Temporal Analytics of Workplace-based Assessment Data to Support Self-Regulated Learning

One of the most effective ways to develop self-regulated learning skills in higher education is to include work placements. Workplace-based assessment (WBA) provides opportunities for students to gain feedback on their practical skills, reflect on their performance, and set goals and actions for further development. This requires identifying temporal patterns, as placements usually span extended periods of time. In this paper we explore two intelligent computational methods (burst detection and process mining) to derive temporal patterns. We apply both methods on WBA data from a cohort of first-year medical students. Through this we identify interesting temporal patterns, and gather educators' feedback on their usefulness for self-regulated learning

Quantified Self Analytics Tools for Self-regulated Learning with myPAL

One of the major challenges in higher education is developing self-regulation skills for lifelong learning. We address this challenge within the myPAL project, in medical education context, utilising the vast amount of student assessment and feedback data collected throughout the programme. The underlying principle of myPAL is Quantified Self -- the use of personal data to enable students to become lifelong learners. myPAL is facilitating this with learning analytics combined with interactive nudges. This paper reviews the state of the art in Quantified Self analytics tools to identify what approaches can be adopted in myPAL and what gaps require further research. The paper contributes to awareness and reflection in technology-enhanced learning by: (i) identifying requirements for intelligent personal adaptive learning systems that foster self-regulation (using myPAL as an example); (ii) analysing the state of the art in text analytics and visualisation related to Quantified Self for self-regulated learning; and (iii) identifying open issues and suggesting possible ways to address them

Framing Professional Learning Analytics as Reframing Oneself

Central to imagining the future of technology-enhanced professional learning is the question of how data are gathered, analyzed, and fed back to stakeholders. The field of learning analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centered and instructional design, and organizational change, and so could in principle inform how data can be gathered and analyzed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated, and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this article proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of “reframing oneself” holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: first, analyzing personal and team skills profiles (skills analytics); second, making sense of challenging workplace experiences (reflective writing analytics); and third, reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically

Educational Theories and Learning Analytics : From Data to Knowledge

Under embargo until 17.01.21.acceptedVersio

Proceedings of the 4th Workshop on Awareness and Reflection in Technology Enhanced Learning. In conjunction with the 9th European Conference on Technology Enhanced Learning: Open Learning and Teaching in Educational Communities

Awareness and reflection can be viewed from the differing perspectives of the disciplines informing Technology-Enhanced Learning, such as Psychology, Educational and Learning Sciences, or Computer Science. A common denominator can be identified, though, and enhancing ’awareness’ of learners and other participants involved in learning processes by technology means augmenting formal or informal learning experiences, typically in real-time, with information on progress, presence, outcomes, workspace, and the like. Supporting ’reflection’ then means enabling learners to capture, adapt, re-evaluate, and share experience in anticipation of future situations it will prove relevant to. Reflection supported digitally is a creative act, adding sense and meaning to experiences made. Combining support for 'awareness' and 'reflection' bears huge potential for improving the learning and training with respect to utility, self-regulation, usability, and user experience. The ARTEL workshop series brings - for the 4th time in 2014 - together researchers and professionals from different backgrounds to provide a forum for discussing the multi-faceted area of awareness and reflection. For this year 2014, the workshop organizes discussion and meta-reflection amongst researchers around the application of awareness and reflection in practice, its impact on learners and questions of feasibility, and sustainability for awareness and reflection in education and work. This year's workshop theme is: How does computer-support for awareness and reflection need to be embedded into practical (working or learning) contexts in order for learners to benefit from such computer support

Evolutionary Clustering of Apprentices' Self- Regulated Learning Behavior in Learning Journals

Learning journals are increasingly used in vocational education to foster self-regulated learning and reflective learning practices. However, for many apprentices, documenting working experiences is a difficult task. In this article, we profile apprentices' learning behavior in an online learning journal. Based on a pedagogical framework, we propose a novel multistep clustering pipeline that integrates different learning dimensions into a combined profile. Specifically, the profiles are described in terms of effort, consistency, regularity, help-seeking behavior, and quality of the written entries. Our results on two populations of chef apprentices (183 apprentices) interacting with an online learning journal (over 121K entries) show that our pipeline captures changes in learning patterns over time and yields interpretable profiles that can be related to academic performance. The obtained profiles can be used as a basis for personalized interventions, with the ultimate goal of improving the apprentices' learning experience

Designing Learning Analytics Dashboards for Digital Learning Environments: Investigating Learner Preferences, Usage, and Self-Efficacy

This dissertation, a product of the European Union's CHARMING project, investigates the intersection of technology and learning, focusing on the design of learning analytics for lifelong learning. It emphasizes the importance of effective learning design and the innovative use of technology in digital learning environments. Chapter 1 presents the problem statement, highlighting the knowledge gap related to learning analytics design and the overarching research question: How does learning analytics dashboard (LAD) design influence learner preferences, interaction, and self-efficacy in training and education? Chapter 2 investigates workplace learner preferences for LADs designed for different phases of the self-regulated learning (SRL) cycle. The study reveals a preference for progress reference frames before and after task performance, while social reference frames are least preferred. Chapter 3 examines the impact of LADs with progress and social reference frames on occupational self-efficacy in virtual reality simulation-based training environments. The findings suggest that both reference frames could elicit equal change in self-efficacy, with social reference frames potentially inducing more significant change. Chapter 4 analyzes log-file data to understand chemical plant employees' engagement with LADs. The results indicate that progress reference frames might foster mastery goal orientation behaviors, while social reference frames seem to promote performance goal orientation behaviors. Chapter 5 investigates the impact of LAD reference frame type and direction of comparison on academic self-efficacy among university students. The findings highlight the influence of both comparison type and direction on changes in academic self-efficacy. Chapter 6 discusses the main research findings, theoretical and practical implications, limitations, and future research opportunities. The dissertation contributes to the understanding of LAD design and its influence on learning-related variables, providing valuable insights for educational stakeholders and researchers. This dissertation advances the understanding of learning analytics dashboard design and its impact on learner preferences, interaction, and self-efficacy in various educational contexts. The findings provide a foundation for future research and the development of more effective digital learning environments