3,332 research outputs found
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
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A Social Learning Space Grid for MOOCs: Exploring a FutureLearn Case
Collaborative and social engagement promote active learning through knowledge intensive interactions. Massive Open Online Courses (MOOCs) are dynamic and diversified learning spaces with varying factors like flexible time frames, student count, demographics requiring higher engagement and motivation to continue learning and for designers to implement novel pedagogies including collaborative learning activities. This paper looks into available and potential collaborative and social learning spaces within MOOCs and proposes a social learning space grid that can aid MOOC designers to implement such spaces, considering the related requirements. Furthermore, it describes a MOOC case study incorporating three collaborative and social learning spaces and discusses challenges faced. Interesting lessons learned from the case give an insight on which spaces to be implemented and the scenarios and factors to be considered
¿Pueden los MOOC cerrar la brecha de oportunidades?: La contribución del diseño pedagógico social inclusivo
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
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
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)
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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How to design for persistence and retention in MOOCs?
Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme.
In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context
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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
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