Automatic feedback, self-regulated learning and social comparison: A case study

Abstract

Formative assessment is one of the main challenges facing MOOC research and practice. Providing timely and personalized feedback to large cohorts of learners poses issues in terms of scalability and sustainability. This paper puts forward a proposal for automated feedback well suited for assessing non-declarative knowledge. The proposed feedback strategy consists in displaying a comparison of responses and behaviors of individual participants with descriptive statistics reflecting the same data for the entire cohort. To investigate the usefulness and potential of this feedback strategy, quali-quantitative data were collected during a MOOC on learning design. Self-reported data about usefulness (for both responses and behaviors) were statistically above the mid-point of the scale, with no significant difference between the two types of data. Suggestions on how to improve this feedback strategy were also drawn from interviews with subjects.