Investigating the viability of mental models held by novice programmers

Abstract

This paper describes an investigation into the viability of mental models used by novice programmers at the end of a first year Java programming course. The qualitative findings identify the range of mental models of value and reference assignment held by the participants. The quantitative analysis reveals that approximately one third of students held non-viable mental models of value assignment and only 17% of students held a viable mental model of reference assignment. Further, in terms of a comparison between the participants' mental models and their performance in incourse assessments and final examination, it was found that students with viable mental models performed significantly better than those with non-viable models. These findings are used to propose a more "constructivist" approach to teaching programming based on the integration of "cognitive conflict" and program visualisation.

Teaching Introductory Programming

“…These high attrition rates are associated with considerable failure rates in introductory programming courses and/or disenchantment with programming [McGettrick et al 2004, in Grand Challenges in Computing: Education;Nikula et al 2011]. This disenchantment persists with graduates "expressing a dislike of programming and reluctance to undertake it" [McGettrick et al 2004 p.12; see also Ma et al 2007]. This is also reflected in the outcomes achieved by students, with large, multinational studies reporting that even at the conclusion of their introductory programming courses, a large number of students show substandard performance in elementary programming tasks [McCracken et al 2001;Lister et al 2004].…”

Developing a pre- and post-course concept inventory to gauge operating systems learning

“…These preconceptions can be leveraged to guide them to a deeper understanding of computer science concepts [5], or it may lead to misconceptions, which if not corrected by the instructor, may persist and hinder their understanding of a subject. In computer science, previous work has shown that students have trouble with even the most basic concepts of programming [4,18,22].…”

Assessing the Success of an Introductory Programming Course

“…Ma, Ferguson, Roper, and Wood (2007) have suggested that students' poor performance in programming is most likely the main reason computer science courses have such high attrition rates, around 30-50% according to Denning & McGettrick (2005;also see De Raadt et al, 2005). Not only are departments that teach programming faced with high attrition rates and pressure to limit failure rates, but since the dot com bust in early 2000 there has been a marked decrease in students enrolling in computer science programs (De Raadt, Watson, & Toleman, 2004;Denning & McGettrick, 2005).…”

“…Thus, while the written test of the McCracken group or even the reading test of the Lister group might be too hard, at least the skills tested by the more fundamental test of Dehnadi should be mastered. Ma et al (2007) tested 90 volunteer students on the Bornat et al (2008) test at the end of an introductory programming course. They acknowledged that these 90 students came from a possible pool of 124 students and that it seemed that weaker students did not volunteer.…”

“…They acknowledged that these 90 students came from a possible pool of 124 students and that it seemed that weaker students did not volunteer. Ma et al (2007) found that 63% of the students gave consistently correct responses at the end of the course (also see Ma et al, 2008). Ma et al (2007) expressed concern that approximately one-third of the students did not have an understanding of assignment and sequencing after an introductory programming course.…”