Carolien Van Soom

Profiling First-Year Students in STEM Programs Based on Autonomous Motivation and Academic Self-Concept and Relationship with Academic Achievement

The low success rate of first-year college students in Science, Technology, Engineering, and Mathematics (STEM) programs has spurred many academic achievement studies in which explanatory factors are studied. In this study, we investigated from a person-oriented perspective whether different motivational and academic self-concept profiles could be discerned between male and female first-year college students in STEM and whether differences in early academic achievement were associated with these student groups. Data on autonomous motivation, academic self-concept, and early academic achievement of 1,400 first-year STEM college students were collected. Cluster analyses were used to distinguish motivational profiles based on the relative levels of autonomous motivation and academic self-concept for male and female students. Differences in early academic achievement of the various profiles were studied by means of ANCOVA. Four different motivational profiles were discerned based on the dimensions of autonomous motivation (A) and academic self-concept (S): students scoring high and respectively low on both dimensions (HA-HS or LA-LS), and students scoring high on one dimension and low on the other (HA-LS or LA-HS). Also gender differences were found in this study: male students with high levels of academic self-concept and autonomous motivation had higher academic achievement compared to male students with low levels on both motivational dimensions. For female students, motivational profiles were not associated with academic achievement. The findings partially confirm the internal and external validity of the motivational theories underpinning this study and extend the present insights on identifying subgroup(s) of at risk students in contemporary STEM programs at university level.

Dashboard for Actionable Feedback on Learning Skills: Scalability and Usefulness

In the transition from secondary to higher education, students are expected to develop a set of learning skills. This paper reports on a dashboard implemented and designed to support this development, hereby bridging the gap between Learning Analytics research and the daily practice of supporting students. To demonstrate the scalability and usefulness of the dashboard, this paper reports on an intervention with 1406 first-year students in 12 different programs. The results show that the dashboard is perceived as clear and useful. While students not accessing the dashboard have lower learning skills, they make more use of the extra remediation possibilities in the dashboard.

Multi-institutional positioning test feedback dashboard for aspiring students: lessons learnt from a case study in flanders

Our work focuses on a multi-institutional implementation and evaluation of a Learning Analytics Dashboards (LAD) at scale, providing feedback to N=337 aspiring STEM (science, technology, engineering and mathematics) students participating in a region-wide positioning test before entering the study program. Study advisors were closely involved in the design and evaluation of the dashboard. The multi-institutional context of our case study requires careful consideration of external stakeholders and data ownership and portability issues, which gives shape to the technical design of the LAD. Our approach confirms students as active agents with data ownership, using an anonymous feedback code to access the LAD and to enable students to share their data with institutions at their discretion. Other distinguishing features of the LAD are the support for active content contribution by study advisors and LATEX type-setting of question item feedback to enhance visual recognizability. We present our lessons learnt from a first iteration in production.