A Quality Data Set for Data Challenge: Featuring 160 Students' Learning Behaviors and Learning Strategies in a Programming Course

Owen H.T. Lu, Anna Y.Q. Huang, Brendan Flanagan, Hiroaki Ogata, Stephen J.H. Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Emerging science requires data collection to support the research and development of advanced methodologies. In the educational field, conceptual frameworks such as Learning Analytics (LA) or Intelligent Tutoring System (ITS) also require data. Prior studies demonstrated the efficiency of academic data, for example, risk student prediction and learning strategies unveiling. However, a publicly available data set was lacking for benchmarking these experiments. To contribute to educational science and technology research and development, we conducted a programming course series two years ago and collected 160 students' learning data. The data set includes two well-designed learning systems and measurements of two welldefined learning strategies: Self-regulated Learning (SRL) and Strategy Inventory for Language Learning (SILL). Then we summarized this data set as a Learning Behavior and Learning Strategies data set (LBLS-160) in this study; here, 160 indicates a total of 160 students. Compared to the prior studies, the LBLS data set is focused on students' book reading behaviors, code programming behaviors, and measurement results on students' learning strategies. Additionally, to demonstrate the usability and availability of the LBLS data set, we conducted a simple risk student prediction task, which is in line with the challenge of cross-course testing accuracy. Furthermore, to facilitate the development of educational science, this study summarized three data challenges for the LBLS data set.

Original languageEnglish
Title of host publication30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
EditorsSridhar Iyer, Ju-Ling Shih, Ju-Ling Shih, Weiqin Chen, Weiqin Chen, Mas Nida MD Khambari, Nor Azni Abdul Aziz, Maiga Chang, Anita Diwakar, Shwu Pyng How, Bo Jiang, Atima Kaewsa-Ard, Mi Song Kim, Chiu-Lin Lai, Vwen Yen Awyln Lee, Lydia Yan Liu, Hiroaki Ogata, Muhd Khaizer Omar, Hang Shu, Yanjie Song, Wen Yun
PublisherAsia-Pacific Society for Computers in Education
Pages64-73
Number of pages10
ISBN (Electronic)9786269689002
StatePublished - 28 Nov 2022
Event30th International Conference on Computers in Education Conference, ICCE 2022 - Kuala Lumpur, Malaysia
Duration: 28 Nov 20222 Dec 2022

Publication series

Name30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
Volume2

Conference

Conference30th International Conference on Computers in Education Conference, ICCE 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period28/11/222/12/22

Keywords

  • AIED
  • Educational data
  • learning analytics

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