Coding Decoded: Exploring Course Achievement and Gender Disparities in an Online Flipped Classroom Programming Course

Abstract

In introductory programming courses (IPCs), students encounter various difficulties that are related to low achievement and high dropout and failure rates. Technology-rich approaches that promote self-directed learning while facilitating competency development and knowledge construction through social collaboration may offer advantages in this context. The current study assesses such an instructional approach by (1) identifying antecedents and process variables related to course achievement in an online flipped classroom IPC and (2) testing for gender differences regarding antecedents, process variables, and course achievement. In the winter semester of 2020/21, a sample of 144 Austrian university students participated in a survey with measurements at different points in time. Multiple linear regression was carried out to explore factors related to course achievement. The results indicate that gender, achievement-avoidance goals, academic self-concept, engagement in asynchronous learning, and course satisfaction were positively related to achievement. In contrast, work avoidance was identified as a barrier to achievement. Additionally, multivariate analysis of variance (MANOVA) was employed to test gender differences. MANOVA revealed significant gender differences regarding learning goals, mathematical self-concept, work avoidance, and engagement in synchronous learning. There were no gender differences regarding course satisfaction or achievement. The study has implications for designing innovative programming courses that could foster course satisfaction and achievement and thus reduce dropout and failure rates.