Social presence in massive open online courses

The capacity to foster interpersonal interactions in massive open online courses (MOOCs) has frequently been contested, particularly when learner interactions are limited to MOOC forums. The establishment of social presence-a perceived sense of somebody being present and "real"-is among the strategies to tackle the challenges of online learning and could be applied in MOOCs. Thus far, social presence in MOOCs has been under-researched. Studies that previously examined social presence in MOOCs did not account for the peculiar nature of open online learning. In contrast to the existing work, this study seeks to understand how learners perceive social presence, and the different nuances of social presence in diverse MOOC populations. In particular, we compare perceptions of social presence across the groups of learners with different patterns of forum participation in three edX MOOCs. The findings reveal substantial differences in how learners with varying forum activity perceive social presence. Perceptions of social presence also differed in courses with the varying volume of forum interaction and duration. Finally, learners with sustained forum activity generally reported higher social presence scores that included low affectivity and strong group cohesion perceptions. With this in mind, this study is significant because of the insights into brings to the current body of knowledge around social presence in MOOCs. The study's findings also raise questions about the effectiveness of transferring existing socio-constructivist constructs into the MOOC contexts.System Engineerin

Towards more replicable content analysis for learning analytics

Content analysis (CA) is a method frequently used in the learning sciences and so increasingly applied in learning analytics (LA). Despite this ubiquity, CA is a subtle method, with many complexities and decision points affecting the outcomes it generates. Although appearing to be a neutral quantitative approach, coding CA constructs requires an attention to decision making and context that aligns it with a more subjective, qualitative interpretation of data. Despite these challenges, we increasingly see the labels in CA-derived datasets used as training sets for machine learning (ML) methods in LA. However, the scarcity of widely shareable datasets means research groups usually work independently to generate la- belled data, with few attempts made to compare practice and results across groups. A risk is emerging that different groups are coding constructs in different ways, leading to results that will not prove replicable. We report on two replication studies using a previously reported construct. A failure to achieve high inter-rater reliability suggests that coding of this scheme is not currently replicable across different research groups. We point to potential dangers in this result for those who would use ML to automate the detection of various educationally relevant constructs in LA

How do we model learning at scale?:A systematic review of research on MOOCs

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models. </jats:p

Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?

The proliferation of AI in many aspects of human life—from personal leisure, to collaborative professional work, to global policy decisions—poses a sharp question about how to prepare people for an interconnected, fast-changing world which is increasingly becoming saturated with technological devices and agentic machines. What kinds of capabilities do people need in a world infused with AI? How can we conceptualise these capabilities? How can we help learners develop them? How can we empirically study and assess their development? With this paper, we open the discussion by adopting a dialogical knowledge-making approach. Our team of 11 co-authors participated in an orchestrated written discussion. Engaging in a semi-independent and semi-joint written polylogue, we assembled a pool of ideas of what these capabilities are and how learners could be helped to develop them. Simultaneously, we discussed conceptual and methodological ideas that would enable us to test and refine our hypothetical views. In synthesising these ideas, we propose that there is a need to move beyond AI-centred views of capabilities and consider the ecology of technology, cognition, social interaction, and values