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EX-LAD: Explainable Learning Analytics Dashboard in Higher Education

14 pagesPublished: January 24, 2024

Abstract

This paper introduces an EXplainable Learning Analytic Dashboard (EX-LAD) that presents learning analytics data on student performance, engagement, and perseverance in a clear and easily understandable manner. The main goal of this study is to make this information accessible to both teachers and students, who may not possess extensive knowledge in data analysis, and demonstrate the effectiveness of the relationship between performance, engagement, and perseverance in identifying student difficulties. This dashboard enables teachers to gain valuable information about their student’s progress, identify at-risk learners, and provide targeted support. Similarly, students can use this dashboard to track their own learning journey, identify their strengths and weaknesses, and make informed decisions to improve their academic performance. It integrates visualizations to represent various aspects of student learning, such as performance, engagement, and perseverance. To demonstrate the effectiveness of our dashboard, we conducted a case study using real data collected from ESIEE-IT, an engineering school in France, during the academic year 2021-2022. This case study serves as concrete evidence of the impact and values our dashboard brings to the educational context.

Keyphrases: Dashboard, Explainability, learning analytics, Technology Enhanced Learning, visualization

In: Krishna Kambhampaty, Gongzhu Hu and Indranil Roy (editors). Proceedings of 36th International Conference on Computer Applications in Industry and Engineering, vol 97, pages 38--51

Links:
BibTeX entry
@inproceedings{CAINE2023:EX_LAD_Explainable_Learning_Analytics,
  author    = {Tesnim Khelifi and Nourh\textbackslash{}`ene Ben Rabah and B\textbackslash{}'en\textbackslash{}'edicte Le Grand and Ibtissem Daoudi},
  title     = {EX-LAD: Explainable Learning Analytics Dashboard in Higher Education},
  booktitle = {Proceedings of 36th International Conference on Computer Applications in Industry and Engineering},
  editor    = {Krishna Kambhampaty and Gongzhu Hu and Indranil Roy},
  series    = {EPiC Series in Computing},
  volume    = {97},
  pages     = {38--51},
  year      = {2024},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/jPSG},
  doi       = {10.29007/dsxd}}
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