Socializing on MOOCs: comparing university and self-enrolled students

International audienceMOOCs are becoming more and more integrated in the higher education landscape of learning, with many institutions now pushing their students towards MOOC as part of their curriculum. But what does it mean for other MOOC learners? Are these students socializing the same way when they have an easier possibility to interact with classmates offline? Is the fact that they do not personally choose to enroll in a MOOC also having an effect? In this paper, we compare university-enrolled students to other MOOC participants and in particular other self-enrolled students, to examine how and why they socialize on and around the MOOC. Using data from two French MOOCs in project management, we show that university-enrolled students are less attracted by forums and seem to interact less than others when the workload increases , which could lead to misleading conclusions when analyzing data. We therefore encourage MOOC researchers to be particularly mindful of this new trend when performing social network analyses

Unravelling the dynamics of instructional practice: a longitudinal study on learning design and VLE activities

Substantial progress has been made in understanding how teachers design for learning. However, there remains a paucity of evidence of the actual students' response towards leaning designs. Learning analytics has the power to provide just-in-time support, especially when predictive analytics is married with the way teachers have designed their course, or so-called a learning design. This study investigates how learning designs are configured over time and their impact on student activities by analyzing longitudinal data of 38 modules with a total of 43,099 registered students over 30 weeks at the Open University UK, using social network analysis and panel data analysis. Our analysis unpacked dynamic configurations of learning designs between modules over time, which allows teachers to reflect on their practice in order to anticipate problems and make informed interventions. Furthermore, by controlling for the heterogeneity between modules, our results indicated that learning designs were able to explain up to 60% of the variability in student online activities, which reinforced the importance of pedagogical context in learning analytics

Dynamics of MOOC Discussion Forums

In this integrated study of dynamics in MOOCs discussion forums, we analyze the interplay of temporal patterns, discussion content, and the social structure emerging from the communication using mixed methods. A special focus is on the yet under-explored aspect of time dynamics and influence of the course structure on forum participation. Our analyses show dependencies between the course structure (video opening time and assignment deadlines) and the overall forum activity whereas such a clear link could only be partially observed considering the discussion content. For analyzing the social dimension we apply role modeling techniques from social network analysis. While the types of user roles based on connection patterns are relatively stable over time, the high fluctuation of active contributors lead to frequent changes from active to passive roles during the course. However, while most users do not create many social connections they can play an important role in the content dimension triggering discussions on the course subject. Finally, we show that forum activity level can be predicted one week in advance based on the course structure, forum activity history and attributes of the communication network which enables identification of periods when increased tutor supports in the forum is necessary

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

Analytics-based approach to the study of learning networks in digital education settings

Investigating howgroups communicate, build knowledge and expertise, reach consensus or collaboratively solve complex problems, became one of the main foci of contemporary research in learning and social sciences. Emerging models of communication and empowerment of networks as a form of social organization further reshaped practice and pedagogy of online education, bringing research on learning networks into the mainstream of educational and social science research. In such conditions, massive open online courses (MOOCs) emerged as one of the promising approaches to facilitating learning in networked settings and shifting education towards more open and lifelong learning. Nevertheless, this most recent educational turn highlights the importance of understanding social and technological (i.e., material) factors as mutually interdependent, challenging the existing forms of pedagogy and practice of assessment for learning in online environments. On the other hand, the main focus of the contemporary research on networked learning is primarily oriented towards retrospective analysis of learning networks and informing design of future tasks and recommendations for learning. Although providing invaluable insights for understanding learning in networked settings, the nature of commonly applied approaches does not necessarily allow for providing means for understanding learning as it unfolds. In that sense, learning analytics, as a multidisciplinary research field, presents a complementary research strand to the contemporary research on learning networks. Providing theory-driven and analytics-based methods that would allow for comprehensive assessment of complex learning skills, learning analytics positions itself either as the end point or a part of the pedagogy of learning in networked settings. The thesis contributes to the development of learning analytics-based research in studying learning networks that emerge fromthe context of learning with MOOCs. Being rooted in the well-established evidence-centered design assessment framework, the thesis develops a conceptual analytics-based model that provides means for understanding learning networks from both individual and network levels. The proposed model provides a theory-driven conceptualization of the main constructs, along with their mutual relationships, necessary for studying learning networks. Specifically, to provide comprehensive understanding of learning networks, it is necessary to account for structure of learner interactions, discourse generated in the learning process, and dynamics of structural and discourse properties. These three elements – structure, discourse, and dynamics – should be observed as mutually dependent, taking into account learners’ personal interests, motivation, behavior, and contextual factors that determine the environment in which a specific learning network develops. The thesis also offers an operationalization of the constructs identified in the model with the aim at providing learning analytics-methods for the implementation of assessment for learning. In so doing, I offered a redefinition of the existing educational framework that defines learner engagement in order to account for specific aspects of learning networks emerging from learning with MOOCs. Finally, throughout the empirical work presented in five peer-reviewed studies, the thesis provides an evaluation of the proposed model and introduces novel learning analytics methods that provide different perspectives for understanding learning networks. The empirical work also provides significant theoretical and methodological contributions for research and practice in the context of learning networks emerging from learning with MOOCs

Analytics of student interactions: towards theory-driven, actionable insights

The field of learning analytics arose as a response to the vast quantities of data that are increasingly generated about students, their engagement with learning resources, and their learning and future career outcomes. While the field began as a collage, adopting methods and theories from a variety of disciplines, it has now become a major area of research, and has had a substantial impact on practice, policy, and decision-making. Although the field supports the collection and analysis of a wide array of data, existing work has predominantly focused on the digital traces generated through interactions with technology, learning content, and other students. Yet for any analyses to support students and teachers, the measures derived from these data must (1) offer practical and actionable insight into learning processes and outcomes, and (2) be theoretically grounded. As the field has matured, a number of challenges related to these criteria have become apparent. For instance, concerns have been raised that the literature prioritises predictive modeling over ensuring that these models are capable of informing constructive actions. Furthermore, the methodological validity of much of this work has been challenged, as a swathe of recent research has found many of these models fail to replicate to novel contexts. The work presented in this thesis addresses both of these concerns. In doing so, our research is pervaded by three key concerns: firstly, ensuring that any measures developed are both structurally valid and generalise across contexts; secondly, providing actionable insight with regards to student engagement; and finally, providing representations of student interactions that are predictive of student outcomes, namely, grades and students’ persistence in their studies. This research programme is heavily indebted to the work of Vincent Tinto, who conceptually distinguishes between the interactions students have with the academic and social domains present within their educational institution. This model has been subjected to extensive empirical validation, using a range of methods and data. For instance, while some studies have relied upon survey responses, others have used social network metrics, demographic variables, and students’ time spent in class together to evaluate Tinto’s claims. This model provides a foundation for the thesis, and the work presented may be categorised into two distinct veins aligning with the academic and social aspects of integration that Tinto proposes. These two domains, Tinto argues, continually modify a student’s goals and commitments, resulting in persistence or eventual disengagement and dropout. In the former, academic domain, we present a series of novel methodologies developed for modeling student engagement with academic resources. In doing so, we assessed how an individual student’s behaviour may be modeled using hidden Markov models (HMMs) to provide representations that enable actionable insight. However, in the face of considerable individual differences and cross-course variation, the validity of such methods may be called into question. Accordingly, ensuring that any measurements of student engagement are both structurally valid, and generalise across course contexts and disciplines became a central concern. To address this, we developed our model of student engagement using sticky-HMMs, emphasised the more interpretable insight such an approach provides compared to competing models, demonstrated its cross-course generality, and assessed its structural validity through the successful prediction of student dropout. In the social domain, a critical concern was to ensure any analyses conducted were valid. Accordingly, we assessed how the diversity of social tie definitions may undermine the validity of subsequent modeling practices. We then modeled students’ social integration using graph embedding techniques, and found that not only are student embeddings predictive of their final grades, but also of their persistence in their educational institution. In keeping with Tinto’s model, our research has focused on academic and social interactions separately, but both avenues of investigation have led to the question of student disengagement and dropout, and how this may be represented and remedied through the provision of actionable insight

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data