curriculum-analytics
Filtering by topic curriculum-analytics(3)Clear all filters
- PaperJournal of Learning Analytics15 Mar 2026
Assessing Patterns of Students’ Attainment of Professional Standards in Higher Education
Abhinava Barthakur, Jelena Jovanović, Ryan Baker, Vitomir Kovanović et al.
A novel curriculum analytics method uses psychometric modeling and learning analytics to map assessment grades to professional standards in teacher education. The approach identifies distinct patterns in students' attainment of standards without altering current assessment practices, offering a scalable automated complement to GPA.
Original abstract
It is widely recognized that higher education (HE) graduates require a broad range of professional skills and abilities to succeed in their future careers. However, despite this acknowledgement, assessment practices in HE remain focused on content-based knowledge. This narrow emphasis limits the capacity to effectively and holistically evaluate a student’s professional competency and readiness for employment. This issue is particularly acute for HE degrees that require graduates to demonstrate attainment of externally regulated professional standards. While the curricula are mapped to professional standards for accreditation purposes, demonstrating a student’s attainment of these standards is not straightforward and has mostly been done through self-reported surveys. This study offers a novel curriculum analytics method for mapping assessment grades to the attainment of professional standards across a Teacher Education program. Specifically, we present an approach that uses psychometric modelling and learning analytics to identify distinct patterns in learners’ acquisition of professional standards. This method does not alter current assessment practices in HE. Instead, the approach offers a scalable, automated means to infer a learner’s attainment of documented professional standards, complementing current measures of academic success, such as GPA. The study underscores the advantages of complementing the current HE assessment practises with an outlined curriculum analytics approach, providing a holistic representation of a student’s learning progress.
- PaperJournal of Learning Analytics15 Mar 2026
Assessing 21st Century Competencies
Mónica Hernández-Campos, Isabel Hilliger, Francisco-José García-Peñalvo
Curriculum analytics tools effectively support assessing learning outcomes aligned with 21st-century competencies by providing actionable insights for faculty, leading to more authentic and reflective teaching practices, as shown in a two-case study in higher education.
Original abstract
The growing emphasis on competency-based education (CBE) has heightened the need for clearly defined metrics and robust assessment frameworks to evaluate 21st-century competencies. Curriculum analytics (CA) provides a promising avenue for assessing learning outcomes (LOs) and informing continuous improvement in higher education. However, challenges persist in differentiating academic performance from actual LO development and in translating assessment data into meaningful program-level actions. This study examines how CA tools support the direct assessment of LOs and contribute to continuous improvement processes in higher education. Using a two-case study design, we analyzed CA implementation in two universities through interviews, cognitive walkthroughs, and institutional document analysis. Data triangulation identified 18 themes, nine of which reached full consensus among the three researchers. Findings indicate that CA tools effectively support the assessment of LOs aligned with 21st-century competencies by generating actionable insights that guide faculty toward more authentic and reflective teaching practices. The study contributes to the LA field by providing empirical evidence of how CA tools can bridge assessment and pedagogical improvement, offering both theoretical and practical implications for researchers and practitioners.
- PaperJournal of Learning Analytics25 Feb 2026
Evaluating 21st-Century Competencies in Postsecondary Curricula with Large Language Models
Zhen Xu, Xin Guan, Chenxi Shi, Qinhao Chen et al.
Large language models were tested on mapping postsecondary curricula to 21st-century competencies using 7,600 manually annotated alignment scores. Detailed instructional activity descriptions proved most informative, and open-weight models matched proprietary ones on coarse-grained tasks, though no model reached human-level precision. A reasoning-based prompting strategy (curricular chain-of-thought) modestly improved performance by reducing bias and enhancing detection of nuanced pedagogical evidence.
Original abstract
The growing emphasis on 21st-century competencies in postsecondary education, intensified by the transformative impact of generative artificial intelligence (GenAI) on the economy and society, underscores the urgent need to evaluate how they are embedded in curricula and how effectively academic programs align with evolving workforce and societal demands. Curricular analytics, particularly recent advancements powered by GenAI, offer a promising data-driven approach to this challenge. However, the analysis of 21st-century competencies requires pedagogical reasoning beyond surface-level information retrieval, and the capabilities of large language models (LLMs) in this context remain underexplored. In this study, we extend prior research on curricular analytics of 21st-century competencies across a broader range of curriculum documents, competency frameworks, and models. Using 7,600 manually annotated curriculum-competency alignment scores (38 competencies and 200 courses across five curriculum document types), we evaluate the informativeness of different curriculum document sources, benchmark the performance of general-purpose LLMs on mapping curricula to competencies, and analyze error patterns. We further introduce a reasoning-based prompting strategy, curricular chain-of-thought (CoT), to strengthen LLMs’ pedagogical reasoning. Our results show that detailed instructional activity descriptions are the most informative type of curriculum document for competency analytics. Open-weight LLMs achieve accuracy comparable to proprietary models on coarse-grained tasks, demonstrating their scalability and cost-effectiveness for institutional use. However, no model reaches human-level precision in fine-grained pedagogical reasoning. Our proposed curricular CoT yields modest improvements by reducing bias in instructional keyword inference and improving the detection of nuanced pedagogical evidence in long text. Together, these findings highlight the untapped potential of institutional curriculum documents and provide an empirical foundation for advancing AI-driven curricular analytics.