Analyzing learners' behavior and discourse within large online communities: a Social Learning Analytics Dashboard

Online Learning Communities (OLC) are nowadays one of the most important producers of Big Data in education. However, the investigation of such environments is underrepresented in educational research. There is a lack of methods and tools that characterize the massive learning associated with the student participation in large OLC. This paper presents a Social Learning Analytics Dashboard (SLAD) to analyze temporal trend models that outline the evolution of learners behavior over time. Such models suggest that ongoing collaboration and positive emotion have a fundamental role for knowledge creation and sharing in large scale social learning. These findings can be used to take actions in order to enhance and regulate social interaction within large OLC.

The three-iterative design and implementation of a student-facing social learning analytics tool

The design of social learning analytics (SLA) tools has become a practical means to make available learning information with a goal to improve students’ regulation, reflection, and engagement in online learning. This design-based research uses the multi-method analytics to iteratively design, implement, and modify the SLA tool that makes available social, topic, and cognitive information to students in online discussions. This tool demonstrates students’ online discussions from social, sematic, and cognitive aspects. This research further uses online questionnaire and interview methods to investigate students’ perceptions about the tool’s usefulness and usability. After experiencing this tool, students proposed critical feedback for tool improvement from the network visualization, peer reference, and explanation support perspectives. Overall, the SLA tool is a beneficial tool to foster students’ awareness of their own learning and peer learning process, to improve students’ regulation and reflection of learning, and eventually improve student engagement in online discussions.

Social Learning Analytics

Despite the idea that learning is individual, the YouTube channel has been examined to explain how learning is controlled in social networks. What kind of mechanism does the YouTube channel, which deeply influences the education world, work with and what does this structure tell the educators? What do data such as the number of views and comments on YouTube mean? Also, what kind of a model can be established between the video proposition system and our individual and social learning? This study has been prepared to shed light on the questions mentioned.

Designing a Social Learning Analytics Tool for Open Annotation and Collaborative Learning

This book chapter recounts one approach to ethically co-designing a public dashboard that reports social learning analytics and encourages learners’ collaborative annotation across open texts and contexts. As a design narrative in the learning sciences, this chapter is a reflective, first-hand account organized around three related objectives: 1) Naming the theoretical stances toward open and social learning that informed design and research; 2) Describing key decisions and trade-offs pertinent to four iterations of a social learning analytics dashboard; and 3) Considering epistemological, technological, and infrastructural implications for the development and use of social learning analytics in open, flexible, and distance learning.