Evaluating Learning Outcomes Through Curriculum Analytics: Actionable Insights for Curriculum Decision-making A Design-based research approach to assess learning outcomes in higher education
Keywords: Additional Keywords and PhrasesLearning analytics, Curriculum analytics, Learning outcomes assessment, Actionable insights
Abstract
Learning analytics (LA) emerged with the promise of improving student learning outcomes (LOs), however, its effectiveness in informing actionable insights remains a challenge. Curriculum analytics (CA), a subfield of LA, seeks to address this by using data to inform curriculum development. This study explores using CA to evaluate LOs through direct standardized measures at the subject level, examining how this process informs curriculum decision-making. Conducted at an engineering-focused higher education institution, the research involved 32 administrators and 153 faculty members, serving 9.906 students across nine programs. By utilizing the Integrative Learning Design Framework, we conducted three phases of this framework and present key results. Findings confirm the importance of stakeholder involvement throughout different design phases, highlighting the need for ongoing training and support. Among the actionable insights that emerged from LOs assessments, we identified faculty reflections regarding the need to incorporate active learning strategies, improve course planning, and acknowledge the need for education-specific training for faculty development. Although the study does not demonstrate whether these insights lead to improvements in LOs, this paper contributes to the CA field by offering a practical approach to evaluating LOs and translating these assessments into actionable improvements within an actual-world educational context.
Más información
Título según WOS: | Evaluating Learning Outcomes Through Curriculum Analytics: Actionable Insights for Curriculum Decision-making A Design-based research approach to assess learning outcomes in higher education |
Título de la Revista: | FIFTEENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, LAK 2025 |
Editorial: | ASSOC COMPUTING MACHINERY |
Fecha de publicación: | 2025 |
Página de inicio: | 384 |
Página final: | 394 |
Idioma: | English |
DOI: |
10.1145/3706468.3706518 |
Notas: | ISI |