Contextualization, Authenticity, and the Problem Description Effect

Abstract

In recent years, computing education researchers have investigated the impact of problem context on students’ learning and programming performance. This work continues the investigation motivated, in part, by cognitive load theory and educational research in computer science and other disciplines. The results of this study could help inform computing assessment design. If the context and authenticity of a programming problem aid student performance then, instructors’ time in creating appropriately contextualized programming problem descriptions is time well-spent. On the other hand, if the context of a programming problem hinders performance, then instructors should leave it out of programming problems. Recent studies investigating the impact of programming problem context on student success have arrived at different conclusions. Presented here is a series of experiments, conducted over three years, investigating the impact of context on novice programmers’ success in algorithmic programming assignments using three contextualized tasks and their generic counterparts. This experiment series also looked into the possibility of ‘authenticity’ as a factor affecting performance. Common sense would suggest that a student would perform better on a problem if they understood or cared about it. Contextualization could provide authenticity and authenticity could provide interest. Research suggests that perceiving a problem as authentic has a positive effect on engagement and learning. Alternatively, if a problem is ‘just an abstract set of numbers’, it may be harder to make sense of the details and the lack of context could consequently contribute additional cognitive load to an already challenging assignment. The results of this study show that assignment context and problem context authenticity have no effect on the performance of novice programmers. We think however, that contextualization could be worth investing in to support students’ interest in computing. Additional implications of the results suggest that instructors can assign equivalent versions of the same problem in varied contexts to suit their students’ interests without worrying if the context will hinder performance.