University of Oulu

Chen, H., Tan, E., Lee, Y., Praharaj, S., Specht, M., & Zhao, G. (2020). Developing AI into Explanatory Supporting Models: An Explanation-visualized Deep Learning Prototype for Computer Supported Collaborative Learning. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 1133-1140). Nashville, Tennessee: International Society of the Learning Sciences

Developing AI into explanatory supporting models : An explanation-visualized deep learning prototype

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Author: Chen, Haoyu1; Tan, Esther2; Lee, Yoon2;
Organizations: 1University of Oulu
2Delft University of Technology
3Open University of the Netherlands
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021110453715
Language: English
Published: International Society of the Learning Sciences, 2021
Publish Date: 2021-11-04
Description:

Abstract

Using Artificial Intelligence (AI) and machine learning technologies to automatically mine latent patterns from educational data holds great potential to inform teaching and learning practices. However, the current AI technology mostly works as “black box” — only the inputs and the corresponding outputs are available, which largely impedes researchers from gaining access to explainable feedback. This interdisciplinary work presents an explainable AI prototype with visualized explanations as feedback for computer-supported collaborative learning (CSCL). This research study seeks to provide interpretable insights with machine learning technologies for multimodal learning analytics (MMLA) by introducing two different explanatory machine learning-based models (neural network and Bayesian network) in different manners (end-to-end learning and probabilistic analysis) and for the same goal — provide explainable and actionable feedback. The prototype is applied to the real-world collaborative learning scenario with data-driven learning based on sensor-data from multiple modalities which can assess collaborative learning processes and render explanatory real-time feedback.

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Series: Computer-supported collaborative learning
ISSN: 1573-4552
ISSN-E: 2543-0157
ISSN-L: 1573-4552
ISBN Print: 978-1-7324672-6-2
Pages: 1133 - 1140
DOI: 10.22318/icls2020.1133
OADOI: https://oadoi.org/10.22318/icls2020.1133
Host publication: 14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020
Host publication editor: Gresalfi, M.
Horn, I. S.
Conference: International Conference of the Learning Sciences
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: We thank University of Oulu, Finland and National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore for their advice on this manuscript. The first author is funded by the Academy of Finland for project MiGA (grant 316765), ICT 2023 project (grant 328115), Infotech Oulu, CSC - IT Center for Science, Finland and Chinese Scholarship Council.
Academy of Finland Grant Number: 316765
328115
Detailed Information: 316765 (Academy of Finland Funding decision)
328115 (Academy of Finland Funding decision)
Copyright information: © 2021 International Society of the Learning Sciences, Inc. [ISLS].