Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach

Collaborative learning can improve the pedagogical effectiveness of MOOCs. Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students? engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g. page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of task-completion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course

Toward Multimodal Analytics in Ubiquitous Learning Environments

While Ubiquitous Learning Environments (ULEs) have shown several benefits for learning, they pose challenges for orchestration. Teachers need to be aware of the learning process, which is difficult to achieve when it occurs across a heterogeneous set of spaces, resources and devices. In addition, ULEs can benefit from multimodal analyses due to the heterogeneity of the data sources available (e.g., logs, geolocation, sensor information, learning artifacts). In previous works, we proposed an orchestration system with some analytics features that can gather multimodal datasets during the learning process. Based on this experience, in this paper we describe the technological support provided by the system to collect data from multiple spaces and sources as well as the structure of the generated dataset. We also reflect about the challenges of multimodal learning analytics (MMLA) in ULEs, and we pose some ideas about how the system could better support MMLA in the future to mitigate those challenges

Supporting Teachers in the Design and Implementation of Group Formation Policies in MOOCs: A Case Study

Collaborative learning strategies, which can promote student learning and achievement, have rarely been incorporated into pedagogies of MOOCs. Such strategies, when implemented properly, can boost the quality of MOOC pedagogy. Nonetheless, the use of collaborative groups in MOOCs is scarce due to several yet critical contextual factors (e.g., massiveness, and variable levels of engagement) that hamper the group formation process. Therefore, there is a need for supporting MOOC teachers in the design and implementation of group formation policies when implementing collaborative strategies. This paper presents a study where two instruments were used to explore solutions to this need: a guide to support teachers during the planning of the group formation, and a technological tool to help them implement the collaborative groups designed and to monitor them. According to the results of the study, the design guide made the teachers aware of the contextual factors to consider when forming the collaborative groups, and allowed teachers inform some configuration parameters of the activity (e.g., duration and assessment type) and the group formation (e.g., criteria and parameters needed to build the groups). The technological tool was successfully incorporated into the MOOC platform. Lessons learned from the findings of the study are shared and their potential to inform the design guide is discussed

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past massive open online course, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, such as the target variable to predict and the practical constraints to consider. Two predictive models were built: LD-specific model (with features based on the LD and pedagogical intentions), and a generic model (with cumulative features, not informed by the LD). Although the LD-specific predictive model did not outperform the generic one, some LD-driven features were powerful. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor's opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the problems in the LD. The implications for improving the LD are discussed