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

Technologies for Data-Driven Interventions in Smart Learning Environments [Editorial]

Smart Learning environments (SLEs) are defined [1] as learning ecologies where students engage in learning activities, or where teachers facilitate such activities with the support of tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners and educators’ actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out. Sources from these data may include learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wide range of automatic, human-mediated, or hybrid interventions, which involve learners and teachers in the decision making behind the interventions.This work was supported in part by the Spanish Ministry of Science and Innovation through Smartlet and the H2OLearn Projects under Grant MICIN/AEI/10.13039/501100011033, and in part by the Fondo Europeo de Desarrollo Regional (FEDER) under Grant TIN2017-85179-C3-1-R, Grant TIN2017-85179-C3-2-R, Grant TIN2017-85179-C3-30R, Grant PID2020-112584RB-C31, Grant PID2020-112584RB C32, and Grant GPID2020-112584RB-C33. The work of Davinia Hernández-Leo (Serra Húnter) was supported by ICREA through the ICREA Academia Program.Publicad

Generating actionable predictions regarding MOOC learners' engagement in peer reviews

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples

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

Grid Characteristics and Uses: A Grid Definition

Abstract. This paper discusses the concept of grid towards achieving a complete definition using main grid characteristics and uses found in literature. Ten definitions extracted from main literature sources have been studied allowing the extraction of grid characteristics while grid uses are defined in terms of the different types of application support provided by grids. A grid definition is proposed using these characteristics and uses. This definition may be very useful to determine the limits of the grid concept as well as to explore new application fields in grid computing. In this sense, the extracted characteristics are employed to determine the potential benefits a grid infrastructure may provide to Computer Supported Collaborative Learning applications.

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