Conflicting competencies and their individual variability

Learners may encounter difficulties when acquiring new skills or knowledge conflicts with a pre-existing proficiency in their cognitive repertoire. For instance, learning multiple programming languages can be challenging when the syntactic structures of one language clash with those of another. Individuals experiencing such competency-level conflicts often struggle to make progress and appear to have inherent learning impediments, the root cause of which lies in conflicting prior knowledge. Similarly, software engineers who are experts in one programming language face challenges in acquiring a new one, as they tend to draw parallels between the existing and the new language. While most research on learning has focused on teaching programming to novice learners, the conflicts encountered by experts necessitate investigation. To the best of our knowledge, there is a need for systematic studies that observe these competency-level conflicts through neurological signals and relate them to learning challenges. This paper presents an experiment designed to investigate competency conflicts at a neurological level. Participants were asked to comment errors in C and Python code snippets while connected to an electroencephalogram device. The analysis of EEG signals for conflicting competencies, characterized by longer response times and increased theta power, aligns with the existing literature on conflicting tasks. The EEG signals detected the presence of conflict with an accuracy of 89 %. Further analysis of the EEG signal features revealed individual variability in experiencing conflict, based on the participant’s prior competencies. Neurological evidence for competency-level conflicts can provide new insights into the need for personalized learning pathways that should consider an individual’s pre-existing conflicting competencies.