Individual differences in computer programming: a systematic review

作     者：Navarro-Cota, Christian Molina, Ana I. Redondo, Miguel A. Lacave, Carmen 

作者机构：Autonomous Univ Baja Calif UABC Fac Engn Architecture & Design Ensenada Mexico Univ Castilla La Mancha UCLM Dept Informat Syst & Technol Ciudad Real Spain Autonomous Univ Baja Calif UABC Fac Engn Architecture & Design Ensenada Tijuana Transpeninsular Highway 3917 Ensenada 22860 Mexico 

出 版 物：《BEHAVIOUR & INFORMATION TECHNOLOGY》 (Behav Inf Technol)

年 卷 期：2025年第44卷第2期

页      面：357-375页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：CODIFICA project - MCIN/AEI [PID2021-125122OB-100] European Regional Development Fund (ERDF) "A way of making Europe" 

主　　题：Programming human factors individual differences systematic literature review 

摘      要：The demand for programmers has grown exponentially in recent years, making programming an indispensable skill. However, the complex nature of programming poses various challenges for novice programmers, leading to high dropout rates in programming courses. The recognition of individual differences, encompassing distinct neurocognitive profiles, is acknowledged to influence the accuracy of predicting programming skill outcomes. As a result, an increasing awareness exists regarding the substantial contribution of human factors, including personality or cognitive ability, to programming performance. This study conducts a Systematic Literature Review (SLR) to explore recent research on individual differentiation or programmer profiling in programming tasks. The primary goal is to examine pertinent research exploring personal characteristics influence on programming in various contexts. As a result of this review, a taxonomy has been introduced to enhance the comprehension of programmers multifaceted individual differences, categorising influential factors into nine distinct dimensions. Furthermore, it has been found that individual differences influence performance in programming tasks, as well as in the learning of this discipline. In conclusion, this SLR emphasises human factors critical role in programming and proposes a taxonomy that is a valuable framework for researchers, educators, and practitioners to enhance their understanding of human factors influence on programming performance.