This paper modify Clow’s learning analytics cycle to inform a modified framework describing the intersection of learning analytics with professional learning, and illustrates the potential power of GenAI for supporting professional learning across the framework through three case studies in health professions education.
Bernard Bucalon, Li-Xiang Yu, T. Shaw et al.· Journal of Learning Analytic...· 0 citations
Medical history taking is a dialogue-based clinical reasoning task in which learners must gather, organise, and integrate patient information while the consultation unfolds. Generative AI-powered virtual patients (GenAI VPs) make repeated history taking practice scalable and preserve full turn by turn dialogue. However, these logs are educationally difficult to use directly. Complete transcripts are too detailed for routine teacher review, whereas final scores obscure whether learners followed up patient cues, checked uncertainty, or used summaries to guide later questioning. This study examined whether coded GenAI VP dialogues can provide teacher-interpretable process evidence of clinical reasoning. We analysed 1{,}030 GenAI VP dialogues from 210 second-year medical learners across five weeks chest-pain cases. Each consultation was teacher-scored using a rubric assessing the full history taking dialogue, and consultations were classified within each week as high- or low-rated using the weekly median score. To explain how rated performance was reflected in the dialogue process, we applied three analytic layers to the same coded dialogue data: behavioural prevalence, local co-occurrence using Epistemic Network Analysis, and sequential transition using Transition Network Analysis. High-rated consultations involved more history taking activity, but differences were not simply about volume. High rated consultations more often connected information gathering and symptom exploration with communication, checking, organisation, and synthesis. Summarising and organising moves more often led to verification or mechanism-oriented follow-up. These findings show how layered analysis of GenAI VP dialogue logs can reveal process patterns associated with high rated history taking and support process-focused feedback in medical education.
Xinyu Li, Zijian Li, Mengyu Xia et al.· 0 citations
Feedback is a key factor in improving the writing skills of students, but providing it at a large scale remains a significant challenge. Large Language Models (LLMs) emerge as a promising solution; however, the pedagogical quality of the automatically generated feedback requires further investigation. This study examined how effective teachers perceived feedback texts generated by an LLM when the prompts were designed to reflect different aspects of feedback theory. To this end, 450 feedback texts were generated for 30 essays and evaluated by five teachers with varying feedback literacy profiles. The results showed that teachers' preferences for feedback models were strongly shaped by their profiles, indicating that there was no single universally "best" feedback model. From a learning analytics perspective, these findings demonstrate how combining theory-informed prompt design with evaluations from diverse teacher profiles can generate actionable insights for the development of scalable, AI-powered feedback systems. The study contributes to learning by showing that the effectiveness of LLM-based feedback depends on personalization not only for students but also for teachers, highlighting new directions for designing adaptive, equitable, and context-sensitive feedback analytics.
A. P. Cavalcanti, Luiz Rodrigues, Cleon Xavier et al.· Revista Brasileira de Inform...· 0 citations
