Supporting the less-adaptive student: the role of learning analytics, formative assessment and blended learning

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Abstract

How can we best facilitate students most in need of learning support, entering a challenging quantitative methods module at the start of their bachelor programme? In this empirical study into blended learning and the role of assessment for and as learning, we investigate learning processes of students with different learning profiles. Specifically, we contrast learning episodes of two cluster-analysis based profiles, one profile more directed to deep learning and self-regulation, the other profile more directed toward stepwise learning and external regulation. In a programme based on problem-based learning, where students are supposed being primarily self-directed, this first profile is regarded as being of adaptive type, the second profile as being less adaptive. Making use of a broad spectrum of learning and learner data, collected in the framework of a dispositional learning analytics application, we compare these profiles on learning dispositions, such as learning emotions, motivation and engagement, learning performance, and trace variables collected from the digital learning environments. Outcomes suggest that the blended design of the module with the digital environments offering many opportunities for assessment of learning, for learning and as learning together with actionable learning feedback, is used more intensively by students of the less adaptive profile.
Original languageEnglish
Number of pages15
JournalAssessment & Evaluation in Higher Education
DOIs
Publication statusE-pub ahead of print - 25 Nov 2019

Keywords

  • learning analytics
  • learning dispositions
  • assessment of for and as learning
  • Learning analytics
  • MODEL

Cite this

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title = "Supporting the less-adaptive student: the role of learning analytics, formative assessment and blended learning",
abstract = "How can we best facilitate students most in need of learning support, entering a challenging quantitative methods module at the start of their bachelor programme? In this empirical study into blended learning and the role of assessment for and as learning, we investigate learning processes of students with different learning profiles. Specifically, we contrast learning episodes of two cluster-analysis based profiles, one profile more directed to deep learning and self-regulation, the other profile more directed toward stepwise learning and external regulation. In a programme based on problem-based learning, where students are supposed being primarily self-directed, this first profile is regarded as being of adaptive type, the second profile as being less adaptive. Making use of a broad spectrum of learning and learner data, collected in the framework of a dispositional learning analytics application, we compare these profiles on learning dispositions, such as learning emotions, motivation and engagement, learning performance, and trace variables collected from the digital learning environments. Outcomes suggest that the blended design of the module with the digital environments offering many opportunities for assessment of learning, for learning and as learning together with actionable learning feedback, is used more intensively by students of the less adaptive profile.",
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