2026-03-30
Guillermo Bautista, Roderick Cacuyong, Zsolt Lavicza, Barbara Sabitzer, Mona Emara
This study explores how students across Grades 8 to 12 engage with mathematical functions in creative, visual ways through function art—an innovative STEAM-based educational approach. Grounded in the Trends in International Mathematics and Science Study (TIMSS) framework and employing a Design-Based Research methodology, the project involved 400 students from the Philippines who created digital artworks using GeoGebra. To uncover learner profiles, a person-centred clustering method—hierarchical clustering on principal components—was applied to variables representing the number and types of functions used. The results revealed three distinct student profiles: Repetitivists (high function quantity, low diversity), Simplists (low quantity and diversity), and Multifunctionists (high diversity, low quantity). Further analysis showed meaningful associations between cluster membership, grade level, and function strategies. Qualitative evaluation using TIMSS cognitive domains—Knowing, Applying, and Reasoning—highlighted that students’ use of mathematical strategies and precision in transformations varied widely, often independently of the quantity or diversity of functions used. These findings suggest that function art, when analyzed through learning analytics, provides a rich lens for understanding students’ mathematical thinking and offers valuable insights for tailoring interdisciplinary instruction in STEAM education.
2026-03-18
Paul V. Sargent, Isabel Hilliger, Jorge A. Baier
Student workload analysis has the potential to play a crucial role in providing both actionable insights to inform course design and curricular adjustments that promote student learning and well-being. While numerous studies have emphasized the need for analyzing workload beyond single-value metrics, such as credit hours, the interpretation and practical application of these metrics for educational interventions remains unclear. In this study, we explore the interplay between time-on-task measurements with student-perceived learning and difficulty.We move beyond average indicators of time-on-task by proposing and examining various metrics related to the dynamics of workload over time. Across 14 engineering courses taught at Pontificia Universidad Católica de Chile, we analyze three different sources of data: (1) self-reported time-on-task and perceived difficulty obtained through a weekly timesheet survey, (2) interactions with the learning management system (LMS), and (3) perceived learning attainment obtained from the course evaluation survey. Our results show that LMS-based and self-reported time-on-task were highly correlated. Also, workload dynamics metrics, such as the presence of workload peaks, were highly correlated with perceived learning and perceived difficulty. As such, this study provides evidence in support of considering workload dynamics, rather than average measures of time-on-task, to predict variables related to student learning. The metrics proposed by this framework could be used to implement practical tools for educators and administrators willing to optimize course design and improve learning attainment.
2026-03-15
Abhinava Barthakur, Jelena Jovanović, Ryan Baker, Vitomir Kovanović, Christopher C. Deneen, Shane Dawson
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.
2026-02-25
Zhen Xu, Xin Guan, Chenxi Shi, Qinhao Chen, Renzhe Yu
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.
2026-02-25
Josmario Albuquerque, Bart Rienties, Martin Hlosta, Wayne Holmes
Online learning platforms have expanded access to education but also raise concerns about biased content, particularly in text-based learning materials such as textbooks, lesson plans, and course excerpts. Such biases can perpetuate discrimination, can harm student outcomes, and can often be difficult to detect, as identification typically relies on time-consuming human review. Learning analytics (LA) can enhance this process by supporting human reviewers through automated detection, offering a scalable solution while retaining human judgment for nuanced evaluations. Accordingly, this LA study explores two research questions: RQ1: Which features might support the identification of ethnic bias in text-based online learning materials? and RQ2: Which classification approaches might be suitable for identifying ethnic bias in text-based online learning materials? First, we identified features signalling potential ethnic bias (presence or absence) in textual content using a dataset (N = 345) labelled by 193 students from diverse ethnic backgrounds. Then, we evaluated multiple machine learning (ML) models for their effectiveness in bias classification. The results suggest significant correlations between perceived bias and content from social sciences. Additionally, through bootstrap analysis, support vector machines and random forest classifiers showed consistent performance in bias identification (with F1-scores of 0.71 and 0.70 on the test set, respectively). In contrast, the naive Bayes (NB) model demonstrated the highest precision (0.75 on the test set). We discuss these findings and their implications for LA, emphasizing the importance of quality and inclusive educational tools. As an initial step toward automated bias classification in education, this study provides a foundation for spotting ethnic bias in learning content, supporting fairer technologies for more inclusive learning environments.