Different individual features of the learner data often work as essential indicators of learning and intervention needs. This work exploits the personas in the design thinking process as the theoretical basis to analyze and cluster learners’ learning behavior patterns as groups. To adapt to the learning practice, we develop data-driven personas by clustering learners’ features based on factual learning outcomes (i.e., knowledge gain, perceived learning experience, perceived social presence) based on unsupervised learning, a more accessible and objective intervention design strategy for e-reading practices. Using the Chi-square test, we quantitatively evaluate different clusters driven by various unsupervised learning methods on the multimodal SKEP dataset. Furthermore, for a more practical real-life application, we achieved automatic persona prediction based on the attention regulation behaviors of learners. The subject-independent evaluation results indicate the best classification accuracy of 70% for the four-level classification task, differentiating three personas of learners with needs and another without feedback needs. It also shows that time-based sampling on both independent and cumulative learner behaviors works as robust predictors of learner personas, achieving a stable accuracy range of 65%-70% throughout the e-reading with the SVM classifier. Our work inspires the design of a real-time feedback loop for e-learning based on conversational agents.@en

This study is built upon a behavior-based framework for real-time attention evaluation of higher education learners in e-reading. Significant challenges in AI model developments for learning analytics have been 1) defining valid indicators and 2) connecting the analytics results to interventions, balancing the generalization and personalization needs. To address this, we utilized a public multimodal WEDAR dataset and trained a neural network model based on real-time features of learners, aiming at predicting learners’ moment-to-moment distractions. Real-time features for model training include 30 learners’ attention regulation behaviors annotated every second, reaction times to blur stimuli, and page numbers indicating various reading phases. Our preliminary model based on a neural network has achieved 66.26% accuracy in predicting self-reported distractions. Based on the model, we suggest a framework of a Behavior-based Feedback Loop for Attentive e-reading (BFLAe). It has text blur as feedback, a mechanism responsive to learners’ distractions that also works as data for next-round feedback. The general feedback implementation rules are established on a statistical analysis conducted on all learners. In addition, we propose a strategy for personalizing feedback using a quartile analysis of individual data, promoting learner-specific feedback. Our framework addresses the high demand for an automated e-learning assistant with non-intrusive data collection based on real-world settings and intuitive feedback provision. The feedback system aims to help learners with longer attention spans and less frequent distractions, leading to more engaging e-reading. @en

We present ReproducedPapers.org : an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest that students who do a reproduction project place more value on scientific reproductions and become more critical thinkers. Students and AI researchers agree that our online reproduction repository is valuable.

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Many universities digitize exams or the process of grading the exams. This potentially allows for faster grading, is less labor intensive and less error-prone. But are the grades produced by online grading consistent with how we grade on paper? In this paper we present preliminary results of the comparison between scores given by grading online and grading on paper.

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