Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods

Massive Open Online Courses (MOOCs) offer unprecedented opportunities to learn at scale. Within a few years, the phenomenon of crowd-based learning has gained enormous popularity with millions of learners across the globe participating in courses ranging from Popular Music to Astrophysics. They have captured the imaginations of many, attracting significant media attention - with The New York Times naming 2012 "The Year of the MOOC." For those engaged in learning analytics and educational data mining, MOOCs have provided an exciting opportunity to develop innovative methodologies that harness big data in education.Comment: Preprint of a chapter to appear in "Data Mining and Learning Analytics: Applications in Educational Research

MOOCs Meet Measurement Theory: A Topic-Modelling Approach

This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical models for quantifying a person's attainment of intangible attributes such as attitudes, abilities or intelligence. Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions. The set of items can be used to measure a latent skill if individuals' responses on them conform to a Guttman scale. Such well-scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced. In practice, education researchers manually devise items (quiz questions) while optimising well-scaled conformance. Due to the costly nature and expert requirements of this process, psychometric testing has found limited use in everyday teaching. We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. The challenge is to formalise the Guttman scale educational constraint and incorporate it into topic models. To favour topics that automatically conform to a Guttman scale, we introduce a novel regularisation into non-negative matrix factorisation-based topic modelling. We demonstrate the suitability of our approach with both quantitative experiments on three Coursera MOOCs, and with a qualitative survey of topic interpretability on two MOOCs by domain expert interviews.Comment: 12 pages, 9 figures; accepted into AAAI'201

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the "significant" interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) "significant" peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly "global" exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education.Comment: Pre-print version. Published version available at http://dx.doi.org/10.1038/srep0644

Learning in friendship groups:developing students’ conceptual understanding through social interaction

The role that student friendship groups play in learning was investigated here. Employing a critical realist design, two focus groups on undergraduates were conducted to explore their experience of studying. Data from the "case-by-case" analysis suggested student-to-student friendships produced social contexts which facilitated conceptual understanding through discussion, explanation, and application to "real life" contemporary issues. However, the students did not conceive this as a learning experience or suggest the function of their friendships involved learning. These data therefore challenge the perspective that student groups in higher education are formed and regulated for the primary function of learning. Given these findings, further research is needed to assess the role student friendships play in developing disciplinary conceptual understanding

Iterative discriminant tensor factorization for behavior comparison in massive open online courses

The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology's promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome

Crowdsourcing Cognitive Presence: A Quantitative Content Analysis of a K12 Educator MOOC Discussion Forum

Massively Open Online Courses (MOOCs) offer participants opportunities to engage with content and discussion forums similar to other online courses. Pedagogical components of MOOCs and the nature of learning are worth of examining due to issues involving scale, interaction and the role of the instructor (Ross, Sinclair, Know, Bayne & McLeod, 2014). The Community of Inquiry (CoI) framework provides a basis for measuring cognitive presence in online discussion forums. As voluntary point of entry to a community of learners, it is important to consider the nature of participant contributions in terms of cognitive presence. This study focused on an educator MOOC because MOOCs have been proposed as an efficient vehicle for providing professional development due to the significant self-identification of participants as educators (Ho et al. 2014). Participant attributes have been categorized, however the discussion forum is difficult to study on a massive scale (Kizilcec, Piech, & Schulz, 2013). Automated measures of cognitive presence may not provide the full view of learning behaviors implicit in messages posted to the forums (Wong, Pursel, Divinsky & Jansen, 2015). To address this gap, the forum messages were hand-coded and analyzed using quantitative content analysis (Neuendorf, 2002). The study found that the measure of exploration increased over the duration of the course. Viewing cognitive presence over time provided a new metaphor for explaining the proportions of cognitive presence in the discussion forum of an educator MOOC. This finding suggests that increased instructor presence during the later stages of the course may increase cognitive presence over time (Akyol & Garrison, 2007; Garrison & Cleveland-Innes, 2005)

Learning, technologies, and time in the age of global neoliberal capitalism

Though diverse in nature, the articles in this collection discuss both socio-cultural and temporal transformations linked to technology and learning and can be classified into three broad themes. The first theme is interested in temporal experiences within time and learning; the second theme is about practical implementations of these concerns, and the third theme inquires into relationships between our understanding of time and human nature. In many articles, the boundaries between these themes are blurred and fluid. Yet, this general classification does indicate the present state of the art in studies of time, technology and education

Examining learners’ social presence in a Massive Open Online Course through social network analysis and machine learning

Low engagement has been a longstanding problem in Massive Open Online Courses (MOOCs). However, engagement is crucial in social learning contexts to increase knowledge construction and achieve meaningful learning outcome. To further understand learners’ engagement in MOOC discussion forums, this study focuses on the perspective of social presence, which is defined as learners’ ability to project themselves socially and emotionally in a community of inquiry. Social presence is an important factor that has the potential to affect learners’ learning experience and outcome. This study took place in the context of a professional development MOOC in the field of journalism. The discussion posts, system log data and survey responses were collected and analyzed. The purpose of this study is to understand the learners’ participation patterns in the discussion forums over the six modules of the MOOC, and the relationship between learners’ social presence, their positions in the learner network and their learning outcomes. In terms of data analysis, this study adopted a mixed-method approach to examine the data from both qualitative and quantitative aspects: to qualitatively analyze the posts, a machine learning supported text classification model was developed and applied to automatically analyze the large-scale text data in the forums; social network analysis (SNA) was used to analyze the characteristics of the learner network and determine learners’ centrality (degree, closeness, betweenness and Eigen centrality). Centrality is an important measure because prior studies found it to be an important predictor of learning outcome. Correlation analyses were used to discern the relationship between social presence and learners’ centrality, while regression models were built to investigate how learners’ social presence and posting behaviors (frequency of posting, average length of posts and day of posting) predict learners’ network centrality. Finally, correlation analyses were conducted to understand the association between learners’ network centrality and their certificate status, perceived learning and satisfaction. The purpose of using mixed methods is to see in what ways the qualitative nature of the posts and learners’ posting behaviors impact learners’ positions and influence in the learning community and their learning outcomes. The findings revealed the evolvement of the learner network in relation to the distribution of social presence throughout the MOOC. The results also showed that social presence indicators such as Complimenting others, Expressing agreement, Expressing gratitude and Disagreement/doubts/criticism play important roles in learners’ centrality in the learner network. Beside social presence, frequency of posting has strong effect in predicting learners’ network centrality, while other factors such as the average length of posts and the timing of posting have marginal impact in the prediction. Finally, this study found that learners’ network centrality is correlated with their certificate status as well as their overall satisfaction with the MOOC, but not correlated with their perceived learning in the MOOC. This study is among the first efforts in MOOC research to examine the relationship between social presence, learners’ network centrality and learning outcomes. It provides a critical ground for studying content-related interaction and learning community in MOOC forums. The findings inform MOOC learners in terms of how to strategically present themselves in the discussion forums to increase the possibilities of peer interaction and achieve productive learning outcomes. For examples, findings suggest that learners may obtain more central position in the community by posting more compliments, expressing more gratitude, and communicating agreement and disagreement, doubts etc. While for MOOC instructors, this study will potentially inform them how to effectively mediate the discussions and improve learner engagement as a facilitator, such as paying attention to the changes of learner network, identifying central learners, monitoring learners’ affective states.Curriculum and Instructio