Understanding online learning through data

Working in partnership with researchers from UTS has enabled students and teachers to learn more about the research process

Data Science students and staff from Pymble have now concluded their research collaboration with the team from the University of Technology Sydney Data Science Institute (DSI) and UTS Education. In May, the research team joined us for final meetings to share and discuss findings in preparation for upcoming presentations and publications.

The focus of the project was how learning analytics can be used to enhance student engagement in online learning in secondary school. The pilot set out to see if tracking software can ‘see’ student engagement and, if so, what engagement in online tasks ‘looks like’. The research questions for the project were:

1.How does students’ engagement vary in the different online tasks?

2.How can teachers assess students’ levels of engagement on online tasks?

3.How does this engagement relate to assessable learning outcomes?

4.What are the implications for how Pymble teachers design online tasks that engage and effectively enable learners to complete task successfully?

Data were collected via DSI software loaded onto College laptops which students used for the task and were collected in the form of coding which captured eye gaze, head pose, facial expressions and keyboard and mouse interactions. Once in code, the students’ movements were represented as follows:

A second form of data collection took place in the form of focus groups. These enabled discussion, explanation and questioning, including what was happening at certain times and how students felt while doing the task.

Data analysis by the UTS team arrived at three personas which will be further explained in upcoming publications. In summary, teachers and students have started to explore the three categories of Whiz, Worker and Worrier in order to critique the relevance of these in the school setting. The Pymble Data Science team strongly believe that all mindsets are vital for coding and programming and help students see their strengths and growth opportunities by exploring all these approaches.

Dr Tracey-Ann Palmer from UTS Education has presented on this project at the Australasian Science Education Research Association (ASERA) Conference in Queensland and enjoyed the chance to share results with the interested audience.

This article has been co-written with the UTS team of Nick Hopwood, Tracey-Ann Palmer, Mun Yee Lai, Gloria Koh (Education) and Kun Yun and Yifei Dong (Data Science). We thank Pymble Data Science teachers Anthony England, Kim Maksimovic, Glen McCarthy and Cedric Le Bescont and Research Assistant Victoria Adamovich, and Year 9 and 10 Data Science students (2022).