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  8. Best Paper Award of the 18th International Conference on Wirtschaftsinformatik

Best Paper Award of the 18th International Conference on Wirtschaftsinformatik

At the 18th International Conference on Wirtschaftsinformatik (WI), the article "Addressing Learners' Heterogeneity in Higher Education: An Explainable AI-based Feedback Artifact for Digital Learning Environments", which resulted from a collaboration between the LifBi (Philipp Handschuh, Maria Klose) and the University of Bamberg (Felix Haag, Sebastian Günther, Konstantin Hopf, Thorsten Staake), was awarded with the Best Paper Award. The WI conference is considered the most important scientific conference for information systems in the German-speaking region and took this year place in Paderborn under the theme "Digital Responsibility: Social, Ethical, Ecological Implications of IS". With the award, the conference organizers recognize the valuable contributions of the winning paper.

Abstract: Due to the advent of digital learning environments and the freedom they offer for learners, new challenges arise for students' self-regulated learning. To overcome these challenges, the provision of feedback has led to excellent results, such as less procrastination and improved academic performance. Yet, current feedback artifacts neglect learners’ heterogeneity when it comes to prescriptive feedback that should meet personal characteristics and self-regulated learning skills. In this paper, we derive requirements from self-regulated learning theory for a feedback artifact that takes learners’ heterogeneity into account. Based on these requirements, we design, instantiate, and evaluate an Explainable AI-based approach. The results demonstrate that our artifact is able to detect promising patterns in data on learners' behaviors and characteristics. Moreover, our evaluation suggests that learners perceive our feedback as valuable. Ultimately, our study informs Information Systems research in the design of future Explainable AI-based feedback artifacts that seek to address learners' heterogeneity.

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