Using Data to Inform Computing Education Research and Practice

The analysis of data plays an increasingly critical role in computing education research, enabled by more and larger datasets, more powerful analysis techniques and better infrastructure for sharing. This panel brings together four panellists at various stages of work involving the collection and analysis of large datasets in different fields of computing education. The panellists will each discuss the current state of their work, the unique aspects of their data, and how that data fits into the larger landscape of computing education and research. Panellists will be asked to explain how they are employing AI and data mining techniques to learn about learners, the research methods they have used to make this happen, and any significant key findings they have discovered through this processes. The panel will discuss emerging topics, including: going beyond log data, handling global-scale datasets, efficiently collaborating with cross-dataset analysis, and ethical and privacy considerations. After the panelists present (5 minutes each), the moderator will pose follow-up questions and invite the audience to pose additional questions or provide other feedback. Key takeaways will include how data mining and artificial intelligence can contribute to improved insight and learning gains and how the larger computer education community can participate in data collection or analysis.