Automated essay scoring applications to educational technology

The IEA uses Latent Semantic Analysis LSAwhich is both a computational model of human knowledge representation and a method for extracting semantic similarity of words and passages from text.

Automated essay scoring applications to educational technology

The IEA uses Latent Semantic Analysis LSAwhich is both a computational model of human knowledge representation and a method for extracting semantic similarity of words and passages from text. Simulations of psycholinguistic phenomena show that LSA reflects similarities of human meaning effectively.

To assess essay quality, LSA is first trained on domain-representative text. Then student essays are characterized by LSA representations of the meaning of their contained words and compared with essays of known quality on degree of conceptual relevance and amount of relevant content.

Over many diverse topics, the IEA scores agreed with human experts as accurately as expert scores agreed with each other. Implications are discussed for incorporating automatic essay scoring in more general forms of educational technology. Introduction While writing is an essential part of the educational process, many instructors find it difficult to incorporate large numbers of writing assignments in their courses due to the effort required to evaluate them.

Some older research papers by Peter Foltz

However, the ability to convey information verbally is an important educational achievement in its own right, and one that is not sufficiently well assessed by other kinds of tests.

In addition, essay-based testing is thought to encourage a better conceptual understanding of the material on the part of students and to reflect a deeper, more useful level of knowledge and application by students. Thus grading and criticizing written products is important not only as an assessment method, but also as a feedback device to help students better learn both content and the skills of thinking and writing.

Nevertheless, essays have been neglected in many computer-based assessment applications since there exist few techniques to score essays directly by computer. In this paper we describe a method for performing automated essay scoring of the conceptual content of essays.

Based on a statistical approach to analyzing the essays and content information from the domain, the technique can provide scores that prove to be an accurate measure of the quality of essays. Detailed treatments of LSA, both as a theory of aspects of human knowledge acquisition and representation, and as a method for the extraction of semantic content of text are beyond the scope of this article.

The LSA similarity between words and passages is measured by the cosine of their contained angle in a dimensional "semantic space". The LSA measured similarities have shown to closely mimic human judgments of meaning similarity and human performance based on such similarity in a variety of ways.

This similarity comparison made by LSA is the basis for performing automated scoring of essays through comparing the similarity of meaning between essays. Automated scoring with LSA While other approaches to automatic evaluation of written work have focussed on mechanical features, such as grammar, spelling and punctuation, there are other factors involved in writing a good essay.

For example at an abstract level, one can distinguish three properties of a student essay that are desirable to assess; the correctness and completeness of its contained conceptual knowledge, the soundness of arguments that it presents in discussion of issues, and the fluency, elegance, and comprehensibility of its writing.

Although previous attempts to develop computational techniques for scoring essays have focused primarily on measures of style e. In contrast to earlier approaches, LSA methods concentrate on the conceptual content, the knowledge conveyed in an essay, rather than its style, or even its syntax or argument structure.

To assess the quality of essays, LSA is first trained on domain-representative text. Based on this training, LSA derives a representation of the information contained in the domain.

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Student essays are then characterized by LSA vectors based on the combination of all their words. These vectors can then be compared with vectors for essays or for texts of known content quality. The angle between the two vectors represents the degree to which the two essays discuss information in a similar.

For example, an ungraded essay can be compared to essays that have already been graded. If the angle between two essays is small then those essays should be similar in content. Thus, the semantic or conceptual content of two essays can be compared and a score derived based on their similarity.

Note that two essays can be considered to have almost identical content, even if they contain few or none of the same words, as long as they convey the same meaning. Evaluating the effectiveness of automated scoring Based on comparing conceptual content, several techniques have been developed for assessing essays.

Details of these techniques have been published elsewhere and summaries of particular results will be provided below. One technique is to compare essays to ones that have been previously graded. The holistic method has been tested on a large number of essays over a diverse set of topics.

The essays have ranged in grade level, including middle school, high school, college and college graduate level essays. The topics have included essays from classes in introductory psychology, biology, history, as well as essays from standardized tests, such as analyses of arguments, and analyses of issues from the ETS Graduate Management Achievement Test GMAT.

For each of these sets of essays, LSA is first trained on a set of texts related to the domain.View frequently asked questions on the Criterion Service.

Implications are discussed for incorporating automatic essay scoring in more general forms of educational technology. Introduction While writing is an essential part of the educational process, many instructors find it difficult to incorporate large numbers of writing assignments in their courses due to the effort required to evaluate them.

Turnitin provides instructors with the tools to prevent plagiarism, engage students in the writing process, and provide personalized feedback.

Note: The terms in which a course is normally taught is at the end of each description (F=Fall, Sp=Spring, Su=Summer). Jump to TN eCampus Courses.

The Intelligent Essay Assessor (IEA) is a set of software tools for scoring the quality of essay content.

Automated essay scoring applications to educational technology

The IEA uses Latent Semantic Analysis (LSA), which is both a computational model of human knowledge representation and a method for extracting semantic similarity of words and passages.

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Automated essay scoring applications to educational technology
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