Extraction of Useful Observational Features from Teacher Reports for Student Performance Prediction

Menna Fateen, Tsunenori Mine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Performance prediction models have been proposed countless times due to the benefits that they can provide to educational stakeholders. While many factors have been taken into account when predicting student performance, teachers’ assessment or observation reports have not been commonly used. A teacher’s assessment is a fundamental part of the educational process and has a direct impact on students’ success. In this study, we analyze the topics, and psychological features in teachers’ daily written reports and apply them to the student performance prediction model. Experimental results show the capability of this approach in contributing to the accuracy of performance prediction models.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 23rd International Conference, AIED 2022, Proceedings
EditorsMaria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
PublisherSpringer Science and Business Media Deutschland GmbH
Pages620-625
Number of pages6
ISBN (Print)9783031116438
DOIs
Publication statusPublished - 2022
Event23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, United Kingdom
Duration: Jul 27 2022Jul 31 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13355 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Artificial Intelligence in Education, AIED 2022
Country/TerritoryUnited Kingdom
CityDurham
Period7/27/227/31/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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