A Predictive Model to Evaluate Student Performance
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A new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods using free-style comments written by students after each lesson is proposed.Abstract:
In this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.read more
Citations
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References
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TL;DR: The adequacy of LSA's reflection of human knowledge has been established in a variety of ways, for example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word‐word and passage‐word lexical priming data.
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Knowing What Students Know: The Science and Design of Educational Assessment
TL;DR: In this article, the authors propose a new kind of assessment called Knowing What Students Know (KSS), which aims to make as clear as possible the nature of students' accomplishments and the progress of their learning.
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Using linear algebra for intelligent information retrieval
TL;DR: A lexical match between words in users’ requests and those in or assigned to documents in a database helps retrieve textual materials from scientific databases.