Course-level Learning Analytics is often perceived as an effective tool to help facilitate large-sized classes or MOOC courses, but not necessary useful for small-sized class as instructors can easily observe students engagement and assess their performance at a glance.
This blog is to demonstrate the application of learning analytics specifically designed/targeted for small-sized classes, i.e., how learning data can be leveraged to provide students with personalized evidenced-based real-time feedback.
First, we employed text mining technique to harness student reflective writing on course materials/readings and produce diagrams derived from the corpus that represent word relationships. The link corresponds to the strength of term correlation and the density of terms denotes the degree of centrality in relation to sparsity.
Secondly, we aggregate student activity data and generate animated plot that represents number of valid clicks on a daily basis over a period of time.
Below is a sample of evidence-based feedback to student with the visual representation of student learning data:
As shown in the diagram below, your (the student) description of the research project demonstrates a solid understanding of the hypothesis, meaning of the study, its findings and implications.
Below is the line chart that represents the class activity over time. To comply with the guidance of data privacy and ethics in LA, the class is anonymized. The plot shows the engagement of individual students over time.
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