3,332 research outputs found

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

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    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

    ¿Pueden los MOOC cerrar la brecha de oportunidades?: La contribución del diseño pedagógico social inclusivo

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    Massive Open Online Courses (MOOCs) are open courses made available online at no cost to the user and designed to scale up, allowing for a large number of participants. As such, they are a disruptive new development which has the potential to widen access to higher education since they contribute to social inclusion, the dissemination of knowledge and pedagogical innovation. However, assuring quality learning opportunities to all cannot be simply reduced to allowing free access to higher education. On the contrary, it implies assuring equitable opportunities for every participant to succeed in their learning experience. This goal depends on the quality of the learning design. To be successful, a massive open online learning experience has to empower learners and to facilitate a networked learning environment. In fact, MOOCs are designed to serve a high heterogeneity of profiles, with many differences regarding learning needs and preferences, prior knowledge, contexts of participation and diversity of online platforms. Personalization can play a key role in this process. In this article, the authors describe the iMOOC pedagogical model and its later derivative, the sMOOC model, and explain how they contributed to the introduction of the principles of diversity and learner equity to MOOC design, allowing for a clear differentiation of learning paths and also of virtual environments, while empowering participants to succeed in their learning experiences. Using a design-based research approach, a comparative analysis of two course iterations each representing each model is also presented and discussed.Los cursos en línea abiertos y masivos (MOOC) son cursos abiertos disponibles en línea sin costo para el usuario y diseñados para ampliarse, permitiendo un gran número de participantes. Como tales, son un nuevo desarrollo disruptivo que tiene el potencial de ampliar el acceso a la educación superior, ya que contribuyen a la inclusión social, la difusión del conocimiento y la innovación pedagógica. Sin embargo, garantizar oportunidades de aprendizaje de calidad para todos no puede reducirse simplemente a permitir el acceso gratuito a la educación superior. Por el contrario, implica asegurar oportunidades equitativas para que cada participante tenga éxito en su experiencia de aprendizaje. Este objetivo depende de la calidad del diseño de aprendizaje. Para tener éxito, una experiencia de aprendizaje en línea abierta y masiva debe empoderar a los alumnos y facilitar un entorno de aprendizaje en red. De hecho, los MOOC están diseñados para servir a una gran heterogeneidad de perfiles, con muchas diferencias con respecto a las necesidades y preferencias de aprendizaje, conocimiento previo, contextos de participación y diversidad de plataformas en línea. La personalización puede jugar un papel clave en este proceso. En este artículo, los autores describen el modelo pedagógico iMOOC y su derivada posterior, el modelo sMOOC, y explican cómo contribuyeron a la introducción de los principios de diversidad y equidad en el diseño MOOC, lo que permite una clara diferenciación de las rutas de aprendizaje y también de entornos virtuales, al tiempo que permite a los participantes tener éxito en sus experiencias de aprendizaje. Usando un enfoque de design-based research, también se presenta y discute un análisis comparativo de dos iteraciones del curso, cada una representando cada modelo

    Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

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    Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

    Online Learning and Experimentation via Interactive Learning Resources

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    Recent trends in online learning like Massive Open Online Courses (MOOCs) and Open Educational Resources (OERs) are changing the landscape in the education sector by allowing learners to self-regulate their learning and providing them with an abundant amount of free learning materials. This paper presents FORGE, a new European initiative for online learning and experimentation via interactive learning resources. FORGE provides learners and educators with access to world- class facilities and high quality learning materials, thus enabling them to carry out experiments on e.g. new Internet protocols. In turn, this supports constructivist and self-regulated learning approaches, through the use of interactive learning resources, such as eBooks

    Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

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    Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201
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