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Towards transparent and trustworthy prediction of student learning achievement by including instructors as co-designers: a case study

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Abstract

Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)—contrary to the traditional “black-box” approach—helps fulfilling this important goal. We present a case study of building prediction models for undergraduate students’ learning achievement in a Computer Science course, where the development process involves the course instructor as a co-designer, and with the use of XAI technologies to explain the underlying reasoning of several machine learning predictions. The explanations enhance the transparency of the predictions and open the door for educators to share their judgments and insights. It further enables us to refine the predictions by incorporating the educators’ contextual knowledge of the course and of the students. Through this human-AI collaboration process, we demonstrate how to achieve a more accountable understanding of students’ learning and drive towards transparent and trustworthy student learning achievement prediction by keeping instructors in the loop. Our study highlights that trustworthy AI in education should emphasize not only the interpretability of the predicted outcomes and prediction process, but also the incorporation of subject-matter experts throughout the development of prediction models.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgement

This research was supported in part by the U.S. National Science Foundation through grant IIS1955395.

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Authors and Affiliations

  1. Department of IT, Analytics, and Operations, University of Notre Dame, 219 Mendoza College of Business, Notre Dame, IN, 46556, USA

    Xiaojing Duan

  2. Lucy Family Institute of Data and Society, University of Notre Dame, Notre Dame, IN, 46556, USA

    Bo Pei

  3. University of Notre Dame, 350C DeBartolo Hall, Notre Dame, IN, 46556, USA

    G. Alex Ambrose

  4. School of Education, Tel Aviv University, P.O.Box 39040, Tel-Aviv, IL, 6997801, Israel

    Arnon Hershkovitz

  5. Department of Psychology Notre Dame, University of Notre Dame, Notre Dame, IN, 46556, USA

    Ying Cheng

  6. Department of Computer Science and Engineering, University of Notre Dame, 383 Fitzpatrick Hall, Notre Dame, IN, 46556, USA

    Chaoli Wang

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  1. Xiaojing Duan
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Correspondence to Xiaojing Duan.

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Duan, X., Pei, B., Ambrose, G.A. et al. Towards transparent and trustworthy prediction of student learning achievement by including instructors as co-designers: a case study. Educ Inf Technol 29, 3075–3096 (2024). https://doi.org/10.1007/s10639-023-11954-8

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