Abstract:

As the field of software engineering continues to modernize, LCDPs can offer a viable complement to traditional programming languages by facilitating rapid application development and promoting experiential learning within a controlled, yet flexible, technical environment. These platforms have the potential to allow educators to bridge conceptual understanding with practical implementation while mitigating cognitive barriers typically associated with text-based coding.

LCDPs are defined as development environments that employ visual modeling and component-based logic to automate aspects of software construction. Their incorporation into software engineering curricula can provide a transitional medium through which novices can progress from low-code modeling to traditional coding paradigms. Furthermore, the increasing availability of AI-augmented features within LCDPs provides a potential starting point to integrate artificial intelligence into software development pedagogy.

Motivation has long been recognized in related research as a critical determinant of performance and persistence in software development activities. Motivational research in software development makes uses of several concepts, besides motivation itself: engagement, interest, effort, focus of attention, self-efficacy, confidence etc. The research presented in this paper focuses on the engagement with a LCDP in context of software development. Building upon established frameworks in software engineering motivation research, this study examines the behavioral and motivational patterns of students engaged in low-code development tasks. Log data derived from LCDP usage by 38 Master’s students participating in project-based courses were analyzed to identify latent motivational profiles.

The students were working to develop a low-code application to solve a real-life business requirement. Hence, this research builds on previous work in the context of motivation factors in software development as well as on the analysis of the log data for definition of engagement patterns rooted in the study of the interactions with a Learning Management System (LMS).

Cluster analysis was employed to categorize learners based on indicators of behavioral engagement, coding activity, and interaction frequency. Results revealed three distinct motivational profiles, aligned with the dimensions of Source, Engagement, and Energization as defined by Sharp et al. The robustness of the identified clusters was also evaluated.

The examination of log-based behavioral indicators provides an empirical basis for understanding individual differences in student motivation and engagement during low-code development. These findings hold pedagogical implications for the design of software engineering courses: instructors can leverage such data to adapt instructional strategies, optimize group composition, and allocate mentoring resources according to observed engagement levels. Furthermore, recognizing variation in coding intensity and interaction dynamics facilitates the development of targeted interventions aimed at sustaining student motivation and improving learning outcomes.

Keywords:

Low Code development, Log data analysis, motivation profiles, software development, teaching business-IT alignment.