Journal of Computing in Higher Education

Although learning analytics dashboards (LADs) are being recognized as tools that can enhance engagement—a crucial factor for the success of asynchronous online higher education—their impact may be limited without a solid theoretical basis for motivation. Furthermore, the processes through which students make decisions using dashboards and engage are not well understood. This study aimed to design a LAD informed by self-determination theory and to investigate university students’ experiences with it. The findings, including those from stimulated recall interviews using eye-tracking data, shed light on how the LAD fosters student engagement. Interacting with the LAD fulfilled students’ basic psychological needs. Awareness and reflection on learning status facilitated by the LAD boosted enthusiasm for active learning participation. The LAD offered essential information to support autonomous, strategic decisions, empowering students to take proactive actions toward personal goals while reinforcing their belief in achieving them. Despite its potential benefits, various improvements have been identified to further enhance its effectiveness. Based on the findings, we discuss the implications of this study for future research in the field.

Computer science can be included in Early Childhood Education (ECE) through the use of block-based coding and robots. But this requires adequate preparation of ECE teachers to work with coding and robots, and integrate such into high quality lesson plans. In this paper, we investigate predictors of lesson plan quality among preservice, early childhood teachers learning to teach with robots. Motivation variables, academic standing, and collaboration status during lesson planning were entered as predictors of overall lesson plan quality, front-end analysis quality, STEM and robotics integration quality, and instructional activities quality. Achievement emotions in STEM was a positive predictor and mathematics interest was a negative predictor of the overall lesson plan quality score. Achievement emotions in STEM was a significant positive predictor of front-end analysis score. Science and technology interest and individual lesson planning were significant positive predictors of teaching and learning activities design score. Instructional implications are presented.

In recent years, embodied learning has gained currency in the field of education, allowing interactivity between users, thus contributing to collaborative learning in the flow of embodied immersive technology. Evidence based studies conducted in this field tackled the effectiveness of this approach on students’ performance and learning outcomes in children’s education. However, scarcity of scoping reviews treating the outcome of using embodied collaborative learning on higher education students’ performance was detected. Considering the relevance of building a theoretical background guiding further research in this current field, this meta-analysis was conducted to fill the gap. This article describes the current issues on research about embodied collaborative learning in STEM education in XR learning settings to identify advances and gaps. Published papers between 2013 and 2023 were selected from educational databases, identifying 12 documents related to the subject of interest of this meta-analysis. Results show that embodied learning have positive impact on social interactivity and collaboration between students. Positive impact on students’ academic outcomes was also detected. Alongside with the preceding, embodied collaborative learning has a positive impact on overall students’ social flow (motivation, cohesion, emotions, interaffectivity, satisfaction). This article presents a theoretical background for embodied collaborative testing tools based on embodied social presence and collaboration practical criteria.

Institutions of higher education possess large amounts of learning-related data in their student registers and learning management systems (LMS). This data can be mined to gain insights into study paths, study styles and possible bottlenecks on the study paths. In this study, we focused on creating a predictive model for study completion time estimation. Additionally, we explored the data to find out what features may affect the rapid completion of studies for a bachelor’s degree in an institution of higher education. We combined data from two sources: the Moodle LMS and a student register. The study exploited data from the entire study duration of the students. The data we extracted from the Moodle LMS focuses on the student’s diligence in respecting assignment deadlines. Based on the data, we created a model for predicting study duration and achieved an accuracy of 72%. According to this study, among the factors that may be influenced by the student herself, we found out that the most important predictors for fast study completion are a study pace that is more intensive at the end of studies, submitting assignments well before deadline and having a considerable amount of the grade 4.

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.