Advance learning analytics for study success through standardised measures, multimodal data, theory-informed practice, and large-scale longitudinal experimental designs¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The review recommends that learning analytics research and development clearly define standards for reliable and valid measures, informative visualisations, and design guidelines for pedagogically effective interventions. It states that "measures for learning analytics need to include reactive and non-reactive data collection, i.e., multimodal data for supporting learning, teaching, and study success", grounded in a strong theoretical foundation. It further recommends leveraging existing research by designing large-scale, longitudinal, or quasi-experimental studies with well-defined and operationalised constructs.

Design Implications¶

Context¶

Requirements¶

• A strong theoretical foundation of learning analytics as a prerequisite for defining a multimodal or holistic data set

Constraints¶

• The review notes fully automated adaptive learning analytics support systems may reduce a learner's self-regulation and perceived autonomy, so opportunities for personalisation are needed

Target Learners¶

• higher education students
• learning analytics researchers and educators

Target Learning Goals¶

• study success, retention, and dropout prevention

Claims¶

• Learning analytics approaches show promise for supporting study success and identifying students at risk of dropping out, but rigorous large-scale evidence of effectiveness is still lacking [+W]

Related Strategies¶

• Integrate autonomy and agency paradigms, standardise instruments, probe non-demographic moderators, and extend evaluations to longitudinal real-world settings
• Employ experimental and longitudinal designs with standardised measurement tools in future pressure injury prevention research.

Examples¶

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Key Sources¶

• Dirk Ifenthaler, Jane Yin-Kim Yau. (2020). Utilising learning analytics to support study success in higher education: a systematic review. Education Tech Research Dev. https://doi.org/10.1007/s11423-020-09788-z