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- Vol 7 RE: Chapter 10, App D
learning analyticsstudent expectationsstudy planningself-assessmentadaptive recommendationspersonalized feedbackdata privacyJournal article Evidence summaryAt a glance
Schumacher and Ifenthaler investigate what university students want from learning-analytics systems and whether they believe particular features would support their learning. An exploratory qualitative study with 20 students generated a set of expected functions, followed by a quantitative study in which 216 students rated 15 features, their willingness to use them, and perceived learning value.
Key ideas
Students especially valued tools for planning and organizing study, self-assessment, adaptive recommendations, and personalized analysis of learning activity. These expectations map onto phases of self-regulated learning, including preparation, performance monitoring, and reflection. Participants saw potential benefits in timely feedback and individualized support, yet also worried that analytics might become invasive or reduce autonomy. The findings show why dashboard design should begin with learner needs and educational theory rather than the data that happen to be available.
Why it matters
The authors recommend aligning features with self-regulation, feedback, and instruction, while making data practices and purposes transparent. Meaningful user involvement is therefore both a design requirement and a condition for acceptance.
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