matrix education essay writing

Writing based on Texts

• Idea Matrix for Writing

An idea matrix is a way to prewrite once you have an idea of what you want to write about. An idea matrix for writing is just what it sounds like – it’s a table or grid that helps you identify, organize, and develop your ideas. You lay out your ideas in columns and rows, and fill details in the appropriate squares.

How to Create an Idea Matrix

Working Thesis: Write your topic and your assertion about that topic. Your thesis is a “working” thesis because you may find that you need to circle back and revise it as you develop your supporting ideas and details. The thesis and support need to relate directly to one another.

Supporting Ideas: In this column, insert main ideas that support your assertion in the working thesis.  What would convince a reader to support your assertion? These ideas will eventually become your topic sentence assertions.

Details: In each row, insert details and ideas that further develop and exemplify each supporting idea group. Note that you do not have to fill up every square of the grid.

For example, here’s an initial idea matrix a writer developed to identify possible topic sentence ideas:

The writer’s next step was to add notes in each supporting idea row for examples and details to develop that supporting idea:

After adding notes, the writer assessed her idea matrix:

• She decided that she really didn’t have much to say about fiber, so she deleted that supporting idea.
• She realized that “history of heavily-processed foods” did not directly provide an idea that supported her assertion about harming health, so she deleted that concept.
• She realized that she needed to say more about fat intake, so she added more details in that row.
• She tweaked and added details overall.
• Then she turned her supporting ideas into topic sentence assertions to create the following revised idea matrix.

The writer may have two more iterations of this essay planning matrix:

• If she decides on a certain order for her information, she may decide to re-order the rows and tweak the working thesis to indicate a particular pattern of organization.
• If she decides to support some of her assertions with research, she might add rows under each topic sentence row to house appropriate quotations or summaries.

The final idea matrix, if the writer made these two changes, might look like this:

As you might have deduced, after working progressively with an idea matrix to plan your essay, it’s a relatively easy next step to draft that essay.  An idea matrix helps you identify the “bare bones” – the skeletal structure of ideas in an essay that you then can develop with examples and details.  It also helps you stay on track, since you can see at a glance if there’s an idea that doesn’t directly support the assertion in the working thesis, or that doesn’t fit with the other ideas.

Here are two templates that you are welcome to download and adapt to help you write:

• Idea Matrix for Research Writing

Idea Matrices in Other Contexts

Just as a note: Idea matrices are used in many other contexts. The following video focuses on generating ideas in a business to engage consumers. Although the context is different, you’ll see the process of using a matrix to generate ideas is the same.

Idea Matrices and Outlines

Note that an idea matrix essentially puts your planned information for an essay into a visual format, so you can see at a glance where you need more or less information.  If an idea matrix does not resonate with you, you can always do an outline, which has the same purpose of putting planned information for an essay into a visual format. Instead of using rows, columns, and “boxes” of a matrix, an outline uses indentation to show major and minor idea relationships.

For example, using the sample above, an outline for an essay might look like this:

Working Thesis: Eating too much heavily-processed food can lead to a number of harmful effects on a person’s health.

I. Heavily-processed foods often contain empty calories…

A. Body may not be getting nutrients… B. Poor nutrition leads to more frequent illnesses… C. May lead to disease that develops from nutritional deficiencies

II. Many heavily-processed foods are high in fat…

A. May heighten bad type of cholesterol… B. May lead to weight gain… C. Weight gain may lead to development of different cancers…

III. Higher salt and sugar may harm a person’s heart and blood systems…

A. May harm blood pressure… B. Higher risk of cardiovascular disease… C. May lead to elevated blood sugar, pre-diabetes…

Link to the page on Outlines and refer to the section on Outlines for Writing for more information.

• Idea Matrix. Authored by : Susan Oaks. Project : Introduction to College Reading & Writing. License : CC BY-NC: Attribution-NonCommercial
• image of a matrix with colored tiles. Authored by : David Zydd. Provided by : Pixabay. Located at : https://pixabay.com/illustrations/square-background-color-mosaic-2724387/ . License : CC0: No Rights Reserved
• video Design Thinking: Using an Idea Matrix. Provided by : Skillsoft YouTube. Located at : . License : Other . License Terms : YouTube video

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58 The Matrix Essay Topic Ideas & Examples

🏆 best the matrix topic ideas & essay examples, 📌 good essay topics on the matrix, 🔎 most interesting the matrix topics to write about.

• “The Matrix” Trilogy and Greek Mythology: Comparison One of the exceptions of the previous statements is the Matrix trilogy directed by Larry and Andy Wachowski.”The Matrix”, The Matrix: Reloaded”, and “The Matrix: Revolution”, all of which gained a cult status among fans, […]
• Demystifying the Fiction Movie “The Matrix” The second world is a generic world created by the machines in order to pacify the human being as the machines siphon energy from people by plugging the human beings into an artificial intelligence system […] We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Cartesian Skepticism in “The Matrix” Film The purpose of this paper is to explore the philosophical foundation of that theory and see how it is tied to the basis behind The Matrix.
• Descartes’ Epistemology in “The Matrix” The present paper analyzes Descartes’ epistemology in the light of the tripartite theory of knowledge and uses a science fiction film The Matrix to understand Descartes’ influence on the human understanding of reality.
• Film Studies: “The Matrix” by Larry and Andy Wachowski The Matrix tries to bring out the truth, and the reality of issues that affect the world. The Agents have the freedom to control the making of the film and are responsible for all the […]
• “The Matrix” by The Wachowskis Review Interestingly, the script and plot develop in significant accordance with Descartes and Berkeley’s ideas, and answers to the philosophical questions that the film raises can be found in the works of these thinkers.
• Holden Caulfield and Morpheus in “The Matrix” But the fact is that, the feature, that the main characters – Holden Caulfield and Morpheus are searching for truth, and try to obtain it by every effort possible.
• “X-Files: Film” and “The Matrix”: Comparative Analysis One of the significant differences between the X-Files and The Matrix, is that X-Files emphasize the potential of alien control in the end while The Matrix is more optimistic as it shows how one man […]
• Comparison of Approaches of “Fight Club” & “The Matrix” Cultural issues on sexuality have now been changed because media has made the issue open for everyone so that it is now fun to have the feelings and desires of the “other” brought to the […]
• The Philosophical Concepts Behind “The Matrix” The informational development and the widespread of the information is the puppets shown to the people in the cave, that can become their matrix.
• Popular Culture and Contemporary Life. The Matrix The movie The Matrix represents a new reality or a new philosophy of the 21st century. The Matrix may be a new mode of transmedia storytelling, as Jenkins argues, but the value of the movie […]
• Descartes’ Philosophy of Mind in “The Matrix” Film The world of The Matrix is one of the examples of the precarity of that, which may be called the real world as perceived by the beholder.
• “The Matrix” Film by the Wachowskis Analysis This assumption is validated by the fact that in several scenes he is introduced to the concept of agents, programs designed to ensure the stability of the virtual world, and is given the choice of […]
• Narration in “All About My Mother” and “The Matrix” Such structure suggests the presence of three essential parts: in the first one the problem is introduced, in the second one the characters work on the problem, and in the third one the problem is […]
• Neo in “The Matrix” – Film by the Wachowskis The title closely reflects the central conflict as the matrix is the name of the machine that harbors the imprisoned humans and alters their reality.
• “The Matrix“ by Andy and Larry Wachowski: Philosophical Picture of the Post-apocalyptic World Therefore, the Matrix, the system created by computers, could be considered as a dystopian world in which humans are controlled by machines, deprived of the truth about the real world.
• “The Matrix“ by Andy and Larry Wachowski: Themes and Summary Because Morpheus is mythologically attached to sleep it is intriguing that the Morpheus that is feature prominently in the film is in touch with reality.
• Comparing and Contrasting the Followership and Mentorship in “Wall Street” and “The Matrix” He teaches Fox to use the loopholes of business to his advantage. Neo uses the skills that Morpheus has taught him to save his mentor.
• The Film “The Matrix” and Its Effect on Society
• How “The Matrix” Displays Mythic Heroism Through Heroism vs. The Machine
• Parallelism Between “The Matrix” and Christianity
• The Religious Amalgamation Dubbed “The Matrix”
• Marxist and Semiotic Analysis of “The Matrix”
• Religious Themes Within the Film “The Matrix”
• Describing Neo and His Identity in the Movie “The Matrix”
• A Screening of the Deception Hypothesis in “The Matrix”
• The Dark Knight and “The Matrix” Neo-Noir Characteristics
• The Artificial Intelligence Rule as Portrayed in “The Matrix”
• “The Matrix”: Technology Fears of a Dystopian World
• Descartes Doubt and “The Matrix” in Our Society
• The Postmodern Theory in the Ideas of Michael Foucault and the Movie “The Matrix”
• Similarities and Differences Between “The Matrix” and Plato’s “The Allegory of the Cave”
• Religious Archetypes of “The Matrix”
• The Comparison Between the Movies “The Matrix” and “Fight Club”
• Unearthing the Meaning Behind “The Matrix”
• Ideas Form Plato’s Ontology in “The Matrix”
• Interpreting “The Matrix” Through Descartes’s Philosophy
• Social Psychology in the Movie “The Matrix”
• Breaking the Laws of Space and Time to an Illusion in “The Matrix”
• Confusion Between Reality and Virtual Reality in “The Matrix”
• Morpheus, Trinity, and Neo’s Journey to Success in “The Matrix”
• Describing “The Matrix” Film Editing Techniques
• Defining the Elements of a Classic Plot Chart in “The Matrix”
• Implicating “The Matrix” in Youth Violence
• Understanding “The Matrix” and Its Relationship With Religion
• Symbols, Allegory, and Motifs in “The Matrix”
• Relief From “The Matrix”: Arguing Against Skepticism
• The Philosophy of “The Matrix”: From Plato and Descartes to Eastern Philosophy
• The Idea of Skepticism in the Movie “The Matrix”
• The Reality, Media, and the Role of Human Nature in “The Matrix”
• Comparing the Story “Harrison Bergeron” With the Film “The Matrix”
• The Postmodern Age in “The Matrix” Simulation
• Representations of Buddhism in the Modern World in “The Matrix”
• Cartesian and Platonic Philosophical Themes in “The Matrix”
• The Connection Between Senses and Reality in “The Matrix”
• Linking Between “The Matrix” and Descartes’s Theory of Immaterialism
• How the Film “The Matrix” Presents Two Different Versions of Reality
• Similarities Between “The Matrix” and “Charlie’s Angels” Films
• Chicago (A-D)
• Chicago (N-B)

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The writing matrix

How to transform a vague idea into a structured outline in less than 10 minutes.

In this post, I cover my writing method to transform a vague idea into a structured outline in less than 10 minutes. If you’ve got something to write this week, it’s a good place to start from.

Table of contents:

• The creativity distortion; three reasons why we think about creativity wrong
• Writing is thinking; when you sit down to write you need to have already thought
• The writing matrix; visualize the density and time dimensions of an idea and ask a sequence of questions to pull out a natural structure from your head
• Beyond writing; how the writing matrix helps to solve creative problems

Enjoy your reading.

The distorted view of creativity

Everybody writes. But for most people, writing is a struggle. They stare at a blinking cursor for hours and don’t hear a thing.

That’s because people think about creativity wrong. They believe creativity is some voodoo magic plus innate genius. It’s this brilliant idea that strikes you when you’re wandering around dark Edinburgh streets and see a boy with round eyeglasses, and you scream and run back and write Harry Potter at one go.

Why we think of creativity that way?

First, because journalists love it. Their jobs are to write what people read, and people strive for blood and sweat and tears. And if you give a journalist a story of Thomas Edison who failed ten thousand times but then finally figured out a major invention, they’ll scream with joy. But if you tell a journalist that you just thought through the problem, they’ll stare at you with a lame look. “People won’t click.” And if you consider that most humans receive information through journalism in one form or another, it becomes obvious why we think of creativity as a spark of inspiration—the incentives of journalists are screwed up to write about stories that feel inspiring.

The second reason is that most creative work actually gets done that way. I’ve been wondering why for a long time but haven’t found a good answer. For some reason, it’s just hard for people to accept that there might be a better way to think instead of staring at a blank page for days. Tesla once said about Edison that if he had to find a needle in a haystack, he’d spent years taking the whole thing down piece by piece instead of burning the hay in five minutes.

Lastly, hoping for a creative miracle takes the responsibility off your shoulders. It’s way easier to believe in creativity than in knowledge; to assume that creativity is some random event that cannot be controlled in any way whatsoever. Because if you fail, that’s not your fault. That’s why students love wearing t-shirts with Einstein’s quote that creativity is more important than intelligence. They don’t bother looking up Einstein’s work and seeing if he had said something else to say on the matter. Most of them don’t even know who Einstein was.

Misunderstanding of creativity explains why people face difficulties with writing. They think writing is when you sit down in front of a computer, open a new Word document, start typing—and boom, a few hours later, you’ve got a good piece of work.

But writing is actually thinking. To write something down, you need to have already thought what you’re going to say. That’s why people struggle so much to put words on a page; they haven’t thought through the idea [1].

Here’s the method I use to transform vague ideas into structured outlines.

Let’s say you’ve got an idea for an essay.

The first thing to do is to discard your belief that it’s an idea for an essay. Because an essay is a form, and the form must always meet the function. And if you’ve got a one-sentence dense idea, you don’t need an essay to communicate it to the world; a tweet will do. Unfortunately, most writers don’t follow this advice. That’s why we end up having 200-pages long NYT bestsellers covering one thought that could be explained with half a page of dense text.

To avoid producing fluffy prose, I designed a matrix for developing thoughts.

On the vertical axis, there’s a hierarchy of ideas by density. The layers are a sentence, a paragraph, half page, a page, a section, a chapter, a book, etc. Anything from a few words to 3 million characters in Anna Karenina.

On the horizontal dimension, I’ve got time. The process begins with the very inception of an idea to the moment it’s published. Note that for different layers of density, the time dimension is different as well (blue lines); it increases with the complexity of an idea itself. It will take a few minutes to express a sentence-long idea as a tweet and years to write a good 500-pages book.

Here’s a two-step algorithm I follow when I get an idea for writing.

First, I ask myself:

“What’s the density range of this idea?”

Usually, it’s hard to answer this question precisely because an idea is not fully formed yet. As Picasso said, you must begin drawing to start seeing the painting. But you must start from somewhere, and that’s why it’s useful to answer imprecisely; the range provides a rough understanding of what an idea is about. If you have trouble at this step and it’s hard to define even an imprecise range, change the question to an affirmative conclusion of the lowest density. Just tell yourself, “OK, let’s suppose this idea I’m thinking about is a paragraph-dense idea,” and proceed to the next step.

Second, I ask:

“Why do I think so?”

I try to be specific:

• “Why do I think this is a paragraph-dense idea?”
• “Why do I think it will take a page?”

This question pulls out the natural structure of an idea from my head [2]. I immediately hear things like: “Oh that’s because I need to explain such and such, and then I also need an introduction, and then…” I jot down the structure and begin elaborating on each part.

How the writing matrix helps to solve creative problems

You might have already guessed that the matrix applies to many things besides prose.

Let’s take problem-solving as an example. When you start on a problem, you don’t yet know the solution. But you can imagine the answer to the problem written down on a page as text. You can then apply the same density matrix to restructure your thought and pull out relevant information from your head; information that’s already there but hard to reach.

To do that, you ask the same questions:

• “What’s the density range for the solution to this problem written on a page?”
• “Could it be a paragraph-dense?”

This might work for any problem where an outcome can be turned into a knowledge artifact and then worked backwards through the matrix:

• Email. “Let’s assume this email is one sentence dense. Why so?”
• Industry report. “Let’s suppose this report is paragraph-dense. Why do I think so?”
• Facebook post. “OK, I think it’s a sentence-dense post. Why?

That’s how you get pointers to navigate your thought. You won’t see the whole thing, but you’ll start thinking, “Oh, it’s not a paragraph-dense solution because it has to include such and such..” And “such and such” is precisely what you need to get unstuck [3].

Now make yourself smarter.

If you go through the questions below, you will reap the most benefits of reading and create new neurons in your head through neurogenesis . Otherwise, you’ll forget about 80% of what you just read tomorrow. Don’t waste time.

• What are the three reasons why people think about creativity wrong?
• Why you have to discard your idea for an essay?
• What the writing matrix is about?
• What are the two questions you need to ask to pull out a structure of an idea from your head?
• How the writing matrix helps to solve creative problems?

Write well, Vasili

[1] Writing is like running; it always hurts to start. But if you’ve been running for a while, you know that it will get easier after the first ten minutes. You’re less scared of the suck part because you know the process. It’s the same with writing. If you know how a text evolves, you’re not scared to produce a shitty first draft and put it away for a few days. You’re confident because you know precisely where to begin, what to do, and what comes next. And this makes you excited to try new things and write more.

[2] The matrix method won’t work if you haven’t thought about the topic for a while already. It’s more focused on defining the structure than on coming up with ideas. I didn’t include the ideation part of writing in this essay because it goes beyond this work’s scope.

[3] If you’re still struggling, let it go. Write something else that you’re excited about. Or go back to thinking backwards and start from the purpose of the work—answer why you’re writing this in the first place and for who.

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115 The Matrix Essay Topic Ideas & Examples

Inside This Article

The Matrix, a groundbreaking science fiction film released in 1999, has become a cult classic and continues to captivate audiences with its thought-provoking themes and mind-bending concepts. For students and fans of the film who are looking for essay topics to explore in-depth analysis and discussion, here are 115 Matrix essay topic ideas and examples to consider:

• The concept of reality vs. illusion in The Matrix
• The role of technology in controlling human perception in The Matrix
• The significance of the red pill vs. blue pill choice in The Matrix
• The allegorical elements in The Matrix and their symbolic meanings
• The portrayal of artificial intelligence in The Matrix
• The theme of free will vs. determinism in The Matrix
• The influence of philosophy on The Matrix
• The role of morality and ethics in The Matrix
• The use of visual effects in The Matrix and their impact on storytelling
• The representation of gender and identity in The Matrix
• The portrayal of power dynamics in The Matrix
• The role of religion in The Matrix
• The impact of The Matrix on popular culture and society
• The depiction of violence in The Matrix
• The influence of Eastern philosophy on The Matrix
• The representation of race and ethnicity in The Matrix
• The role of Neo as a Christ-like figure in The Matrix
• The symbolism of the Matrix itself in the film
• The portrayal of human nature in The Matrix
• The significance of the Oracle in The Matrix
• The theme of rebellion and resistance in The Matrix
• The portrayal of love and relationships in The Matrix
• The role of fate and destiny in The Matrix
• The influence of science fiction literature on The Matrix
• The representation of government and authority in The Matrix
• The use of allegory in The Matrix
• The impact of The Matrix on the cyberpunk genre
• The portrayal of technology and its consequences in The Matrix
• The symbolism of the red pill in The Matrix
• The significance of the Matrix code in the film
• The representation of reality as a simulation in The Matrix
• The role of memory and perception in The Matrix
• The influence of Greek mythology on The Matrix
• The portrayal of leadership and heroism in The Matrix
• The significance of the Matrix as a metaphor for society
• The representation of truth and deception in The Matrix
• The role of consciousness and self-awareness in The Matrix
• The symbolism of the black cat in The Matrix
• The depiction of technology as a tool of control in The Matrix
• The influence of Buddhism on The Matrix
• The portrayal of sacrifice and redemption in The Matrix
• The significance of the mirror scene in The Matrix
• The role of prophecy and destiny in The Matrix
• The symbolism of the spoon in The Matrix
• The representation of the mind-body problem in The Matrix
• The influence of postmodernism on The Matrix
• The portrayal of virtual reality in The Matrix
• The significance of the Oracle's predictions in The Matrix
• The role of trust and betrayal in The Matrix
• The symbolism of the Matrix agents in the film
• The representation of social inequality in The Matrix
• The influence of existentialism on The Matrix
• The portrayal of the hero's journey in The Matrix
• The significance of the Matrix as a metaphor for the internet
• The role of choice and agency in The Matrix
• The symbolism of the Matrix's green tint in the film
• The representation of fear and uncertainty in The Matrix
• The influence of Gnosticism on The Matrix
• The portrayal of surveillance and control in The Matrix
• The significance of the glitch in the Matrix in the film
• The role of memory and identity in The Matrix
• The symbolism of the Oracle's cookies in The Matrix
• The representation of the body as a prison in The Matrix
• The influence of artificial intelligence on The Matrix
• The portrayal of rebellion and revolution in The Matrix
• The significance of the Matrix as a symbol of oppression
• The role of fate and choice in The Matrix
• The symbolism of the white room in The Matrix
• The representation of the hero as a chosen one in The Matrix
• The influence of determinism on The Matrix
• The portrayal of reality as a construct in The Matrix
• The significance of the phone booth in The Matrix
• The role of memory and trauma in The Matrix
• The symbolism of the deja vu phenomenon in The Matrix
• The representation of the human body in The Matrix
• The influence of skepticism on The Matrix
• The portrayal of the mind as a weapon in The Matrix
• The significance of the Architect's speech in The Matrix
• The role of prophecy and fate in The Matrix
• The symbolism of the mirror in The Matrix
• The representation of the hero's journey in The Matrix Reloaded
• The influence of technology on The Matrix Revolutions
• The portrayal of the Oracle's role in The Matrix trilogy
• The significance of the Matrix as a metaphor for the internet in The Matrix Reloaded
• The role of choice and destiny in The Matrix Revolutions
• The symbolism of the Matrix as a system of control in The Matrix trilogy
• The representation of the Matrix agents as enforcers of order in The Matrix Reloaded
• The influence of rebellion and resistance in The Matrix trilogy
• The portrayal of the human-machine war in The Matrix Revolutions
• The significance of the Oracle's prophecy in The Matrix trilogy
• The role of Neo as the chosen one in The Matrix Reloaded
• The symbolism of the Zion city in The Matrix Revolutions
• The representation of the Matrix as a prison in The Matrix trilogy
• The influence of the Matrix code on human perception in The Matrix Reloaded
• The portrayal of the Merovingian as a symbol of power in The Matrix Revolutions
• The significance of the Matrix's green tint in The Matrix trilogy
• The role of the Architect as the creator of the Matrix in The Matrix Reloaded
• The symbolism of the Oracle's cookies in The Matrix Revolutions
• The representation of the human-machine alliance in The Matrix trilogy
• The influence of the Matrix as a system of control in The Matrix Reloaded
• The portrayal of the Matrix's glitches as signs of instability in The Matrix Revolutions
• The significance of the train station as a liminal space in The Matrix trilogy
• The role of choice and agency in The Matrix Reloaded
• The symbolism of the Oracle's prophecies in The Matrix Revolutions
• The representation of the human-machine alliance as a path to peace in The Matrix trilogy
• The influence of the Matrix's green tint on human perception in The Matrix Reloaded
• The portrayal of the Oracle's role in guiding human destiny in The Matrix Revolutions
• The significance of the Matrix as a system of control in The Matrix trilogy

These essay topics and examples provide a starting point for exploring the rich themes and ideas presented in The Matrix. Whether you are writing an academic paper, a critical analysis, or simply discussing the film with fellow fans, there is no shortage of material to delve into and uncover the deeper meanings and implications of this iconic sci-fi masterpiece.

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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

Selection process

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.

Usages of tools

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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Matrix: Education and Students

Philosophy Matrix Use the Knight textbook to complete this matrix. See the syllabus for detailed instructions. The following list is to help you consider what should be written in each column.

Metaphysics = What is reality/truth? What is purpose and meaning in life? Epistemology = Is it possible to know reality/truth? If so, how? Axiology = What values should be developed in education?

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Learner’s Nature = What is the role of the learner? What is the human condition? Good? Bad? Neutral? What learner factors should be considered in education? Teacher’s Role = What is the most effective approach the teacher should take? Curricular Focus = What content is most important?

Methodology = What pedagogical strategies are most effective? Criticisms = What do opponents of this philosophy/theory say about it?

(The names beside each philosophy are just for reference to help you connect the philosophy with specific philosophers.) Traditional Philosophies IDEALISM (Plato) Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Truth lies in a realm of ideas that is beyond the world of the senses Knowing truth requires it to fit into a harmonious nature of the universe. Values that express goodness and beauty with a life lived in harmony with the universe. Absolute Self-macrocosm and individual human self is microcosmic. Most important aspect of learners is intellect. Idealists concentrate on the mental development of the learner. The teacher’s role is to pass on knowledge of reality and to be examples of the ethical ideal. The curriculum must be formed around those subjects that bring students into contact with ideas Classrooms are an open extension on a library, because it’s a place where books and ideas form the center of attention. Methodology is lecturing, verbal transfer between teacher and student, reading and discussion. Seems as ivory tower experience. School is a sustainer of the status quo. REALISM (Aristotle)

Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms The realist sees reality in terms of things that operate according to the natural law of the physical world in which people live that makes up reality. Truth for the realist is viewed as observable fact. The realist seeks to discover how the world works by examining it. The natural law thereby discovered is believed to be built into the nature of reality. Through natural law values are obtained. One knows the ethical and aesthetic judgment through the study of natural order. Developing of nature as the moral law. A beautiful art form reflects the logic and order of the universe. Student is viewed as a spectator viewing the universal machine. Students must be reinforced, disciplined, and shaped until they have learned to make the proper responses. The role of the teacher is to provide information about reality to the student in the quickest and efficient. The teachers are to demonstrate the regularities and laws of nature and to pass on to the student those facts of the natural world that have been verified by research.

The focus of realist curriculum is on demonstrable facts and the structural frameworks of the academic disciplines that give meaning to those facts. Language and mathematics are also important in the curriculum because they provide the entrance to an academic discipline. Modern realist favor teaching for the mastery of facts to develop an understanding of natural law. Modern realist favor demonstrations in the classroom, filed trips and the use of audio-visual aids in the absence of field trips. In favor of teaching machines and programmed learning Other questioned the naturalistic presuppositions of realism that seen to them to be more in harmony with deism, agnosticism, or even atheism than with personal Christianity. Realism tends to be deterministic and to minimize the importance of humanity in its attempt to be objective and scientific. The lack of free will and human dignity implied in realism is dehumanizing and does not harmonize with Christian revelation. NEO-SCHOLASTICISM (Thomas Aquinas)

Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Two-sided coin, which is the natural world that is open to focus and the supernatural realm, which is understood through intuition. Neo-scholastics hold the nature of the universe to be permanent and unchanging. Neo-Scholastic believe in the hierarchy of the truth. Lower level relies on reason, which is the realm of nature and science that is limited. The higher level is the first principles and faith. The two realms overlap and form two routes to the same truth. They value most highly the truths that are logical, permanent and unchanging. Value developed through education is acting rationally. Good people are the ones whose desires and wills are subservient to their intellect. If they know what is right, they will do it, because it is reasonable to do what is good. Learners are a spiritual being who relate to God. Faculty psychology is what learners are viewed by neo –scholastics. The mind is carefully developed through different potentials. The faculty of reason, faulty of memory and the faculty of will allows the will to be brought under submission to the reason. Teachers are mental disciplinarians with the capability of developing reason, memory and will power in their students.

It’s the teacher’s responsibility to decide what knowledge the child should learn. The decision should harmonize with the child’s interest and curiosity, but subject matter concerns, rather than student’s desires are central to education. The curriculum should give priority to cultivating rationalism. Education should focus on sharpening the intellect by training the mind to think. The end result is for people to understand the Absolute Truth of the cosmos. Thus, the mind must be strengthened and toughened to reach the Truth. Internal logic subject matter is best and should be center of curriculum such as foreign language and Mathematics. Training of the intellectual powers, which is rooted in mental discipline. Relation to discipline inherent in the subject matter strengthens intellect through exercises in reason and memory. The mind is strengthened by strenuous mental exertion. Neo-Scholastic emphasis on the mental and rational side of human nature has not provided a base for the practical and physical in education.

Modern Philosophies

PRAGMATISM (William James, John Dewey) Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Denies their philosophic position has metaphysics. They do not believe in ultimate and absolute realm of reality. Pragmatist relies on the community Knowledge is rooted in experience. Knowledge is different from belief. Individuals make knowledge as they interact. No A priori and Absolut Truth. Truth is relative, changing and expanding. The axiology is directly related to the epistemology. Humanity is ultimately responsible for values. Values are relative and there are no absolute principles on which we can learn. The learner is active in their environment.

Natural problem solvers. Experiential learners. The teachers are the guide, fellow problem solvers, project facilitators. The student’s interest and problems as defined by the group.

The group choice, scientific methods, group work, projects, and experimentation. Most Christians view it as having an insufficient perspective on reality, truth, and value. EXISTENTIALISM (Nietzsche, Kierkegaard, Sartre, Heidegger, etc.)

Metaphysics

Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Existence preceded essence. The act of daily living is a process of defining personal essence. The individual is the authority. The individual has choices The values of individuals are based on their interest. They are free to make choices and actions. Free choosing agents, responsible for making meaning, independent.

Facilitator Explore possibility with students Encourage independent learning styles Open to individual student choice and interest Art, theatre, liberal, humanities, literature Individual goal setting Self assessment Reflection Journal writing Individualization

Difficulty for some Christians in relation to understanding existentialism is that religious existentialists often use the same word (god, creation, or redemption.) as traditional Christianity with different meaning.

Contemporary Educational Theories for Transmission PERENNIALISM (Adler, Hutchins, William Bennett, Allan Bloom, etc) Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Search for unchanging truth found in collective wisdom of western culture Truth and knowledge is changeless and found in western culture Changeless; determined by the very nature of reality Students are passive learners. Students learn what is being taught. Self-contained knowledge- teacher is supposed to know all the answers. Teacher is the expert of content knowledge and pass on wisdom

Basic skills and recurring themes in humanity. Geometry, English literature, world geography, algebra, trigonometry, ancient geography, world history, U.S. history. * Liberal arts formed traditional education.

Teacher-made tests, standardized test, memory work (in is a muscle) spelling bees. Direct instruction and Socratic dialogue

. Critics of perennialism contend that low-ability students simply cannot master subjects such as logic and foreign languages.

ESSENTIALISM (E.D. Hirsch) Metaphysics = What is reality/truth? What is purpose and meaning in life? Epistemology = Is it possible to know reality/truth? If so, how? Axiology = What values should be developed in education?

Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms What is prevalent is what helps the individual live well and what benefits humanity Truth exists in classics and modern science. Must learn process and content. Knowledge is gained through interaction and rational thought Determined by natural order of things. Values exist in the best of culture. Role of the student is there to listen and learn. The learner’s nature should focus on discipline. Because students need t discipline to focus on the task at hand. Effort is more important than interest.

Expert of Knowledge. Teacher’s essential knowledge. Maintain task-oriented focus. The teachers know what the students need to know and are well acquainted with the logical order of the subject matter and the way it should be presented. The teacher role demands respect. The teacher is the classroom authority. Strong emphasis on basic skills and on disciplines knowledge and scholastic achievement in secondary schools. Reading, spelling, language art, mathematics, U.S. and World History. No vocational education. Elementary school should focus on basic skills that contribute to literacy and mastery of arithmetical computations. Secondary education would aim at developing competency in history, mathematics, science, English, literature and foreign language. Tap dancing, basket weaving and etc. are not the business of the school. Avoids methodological frills and soft pedagogy and concentrated o sound, proven instructional methods. The teacher is enacting the curriculum and setting the standards to which the students must meet. The teacher’s evaluation role undermines students’ interest in study. As a result, the students begin to take on more of a passive role in their education as they are forced to meet and learn such standards and information. BEHAVIORISM (Skinner)

Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Reality for the behaviorist is independent of human knower. Events are explained by essences, causes, and inner principles. Observe living organisms including humans in an attempt to discover the laws of behavior. After these laws are discovered they will provide a foundation for technology of behavior. Efficiency, economy, precision and objectivity are central values considerations in education. Students learn in daily life through the consequences of their acts. The learner does not have any opportunity for evaluation or reflection within the learning process; they are simply told what is right or wrong. The teacher’s role is to create an effective learning environment. The teacher is the dominant person in the classroom and takes complete control; evaluation of learning comes from the teacher who decided what is right or wrong. Behaviorists are at least as concerned about how people behave as that they know. They do not tend to be big innovators in curriculum.

They will however give a fair trial to any new curricula that someone else might write. Empirical verification is central in behavioral methodology. Positive reinforcement is the missing ingredient in most school environments. Popular as a method of discipline and computer aided instruction. Cannot study sensation & perception as it denies mental aspects •

Study of after images through verbal reports is a kind of defeat of methodological behaviorism A Muscle Twitch Psychology which fails to take into account the purposive behavior

Contemporary Educational Theories for Transformation PROGRESSIVISM (Dewey) Metaphysics = What is reality/truth? What is purpose and meaning in life? Epistemology = Is it possible to know reality/truth? If so, how? Axiology = What values should be developed in education?

(The names beside each philosophy are just for reference to help you connect the philosophy with specific philosophers.) Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Students should understand the major problems facing society The curriculum should move from the abstract to the concrete, from the theoretical to the practical – learning by doing Values are relative and rules are sometimes inadequate in guiding complex decision-making Students are active beings. Students naturally want to learn and will learn if they are not frustrated in their learning by adults and authorities who seek to impose their wills and goals. Teacher role is as an advisor, guide, and fellow traveler rather than of authoritarian and classroom director. Teachers have more experience than their students. The child is the focal point of the school. They sought to develop a curriculum and teaching method that grew out of students’ needs, interest and initiatives. Curriculum content is derived from student interests and questions. The scientific method is used by progressivism educators So that student

can study matter and events systematically and first hand. Shared decision making, planning of teachers with students, student selected topics are all aspects. Books are tools , rather than authority

Focus on problem solving, knowledge is seen as an instrument for managing experience. *project method allows a teacher to guide to lead students through a larger part of curriculum in an almost painless manner. One way in which progressives differ a great deal from classical educators is that they often permit some degree of student self-evaluation to figure into the grading process. Critics charge that students are not equipped to do this fairly and objectively and that the practice demonstrates that progressives do not enforce high academic standards.

RECONSTRUCTIONISM (Counts) Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Reconstructionist agrees that everyday, personal experience constitutes reality. The reconstructionist claims that truth is what works, and we arrive at truth through a process of trial and error. The deconstructionist’s answer to this is whatever the public consensus says it is.

The role of the student in the reconstructionist learning environment is to be an active participant. Students are encouraged to think critically about the world in which they live in and how it can be changed for the better. Challenged teachers to become “transformative intellectuals. Teachers must awaken the students self conscious to social problems. Working with student interests and needs, teachers serve as guides and facilitators in assisting students to reach their goals. The emphasis is on the future, and on preparing students to be independent-thinking adults. Teaching methods should focus on democratic principles in classroom as students are led into opportunities to choose between varying social, political, and economic options. Social sciences used as reconstructive tools. (Anthropology, economics, sociology, political science, and psychology) Critical investigation and discussion. Problem solving, pupil teacher dialogue. Flexible seating arrangements. Too much confidence is placed on the powers of teachers and educators to act as primary instruments of social change. CRITICAL PEDAGOGY (Friere, Giroux, Noddings, Gloria Watkins [bell hooks], Peter McLaren, etc) This section might be a bit more challenging than the ones above. You are encouraged to supplement the textbook information with internet searches on the topic. Metaphysics

Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms Promotes an educationally conscious social reality and strives to eliminate the tremendous inequalities in the world. More radical the person is, the more fully he or she enters into reality so that, knowing it better, he or she can transform it. This individual is not afraid to confront, to listen, to see the world unveiled. draws attention to questions concerning who has control over the conditions for the production of knowledge, values, and skills, and it illuminates how knowledge, identities, and authority are constructed within particular sets of social relations learners are teachers. Therefore, learners are not recipients of knowledge rather they become  creators students must actively question and participate in the construction of their own reality. This requires a close examination of their political and social ideologies and assumptions.

Engage in a dialogue with our students and allow them to build their own understanding and subject matter schema vast enough that they are able to change their own situations without our push. Teachers should construct curricula that draw upon the cultural resources that students bring with them to the school. This suggests not only taking the languages, histories, experiences, and voices of the students seriously, but also integrating what is taught in schools to the dynamics of everyday life Revolutionary role for schools. Critical pedagogy argues for the importance of developing multiple literacies. We need to develop social literacies that are functional, cultural, and critical. In this sense, we need literacies that both recognize the importance of cultural differences and the importance of individuals communicating across various social, cultural, and political borders. Critical pedagogy also encourages students to be aware or critical of their own attitudes, values and prejudices.

However, postmodernist composition theorists argue that it is unethical to position the teacher in the powerful role of the liberator who controls students for the sake of the students’ own good CONSTRUCTIVISM (Piaget, Vygotsky, Constance Kamii, etc)

This section might be a bit more challenging than the ones above. You are encouraged to supplement the textbook information with internet searches on the topic. Metaphysics Epistemology Axiology Learner’s Nature Teacher’s Role Curricular Focus Methodology Criticisms In metaphysics constructivists believe that human beings enter the world neither inherently good nor evil but rather neutral in genetic orientation, behaviorally active and with free will. Believe that we know reality through the lens of our cultural and historically derived personal conduct. In axiology constructivists believe that values are existential. Values emerge in the context of living and experiencing as opposed to having been divinely ordained or being inherent in nature. To constructivists values emerge through the social interaction of human beings as they engage in experience

Active learner, Self directed, creative and innovative. Learners construct their own knowledge beginning with what they already know, exploring what needs to be known next and determining the quality and effectiveness of their pursuit through authentic assessment and application The teacher as a facilitator of learning as opposed to a dispenser of knowledge. Facilitators help the learner to get his or her own understanding of the content. It promotes using curricula customized to the students’ prior knowledge. Elimination of grades and standardized testing. Instead, assessment becomes part of the learning process so that students play a larger role in judging their own progress. – See more at: http://www.funderstanding.com/theory/constructivism/#sthash.zsXk0CmA.dpuf

Provides students with hands-on activities. Hands on problem solving. Reciprocal questioning- students work together to ask and answer questions. Jigsaw classroom- students become experts on one part of a group project and teach it to others in their groups. Structured controversies- students work together to research a particular controversy. While some constructivists argue that “learning by doing” enhances learning, critics of this instructional strategy argue that little empirical evidence exists to support this statement given novice learners

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Get 150+ pages of expertly designed content backed by over 30 years of collective teaching experience delivered right to your doorstep.

Clear, structured Theory Lesson Videos

Learn from the best. Each Theory Lesson is accompanied by online video lessons led by experienced teachers.

Weekly writing tasks and quizzes

Get regular practice and teacher feedback with weekly online homework consisting of writing tasks and quizzes.

Matrix Learning Management System (LMS)

Get access to Theory Lesson Videos, online resources, and track your progress easily via the Matrix LMS App.

Stuck on a question? Address individual learning needs using the Q&A boards and receive teacher responses within 1 working day.

End of term Topic Test

Review your understanding with the end of term Topic Test performed under strict exam-style conditions in preparation for school assessments.

• Year-round access to content

Enjoy access to all course resources until the end of the academic year.

Intelligent, structured learning

Enjoy the flexibility of online learning with all the benefits of being on-campus. Lesson content progression is automatically unlocked for the best learning experience, ensuring students stay on track for success.

Expert academic advice

Want advice on your child’s learning? Get exclusive access to academic advice, and invitations to events and seminars from the Matrix academic team.

Access Period

Matrix+ online

Term course

Holiday course

Who is it for?

Matrix resources

Additional support

Assessments

Meet our English teachers. Experts with real teaching experience.

years teaching

Qualifications

Year levels

Subjects taught

Years at Matrix

Teaching experience

Teaching methods

Year 8 English Pricing

Flexible payment options: Pay quarterly or monthly over 3 months.

9 lessons anytime anywhere

per month for 3 months

• Taught by experienced Matrix teachers
• Video Lessons paired with Theory Book
• New edition Theory Book delivered to your home
• Weekly homework
• Personalised feedback on written work
• Access to Matrix LMS resources
• Q&A Boards for individual help
• Progress tracking for students and parents
• Topic Test with marking and feedback

A word from our Year 8 English students

The English teachers at Matrix are really helpful in giving feedback on your writing and even school assessment tasks. Matrix teachers give you more thorough advice on how to improve for the future.

Sydney Girls High School

The learning facilities are so great! The things I’m taught are not only useful for school study but also better developed my knowledge on reading, writing and thinking skills.

Hurlstone Agricultural High School

Year 8 English class has benefited me in ways such as, teaching me more about Shakespeare and it allowed me to socialise with new faces. Matrix English classes create a great atmosphere for a student to be in, and the teachers are truly very friendly and give outstanding feedback.

Al-Faisal College

I’m enjoying English very much at Matrix. I find it very helpful for my schoolwork and have been getting outstanding results lately. My teacher is also very helpful and teaches valuable things. Highly recommend Matrix.

Chatswood High School

English at Matrix allows me to enhance my literature skills across a wide branch of areas. The teachers in the Matrix environment are supportive and assist me kindly in whatever questions I need assistance with.

This class was the best ever. My teacher taught me how to master writing essays and I had the best time learning about Romeo and Juliet.

St George Girls High School

Studying English in Year 8 at Matrix enhances communication, critical thinking, and creativity. Through exploring diverse literature, like the novel we’re studying, students develop essential skills while gaining deeper insights into complex characters and themes.

Matrix English is a very helpful experience that addresses all the important parts and aspects of the curriculum. This course has helped me heaps and now I understand a lot more of what I learn in class.

Sydney Technical High School

Matrix is an amazing place to learn with approachable staff and the perfect work environment. Along with having one of the best teachers and educational textbooks, this forms the ultimate place to learn about the most important subjects. Highly recommended.

The King's School

Frequently asked questions

Who are Matrix teachers?

Matrix teachers are experienced educators who have degrees in related disciplines. They are knowledgeable, know how to explain things clearly and make learning fun.

What are the teaching styles of Matrix English teachers?

Matrix Year 8 English tutoring teachers deliver a structured and engaging learning experience through instructional and interactive teaching.

Is the Matrix Year 8 English program mapped to the NSW syllabus?

Yes, the Matrix Year 8 English tutoring course covers every aspect of the NSW Education Standards Authority (NESA) Syllabus thoroughly. The course helps students to develop strong foundations in all aspects of English.

Does the Matrix Year 8 English program align with my child's school program?

Matrix teaches one term ahead of the school program with Term 1 starting in October each year. This means students learn ahead and gain a competitive advantage over their school peers when the topic is revisited at their school.

Matrix+ Online Courses

What is Matrix+?

Matrix+ is an online course consisting of video lessons paired with Matrix Theory Books . It’s accessible, more affordable and flexible compared to Matrix On-Campus courses. It’s an effective learning option if you prefer to learn at your own pace.

Is the online lesson live streamed classes?

No, you can learn at any time. Matrix+ lessons are pre-recorded to ensure the highest quality delivery for all types of learners! Our detailed video lessons allow you to view and follow the lesson at your own pace – pause, replay, or speed up videos for an on-demand learning experience.

Pause to view, take a break, take notes in your Theory Book, and rewind to view parts of the lessons again.

How long do I have access to a Matrix+ course?

Students will still be able to access Theory Lesson Videos, Q&A Discussion Boards, and online resources until the end of the calendar year. For example, students enrolled in the 2024 Term 1 course (commences October 2023) will have access until December 2024.

How will I receive the Matrix+ Theory Book?

Matrix Theory Books will be mailed to your delivery address via Australia Post.

Other Year 8 courses

Year 8 english.

Year 8 English tutoring at Matrix is known for helping students build strong reading and writing skills.

Learning methods available

Year 8 Maths tutoring at Matrix is known to help students improve their marks and confidence at school.