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

Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses

Big data and analytics for educational information systems, despite having gained researchers’ attention, are still in their infancy and will take years to mature. Massive open online courses (MOOCs), which record learner-computer interactions, bring unprecedented opportunities to analyse learner activities at a very fine granularity, using very large datasets. To date, studies have focused mainly on dropout and completion rates. This study explores learning activities in MOOCs against their demographic indicators. In particular, pre-course survey data and online learner interaction data collected from two MOOCs, delivered by the University of Warwick, in 2015, 2016, and 2017, are used, to explore how learnerdemographic indicatorsmay influence learner activities. Recommendations for educational information system development and instructional design, especially when a course attracts a diverse group of learners, are provided

Comprendiendo el potencial y los desafíos del Big Data en las escuelas y la educación

In recent years, the world has experienced a huge revolution centered around the gathering and application of big data in various fields. This has affected many aspects of our daily life, including government, manufacturing, commerce, health, communication, entertainment, and many more. So far, education has benefited only a little from the big data revolution. In this article, we review the potential of big data in the context of education systems. Such data may include log files drawn from online learning environments, messages on online discussion forums, answers to open-ended questions, grades on various tasks, demographic and administrative information, speech, handwritten notes, illustrations, gestures and movements, neurophysiologic signals, eye movements, and many more. Analyzing this data, it is possible to calculate a wide range of measurements of the learning process and to support various educational stakeholders with informed decision-making. We offer a framework for better understanding of how big data can be used in education. The framework comprises several elements that need to be addressed in this context: defining the data; formulating data-collecting and storage apparatuses; data analysis and the application of analysis products. We further review some key opportunities and some important challenges of using big data in educationEn los últimos años, el mundo ha experimentado una gran revolución centrada en la recopilación y aplicación de big data en varios campos. Esto ha afectado muchos aspectos de nuestra vida diaria, incluidos el gobierno, la manufactura, el comercio, la salud, la comunicación, el entretenimiento y muchos más. Hasta ahora, la educación se ha beneficiado muy poco de la revolución del big data. En este artículo revisamos el potencial de los macrodatos en el contexto de los sistemas educativos. Dichos datos pueden incluir archivos de registro extraídos de entornos de aprendizaje en línea, mensajes en foros de discusión en línea, respuestas a preguntas abiertas, calificaciones en diversas tareas, información demográfica y administrativa, discurso, notas escritas a mano, ilustraciones, gestos y movimientos, señales neurofisiológicas, movimientos oculares y muchos más. Analizando estos datos es posible calcular una amplia gama de mediciones del proceso de aprendizaje y apoyar a diversos interesados educativos con una toma de decisiones informada. Ofrecemos un marco para una mejor comprensión de cómo se puede utilizar el big data en la educación. El marco comprende varios elementos que deben abordarse en este contexto: definición de los datos; formulación de aparatos de recolección y almacenamiento de datos; análisis de datos y aplicación de productos de análisis. Además, revisamos algunas oportunidades clave y algunos desafíos importantes del uso de big data en la educació

Motivation in a language MOOC: issues for course designers

Whilst several existing studies on foreign language learning have explored motivation in more traditional settings (Dörnyei, 2003), this paper presents one of the first studies on the motivation of participants in a MOOC. The MOOC, Travailler en français (https://sites.google.com/site/mooctravaillerenfrancais/home), was a 5-week open online course for learners of French at level B1 of the CEFR, and aimed to develop language and employability skills for working in a francophone country. It took place in early 2014 and attracted more than 1000 participants. Intrinsic motivation (Wigfield & Eccles, 2000), is directly linked to one’s enjoyment of accomplishing a task. We conducted a study based on the cognitive variables of the Self-Determination Theory (Deci & Ryan, 1985), and adapted the Intrinsic Motivation Inventory to the context of a MOOC in order to understand the expectancy beliefs and task values of participants engaging with the MOOC. Participants answered a 40 Likert-type questions on enjoyment/ interest (i.e. I will enjoy doing this MOOC very much), perceived competence (i.e. I think I will be able to perform successfully in the MOOC), effort (i.e. I will put a lot of effort in this MOOC), value/usefulness (i.e. I think that doing this MOOC will be useful for developing my skills), felt pressure and tension (i.e. I think I might feel pressured while doing the MOOC) and relatedness (i.e. I think I will feel like I can really trust the other participants). Results highlight significant factors that could directly influence intrinsic motivation for learning in a MOOC environment. The chapter makes recommendations for LMOOC designers based on the emerging profile of MOOC participants, on their motivation and self-determination, as well as on the pressures they might feel, including time pressures. Finally, the extent to which participants relate to each other, and are able to engage in social learning and interaction, is a real challenge for LMOOC designers

Quality in MOOCs: Surveying the Terrain

The purpose of this review is to identify quality measures and to highlight some of the tensions surrounding notions of quality, as well as the need for new ways of thinking about and approaching quality in MOOCs. It draws on the literature on both MOOCs and quality in education more generally in order to provide a framework for thinking about quality and the different variables and questions that must be considered when conceptualising quality in MOOCs. The review adopts a relativist approach, positioning quality as a measure for a specific purpose. The review draws upon Biggs’s (1993) 3P model to explore notions and dimensions of quality in relation to MOOCs — presage, process and product variables — which correspond to an input–environment–output model. The review brings together literature examining how quality should be interpreted and assessed in MOOCs at a more general and theoretical level, as well as empirical research studies that explore how these ideas about quality can be operationalised, including the measures and instruments that can be employed. What emerges from the literature are the complexities involved in interpreting and measuring quality in MOOCs and the importance of both context and perspective to discussions of quality

EU–originated MOOCs, with focus on multi- and single-institution platforms

No abstract available

The Digital Scholar Revisited

The book The Digital Scholar was published in 2011, and used Boyer’s framework of scholarship to examine the possible impact of digital, networked technology on scholarly practice. In 2011 the general attitude towards digital scholarship was cautious, although areas of innovative practice were emerging. Using this book as a basis, the author considers changes in digital scholarship since its publication. Five key themes are identified: mainstreaming of digital scholarship, so that it is a widely accepted and encouraged practice; the shift to open, with the emphasis on the benefits that open practice brings rather than the digital or networked aspects; policy implementation, particularly in areas of educational technology platforms, open access policies and open educational resources; network identity, emphasising the development of academic identity through social media and other tools; criticality of digital scholarship, which examines the negative issues associated with online abuse, privacy and data usage. Each of these themes is explored, and their impact in terms of Boyer’s original framing of scholarly activity considered. Boyer’s four scholarly activities of discovery, integration, application and teaching can be viewed from the perspective of these five themes. In conclusion what has been realised does not constitute a revolution in academic practice, but rather a gradual acceptance and utilisation of digital scholarship techniques, practices and values. It is simultaneously true that both radical change has taken place, and nothing has fundamentally altered. Much of the increased adoption in academia mirrors the wider penetration of social media tools amongst society in general, so academics are more likely to have an identity in such places that mixes professional and personal