In this paper, we present our preliminary results of identifying serendipitous findings from discussion forums of students by using a text-mining analytical tool to predict their academic performances. The analytical results were visualized by constructing KeyGraphs so that teachers can assess the effectiveness of teaching and innovation of learning respectively through the visualization of hidden patterns in the online learning environment. Our results show that the serendipitous findings have shown a traceable pattern, which is statistically significant to predict the academic performance of students. The research findings can lead to adaptive pedagogical designs for teaching and learning by finding hidden patterns and linkages among the students' serendipitous learning. The identified results are expected to support both teachers and students on how to improve teaching and learning with feedbacks from this new tool. Ultimately, this creates a new approach for transformative learning and teaching in education by using the advanced mining technology to assess the students' knowledge discovery process.
|Publication status||Published - Jun 2016|
Wong, G. K. W., & Li, S. Y. K. (2016, June). Academic performance prediction using chance discovery from online discussion forums. Paper presented at The 40th IEEE Computer Society International Conference on Computers, Software & Applications (COMPSAC 2016): Connected world: New challenges for data, systems & applications, Sheraton Atlanta Hotel, Georgia, USA.
- Teaching Development Grant (TDG)
- TDG project code: T0165
- Period: TDG 2015-2016
- Big data
- Educational data mining
- Topic detection
- Serendipitous learning
- Learning analytics
- Chance discovery
- Teaching Development Grant (TDG) Output
- No of participants: Around 20