{"id":1633,"date":"2024-06-18T13:19:34","date_gmt":"2024-06-18T12:19:34","guid":{"rendered":"https:\/\/www.open.ac.uk\/blogs\/CALRG\/?p=1633"},"modified":"2024-08-16T09:43:25","modified_gmt":"2024-08-16T08:43:25","slug":"calrg-conference-4-july-2024","status":"publish","type":"post","link":"https:\/\/www.open.ac.uk\/blogs\/CALRG\/calrg-conference-4-july-2024\/","title":{"rendered":"CALRG Conference: 4 July 2024"},"content":{"rendered":"

Beyond Boundaries: The Role of Learning Types in Shaping MOOC Learner Engagement and Progression (Hannah John, John Kerr & Guillaume Andrieux, Glasgow) \u00a0<\/strong><\/h2>\n

Utilising the ABC Learning Design based on the ABC curriculum design method (Young & Perovi\u0107 2016) and the Conversational Framework (Laurillard <\/span>2012<\/span>), specifically the six learning types that underpin that model, eight Massive Open Online Courses (MOOCs) from Coursera or FutureLearn were examined. These MOOCs were selected because they represented a wide range of disciplines, assessment options, and course structures.\u202fThis paper will demonstrate how the application of various learning types impacts how learners engage with the material, progress through the course, and how it influences their commitment to continue learning (Martin and Bolliger 2018). Additionally, results from this research provide evidence of how the frequency and sequencing of learning types create opportunities for learners to engage with content in a meaningful way.<\/span>\u00a0<\/span><\/p>\n

By synthesising the secondary data from pre-course surveys, exit surveys, end-of-course surveys, comment sections, and several other course metrics, including but not limited to the watch-through rates and technical feedback of over 400 videos, course completion, assessment completion, and learner satisfaction, seven key areas of impactful course design were identified and will be explored throughout this paper. These key areas will focus on the following elements of course design (1) quality and duration of videos, (2) balance and distribution of acquisition learning types (3) structure of discussions, (4) effective guidance for exploration activities, (5) balance of assessment and feedback opportunities, (6) Utilisation of e-learning tools and plug-ins, and (7) Successfully leveraging the synergies between the learning types in online course design. This empirical research will present evidence on how learning types can be successfully deployed in course design and course design sequencing.\u00a0<\/span>\u00a0<\/span><\/p>\n

Understanding engagement patterns of game-based learning for children in <\/span>CatnClever<\/span> using learning analytics<\/span><\/span> (<\/span>Josmario Albuquerque, Kristina Corbero, Sebastian Hahnel & Bart Rienties, OU UK)<\/h2>\n

\u00a0<\/span>It is widely acknowledged that games and game-based learning (GBL) opportunities can spark engaged learning opportunities <\/span>(<\/span>Sun et al., 2023<\/span><\/a>)<\/span>. Well-designed games can lead to enjoyable and engaging experiences, in particular for young people and children <\/span>(<\/span>Maureen et al., 2022<\/span><\/a>; <\/span>Plass et al., 2015<\/span><\/a>)<\/span>. Furthermore, games can be fun, interesting, motivating, and playful. However, there is mixed evidence whether GBL has positive (e.g., knowledge, skills, attitudes) or negative impacts of playing games for young children <\/span>(<\/span>Guan et al., 2024<\/span><\/a>)<\/span>. In particular, there is paucity of research in how young learners engage in GBL. In this explorative study we aimed to investigate how learners engaged in one specific game-based mobile app called CatnClever that is designed specifically for children aged between 3-6. Using principles of learning analytics and artificial intelligence <\/span>(<\/span>Banihashem et al., 2023<\/span><\/a>)<\/span>, we were specifically interested in exploring whether we can use engagement data <\/span>from 8,365 German preschoolers across 60,279 activities<\/span> how children progressed over time in CatnClever to predict learning performance without formal assessment data. As extensive testing of young children might not be appropriate in terms of gameplay, motivation, and data collection <\/span>(<\/span>Kucirkova et al., 2024<\/span><\/a>)<\/span>, in this study we wanted to explore whether we could estimate learning performance and activity difficulty of 170 Tool X activities in four subjects (i.e., mathematics, language, social and emotional learning, and sport) purely from children engagement data. <\/span>Findings suggest that difficulty aligns well with effort, suggesting optimal challenge levels. Key engagement moments were also identified, potentially informing further interventions. Overall, we stress the potential of learning analytics to deepen our understanding of young learners’ interactions in GBL, paving the way for tailored educational strategies. <\/span>However, ethical considerations regarding data collection and analysis in GBL environments warrant careful attention.<\/span>\u00a0<\/span><\/p>\n

References<\/span><\/b>\u00a0<\/span><\/p>\n