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This exploratory study focuses on the design and evaluation of teaching analytics that relate social learning structure with performance measures in a massive open online course MOOC prototype environment. Using reflexive analysis of online learning trace data and qualitative performance measures we present an exploratory empirical study that: a rigorously evaluates a novel, multi-dimensional performance construct; b describes differences in small group dynamics and structure; and c draws a connection between learning performance and group structure. Performance is operationalized using a combination of knowledge construction measurement from discussion boards, rigorous analysis of student work products and several indicators of small group identity in the course examined. Interview and observational data are used to develop an approach for deriving and validating a model of the social structure of students in the course using traces of interaction data. The connection between performance and structure is developed at the small group unit of analysis. Implications for MOOC design, scaling MOOC analytics and a vision for developing social sensors in MOOC environments are presented in the discussion and conclusion.

Context: the ability to follow other users and projects on GitHub has introduced a new layer of open source software development participants who observe but do not contribute to projects. It has not been fully explored how following others influences the actions of GitHub users. Objective: this paper studies the motivation behind following (or not following) others and the influence of popular users on their followers. Method: a mixed methods research approach was used including a survey of 800 GitHub users to uncover the reasons for following on GitHub and a complementary quantitative analysis of the activity of GitHub users to examine influence. Our quantitative analysis studied 199 popular (most followed) users and their followers. Results: we found that popular users do influence their followers by guiding them to new projects. As a user's popularity increases, so does their rate of influence, yet the same is not true for a popular user's rate of contribution. Conclusions: these results indicate that a new type of leadership is emerging through GitHub's following feature and popularity can be more important than contribution in influencing others. We discuss implications of popularity and influence and their impact on social structure and leadership on OSS projects.

A theory-based method for a computational student performance prediction.A Genetic Programming model for grade prediction is described and tested.Model evaluation suggests high success rates for predicting student grades. Building a student performance prediction model that is both practical and understandable for users is a challenging task fraught with confounding factors to collect and measure. Most current prediction models are difficult for teachers to interpret. This poses significant problems for model use (e.g. personalizing education and intervention) as well as model evaluation. In this paper, we synthesize learning analytics approaches, educational data mining (EDM) and HCI theory to explore the development of more usable prediction models and prediction model representations using data from a collaborative geometry problem solving environment: Virtual Math Teams with Geogebra (VMTwG). First, based on theory proposed by Hrastinski (2009) establishing online learning as online participation, we operationalized activity theory to holistically quantify students' participation in the CSCL (Computer-supported Collaborative Learning) course. As a result, 6 variables, Subject, Rules, Tools, Division of Labor, Community, and Object, are constructed. This analysis of variables prior to the application of a model distinguishes our approach from prior approaches (feature selection, Ad-hoc guesswork etc.). The approach described diminishes data dimensionality and systematically contextualizes data in a semantic background. Secondly, an advanced modeling technique, Genetic Programming (GP), underlies the developed prediction model. We demonstrate how connecting the structure of VMTwG trace data to a theoretical framework and processing that data using the GP algorithmic approach outperforms traditional models in prediction rate and interpretability. Theoretical and practical implications are then discussed.

In this article, the authors synthesize 3 years of social technologies research, including studies of Facebook, Twitter, and GitHub, to present a theory driven framework to guide future social scientific research using “Big Data.” They connect levels of analysis derived from empirical study of influence to the electronic trace data generated by social technologies. Specifically, the authors outline a relationship between social media technology platforms, individual goals for participation, and emergent small groups to inform future research on influence in social technologies. They incorporate theory from small group literature, communities and networks of practice, and media theory to explicate a contextual framework for measuring influence. In their discussion, the authors build on the contrast between influence indicators in Facebook, Twitter, and GitHub to argue for a greater focus on the influence abstractions of articulation and affiliation.