Introduction to Learning Analytics

(3 Credit Hours) · Undergraduate

The popularity of massive open online courses (MOOCs) and shifting to online learning during the COVID-19 pandemic have witnessed the power of learning analytics (LA) of using large-scale learning data to support the teaching and learning practices. Although promising milestones have been achieved, the widespread adoption of LA is still at its infancy stage. In this course, we will introduce the most current topics in LA including: What are the main concerns in the LA field? How do we build artificial intelligence (AI) models to identify patterns in historical learning data and make predictions about the future learning? How to use Text Mining approaches to analyze forum discussion data to track changes in student emotional status? What are the data visualization skills we should have to support the analytical processes and present results? Apart from this, we will also talk about the topics about ethical issues such as the unintentional discriminations from AI algorithms, trustworthy concerns in AI predictions as well as privacy concerns related to large availability of learning data. Students in this course will be engaged in multiple projects based on publicly available learning datasets with modularized python function blocks provided for the corresponding tasks.