With the rise of social networking sites and the arrival of an open education era characterized by Massive Open Online Courses MOOCs, learning is undergoing a paradigm shift which requires new assessment strategies. The boundaries between what we know, how we know it and the ways we assess and evaluate knowledge in formal and informal settings are now blurred , . In these environments, students often interact with one another to produce and reproduce knowledge and transfer it into a new context to reach a mastery level of learning . The massive amount of data being generated by learners makes it easier to assess performance than ever before , . Every learner action is logged and factored in as a source of evidence to contribute to the overall learner assessment both from a summative perspective, and also in a formative way where immediate feedback is actionable. The integration of learning analytics tools and machine learning techniques can facilitate the process of assessment. In this paper we present a case study to show how the integration of learning analytics benefited learners and improved their performance in an online educational course at the University of Illinois Urbana-Champaign, while also holding them accountable for their own learning. The study utilized a survey method for data collection and quantitative and qualitative data analysis to interpret learners’ experiences after taking the course.
- learning analytics
- big data