article

Data mining based analysis to explore the effect of teaching on student performance

Authors:

Soumya K. GhoshAuthors Info & Claims

Education and Information Technologies, Volume 23, Issue 4

Pages 1677 - 1697

https://doi.org/10.1007/s10639-017-9685-z

Published: 01 July 2018 Publication History

Abstract

Analysing the behaviour of student performance in classroom education is an active area in educational research. Early prediction of student performance may be helpful for both teacher and the student. However, the influencing factors of the student performance need to be identified first to build up such early prediction model. The existing data mining literature on student performance primarily focuses on student-related factors, though it may be influenced by many external factors also. Superior teaching acts as a catalyst which improves the knowledge dissemination process from teacher to the student. It also motivates the student to put more effort on the study. However, the research question, how the performance or grade correlates with teaching, is still relevant in present days. In this work, we propose a quantifiable measure of improvement with respect to the expected performance of a student. Furthermore, this study analyses the impact of teaching on performance improvement in theoretical courses of classroom-based education. It explores nearly 0.2 million academic records collected from an online system of an academic institute of national importance in India. The association mining approach has been adopted here and the result shows that confidence of both non-negative and positive improvements increase with superior teaching. This result indeed establishes the fact that teaching has a positive impact on student performance. To be more specific, the growing confidence of non-negative and positive improvements indicate that superior teaching facilitates more students to obtain either expected or better than expected grade.

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Cited By

Al-Alawi LAl Shaqsi JTarhini AAl-Busaidi A(2023)Using machine learning to predict factors affecting academic performance: the case of college students on academic probationEducation and Information Technologies10.1007/s10639-023-11700-028:10(12407-12432)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.1007/s10639-023-11700-0
Liu HZhu YZang TXu YYu JTang F(2021)Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction TasksACM Transactions on Knowledge Discovery from Data10.1145/345802316:1(1-24)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3458023
Nikolic SSuesse TJovanovic KStanisavljevic Z(2021)Laboratory Learning Objectives Measurement: Relationships Between Student Evaluation Scores and Perceived LearningIEEE Transactions on Education10.1109/TE.2020.302266664:2(163-171)Online publication date: 1-May-2021
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cover image Education and Information Technologies

Education and Information Technologies Volume 23, Issue 4

July 2018

337 pages

ISSN:1360-2357

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2018

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Cited By

Al-Alawi LAl Shaqsi JTarhini AAl-Busaidi A(2023)Using machine learning to predict factors affecting academic performance: the case of college students on academic probationEducation and Information Technologies10.1007/s10639-023-11700-028:10(12407-12432)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.1007/s10639-023-11700-0
Liu HZhu YZang TXu YYu JTang F(2021)Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction TasksACM Transactions on Knowledge Discovery from Data10.1145/345802316:1(1-24)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3458023
Nikolic SSuesse TJovanovic KStanisavljevic Z(2021)Laboratory Learning Objectives Measurement: Relationships Between Student Evaluation Scores and Perceived LearningIEEE Transactions on Education10.1109/TE.2020.302266664:2(163-171)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1109/TE.2020.3022666
Khan AGhosh S(2021)Student performance analysis and prediction in classroom learning: A review of educational data mining studiesEducation and Information Technologies10.1007/s10639-020-10230-326:1(205-240)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10639-020-10230-3
Kazanidis IValsamidis SGounopoulos EKontogiannis S(2020)Proposed S-Algo+ data mining algorithm for web platforms course content and usage evaluationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04841-824:19(14861-14883)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s00500-020-04841-8
Adekitan ANoma-Osaghae E(2019)Data mining approach to predicting the performance of first year student in a university using the admission requirementsEducation and Information Technologies10.1007/s10639-018-9839-724:2(1527-1543)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10639-018-9839-7

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