Journal of Computing in Higher Education

This study investigates the role of learning analytics in enhancing the learning experience within Massive Open Online Courses (MOOCs) through a two-phase design-based research approach, focusing on a Social Work MOOC. Initial engagement analysis revealed strong interactions with course content, especially with introductory elements and reflection quizzes, underscoring their importance in sustaining learner commitment. The subsequent empirical design refinement identified two primary learner clusters: Comprehensive Sequential Engagers and Interactive Early Engagers. The Comprehensive Sequential Engagers demonstrate a methodical approach, starting later and favoring a structured knowledge acquisition process, suggesting the need for adaptable course structures and early checkpoints to track progress. Conversely, the Interactive Early Engagers engage early and actively, driven by curiosity and a preference for exploratory learning, indicating a need for flexible content navigation and personalized learning pathways. These findings highlight that learning analytics can significantly inform MOOC design, providing valuable insights into tailoring educational experiences to meet diverse learner needs and behaviors. Despite these benefits, challenges remain in integrating learning analytics into course design, including obtaining timely and accurate data, ensuring data literacy among educators, and addressing cultural resistance to data-driven approaches. This study calls for further research to expand the adoption of learning analytics, examine the barriers to its integration, and improve its scalability across different educational contexts.

This study had two objectives: (1) to evaluate the validity of an instrument for measuring differentiated cognitive loads in its Spanish version; and (2) to evaluate the three types of cognitive loads and their relationship with self-efficacy, self-concept, and interest in programming of students in an introductory course. Understanding and assessing cognitive loads when learning computer programming is key to supporting student learning. While there are instruments in English and German assessing the different types of cognitive loads, there is no validated instrument in Spanish. This study took place during the implementation of an online training program in basic programming, with a sample of 1162 students. We used Exploratory Factor Analysis and Confirmatory Factor Analysis to validate the structure of the instrument. The results allowed us to establish a factorial structure of the subjective scale of differentiated cognitive loads, managing to measure the germane, intrinsic, and extraneous cognitive loads. The bivariate correlation analysis allowed identifying statistically significant associations between the study variables, including (a) the negative relationship between extraneous cognitive load and germane cognitive load and (b) the negative relationship between extraneous cognitive load and self-efficacy in programming.