NDLI: Correlation of Grade Prediction Performance with Characteristics of Lesson Subject

Content Provider	IEEE Xplore Digital Library
Author	Sorour, S.E. Jingyi Luo Goda, K. Mine, T.
Copyright Year	2015
Description	Author affiliation: Inf. Sci. & Electr. Eng., Fukuoka, Japan (Jingyi Luo) \|\| Fac. of Specific Educ., Kafr Elsheik Univ., KafrElsheikh, Egypt (Sorour, S.E.) \|\| Fac. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan (Mine, T.) \|\| Kyushu Inst. of Inf. Sci., Fukuoka, Japan (Goda, K.)
Abstract	Learning analytics is valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities. Analyzing comment data written by students after each lesson helps to grasp their learning attitudes and situations. They can be a powerful source of data for all forms of assessment. In the current study, we break down student comments into different topics by employing two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA), to discover the topics that help to predict final student grades as their performance. The objectives of this paper are twofold: First, determine how the three time-series items: P-, C- and N-comments and the difficulty of a subject affect the prediction results of final student grades. Second, evaluate the reliability of predicting student grades by considering the differences between prediction results of two consecutive lessons. The results obtained can help to understand student behavior during the period of the semester, grasp prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.
Starting Page	247
Ending Page	249
File Size	304423
Page Count	3
File Format	PDF
e-ISBN	9781467373340
DOI	10.1109/ICALT.2015.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Correlation Analytical models Accuracy Reliability Education Programming Student Grade Prediction Free-style comment Topic Models Learning Activity
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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