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

Donatella Persico, Marcello Passarelli, Flavio Manganello, Francesca Pozzi, Francesca Maria Dagnino, Andrea Ceregini, Giovanni Caruso

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.

Parole chiave


Social Comparison; Automatic Feedback; Learning Analytics; Self- Regulated Learning

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