer with student response, they find the essay's completeness and provide feedback. In this approach, model vocabulary plays a vital role in grading, and with this model vocabulary, the grade will be assigned to the student's response and provides feedback. The correlation between the student answer to model answer is 0.81.

Classification based Models

Persing and Ng ( 2013 ) used a support vector machine to score the essay. The features extracted are OS, N-gram, and semantic text to train the model and identified the keywords from the essay to give the final score.

Sakaguchi et al. ( 2015 ) proposed two methods: response-based and reference-based. In response-based scoring, the extracted features are response length, n-gram model, and syntactic elements to train the support vector regression model. In reference-based scoring, features such as sentence similarity using word2vec is used to find the cosine similarity of the sentences that is the final score of the response. First, the scores were discovered individually and later combined two features to find a final score. This system gave a remarkable increase in performance by combining the scores.

Mathias and Bhattacharyya ( 2018a ; b ) Proposed Automated Essay Grading Dataset with Essay Attribute Scores. The first concept features selection depends on the essay type. So the common attributes are Content, Organization, Word Choice, Sentence Fluency, Conventions. In this system, each attribute is scored individually, with the strength of each attribute identified. The model they used is a random forest classifier to assign scores to individual attributes. The accuracy they got with QWK is 0.74 for prompt 1 of the ASAS dataset ( https://www.kaggle.com/c/asap-sas/ ).

Ke et al. ( 2019 ) used a support vector machine to find the response score. In this method, features like Agreeability, Specificity, Clarity, Relevance to prompt, Conciseness, Eloquence, Confidence, Direction of development, Justification of opinion, and Justification of importance. First, the individual parameter score obtained was later combined with all scores to give a final response score. The features are used in the neural network to find whether the sentence is relevant to the topic or not.

Salim et al. ( 2019 ) proposed an XGBoost Machine Learning classifier to assess the essays. The algorithm trained on features like word count, POS, parse tree depth, and coherence in the articles with sentence similarity percentage; cohesion and coherence are considered for training. And they implemented K-fold cross-validation for a result the average accuracy after specific validations is 68.12.

Neural network models

Shehab et al. ( 2016 ) proposed a neural network method that used learning vector quantization to train human scored essays. After training, the network can provide a score to the ungraded essays. First, we should process the essay to remove Spell checking and then perform preprocessing steps like Document Tokenization, stop word removal, Stemming, and submit it to the neural network. Finally, the model will provide feedback on the essay, whether it is relevant to the topic. And the correlation coefficient between human rater and system score is 0.7665.

Kopparapu and De ( 2016 ) proposed the Automatic Ranking of Essays using Structural and Semantic Features. This approach constructed a super essay with all the responses. Next, ranking for a student essay is done based on the super-essay. The structural and semantic features derived helps to obtain the scores. In a paragraph, 15 Structural features like an average number of sentences, the average length of sentences, and the count of words, nouns, verbs, adjectives, etc., are used to obtain a syntactic score. A similarity score is used as semantic features to calculate the overall score.

Dong and Zhang ( 2016 ) proposed a hierarchical CNN model. The model builds two layers with word embedding to represents the words as the first layer. The second layer is a word convolution layer with max-pooling to find word vectors. The next layer is a sentence-level convolution layer with max-pooling to find the sentence's content and synonyms. A fully connected dense layer produces an output score for an essay. The accuracy with the hierarchical CNN model resulted in an average QWK of 0.754.

Taghipour and Ng ( 2016 ) proposed a first neural approach for essay scoring build in which convolution and recurrent neural network concepts help in scoring an essay. The network uses a lookup table with the one-hot representation of the word vector of an essay. The final efficiency of the network model with LSTM resulted in an average QWK of 0.708.

Dong et al. ( 2017 ). Proposed an Attention-based scoring system with CNN + LSTM to score an essay. For CNN, the input parameters were character embedding and word embedding, and it has attention pooling layers and used NLTK to obtain word and character embedding. The output gives a sentence vector, which provides sentence weight. After CNN, it will have an LSTM layer with an attention pooling layer, and this final layer results in the final score of the responses. The average QWK score is 0.764.

Riordan et al. ( 2017 ) proposed a neural network with CNN and LSTM layers. Word embedding, given as input to a neural network. An LSTM network layer will retrieve the window features and delivers them to the aggregation layer. The aggregation layer is a superficial layer that takes a correct window of words and gives successive layers to predict the answer's sore. The accuracy of the neural network resulted in a QWK of 0.90.

Zhao et al. ( 2017 ) proposed a new concept called Memory-Augmented Neural network with four layers, input representation layer, memory addressing layer, memory reading layer, and output layer. An input layer represents all essays in a vector form based on essay length. After converting the word vector, the memory addressing layer takes a sample of the essay and weighs all the terms. The memory reading layer takes the input from memory addressing segment and finds the content to finalize the score. Finally, the output layer will provide the final score of the essay. The accuracy of essay scores is 0.78, which is far better than the LSTM neural network.

Mathias and Bhattacharyya ( 2018a ; b ) proposed deep learning networks using LSTM with the CNN layer and GloVe pre-trained word embeddings. For this, they retrieved features like Sentence count essays, word count per sentence, Number of OOVs in the sentence, Language model score, and the text's perplexity. The network predicted the goodness scores of each essay. The higher the goodness scores, means higher the rank and vice versa.

Nguyen and Dery ( 2016 ). Proposed Neural Networks for Automated Essay Grading. In this method, a single layer bi-directional LSTM accepting word vector as input. Glove vectors used in this method resulted in an accuracy of 90%.

Ruseti et al. ( 2018 ) proposed a recurrent neural network that is capable of memorizing the text and generate a summary of an essay. The Bi-GRU network with the max-pooling layer molded on the word embedding of each document. It will provide scoring to the essay by comparing it with a summary of the essay from another Bi-GRU network. The result obtained an accuracy of 0.55.

Wang et al. ( 2018a ; b ) proposed an automatic scoring system with the bi-LSTM recurrent neural network model and retrieved the features using the word2vec technique. This method generated word embeddings from the essay words using the skip-gram model. And later, word embedding is used to train the neural network to find the final score. The softmax layer in LSTM obtains the importance of each word. This method used a QWK score of 0.83%.

Dasgupta et al. ( 2018 ) proposed a technique for essay scoring with augmenting textual qualitative Features. It extracted three types of linguistic, cognitive, and psychological features associated with a text document. The linguistic features are Part of Speech (POS), Universal Dependency relations, Structural Well-formedness, Lexical Diversity, Sentence Cohesion, Causality, and Informativeness of the text. The psychological features derived from the Linguistic Information and Word Count (LIWC) tool. They implemented a convolution recurrent neural network that takes input as word embedding and sentence vector, retrieved from the GloVe word vector. And the second layer is the Convolution Layer to find local features. The next layer is the recurrent neural network (LSTM) to find corresponding of the text. The accuracy of this method resulted in an average QWK of 0.764.

Liang et al. ( 2018 ) proposed a symmetrical neural network AES model with Bi-LSTM. They are extracting features from sample essays and student essays and preparing an embedding layer as input. The embedding layer output is transfer to the convolution layer from that LSTM will be trained. Hear the LSRM model has self-features extraction layer, which will find the essay's coherence. The average QWK score of SBLSTMA is 0.801.

Liu et al. ( 2019 ) proposed two-stage learning. In the first stage, they are assigning a score based on semantic data from the essay. The second stage scoring is based on some handcrafted features like grammar correction, essay length, number of sentences, etc. The average score of the two stages is 0.709.

Pedro Uria Rodriguez et al. ( 2019 ) proposed a sequence-to-sequence learning model for automatic essay scoring. They used BERT (Bidirectional Encoder Representations from Transformers), which extracts the semantics from a sentence from both directions. And XLnet sequence to sequence learning model to extract features like the next sentence in an essay. With this pre-trained model, they attained coherence from the essay to give the final score. The average QWK score of the model is 75.5.

Xia et al. ( 2019 ) proposed a two-layer Bi-directional LSTM neural network for the scoring of essays. The features extracted with word2vec to train the LSTM and accuracy of the model in an average of QWK is 0.870.

Kumar et al. ( 2019 ) Proposed an AutoSAS for short answer scoring. It used pre-trained Word2Vec and Doc2Vec models trained on Google News corpus and Wikipedia dump, respectively, to retrieve the features. First, they tagged every word POS and they found weighted words from the response. It also found prompt overlap to observe how the answer is relevant to the topic, and they defined lexical overlaps like noun overlap, argument overlap, and content overlap. This method used some statistical features like word frequency, difficulty, diversity, number of unique words in each response, type-token ratio, statistics of the sentence, word length, and logical operator-based features. This method uses a random forest model to train the dataset. The data set has sample responses with their associated score. The model will retrieve the features from both responses like graded and ungraded short answers with questions. The accuracy of AutoSAS with QWK is 0.78. It will work on any topics like Science, Arts, Biology, and English.

Jiaqi Lun et al. ( 2020 ) proposed an automatic short answer scoring with BERT. In this with a reference answer comparing student responses and assigning scores. The data augmentation is done with a neural network and with one correct answer from the dataset classifying reaming responses as correct or incorrect.

Zhu and Sun ( 2020 ) proposed a multimodal Machine Learning approach for automated essay scoring. First, they count the grammar score with the spaCy library and numerical count as the number of words and sentences with the same library. With this input, they trained a single and Bi LSTM neural network for finding the final score. For the LSTM model, they prepared sentence vectors with GloVe and word embedding with NLTK. Bi-LSTM will check each sentence in both directions to find semantic from the essay. The average QWK score with multiple models is 0.70.

Ontology based approach

Mohler et al. ( 2011 ) proposed a graph-based method to find semantic similarity in short answer scoring. For the ranking of answers, they used the support vector regression model. The bag of words is the main feature extracted in the system.

Ramachandran et al. ( 2015 ) also proposed a graph-based approach to find lexical based semantics. Identified phrase patterns and text patterns are the features to train a random forest regression model to score the essays. The accuracy of the model in a QWK is 0.78.

Zupanc et al. ( 2017 ) proposed sentence similarity networks to find the essay's score. Ajetunmobi and Daramola ( 2017 ) recommended an ontology-based information extraction approach and domain-based ontology to find the score.

Speech response scoring

Automatic scoring is in two ways one is text-based scoring, other is speech-based scoring. This paper discussed text-based scoring and its challenges, and now we cover speech scoring and common points between text and speech-based scoring. Evanini and Wang ( 2013 ), Worked on speech scoring of non-native school students, extracted features with speech ratter, and trained a linear regression model, concluding that accuracy varies based on voice pitching. Loukina et al. ( 2015 ) worked on feature selection from speech data and trained SVM. Malinin et al. ( 2016 ) used neural network models to train the data. Loukina et al. ( 2017 ). Proposed speech and text-based automatic scoring. Extracted text-based features, speech-based features and trained a deep neural network for speech-based scoring. They extracted 33 types of features based on acoustic signals. Malinin et al. ( 2017 ). Wu Xixin et al. ( 2020 ) Worked on deep neural networks for spoken language assessment. Incorporated different types of models and tested them. Ramanarayanan et al. ( 2017 ) worked on feature extraction methods and extracted punctuation, fluency, and stress and trained different Machine Learning models for scoring. Knill et al. ( 2018 ). Worked on Automatic speech recognizer and its errors how its impacts the speech assessment.

The state of the art

This section provides an overview of the existing AES systems with a comparative study w. r. t models, features applied, datasets, and evaluation metrics used for building the automated essay grading systems. We divided all 62 papers into two sets of the first set of review papers in Table Table5 5 with a comparative study of the AES systems.

State of the art

Comparison of all approaches

In our study, we divided major AES approaches into three categories. Regression models, classification models, and neural network models. The regression models failed to find cohesion and coherence from the essay because it trained on BoW(Bag of Words) features. In processing data from input to output, the regression models are less complicated than neural networks. There are unable to find many intricate patterns from the essay and unable to find sentence connectivity. If we train the model with BoW features in the neural network approach, the model never considers the essay's coherence and coherence.

First, to train a Machine Learning algorithm with essays, all the essays are converted to vector form. We can form a vector with BoW and Word2vec, TF-IDF. The BoW and Word2vec vector representation of essays represented in Table Table6. 6 . The vector representation of BoW with TF-IDF is not incorporating the essays semantic, and it’s just statistical learning from a given vector. Word2vec vector comprises semantic of essay in a unidirectional way.

Vector representation of essays

In BoW, the vector contains the frequency of word occurrences in the essay. The vector represents 1 and more based on the happenings of words in the essay and 0 for not present. So, in BoW, the vector does not maintain the relationship with adjacent words; it’s just for single words. In word2vec, the vector represents the relationship between words with other words and sentences prompt in multiple dimensional ways. But word2vec prepares vectors in a unidirectional way, not in a bidirectional way; word2vec fails to find semantic vectors when a word has two meanings, and the meaning depends on adjacent words. Table Table7 7 represents a comparison of Machine Learning models and features extracting methods.

Comparison of models

In AES, cohesion and coherence will check the content of the essay concerning the essay prompt these can be extracted from essay in the vector from. Two more parameters are there to access an essay is completeness and feedback. Completeness will check whether student’s response is sufficient or not though the student wrote correctly. Table Table8 8 represents all four parameters comparison for essay grading. Table Table9 9 illustrates comparison of all approaches based on various features like grammar, spelling, organization of essay, relevance.

Comparison of all models with respect to cohesion, coherence, completeness, feedback

comparison of all approaches on various features

What are the challenges/limitations in the current research?

From our study and results discussed in the previous sections, many researchers worked on automated essay scoring systems with numerous techniques. We have statistical methods, classification methods, and neural network approaches to evaluate the essay automatically. The main goal of the automated essay grading system is to reduce human effort and improve consistency.

The vast majority of essay scoring systems are dealing with the efficiency of the algorithm. But there are many challenges in automated essay grading systems. One should assess the essay by following parameters like the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge.

No model works on the relevance of content, which means whether student response or explanation is relevant to the given prompt or not if it is relevant to how much it is appropriate, and there is no discussion about the cohesion and coherence of the essays. All researches concentrated on extracting the features using some NLP libraries, trained their models, and testing the results. But there is no explanation in the essay evaluation system about consistency and completeness, But Palma and Atkinson ( 2018 ) explained coherence-based essay evaluation. And Zupanc and Bosnic ( 2014 ) also used the word coherence to evaluate essays. And they found consistency with latent semantic analysis (LSA) for finding coherence from essays, but the dictionary meaning of coherence is "The quality of being logical and consistent."

Another limitation is there is no domain knowledge-based evaluation of essays using Machine Learning models. For example, the meaning of a cell is different from biology to physics. Many Machine Learning models extract features with WordVec and GloVec; these NLP libraries cannot convert the words into vectors when they have two or more meanings.

Other challenges that influence the Automated Essay Scoring Systems.

All these approaches worked to improve the QWK score of their models. But QWK will not assess the model in terms of features extraction and constructed irrelevant answers. The QWK is not evaluating models whether the model is correctly assessing the answer or not. There are many challenges concerning students' responses to the Automatic scoring system. Like in evaluating approach, no model has examined how to evaluate the constructed irrelevant and adversarial answers. Especially the black box type of approaches like deep learning models provides more options to the students to bluff the automated scoring systems.

The Machine Learning models that work on statistical features are very vulnerable. Based on Powers et al. ( 2001 ) and Bejar Isaac et al. ( 2014 ), the E-rater was failed on Constructed Irrelevant Responses Strategy (CIRS). From the study of Bejar et al. ( 2013 ), Higgins and Heilman ( 2014 ), observed that when student response contain irrelevant content or shell language concurring to prompt will influence the final score of essays in an automated scoring system.

In deep learning approaches, most of the models automatically read the essay's features, and some methods work on word-based embedding and other character-based embedding features. From the study of Riordan Brain et al. ( 2019 ), The character-based embedding systems do not prioritize spelling correction. However, it is influencing the final score of the essay. From the study of Horbach and Zesch ( 2019 ), Various factors are influencing AES systems. For example, there are data set size, prompt type, answer length, training set, and human scorers for content-based scoring.

Ding et al. ( 2020 ) reviewed that the automated scoring system is vulnerable when a student response contains more words from prompt, like prompt vocabulary repeated in the response. Parekh et al. ( 2020 ) and Kumar et al. ( 2020 ) tested various neural network models of AES by iteratively adding important words, deleting unimportant words, shuffle the words, and repeating sentences in an essay and found that no change in the final score of essays. These neural network models failed to recognize common sense in adversaries' essays and give more options for the students to bluff the automated systems.

Other than NLP and ML techniques for AES. From Wresch ( 1993 ) to Madnani and Cahill ( 2018 ). discussed the complexity of AES systems, standards need to be followed. Like assessment rubrics to test subject knowledge, irrelevant responses, and ethical aspects of an algorithm like measuring the fairness of student response.

Fairness is an essential factor for automated systems. For example, in AES, fairness can be measure in an agreement between human score to machine score. Besides this, From Loukina et al. ( 2019 ), the fairness standards include overall score accuracy, overall score differences, and condition score differences between human and system scores. In addition, scoring different responses in the prospect of constructive relevant and irrelevant will improve fairness.

Madnani et al. ( 2017a ; b ). Discussed the fairness of AES systems for constructed responses and presented RMS open-source tool for detecting biases in the models. With this, one can change fairness standards according to their analysis of fairness.

From Berzak et al.'s ( 2018 ) approach, behavior factors are a significant challenge in automated scoring systems. That helps to find language proficiency, word characteristics (essential words from the text), predict the critical patterns from the text, find related sentences in an essay, and give a more accurate score.

Rupp ( 2018 ), has discussed the designing, evaluating, and deployment methodologies for AES systems. They provided notable characteristics of AES systems for deployment. They are like model performance, evaluation metrics for a model, threshold values, dynamically updated models, and framework.

First, we should check the model performance on different datasets and parameters for operational deployment. Selecting Evaluation metrics for AES models are like QWK, correlation coefficient, or sometimes both. Kelley and Preacher ( 2012 ) have discussed three categories of threshold values: marginal, borderline, and acceptable. The values can be varied based on data size, model performance, type of model (single scoring, multiple scoring models). Once a model is deployed and evaluates millions of responses every time for optimal responses, we need a dynamically updated model based on prompt and data. Finally, framework designing of AES model, hear a framework contains prompts where test-takers can write the responses. One can design two frameworks: a single scoring model for a single methodology and multiple scoring models for multiple concepts. When we deploy multiple scoring models, each prompt could be trained separately, or we can provide generalized models for all prompts with this accuracy may vary, and it is challenging.

Our Systematic literature review on the automated essay grading system first collected 542 papers with selected keywords from various databases. After inclusion and exclusion criteria, we left with 139 articles; on these selected papers, we applied Quality assessment criteria with two reviewers, and finally, we selected 62 writings for final review.

Our observations on automated essay grading systems from 2010 to 2020 are as followed:

The implementation techniques of automated essay grading systems are classified into four buckets; there are 1. regression models 2. Classification models 3. Neural networks 4. Ontology-based methodology, but using neural networks, the researchers are more accurate than other techniques, and all the methods state of the art provided in Table Table3 3 .
The majority of the regression and classification models on essay scoring used statistical features to find the final score. It means the systems or models trained on such parameters as word count, sentence count, etc. though the parameters extracted from the essay, the algorithm are not directly training on essays. The algorithms trained on some numbers obtained from the essay and hear if numbers matched the composition will get a good score; otherwise, the rating is less. In these models, the evaluation process is entirely on numbers, irrespective of the essay. So, there is a lot of chance to miss the coherence, relevance of the essay if we train our algorithm on statistical parameters.
In the neural network approach, the models trained on Bag of Words (BoW) features. The BoW feature is missing the relationship between a word to word and the semantic meaning of the sentence. E.g., Sentence 1: John killed bob. Sentence 2: bob killed John. In these two sentences, the BoW is "John," "killed," "bob."
In the Word2Vec library, if we are prepared a word vector from an essay in a unidirectional way, the vector will have a dependency with other words and finds the semantic relationship with other words. But if a word has two or more meanings like "Bank loan" and "River Bank," hear bank has two implications, and its adjacent words decide the sentence meaning; in this case, Word2Vec is not finding the real meaning of the word from the sentence.
The features extracted from essays in the essay scoring system are classified into 3 type's features like statistical features, style-based features, and content-based features, which are explained in RQ2 and Table Table3. 3 . But statistical features, are playing a significant role in some systems and negligible in some systems. In Shehab et al. ( 2016 ); Cummins et al. ( 2016 ). Dong et al. ( 2017 ). Dong and Zhang ( 2016 ). Mathias and Bhattacharyya ( 2018a ; b ) Systems the assessment is entirely on statistical and style-based features they have not retrieved any content-based features. And in other systems that extract content from the essays, the role of statistical features is for only preprocessing essays but not included in the final grading.
In AES systems, coherence is the main feature to be considered while evaluating essays. The actual meaning of coherence is to stick together. That is the logical connection of sentences (local level coherence) and paragraphs (global level coherence) in a story. Without coherence, all sentences in a paragraph are independent and meaningless. In an Essay, coherence is a significant feature that is explaining everything in a flow and its meaning. It is a powerful feature in AES system to find the semantics of essay. With coherence, one can assess whether all sentences are connected in a flow and all paragraphs are related to justify the prompt. Retrieving the coherence level from an essay is a critical task for all researchers in AES systems.
In automatic essay grading systems, the assessment of essays concerning content is critical. That will give the actual score for the student. Most of the researches used statistical features like sentence length, word count, number of sentences, etc. But according to collected results, 32% of the systems used content-based features for the essay scoring. Example papers which are on content-based assessment are Taghipour and Ng ( 2016 ); Persing and Ng ( 2013 ); Wang et al. ( 2018a , 2018b ); Zhao et al. ( 2017 ); Kopparapu and De ( 2016 ), Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ); Mohler and Mihalcea ( 2009 ) are used content and statistical-based features. The results are shown in Fig. Fig.3. 3 . And mainly the content-based features extracted with word2vec NLP library, but word2vec is capable of capturing the context of a word in a document, semantic and syntactic similarity, relation with other terms, but word2vec is capable of capturing the context word in a uni-direction either left or right. If a word has multiple meanings, there is a chance of missing the context in the essay. After analyzing all the papers, we found that content-based assessment is a qualitative assessment of essays.
On the other hand, Horbach and Zesch ( 2019 ); Riordan Brain et al. ( 2019 ); Ding et al. ( 2020 ); Kumar et al. ( 2020 ) proved that neural network models are vulnerable when a student response contains constructed irrelevant, adversarial answers. And a student can easily bluff an automated scoring system by submitting different responses like repeating sentences and repeating prompt words in an essay. From Loukina et al. ( 2019 ), and Madnani et al. ( 2017b ). The fairness of an algorithm is an essential factor to be considered in AES systems.
While talking about speech assessment, the data set contains audios of duration up to one minute. Feature extraction techniques are entirely different from text assessment, and accuracy varies based on speaking fluency, pitching, male to female voice and boy to adult voice. But the training algorithms are the same for text and speech assessment.
Once an AES system evaluates essays and short answers accurately in all directions, there is a massive demand for automated systems in the educational and related world. Now AES systems are deployed in GRE, TOEFL exams; other than these, we can deploy AES systems in massive open online courses like Coursera(“ https://coursera.org/learn//machine-learning//exam ”), NPTEL ( https://swayam.gov.in/explorer ), etc. still they are assessing student performance with multiple-choice questions. In another perspective, AES systems can be deployed in information retrieval systems like Quora, stack overflow, etc., to check whether the retrieved response is appropriate to the question or not and can give ranking to the retrieved answers.

Conclusion and future work

As per our Systematic literature review, we studied 62 papers. There exist significant challenges for researchers in implementing automated essay grading systems. Several researchers are working rigorously on building a robust AES system despite its difficulty in solving this problem. All evaluating methods are not evaluated based on coherence, relevance, completeness, feedback, and knowledge-based. And 90% of essay grading systems are used Kaggle ASAP (2012) dataset, which has general essays from students and not required any domain knowledge, so there is a need for domain-specific essay datasets to train and test. Feature extraction is with NLTK, WordVec, and GloVec NLP libraries; these libraries have many limitations while converting a sentence into vector form. Apart from feature extraction and training Machine Learning models, no system is accessing the essay's completeness. No system provides feedback to the student response and not retrieving coherence vectors from the essay—another perspective the constructive irrelevant and adversarial student responses still questioning AES systems.

Our proposed research work will go on the content-based assessment of essays with domain knowledge and find a score for the essays with internal and external consistency. And we will create a new dataset concerning one domain. And another area in which we can improve is the feature extraction techniques.

This study includes only four digital databases for study selection may miss some functional studies on the topic. However, we hope that we covered most of the significant studies as we manually collected some papers published in useful journals.

Below is the link to the electronic supplementary material.

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Contributor Information

Dadi Ramesh, Email: moc.liamg@44hsemaridad .

Suresh Kumar Sanampudi, Email: ni.ca.hutnj@idupmanashserus .

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An automated essay scoring systems: a systematic literature review

Published: 23 September 2021
Volume 55 , pages 2495–2527, ( 2022 )

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Dadi Ramesh ORCID: orcid.org/0000-0002-3967-8914 1 , 2 &
Suresh Kumar Sanampudi 3

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Automated Essay Scoring Systems

Automated Essay Scoring System Based on Rubric

Avoid common mistakes on your manuscript.

1 Introduction

2 Research method

We framed the research questions with PICOC criteria.

Population (P) Student essays and answers evaluation systems.

Intervention (I) evaluation techniques, data sets, features extraction methods.

Comparison (C) Comparison of various approaches and results.

Outcomes (O) Estimate the accuracy of AES systems,

Context (C) NA.

2.1 Research questions

To collect and provide research evidence from the available studies in the domain of automated essay grading, we framed the following research questions (RQ):

RQ1 what are the datasets available for research on automated essay grading?

The answer to the question can provide a list of the available datasets, their domain, and access to the datasets. It also provides a number of essays and corresponding prompts.

RQ2 what are the features extracted for the assessment of essays?

The answer to the question can provide an insight into various features so far extracted, and the libraries used to extract those features.

RQ3, which are the evaluation metrics available for measuring the accuracy of algorithms?

The answer will provide different evaluation metrics for accurate measurement of each Machine Learning approach and commonly used measurement technique.

RQ4 What are the Machine Learning techniques used for automatic essay grading, and how are they implemented?

RQ5 What are the challenges/limitations in the current research?

The answer to the question provides limitations of existing research approaches like cohesion, coherence, completeness, and feedback.

2.2 Search process

2.3 Selection criteria

Inclusion criteria 1 Our approach is to work with datasets comprise of essays written in English. We excluded the essays written in other languages.

Inclusion criteria 2 We included the papers implemented on the AI approach and excluded the traditional methods for the review.

Inclusion criteria 3 The study is on essay scoring systems, so we exclusively included the research carried out on only text data sets rather than other datasets like image or speech.

Exclusion criteria We removed the papers in the form of review papers, survey papers, and state of the art papers.

2.4 Quality assessment

Quality Assessment 1: Internal validity.

Quality Assessment 2: External validity.

Quality Assessment 3: Bias.

The two reviewers review each paper to select the final list of documents. We used the Quadratic Weighted Kappa score to measure the final agreement between the two reviewers. The average resulted from the kappa score is 0.6942, a substantial agreement between the reviewers. The result of evolution criteria shown in Table 1 . After Quality Assessment, the final list of papers for review is shown in Table 2 . The complete selection process is shown in Fig. 1 . The total number of selected papers in year wise as shown in Fig. 2 .

Selection process

Year wise publications

3.1 What are the datasets available for research on automated essay grading?

3.2 RQ2 what are the features extracted for the assessment of essays?

Features play a major role in the neural network and other supervised Machine Learning approaches. The automatic essay grading systems scores student essays based on different types of features, which play a prominent role in training the models. Based on their syntax and semantics and they are categorized into three groups. 1. statistical-based features Contreras et al. ( 2018 ); Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ) 2. Style-based (Syntax) features Cummins et al. ( 2016 ); Darwish and Mohamed ( 2020 ); Ke et al. ( 2019 ). 3. Content-based features Dong et al. ( 2017 ). A good set of features appropriate models evolved better AES systems. The vast majority of the researchers are using regression models if features are statistical-based. For Neural Networks models, researches are using both style-based and content-based features. The following table shows the list of various features used in existing AES Systems. Table 4 represents all set of features used for essay grading.

We studied all the feature extracting NLP libraries as shown in Fig. 3 . that are used in the papers. The NLTK is an NLP tool used to retrieve statistical features like POS, word count, sentence count, etc. With NLTK, we can miss the essay's semantic features. To find semantic features Word2Vec Mikolov et al. ( 2013 ), GloVe Jeffrey Pennington et al. ( 2014 ) is the most used libraries to retrieve the semantic text from the essays. And in some systems, they directly trained the model with word embeddings to find the score. From Fig. 4 as observed that non-content-based feature extraction is higher than content-based.

Usages of tools

Number of papers on content based features

3.3 RQ3 which are the evaluation metrics available for measuring the accuracy of algorithms?

3.4 RQ4 what are the Machine Learning techniques being used for automatic essay grading, and how are they implemented?

3.4.1 Regression based models

3.4.2 Classification based Models

3.4.3 Neural network models

3.4.4 Ontology based approach

3.4.5 Speech response scoring

3.4.5.1 The state of the art

3.4.6 Comparison of all approaches

First, to train a Machine Learning algorithm with essays, all the essays are converted to vector form. We can form a vector with BoW and Word2vec, TF-IDF. The BoW and Word2vec vector representation of essays represented in Table 6 . The vector representation of BoW with TF-IDF is not incorporating the essays semantic, and it’s just statistical learning from a given vector. Word2vec vector comprises semantic of essay in a unidirectional way.

In BoW, the vector contains the frequency of word occurrences in the essay. The vector represents 1 and more based on the happenings of words in the essay and 0 for not present. So, in BoW, the vector does not maintain the relationship with adjacent words; it’s just for single words. In word2vec, the vector represents the relationship between words with other words and sentences prompt in multiple dimensional ways. But word2vec prepares vectors in a unidirectional way, not in a bidirectional way; word2vec fails to find semantic vectors when a word has two meanings, and the meaning depends on adjacent words. Table 7 represents a comparison of Machine Learning models and features extracting methods.

In AES, cohesion and coherence will check the content of the essay concerning the essay prompt these can be extracted from essay in the vector from. Two more parameters are there to access an essay is completeness and feedback. Completeness will check whether student’s response is sufficient or not though the student wrote correctly. Table 8 represents all four parameters comparison for essay grading. Table 9 illustrates comparison of all approaches based on various features like grammar, spelling, organization of essay, relevance.

3.5 What are the challenges/limitations in the current research?

3.5.1 Other challenges that influence the Automated Essay Scoring Systems.

4 Synthesis

Our observations on automated essay grading systems from 2010 to 2020 are as followed:

The implementation techniques of automated essay grading systems are classified into four buckets; there are 1. regression models 2. Classification models 3. Neural networks 4. Ontology-based methodology, but using neural networks, the researchers are more accurate than other techniques, and all the methods state of the art provided in Table 3 .

The majority of the regression and classification models on essay scoring used statistical features to find the final score. It means the systems or models trained on such parameters as word count, sentence count, etc. though the parameters extracted from the essay, the algorithm are not directly training on essays. The algorithms trained on some numbers obtained from the essay and hear if numbers matched the composition will get a good score; otherwise, the rating is less. In these models, the evaluation process is entirely on numbers, irrespective of the essay. So, there is a lot of chance to miss the coherence, relevance of the essay if we train our algorithm on statistical parameters.

In the neural network approach, the models trained on Bag of Words (BoW) features. The BoW feature is missing the relationship between a word to word and the semantic meaning of the sentence. E.g., Sentence 1: John killed bob. Sentence 2: bob killed John. In these two sentences, the BoW is "John," "killed," "bob."

In the Word2Vec library, if we are prepared a word vector from an essay in a unidirectional way, the vector will have a dependency with other words and finds the semantic relationship with other words. But if a word has two or more meanings like "Bank loan" and "River Bank," hear bank has two implications, and its adjacent words decide the sentence meaning; in this case, Word2Vec is not finding the real meaning of the word from the sentence.

The features extracted from essays in the essay scoring system are classified into 3 type's features like statistical features, style-based features, and content-based features, which are explained in RQ2 and Table 3 . But statistical features, are playing a significant role in some systems and negligible in some systems. In Shehab et al. ( 2016 ); Cummins et al. ( 2016 ). Dong et al. ( 2017 ). Dong and Zhang ( 2016 ). Mathias and Bhattacharyya ( 2018a ; b ) Systems the assessment is entirely on statistical and style-based features they have not retrieved any content-based features. And in other systems that extract content from the essays, the role of statistical features is for only preprocessing essays but not included in the final grading.

In AES systems, coherence is the main feature to be considered while evaluating essays. The actual meaning of coherence is to stick together. That is the logical connection of sentences (local level coherence) and paragraphs (global level coherence) in a story. Without coherence, all sentences in a paragraph are independent and meaningless. In an Essay, coherence is a significant feature that is explaining everything in a flow and its meaning. It is a powerful feature in AES system to find the semantics of essay. With coherence, one can assess whether all sentences are connected in a flow and all paragraphs are related to justify the prompt. Retrieving the coherence level from an essay is a critical task for all researchers in AES systems.

In automatic essay grading systems, the assessment of essays concerning content is critical. That will give the actual score for the student. Most of the researches used statistical features like sentence length, word count, number of sentences, etc. But according to collected results, 32% of the systems used content-based features for the essay scoring. Example papers which are on content-based assessment are Taghipour and Ng ( 2016 ); Persing and Ng ( 2013 ); Wang et al. ( 2018a , 2018b ); Zhao et al. ( 2017 ); Kopparapu and De ( 2016 ), Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ); Mohler and Mihalcea ( 2009 ) are used content and statistical-based features. The results are shown in Fig. 3 . And mainly the content-based features extracted with word2vec NLP library, but word2vec is capable of capturing the context of a word in a document, semantic and syntactic similarity, relation with other terms, but word2vec is capable of capturing the context word in a uni-direction either left or right. If a word has multiple meanings, there is a chance of missing the context in the essay. After analyzing all the papers, we found that content-based assessment is a qualitative assessment of essays.

On the other hand, Horbach and Zesch ( 2019 ); Riordan Brain et al. ( 2019 ); Ding et al. ( 2020 ); Kumar et al. ( 2020 ) proved that neural network models are vulnerable when a student response contains constructed irrelevant, adversarial answers. And a student can easily bluff an automated scoring system by submitting different responses like repeating sentences and repeating prompt words in an essay. From Loukina et al. ( 2019 ), and Madnani et al. ( 2017b ). The fairness of an algorithm is an essential factor to be considered in AES systems.

While talking about speech assessment, the data set contains audios of duration up to one minute. Feature extraction techniques are entirely different from text assessment, and accuracy varies based on speaking fluency, pitching, male to female voice and boy to adult voice. But the training algorithms are the same for text and speech assessment.

Once an AES system evaluates essays and short answers accurately in all directions, there is a massive demand for automated systems in the educational and related world. Now AES systems are deployed in GRE, TOEFL exams; other than these, we can deploy AES systems in massive open online courses like Coursera(“ https://coursera.org/learn//machine-learning//exam ”), NPTEL ( https://swayam.gov.in/explorer ), etc. still they are assessing student performance with multiple-choice questions. In another perspective, AES systems can be deployed in information retrieval systems like Quora, stack overflow, etc., to check whether the retrieved response is appropriate to the question or not and can give ranking to the retrieved answers.

5 Conclusion and future work

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Ramesh, D., Sanampudi, S.K. An automated essay scoring systems: a systematic literature review. Artif Intell Rev 55 , 2495–2527 (2022). https://doi.org/10.1007/s10462-021-10068-2

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LEEWS (Law Essay Exam Writing System)

The Law School (Bar) Exam Writing / Study / Preparation System (LEEWS) will help you prepare for and write A exams (B's guaranteed) and/or pass the bar. Wentworth Miller - attorney, Yale law graduate ('77), Rhodes scholar has developed polished for over 25 years comprehensive system of preparation exam taking, applicable to any essay-type in legal subject, that is remarkably effective.

Audio Program now online

(click on free trial segment below), reactions to leews ... ( click for near 1,000 others. ).

A friend told me that his friend at Georgetown took LEEWS and scored the highest in two of his classes. Great lecturer. (Mr. Wu took LEEWS as a first semester 1L. He went on to be named editor-in-chief of the Columbia Law Review for ’98-’99.)

There are lots of commercial programs and aids designed to assist law students in studying for and writing exams, but—trust us—Wentworth Miller's [“LEEWS”] is the best of the bunch. [LEEWS] is the secret behind the success of more law review members than you can shake a stick at. You really should check it out.

LEEWS takes the old IRAC approach and vastly improves on it. LEEWS is a Godsend ...It is truly disgraceful that a LEEWS-type program is not part-and-parcel of every law school's pedagogy.

Mr. Miller: I want to thank you for the help I received by way of the CD program I ordered. My GPA was about 2.49 after my first year. I saw more C's than I care to enumerate. I listened to the program, utilized the strategy, and my grades this last semester [fall 2008] were much better—A, A-, B+, B. B. Put simply, it worked. Thanks.

I’m a 3L and currently “Grand Chancellor,” class of 2011. (1st in my class w/a 4.13 GPA.) I believe in giving credit where credit is due. LEEWS factored significantly in my academic success. [Audio program, summer before starting.] (This despite a median—for my school—LSAT score.) It framed not just my approach to taking exams, but my approach to thinking about the law, and thus what I took away from my classes. Scored several highest grade awards. In one where the final was out of 50 points, I ended up with 37—and the median score was 12!

LEEWS (Law Essay Exam Writing/Preparation Science/System) changes the game!

— Art Jarrett, Temple '91 (Who then raised every grade a full point and made dean's list.)

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100 Words Essay on Examination System
250 Words Essay on Examination System Introduction. The examination system is an integral part of the education system, serving as a yardstick to gauge a learner's understanding of the subjects taught. It is a time-tested method that has been used for centuries to measure students' intellectual capabilities.
Essay on Examination with Outline Quotations and tips
In this post, you will find an essay discussing the examination system in Pakistan, accompanied by quotations. It is intended for students studying in Matric, FFSC, and the 2nd year. Class 12 students can utilize this essay as a practice tool for their annual exams. Similarly, FSC students can write a similar essay titled "Essay on ...
Short Essay on Our Examination System
Our Examination System Essay (400 Words) Examinations are of great use. Examinations are a means (way) of judging or knowing the ability of candidates. Good results in examinations are taken as a sign of knowledge and ability. One important reason for the decrease in the importance of examinations is the teaching standards.
Examinations system essay for 2nd year with outline
The students of 2nd year in Pakistan have to write an essay on the given topic in their board exams. An essay on our examination system is the way of expressing your views about the examinations in Pakistan. This essay gives the students, an understanding of how they can write this essay using the given information. 1.
Essay on Our Examination System in Pakistan with Quotations
Examination System of Pakistan Essay with Quotations for FSC, 2nd Year Students. Education makes life worth living. A country without a proper, system of education can make no progress. Only the educated and skilled people can pave the way to progress. For good education, an adequate system of examination is necessary.
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The examination system in Pakistan holds a vital position in determining the academic prowess of. students. It serves as a mark for assessing their understanding of the class, critical thinking capacities, and overall academic performance. Examinations, in various forms, have been an integral part of the. educational environment in Pakistan.
Examination System In Pakistan Essay
Examination System In Pakistan Essay. Educational system Pakistan is divided into five levels such as: Primary level. Middle level. Secondary level. Intermediate level. University level. All the schools, colleges and universities in Pakistan have been set in three categories namely: Government schools.
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Our examination system is charged with "Booty-Mafia" cheating and corruption. It cannot be called a fair hundred percent. The trend and tendency of the use of unfair means are increasing with every passing day. The idle and sluggish students employ a large variety of unfair means. The students write down the relevant material on small chits ...
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The examination is the center of studies and hard work. It is a motivating force to work. Its importance and efficacy have been called in question. The most important point is that examinations are not the real test of knowledge and understanding. They are the test of ignorance or cramming. Still, we can say that examinations are necessary evil ...
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The examination system, also known as "civil service examinations" or "imperial examinations"—and, in Chinese, as keju 科舉, keju zhidu 科舉制度, gongju 貢舉, xuanju 選舉 or zhiju 制舉—was the imperial Chinese bureaucracy's central institution for recruiting its officials. Following both real and idealized models from ...
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This essay delves into the examination system in India, examining its strengths, weaknesses, and the ongoing discourse around its effectiveness. Quick Overview: Assessment of Knowledge: The primary purpose of examinations is to assess students' comprehension of academic content. They provide a standardized method to evaluate the knowledge ...
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Even otherwise , the prevalent examination system encourages cramming. Those who have a good memory or can indulge. In cramming, steal a march over others who cannot do this. Then, it is extremely painful to all lovers of transparency that sometimes even the question papers are sold in the market a day or so before an examination.
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Abstract. This paper aims to compare and contrast the public examination system and the school assessment system in both countries. It also explores the different emphasis and the possible factors of higher mathematics performance of Chinese students as compared to Malaysian students. These possible factors might include: a) cultural and ...
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An online e xamination system is an application that allows an institution conduct examination via the. Internet (or intranet). Various companies, institutions and org anizations have opted for ...
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Essay on Examination 500+ Words. Examinations, often called "exams," are a common part of education. They are tests that help us learn, measure our knowledge, and prepare for the future. In this essay, we will explore the importance of examinations in education, how they help us grow, and why they are necessary.
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Our Examination System. Decline in the standard of education 2. Fault lies with the faulty examination system 3. Only a test of memory 4. Causes worry but not induces students to work hard 5. Teachers are over burdened 6. Profitable business 7. Need to change the system The standard of education is declining in Pakistan day by day.
Examination System
The answer is simple, year round school. Having school year round will enable students to expand their knowledge, decrease the time during which kids are idle when they tend to get into the most trouble, and reduce the stress levels of both teachers and students.…. 623 Words. 3 Pages. Satisfactory Essays.
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This is just a sample. You can get a custom paper by one of our expert writers. Get your custom essay. Helping students since 2015. Essay Sample: In 2016, thousands of children and teenagers turned to Childline, unable to cope with the stress and pressure of exams. 1 in 5 children have a diagnosed.
An automated essay scoring systems: a systematic literature review
This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers.
An automated essay scoring systems: a systematic literature review
This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer ...
LEEWS (Law Essay Exam Writing System)
The Law School (Bar) Exam Writing / Study / Preparation System (LEEWS) will help you prepare for and write A exams (B's guaranteed) and/or pass the bar. Wentworth Miller - attorney, Yale law graduate ('77), Rhodes scholar has developed polished for over 25 years comprehensive system of preparation exam taking, applicable to any essay-type in legal subject, that is remarkably effective.
Essay on the Examination System of India
Essay on the Examination System of India. Examination means the test of a student's knowledge in prescribed subjects. An examination creates a sort of care in students to prepare their studies sincerely. Examination may be of various types such as oral, written and practical. In lower classes oral test is conducted in lieu of written test ...