Constructing feedback for computer science MCQ wrong answers using semantic profiling (Discussion Paper)
Year: 2023
Type: conference-paper
Abstract: Computer science (CS) for K-12 students is a hard subject with abstract concepts to learn. Multiple-choice questions (MCQ) are often used to supplement classroom learning, with feedback from wrong answers playing a role in addressing misunderstandings. Semantics, a dimension of Legitimation Code Theory, a framework for understanding knowledge practices, has been suggested as a useful theory for reviewing and structuring CS learning events. In this discussion paper, we explore the use of ‘semantic profiling’ to improve feedback to wrong answers in MCQ for post-16 students studying SQL and relational databases. We describe the reflexive review process we developed and present the semantic profiles of two case studies of new feedback for wrong answers. New answer feedback to five questions was trialed in a pilot study with five students, and students self-reported the new feedback as useful. For example, students liked the metacognitive aspect of feedback that explained why answers were wrong or right and liked the generalised summaries. From our reflexive experience, we suggest reviewing MCQ questions and using semantic profiling for feedback can help MCQ authors and students develop their feedback literacy, particularly for creating learner ‘feedforward’ (take-away) opportunities. The approach we used has promise that we will build on, and we invite other researchers to explore and evaluate this approach.
Authors: Jane Waite, Eirini Kolaiti, Meurig Thomas, Karl Maton
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4391584351