Learning Analytics to Inform the Learning Design: Supporting Instructor’s Inquiry into Student Learning in Unsupervised Technology-Enhanced Platforms

Priya Harindranathan, James Folkestad

Abstract


Instructors may design and implement formative assessments on technology-enhanced platforms (e.g., online quizzes) with the intention of encouraging the use of effective learning strategies like active retrieval of information and spaced practice among their students. However, when students interact with unsupervised technology-enhanced learning platforms, instructors are often unaware of students’ actual use of the learning tools with respect to the pedagogical design. In this study, we designed and extracted five variables from the Canvas quiz-log data, which can provide insights into students’ learning behaviors. Anchoring our conceptual basis on the ‘influential conversational framework’, we find that learning analytics (LA) can provide instructors with critical information related to students’ learning behaviors, thereby supporting instructors’ inquiry into student learning in unsupervised technology-enhanced platforms. Our findings suggest that the information that LA provides may enable instructors to provide meaningful feedback to learners and improve the existing learning designs.


Keywords


effective learning strategies, learning design, learning analytics, unsupervised technology-enhanced platforms

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References


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DOI: http://dx.doi.org/10.24059/olj.v23i3.2057



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