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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Dataset . 2025
License: CC BY NC ND
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY NC ND
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY NC ND
Data sources: Datacite
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DPOTS: eXplainable Artificial Intelligence Dataset for Predicting Outcomes from Time Sequences and Student Behaviors

Authors: Jiang, Yuan-Hao; Chen, Zi-Wei; Zhao, Cong; Tang, Kezong; Duan, Jicong; Zhou, Yizhou;

DPOTS: eXplainable Artificial Intelligence Dataset for Predicting Outcomes from Time Sequences and Student Behaviors

Abstract

DPOTS: eXplainable Artificial Intelligence (XAI) Dataset for Predicting Outcomes from Time Sequences and Student Behaviors Accurately and timely predicting learners' outcomes can assist educators in making instructional decisions or interventions. This helps prevent students from falling into a vicious cycle of decreased academic achievement and increased aversion to learning, potentially leading to dropout. Data-driven models often outperform eXplainable Artificial Intelligence (XAI) models in predicting learning outcomes, yet their lack of interpretability can hinder trust from educators. Therefore, this study developed an XAI information fusion framework that not only extracts potential trends from the time series of student grades to enhance predictive performance but also mines explicit relationships between classroom behaviors and learning outcomes. This reveals the behavioral causes behind changes in grades. Furthermore, we have made public the Dataset for Predicting Outcomes from Time sequences and Student behaviors (DPOTS), and validated the effectiveness of the developed XAI information fusion framework based on DPOTS. The results indicate that, the MAE of CEO-IF was reduced by an average of 26.32% compared to the baseline algorithms, and it showed a 22.63% reduction compared to the averaging-based information fusion method. Copyright The Copyright of the DPOTS belongs to the authors and their affiliates. You are free to use DPOTS for research purposes. The authors is not responsible for any consequences of the user's use of the data or code. Papers containing data generated using the data or code should declare the use of the DPOTS , and cite the corresponding references correctly: Code: https://doi.org/10.5281/zenodo.14958102 Paper: https://doi.org/10.1145/3706599.3721212 Support According to ethical requirements, if you need to access this data, please send an email to Zi Wei Chen to apply. If you have any comments or suggestions, please contact 2220042009@stu.jcu.edu.cn (Zi-Wei Chen). Finally, thank you again for using DPOTS.

Related Organizations
Keywords

Artificial intelligence, AI for education, Sustainable development goals, Education

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average