Identifying novice student programming misconceptions and errors from summative assessments

This article presents a study aimed at examining the novice student answers in an introductory programming final e-exam to identify misconceptions and types of errors. Our study used the Delphi concept inventory to identify student misconceptions and skill, rule, and knowledge-based errors approach to identify the types of errors made by novices in Python programming. The students' responses to each question were scrutinized by using the Delphi concept inventory, heuristic-analytic theory, and neo-Piagetian theory of cognitive development for qualitative data analysis. Moreover, the motivation for this exploratory study was to also address the misconceptions that students held in programming and help educators to redefine the teaching methods to correct those alternative conceptions. Student misconceptions were spotted in list referencing and inbuilt functions in Python. In a further quantitative analysis, the study found that students who had misconceptions made knowledge errors and failed to complete the coding tasks. Surprisingly, and coincidentally, it was identified that only a few students were able to write code related to mathematical problems.