Megan Taylor

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.

Self-reflection and reflective writing have been pivotal for developing a deep understanding of concepts and fostering professional competency in learners. The confluence of the importance of reflective practices within the educational curriculum and the increased proliferation of technology have resulted in numerous studies of how to use automated approaches to analyze broad themes of reflection exhibited by learners in higher educational settings. However, there is a dearth of research in the context of automated analysis of reflective writing demonstrated by professionals within workplace learning. Building on a four-level reflective content analysis model, this study evaluates the depth of reflection exhibited by learners within a professional development MOOC using an automated assessment classifier. Our results identify the varying association of linguistic features across these different levels of reflection. The proposed approach can effectively support the deployment at scale of the labor-intensive evaluation of reflective writing by highly trained professionals.

Peak period traffic congestion is a problem faced by many urban areas around the world as the supply of transport infrastructure struggles to keep...