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A systematic review of student engagement research in adaptive learning platforms

● high · created 2026-08-23 · updated 2026-08-23 · adaptive-learning, student-engagement, personalized-learning, intelligent-tutoring, motivation, online-teaching-and-learning

Synthesis: Simon, Zeng, and Fryer (2026) conducted a PRISMA-aligned systematic review of the factors that shape Student Engagement in Adaptive Learning platforms and of which engagement types dominate the literature. Searching six databases for 2012–2022 and retaining 44 peer-reviewed studies, they applied a hybrid inductive/deductive thematic analysis organized around a four-component engagement framework (behavioral, affective, cognitive, agentic). Four overarching themes emerged — system quality, information quality, learner characteristics and preferences, and affordances that mimic real-life social interactions. Behavioral engagement was the most discussed type, agentic engagement the least, prompting the authors to call for an extended engagement framework for human–computer interaction. All four themes were found to interact, and most factors ultimately trace back to system design.

Core Finding

Student engagement in adaptive learning platforms is driven by four interacting theme clusters — system quality, information quality, learner characteristics and preferences, and social-interaction affordances — and these factors are entangled: content quality, learner agency, and social support all depend on how the platform is designed. Across the literature, behavioral engagement is the most studied type and agentic engagement the least studied, a gap the authors attribute to adaptive platforms reducing the student–teacher interactions through which agentic engagement is normally expressed.

The Four Engagement Themes

Drawing partly on the Information Systems Success Model and partly on inductive coding, the review organizes the drivers of engagement into four clusters.

  • System quality — how well the platform works: adaptivity (tailoring activities to the learner's needs and emotional state), Accessibility and "anytime, anywhere" navigation, gamification, structure and design, progress tracking, and novelty. Adaptive tailoring and the exploratory novelty of environments like iTalk2Learn kept primary students on task.
  • Information quality — the value of the content: conciseness and relevance, challenging tasks with progressive difficulty, diversity of formats (texts, visuals, animations, video), and interactive practice questions that must be answered until correct, delivering immediate feedback.
  • Learner characteristics and preferences — self-directed learning at one's own pace, plus socio-demographic variables (educational aspiration, parental education/SES, gender, age/year level) and learning emotions.
  • Affordances mimicking real-life social interactions — affective learning companions, adaptive peer robots that switch between tutee and tutor roles, regular/immediate elaborated feedback, positive reinforcement, and a "teammate effect" (as in Wayang Outpost) that increases Help Seeking.

Engagement Types: Behavioral, Affective, Cognitive, Agentic

The review classified studies using the extended framework of Fredricks et al. (behavioral, affective, cognitive) plus Reeve and Tseng's agentic dimension. Rater counts showed behavioral engagement discussed most (≈29–35 articles), followed by affective (25–27), cognitive (17–19), and agentic (3–6). The relative omission of agentic engagement is telling: agency in Reeve and Tseng's conception is typically expressed in interaction with a teacher, which adaptive platforms minimize. The authors argue students should be given ways to express preferences, customize their environment (as ALEKS allows), and use help buttons to support agentic engagement, and they call for an engagement framework revised for human–computer interaction.

Disengagement and the Novelty Effect

The review also surfaces the "negative side" of adaptive learning. Disengagement arose from complicated, frustrating layouts, browser/technical issues, overly long or irrelevant modules, lack of detailed feedback, and — notably — a novelty effect: engagement declined over time in longitudinal studies of ALEKS and W-Pal once the novelty of the tool faded. Sustaining motivation after the exploration period is flagged as a key open problem.

Self-Determination and Design Implications

The authors interpret the social-interaction findings through self-determination theory: positive reinforcement, teammate effects, and learning companions affirm competence, convey relatedness, and support autonomy. Practically, schools should invest in platforms matched to their students' needs, involve experienced teachers in content creation, orient students to platform features, balance structure and flexibility, and integrate adaptive peer robots/companions to reduce the solitude of online learning. Well-designed feedback systems can also unburden teachers in blended settings so they can focus on quality student–teacher relationships.

Methodological Notes and Limitations

The review was preregistered and followed What Works Clearinghouse (v4.0) and PRISMA procedures; quality was appraised with the MMAT. It excluded gray literature and non-English manuscripts, and the fixed keywords may have missed studies using alternative terms (e.g., "AI," "intelligent tutoring"). Effect sizes were out of scope, so the authors recommend meta-analysis and meta-synthesis, plus experience sampling methods (ESM) for more ecologically valid engagement measurement.

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Citation

Simon, P. D., Zeng, L. M., & Fryer, L. K. (2026). A systematic review of student engagement research in adaptive learning platforms. Computers and Education Open, 10, 100360.

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