Liyin Zhang, Mian Wu and Fan Ouyang

Education and Information Technologies   

2023   

https://doi.org/10.1007/s10639-023-11940-0

Abstract：

The data-intensive research paradigm calls for using educational and learning data to generate actionable insights and improve the instruction and learning quality. Although previous research designed and employed teaching analytics or learning analytics tools, few research had incorporated multiple data sources to assess the overall teaching and learning processes comprehensively. To address this gap, we proposed a teaching and learning analytics (TLA) tool that integrated multiple data sources from the instructor and students during educational process, leveraged multiple analytic methods to visualize results and provide supportive feedforward, with the goal to provide data-driven evidence for educational improvement. Mixed methods were conducted from quantitative and qualitative ways to examine the tool’s effects on actual instruction and learning processes. Our results showed that the designed TLA tool with feedforward suggestions had positive effects on instruction, learning, and instructor-student interactions. Based on the results, this research proposed implications for TLA tool design and pedagogical strategies.

Keywords: Feedforward; Higher education; Learning analytics; Teaching analytics; Teaching and learning analitics tool