Higher education faces increasing pressure from governments and employers to ensure graduates are equipped with the knowledge and capabilities required for the future workplace. While technical knowledge is assessed through grades, professional skills are complex and remain difficult to evaluate systematically. While curriculum mapping offers a potential solution, it is often applied in a simplistic, accreditation-driven manner that merely records the presence or absence of skills embedded in assessments. Such an approach overlooks the relative contribution of each skill to the assessments and, hence, cannot be used to estimate skill development. Other noted approaches have relied on the use of self-assessment reports or surveys, and as such are subjective and cannot provide longitudinal evidence of skill development. This study addresses these limitations by proposing a novel curriculum analytics method, weighted Performance Factor Analysis, to model skill scores using assessment grades and granular weighted skill-assessment mapping. Students’ development of seven professional skills were examined along with how they transition across an accounting degree program. The findings show distinct patterns and trajectories of skill development. Overall, the study makes a significant methodological contribution to measuring skills and offers insights into how graduates develop their professional skills across the curriculum.

The proliferation of Artificial Intelligence (AI) is fundamentally reshaping creative work and education. While research has demonstrated AI's potential to enhance creative outcomes, the processes underlying human-AI co-creation remain insufficiently explored. This study investigates the dynamics of human-AI co-creation in an undergraduate game design course by leveraging Learning Analytics and Epistemic Network Analysis. Specifically, it examines how students’ creative thinking types (divergent, convergent, and evaluative thinking) interact with their contribution types (creating new ideas, extending ideas, refining ideas, and transforming ideas) during co-creation with AI. Results reveal the collaborative role of different thinking types in the student-AI co-creative process. Divergent thinking primarily drives the generation of new ideas, while convergent and evaluative thinking are essential for refinement and quality control. These cognitive-behavioral relationships evolve across learning phases, shifting from open exploration to focused integration. Furthermore, high-achieving students strategically employ convergent thinking and engage in continuous refinement and transformation with AI, whereas lower-achieving students tend to remain at the exploratory stage and rely on initial AI outputs. Practically, the findings underscore the importance of supporting students in shifting between creative thinking modes, promoting critical evaluation, and offering phase-specific guidance to facilitate effective and meaningful co-creation with AI in educational settings.

Ensuring constructive alignment between learning outcomes (LOs) and assessment design is crucial to effective learning design (LD). While previous research has explored the alignment of LOs with assessments, there is a lack of empirical studies on how assessment design influences LO mastery, particularly the relationship between formative and summative assessments. To address this gap, we conducted an empirical study within an undergraduate mathematics course. First, we evaluated the course's learning design to identify potential gaps in constructive alignment. Then, using a sample of 169 students, we analysed their assessment results to explore how LO mastery is demonstrated through formative and summative assessments. This study provides a novel learning analytics (LA) methodology by combining cognitive diagnostic models, epistemic network analysis, and social network analysis to examine LO mastery and interdependencies. Our findings reveal a strong connection between the mastery of LOs through formative and summative assessments, underscoring the importance of well-constructed LD. The practical implications suggest that LA can serve as a critical tool for quality assurance by guiding the revision of LOs and optimising LD to foster deeper student engagement and mastery of critical concepts. These insights offer actionable pathways for more targeted, student-centered teaching practices.

In response to the evolving demands of the contemporary workplace, higher education (HE) institutions are increasingly emphasising the development of transversal skills and graduate attributes (GAs). The development of GAs, such as effective communication, collaboration, and lifelong learning, are non-linear and follow distinct trajectories for individual learners. The ability to trace and measure the progression of GA remains a significant challenge. While previous studies have focused on empirical methods for measuring GAs in individual courses, a notable gap exists in understanding their longitudinal development within HE programs. To address this research gap, our study focuses on measuring and tracing the development of GAs in an Initial Teacher Education (ITE) undergraduate program at a large public university in Australia. By combining learning analytics (LA) with psychometric models, we analysed students’ assessment grades to measure learners’ GA development in each year of the ITE program. The resulting measurements enabled the identification of distinct profiles of GA attainment, as demonstrated by learners and their distinct pathways. The overall approach allows for a comprehensive representation of a learner's progress throughout the program of study. As such, the developed approach sets the grounds for more personalised learning support, program evaluation, and improvement of students’ GA attainment.

Complex problem solving (CPS) is a critical skill with far-reaching implications for personal success and professional development. While CPS research has made extensive progress, additional investigation is needed to explore CPS processes beyond online contexts and performance outcomes. This study, conducted with Year 9 students aged between thirteen and fourteen, focuses on collaborative CPS. It utilises audio and video recordings to capture group communications during a CPS classroom activity. To analyse these interactions, we introduce a novel CPS framework as a dynamic, cognitive and social process involving interrelated main skills, sub-skills, and indicators. Through sequential pattern mining, we identify recurring subskill patterns that reflect CPS processes in an educational environment. Our research underscores the importance of employing diverse patterns before plan execution, particularly building shared knowledge, planning, and negotiation. We uncover patterns related to groups going off-task and highlight the significance of effective communication and maintaining focus for keeping groups on track. Furthermore, we indicate patterns following the detection of emergent issues, recognising the value of cultivating clarity and adaptability among team members. Our CPS framework, combined with our research results, offers practical implications for teaching, learning, and assessment approaches in educational, professional and industry sectors.

The term Learner Profile has proliferated over the years, and more recently, with the increased advocacy around personalising learning experiences. Learner profiles are at the center of personalised learning, and the characterisation of diversity in classrooms is made possible by profiling learners based on their strengths and weaknesses, backgrounds and other factors influencing learning. In this paper, we discuss three common approaches of profiling learners based on students’ cognitive knowledge, skills and competencies and behavioral patterns, all latter commonly used within Learning Analytics (LA). Although each approach has its strengths and merits, there are also several disadvantages that have impeded adoption at scale. We propose that the broader adoption of learner profiles can benefit from careful combination of the methods and practices of three primary approaches, allowing for scalable implementation of learner profiles across educational systems. In this regard, LA can leverage from other aligned domains to develop valid and rigorous measures of students' learning and propel learner profiles from education research to more mainstream educational practice. LA could provide the scope for monitoring and reporting beyond an individualised context and allow holistic evaluations of progress. There is promise in LA research to leverage the growing momentum surrounding learner profiles and make a substantial impact on the field's core aim - understanding and optimising learning as it occurs.