An Attributional Approach to Computer Programming Achievement of Undergraduate Business Computing Students in a University Computer Science Department

摘要

Despite the existence of nineteen universities in Lebanon, student motivation and achievement have not received attention in relation to attribution theory by Lebanese researchers. In the present study, attribution theory is used as a conceptual framework for investigating the motivation of undergraduate business computing students at a Mediterranean university based on their academic achievement in an introductory computer programming course.\udWhile numerous studies have used attribution theory as a framework to study student motivation based on hypothetical scenarios or laboratory tasks, this study investigated forty-five male and female business computing students who completed a computer programming course that lasted for a thirteen-week semester. Instead of focusing on either success or failure, the study explored five strata of achievement outcomes. Semi-structured interviews were conducted to obtain students' perceptions.\udThe participants made 11 causal attributions for their achievement outcomes. Only two of those 11 causes appeared in the original attribution theory model (Weiner et al. 1971, p.96), but they were amongst those least cited in this study. This study also shows that of the 11 causes, 'lack of study' and 'appropriate learning strategy' were the leading ones. The latter was cited by all high achievers. While there was total agreement on some of the underlying causal properties of some causal attributions, other causal attributions were perceived differently in the causal space. In addition, there was strong evidence that globality is a fourth dimension in this achievement context. Furthermore, the two dimensions of the Expectancy-Value motivation model (Amone 2005, p.4) do not seem to relate to attribution theory dimensions in this study, especially for low achievers. Finally, it was possible to identify some attribution styles that lead to either success or failure, thus supporting the predictive power of attribution theory.