Guiding analytics for learners¶

Theory · All theories
Evidence · 4 claims (4 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 4 claims rest on one study

Description¶

The review proposes a new class of learning analytics, guiding analytics for learners, which it "define[s] as analytics based on the analysis of student (log) data according to learning theories or content-oriented structures which immediately presents learners with appropriate learning options". Unlike extracted analytics that teachers must translate into pedagogical action, guiding analytics give students an explicit interpreted output — such as "appropriate feedback", "appropriate guidance and feedback", or an "improvement plan" — directly within the digital learning system. Three included studies instantiated it via a game-based system integrated with the Prediction-Observation-Explanation strategy, two-tier testing based on a concept map, and intelligent assessment of learning strategies.

Design Implications¶

Context¶

Requirements¶

• Analytics must be built according to learning theories or content-oriented structures so the output reflects ideas of learning mathematics.
• Teacher support is described as helping students understand diagnostics and implement improvement strategies into daily learning activities.

Constraints¶

• The review notes "Appropriate feedback" alone does not necessarily lead to improved learning; guidance, not feedback alone, drove the achievement and retention gains.

Target Learners¶

• K–12 mathematics students (ages 6–19) in primary and secondary education

Target Learning Objectives¶

• Mathematics achievement and retention
• Self-regulated learning strategies
• Reduced math anxiety and prevention of random-guessing misuse

Claims¶

• Guiding Analytics Improves Achievement And Strategies [+M]
• Formative-assessment-based LA shows positive effects on achievement, motivation, and understanding, with personalized feedback reading time mattering [+W]
• LA usage in K–12 mathematics has a generally positive effect on student learning, with high-performing students benefiting most [+W]
• Learning paths used for SRL improved accuracy and performance without increasing effort, and tutoring-system benefits required sustained practice [+W]

Related Theories¶

Examples¶

-

Key Sources¶

• Rebecka Rundquist, Kristina Holmberg, John Rack, Zeynab Mohseni and Italo Masiello. (2024). Use of Learning Analytics in K–12 Mathematics Education: Systematic Scoping Review of the Impact on Teaching and Learning. Journal of Learning Analytics 11(3). https://doi.org/10.18608/jla.2024.8299