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#learning analytics

learning analytics

6 artículos

A student adviser and two adult learners review a feasible intervention timeline, a resource budget, and a learner-support dashboard in a university advising room
Herramienta / conjunto de datos2026
Herramienta / conjunto de datos 106

SC2R made student-risk recommendations machine-checkable without claiming causal improvement

Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge

arXiv preprint

Le, Abel and Laforge introduce SC2R, a counterfactual-recourse pipeline that combines calibrated risk prediction, integer programming, an RDF intervention vocabulary, and SHACL validation. Offline OULAD experiments show that semantic checks can reject plans that ignore timing, budget, immutability, or availability. The authors explicitly avoid causal outcome claims, so the contribution is operational feasibility rather than proof that an intervention helps students.

learning analyticscounterfactual recoursesemantic constraints
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A programming lecturer and two university students review an educator-verified lecture clip timeline, code diagrams and study notes in a bright computing studio
Herramienta / conjunto de datos2026
Herramienta / conjunto de datos 96

Lecture-video curation grounded AI help in course material, but the pilot measured engagement rather than learning

Owen Tang, Alexandra Vassar, Jake Renzella

arXiv preprint

Tang, Vassar and Renzella tested an alternative to open-ended AI answers: use LLMs to retrieve short, educator-delivered lecture clips for novice programming questions. Proprietary models produced relevant and sufficient selections across five benchmark queries, and a 903-student pilot showed repeat use and positive voluntary ratings. However, low overlap with one lecturer, LLM-only quality judgments and no learning-outcome measure mean the study demonstrates retrieval feasibility and engagement, not safer or better learning.

lecture video curationCS1retrieval-augmented generation
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A diverse group of university students and a lecturer examine clustered dialogue cards, a ten-trait matrix and an exam-progress chart in a bright learning analytics studio
Herramienta / conjunto de datos2026
Herramienta / conjunto de datos 94

Principal Trait Analysis linked AI-tutor dialogue patterns to outcomes, but not yet to transferable skills

Hunter McNichols, Kai Du, Andrew Lan

arXiv preprint

McNichols, Du and Lan introduce Principal Trait Analysis, an LLM-assisted pipeline that turns human-AI conversation traces into interpretable behavioral traits. On 1,540 university AI-tutor sessions and 2,774 professional coding-agent sessions, selected traits added explanatory or predictive signal beyond prior performance. Conceptual questioning aligned positively with some exam outcomes, but cross-semester inconsistency, contradictory coefficients and mostly flat temporal patterns mean the traits cannot yet be treated as transferable AI collaboration skills.

Principal Trait AnalysisAI tutoring dialoguehuman-AI collaboration
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An academic adviser and two university students compare concurrent course schedules, predicted grade ranges, and an advising decision map
Herramienta / conjunto de datos2026
Herramienta / conjunto de datos 100

TRACE predicted courses and grades jointly, cutting grade error without establishing intervention benefit

Paul Savala

arXiv preprint

Savala introduces TRACE, a transformer that represents courses by semester and jointly predicts a student's next course set and corresponding grades. On ten years of institutional data, joint training reduced mean absolute grade error by nearly 50 percent compared with the same architecture predicting grades alone and outperformed LSTM and graph baselines. External validity, fairness, calibration, and intervention effects remain open.

course predictiongrade predictiontransformer
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A diverse computer science class uses a learning dashboard to record self-assessments and discuss algorithms with a teacher and an AI dialogue panel
Artículo de revista2026
Artículo de revista 76

An eliciting LLM dashboard prompted more reflective dialogue and showed a trend toward stronger learning-judgment calibration

Laura Graf, Patrick Bassner, Maximilian Anzinger, Felix Dietrich, Stephan Krusche, Oleksandra Poquet

Education and Information Technologies

A five-week exploratory study with 30 computer-science students compared no agent, a telling LLM agent and an eliciting agent inside a learning dashboard. The eliciting condition produced more reflective messages and showed descriptive trends toward clearer relationships between self-judgments and mastery estimates, but the study did not test achievement gains.

learning analyticsLLM pedagogical agentlearning dashboard
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