arXiv subjects

Hannah Deininger

Publications and source records attributed to Hannah Deininger.

4 recordsLinked to original sources

arXiv2026

The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?

With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in the scholarship, identify areas of debate and unresolved challenges, and outline open questions and future directions for research and educational practice that emerged from structured small-group activities designed to bridge disciplinary barriers.

cs.CY↗

arXiv2026

Toward Trait-Aware Learning Analytics

Learning analytics (LA) draws from the learning sciences to interpret learner behavior and inform system design. Yet, past personalization remains largely at the content or performance level (during learner-system interactions), overlooking relatively stable individual differences such as personality (unfolding over long-term learning trajectories such as college degrees). The latter could bring underappreciated benefits to the design, implementation, and impact of LA. In this position paper, we conduct an ad hoc literature review and argue for an expanded framing of LA that centers on learner traits as key to both interpreting and designing close-the-loop experiments in LA. We show that personality traits are relevant to LA's central outcomes (e.g., engagement and achievement) and conducive to action, as their established ties to human-computer interaction (HCI) inform how systems time, frame, and personalize support. Drawing inspiration from HCI, where psychometrics inform personalization strategies, we propose that LA can evolve by treating traits not only as predictive features but as design resources and moderators of analytics efficacy. In line with past position papers published at LAK, we present a research agenda grounded in the LA cycle and discuss methodological and ethical challenges.

cs.HC↗

arXiv2024

On Task and in Sync: Examining the Relationship between Gaze Synchrony and Self-Reported Attention During Video Lecture Learning

Successful learning depends on learners' ability to sustain attention, which is particularly challenging in online education due to limited teacher interaction. A potential indicator for attention is gaze synchrony, demonstrating predictive power for learning achievements in video-based learning in controlled experiments focusing on manipulating attention. This study (N=84) examines the relationship between gaze synchronization and self-reported attention of learners, using experience sampling, during realistic online video learning. Gaze synchrony was assessed through Kullback-Leibler Divergence of gaze density maps and MultiMatch algorithm scanpath comparisons. Results indicated significantly higher gaze synchronization in attentive participants for both measures and self-reported attention significantly predicted post-test scores. In contrast, synchrony measures did not correlate with learning outcomes. While supporting the hypothesis that attentive learners exhibit similar eye movements, the direct use of synchrony as an attention indicator poses challenges, requiring further research on the interplay of attention, gaze synchrony, and video content type.

cs.HC↗

arXiv2019

Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph

With the increasing number of deep learning applications, there is a growing demand for explanations. Visual explanations provide information about which parts of an image are relevant for a classifier's decision. However, highlighting of image parts (e.g., an eye) cannot capture the relevance of a specific feature value for a class (e.g., that the eye is wide open). Furthermore, highlighting cannot convey whether the classification depends on the mere presence of parts or on a specific spatial relation between them. Consequently, we present an approach that is capable of explaining a classifier's decision in terms of logic rules obtained by the Inductive Logic Programming system Aleph. The examples and the background knowledge needed for Aleph are based on the explanation generation method LIME. We demonstrate our approach with images of a blocksworld domain. First, we show that our approach is capable of identifying a single relation as important explanatory construct. Afterwards, we present the more complex relational concept of towers. Finally, we show how the generated relational rules can be explicitly related with the input image, resulting in richer explanations.

cs.LG↗