Social Learning Analytics in Computer-Supported Collaborative Learning Environments: A Systematic Review of Empirical Studies

Social learning analytics (SLA) is a promising approach for identifying students’ social learning processes in computer-supported collaborative learning (CSCL) environments. To identify the main characteristics of SLA, gaps and future opportunities for this emerging approach, we systematically identified and analyzed 36 SLA-related studies conducted between 2011 and 2020. We focus on SLA implementation and methodological characteristics, educational focus, and the studies’ theoretical perspectives. The results show the predominance of SLA in formal and fully online settings with social network analysis (SNA) a dominant analytical technique. Most SLA studies aimed to understand students’ learning processes and applied the social constructivist perspective as a lens to interpret students’ learning behaviors. However, (i) few studies involve teachers in developing SLA tools, and rarely share SLA visualizations with teachers to support teaching decisions; (ii) some SLA studies are atheoretical; and (iii) the number of SLA studies integrating more than one analytical approach remains limited. Moreover, (iv) few studies leveraged innovative network approaches (e.g., epistemic network analysis, multimodal network analysis), and (v) studies rarely focused on temporal patterns of students’ interactions to assess how students’ social and knowledge networks evolve over time. Based on the findings and the gaps identified, we present methodological, theoretical and practical recommendations for conducting research and creating tools that can advance the field of SLA

A Multivocal Approach in the Analysis of Online Dialogue in the Language-focused Classroom in Higher Education

The study presented here is interested in understanding the ways in which social interaction in technology mediated institutional settings is constrained and afforded by what Pennycook defines as "critical moments" in the educational experience. Drawing on Social Learning Analytics and on the concepts of heteroglossia, contingency and chaining, this paper critically discusses a methodology that allows the analysts, and ultimately learners and educators, to follow and visually represent the mobility of the learners-in-concert-with-tools across space, time and language varieties and modalities in technology-mediated communication. The empirical data focused on here is drawn from a large project which includes 40 hours of naturally occurring interactional materials, generated through screen recordings of online sessions of an Italian for Beginners course offered by a Swedish university in the videoconferencing platform Adobe Connect. Preliminary findings suggest that the environment, both in terms of what happens inside and outside the virtual learning site, is of primary importance when it comes to the organization of the interaction among individuals in terms of what becomes the participants' focus during the encounters. A visual representation through a multivocal approach of this mobility of learners, topics and tools will, it is suggested, support learners, educators and designers in locating where and when in the interaction "critical moments" have occurred, in order to understand how such shifts in focus support or hinder the learning experience.CC BY-NC-ND 3.0</p

Analytics of time management strategies in online learning environments: a novel methodological approach

The emergence of technology-supported education, e.g., blended and online, has changed the global higher education landscape. Importantly, the new learning modes involve more complex tasks and challenging ways of learning that require effective time management and strong self-regulation skills. In this regard, one of the most prevalent theoretical lenses to understand learning processes is Self-Regulated Learning (SRL). In reference to SRL models, time is a major resource in learning. The way learners schedule, plan, and enact tactics and strategies on their learning time could tremendously impact their academic achievement. However, the assessment of how learners make time-related decisions in learning is a daunting task, particularly given its latent nature and inherent autonomous learning capacity. One way to address this problem is to make use of unprecedented volumes of data collected by digital learning environments that are precisely timestamped records of actions that learners take while studying. This thesis presents a set of novel learning analytics methods for detecting and understanding time management strategies based on the analysis of digital trace data collected in online learning environments. First, the thesis proposes a new method to detect time management tactics and strategies using a combination of sequence mining and clustering techniques. The thesis also describes how time management tactics and strategies detected with this method are aligned with an SRL model that is used as a theoretical foundation of this thesis. Second, the thesis introduces a novel learning analytics method for the detection of time management tactics and strategies. This method uses a combination of process mining and clustering techniques followed by a complementary process mining technique that has a unique feature to bring insights into the temporal learning processes. This new method also has a strong potential to inform and enhance understanding of how learners make complex decisions about their learning. Third, the thesis investigates mutual connections between time management and learning strategies and their combined connections with academic performance using epistemic network analysis. This analysis provides empirical evidence that supports the proposition that time management is a critical characteristic of effective self-regulated learners. Fourth, the thesis proposes a novel method that integrates computational and visualization techniques to explore the frequency, connections, ordering, and the time of the execution of time management and learning tactics, which usually been done in isolation in the existing literature. Then, the thesis quantitatively and theoretically compare time management and learning strategies detected with this new method to explore the role of time management and learning strategies in learning as drawing on theories of educational psychology. Fifth, this new method was validated in a study that was conducted on the trace data of different learning modalities and interaction modes, where large cohorts are involved. This final study emphasizes the importance of multivocality approach in the study of time management and other relevant learning constructs. Finally, the thesis concludes with a discussion of practical implications, the significance of the results, and future research directions