arXiv:2407. 13053v2 Announce Type: replace-cross Abstract: Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data.
arXiv:2508. 17092v2 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content.
arXiv:2503. 06525v2 Announce Type: replace-cross Abstract: Analyzing student behavior in educational scenarios is crucial for enhancing teaching quality and student engagement.
arXiv:2606. 01224v1 Announce Type: new Abstract: Early detection of at-risk students and timely academic intervention pose major challenges in advanced mathematics education, where complex conceptual hierarchies and nonlinear learning trajectories often hold back students' academic performance.
arXiv:2606. 24770v1 Announce Type: cross Abstract: Educational platforms often predict student performance from prior interactions, but the assessment content itself also varies in linguistic and visual complexity.
arXiv:2601. 14968v2 Announce Type: replace-cross Abstract: Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels.
arXiv:2606. 14654v1 Announce Type: new Abstract: Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work.
arXiv:2607. 20556v1 Announce Type: new Abstract: In large-scale text analysis tasks, pre-trained language models are often used to embed text corpora for downstream analysis.
arXiv:2608. 13409v1 Announce Type: new Abstract: Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses taken within a semester.
arXiv:2604. 22476v2 Announce Type: replace-cross Abstract: Disciplines such as business process management and process mining aid organizations by discovering insights about processes on the basis of recorded event data.
arXiv:2606. 03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model.
arXiv:2604. 23336v3 Announce Type: replace-cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial computational costs.
arXiv:2603. 02830v2 Announce Type: replace-cross Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interventions.