🏷️ engagement-metrics
33 pages tagged with engagement-metrics(30 articles, 3 concepts)
📄 Acceptance of AI-Assisted English Language Learning Tools in Higher Education: Psychological Correlates Across Disciplinary and Proficiency Groups
> **Synthesis:** Wu et al. (2026) examined how learning motivation, self-efficacy, anxiety, and risk perception relate to acceptance of AI-assisted English language learning in a Chinese higher-educat…
📄 Methodologies for Improving the Quality of AI Tutoring in K-12 Education
> **Synthesis:** Udeshi et al. (2026), the team behind **Khanmigo** (Khan Academy's K-12 AI tutor, launched 2023), describe the metrics they use to measure AI tutoring quality and student engagement, …
📄 Examining the Impact of Generative AI on Student Motivation and Engagement: The Mediating Role of Autonomy-Support and Autonomous Motivation in Education
> **Synthesis:** Ahmed and Sultan (2026) investigated how perceived autonomy, competence, relatedness, expectancy, and value influence autonomy support for AI use, autonomous motivation, and ultimatel…
📄 AI-Generated Interactive Fiction for Educational Use: A Pilot Study of Perceived Comprehensibility, Coherence, and Engagement
> **Synthesis:** This pilot study (N = 22 STEM higher-education students) evaluates AI-generated interactive fiction as an educational medium. Narrative clarity and length acceptance rated positively,…
📄 Beyond MOOCs: How technical and structural factors shape learner engagement, retention and inclusivity across online learning platforms
> **Synthesis:** This study examines the critical influence of technical and structural factors on learner Engagement, Retention and Inclusivity (ERI) in MOOCs and other large-scale online learning pl…
📄 Enhancing creative writing with robot-LLM integration: The interplay of embodiment, AI creativity and user engagement
> **Synthesis:** This study explores the impact of robot-LLM integration on collaborative creative writing, focusing on how embodiment and AI creativity influence creative output. With 150 undergradua…
🏷️ Motivation
> **Motivation** — the psychological processes that initiate, direct, and sustain goal-directed behavior. In AI in education, motivation research examines how AI tools affect learners' and teachers' m…
2026-08-10 · motivation, student-experience, affective-computing, self-determination-theory, ai-education
🏷️ Engagement Metrics
> **Engagement metrics** — the range of observable signals and measurement approaches researchers and systems use to operationalize [[student-engagement|student engagement]] in AI-supported learning: …
🏷️ Learning Analytics
> **Learning analytics** — the measurement, collection, analysis, and reporting of data about learners and their contexts for the purpose of understanding and optimizing learning. AI has transformed l…
📄 Interactive learning dashboards: rethinking learning visualisations as engagement tools
> **Synthesis:** Graf et al. (2026) transformed a conventional Learning Analytics Dashboard (LAD) into an interactive ILAD by adding an LLM-powered pedagogical agent and a Judgement of Learning (JoL) …
📄 Access is Not Enough: Human Support Improves Engagement with AI Tutoring
> Robinson, Gormley, Ribeiro & Loeb (2026) ran two RCTs showing that AI tutoring's binding constraint is **take-up, not capability**: despite dedicated session time, nearly half of students never used…
📄 Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness
The largest study in the AEHE 51(5) special issue: a **cross-sectional survey across four Australian universities** (≈192,000 invited; 10,132 volunteered; this paper analyses **6,960 students** who an…
📄 Hypergamigication Through Integrating Game Engines and Learning Management Systems: Ender's Game
> **Araz Yusubov, Michael Bechtel, Tangiz Alizada** — arXiv preprint (2026).…
📄 Let''s Chat: Leveraging Chatbot Outreach for Improved Course Performance
> Meyer, Page, Mata et al. (2026) ran two pre-registered RCTs at Georgia State University testing a **non-generative** academic chatbot that texted students 2–3 customized nudges per week in large-enr…
📄 To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions
> **Dimitris Tsirmpas, Katerina Korre, John Pavlopoulos** — arXiv preprint (2026).…
📄 SAVVY: Student Attention Visualization for Video-based Learning Analysis
> **Shixian Zhou, Minghuan Shen, Xiaolin Wen, Zijun Qiu, Yongliang Jiang, Xiangyang Wu, Fei Wu, Yong Wang, Zhiguang Zhou** — arXiv preprint (2026).…
📄 Designing a mobile chatbot-based learning journaling system for intrinsic motivation and engagement
A **randomized 2×2 full-factorial field experiment** (N = 179 German university students, 22 days of app use, 12-week follow-up) testing two design principles for a **mobile chatbot-based learning jou…
📄 MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education
MedGame transforms static clinical cases into structured, executable storytelling games for medical education, moving beyond the localized question-answering and single-turn feedback that characterize…
📄 Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking
Machine learning courses typically hand students pre-labeled datasets, hiding the subjectivity baked into human annotation and cultivating an overly trusting view of AI data pipelines. This two-univer…
📄 Less Deliberate in Teams: Student LLM Use Across Individual and Collaborative Work
> **Sehrish Basir Nizamani, Zannah Ziew, Saad Nizamani, Khyati Goyal** — ACM SIGCSE Virtual 2026, submitted 29 Jun 2026…
📄 An exploratory behavioral and electroencephalographic study of artificial intelligence-assisted learning modes in high school students
📄 [PDF](https://arxiv.org/pdf/2606.26579) This study investigates how different modes of AI interaction affect cognitive engagement and learning outcomes in high school students. Using a within-subje…
📄 Cross-Subject Predictive Validity for Learning Outcomes of Delayed Start Behavior
This study examines the [[student-modeling]] validity of **delayed start behavior** — when students begin assignments or practice sessions past a recommended start time — as a predictor of learning-ga…
2026-06-25 · learning-analytics, student-modeling, higher-ed, efficacy-study, self-regulated-learning
📄 Framing the 5% Problem: Teachers'' Perspectives on Persistence in Educational Technology
Borchers (2026) reports on a 90-minute participatory design workshop with 12 U.S. middle school mathematics teachers using i-Ready Math weekly. Thematic analysis identified four recurring dimensions o…
📄 Confident yet Concerned: Inconsistencies in Computing Students'' Attitudes on Cybersecurity
Computing students show inconsistencies between confidence in cybersecurity knowledge and actual safe practices; educational interventions are needed to close the gap. Confident yet Concerned: Inconsi…
📄 Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction
> Engagement intensity during AI ethics instruction serves as an effective learner-modeling signal for adaptive instruction; prior LLM experience influences engagement patterns.…
📄 Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning
> Investigates ML models to predict exam outcomes from physiological data (electrodermal activity, heart rate, skin temperature) collected during exams. Evaluates logistic regression, random forest, S…
📄 Design and Implementation of a Real-time Multi-site Immersive Learning System Using Photon Fusion
> This paper develops a VR-based immersive learning environment using Photon Fusion that allows teachers and students to be present in the same virtual space regardless of physical locations. The syst…
📄 Simulating Learners' Task-Selection Strategies and System Constraints in Mastery Learning
Intelligent Tutoring Systems often grant learners shared control over skill and problem selection. We propose a simulation-based framework to examine how learner task-selection strategies and system c…
📄 From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
This paper tackles a core ITS challenge: predicting when students will disengage so tutors can intervene before it's too late. It introduces **engagement forecasting** as a supervised prediction task …
📄 Understanding Student Effort Using Response-Time Propensities During Problem Solving
Adaptive learning systems produce substantial learning gains, yet many students engage too briefly or superficially to benefit. This paper addresses the central challenge of **measuring student effort…
📄 When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook Community
> **Authors:** Eason Chen, Ce Guan, A Elshafiey, Zhonghao Zhao, Joshua Zekeri, Afeez Edeifo Shaibu, Emmanuel Osadebe Prince **Year:** 2026 **Venue:** arXiv (cs.HC) > Mining discourse from Moltbook, a …
📄 Not All Students Engage Alike: Multi-Institution Patterns in GenAI Tutor Use
> **Authors:** Youjie Chen, Xixi Shi, Xinyu Liu, Shuaiguo Wang, Tracy Xiao Liu, Dragan Gašević **Year:** 2026 **Venue:** arXiv (cs.CY) > Large-scale analysis (N=11,406 students, 200 classes, 10 instit…
📄 The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness
> A framework for evaluating AI tutoring systems that extends beyond pedagogical quality of feedback to measure what students actually *do* with that feedback — whether they act on it and whether they…