1,863 research outputs found
Inferring Concept Prerequisite Relations from Online Educational Resources
The Internet has rich and rapidly increasing sources of high quality
educational content. Inferring prerequisite relations between educational
concepts is required for modern large-scale online educational technology
applications such as personalized recommendations and automatic curriculum
creation. We present PREREQ, a new supervised learning method for inferring
concept prerequisite relations. PREREQ is designed using latent representations
of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a
neural network based on the Siamese network architecture. PREREQ can learn
unknown concept prerequisites from course prerequisites and labeled concept
prerequisite data. It outperforms state-of-the-art approaches on benchmark
datasets and can effectively learn from very less training data. PREREQ can
also use unlabeled video playlists, a steadily growing source of training data,
to learn concept prerequisites, thus obviating the need for manual annotation
of course prerequisites.Comment: Accepted at the AAAI Conference on Innovative Applications of
Artificial Intelligence (IAAI-19
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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
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
Toward a user-adapted question/answering educational approach
This paper addresses the design of a model for Question/Answering in an interactive and mobile learning environment. The learner's question can be made through vocal interaction or typed text and the answer is the generation of a personalized learning path. This takes into account the focus and type of the question and some personal features of the learner extracted both from the question and prosodic features, in case of vocal questions. The response is a learning path that preserves the precedence of the prerequisite relations and contains all the relevant concepts for answering the user's question. The main contribution of the paper is to investigate the possibility to exploit educational concept maps in a Q/A interactive learning system
The Future Affordances of Digital Learning and Teaching within The School of Education
This report illustrates the discussion outcome on digital education within the University of Glasgow School of Education. It is not a strategy document but it does explore the conditions for nurturing digital culture and how these can be channelled into a strategy on digital learning and teaching. The report is based on a review of literature and on a number of local, national and international case study vignettes
Video Augmentation in Education: in-context support for learners through prerequisite graphs
The field of education is experiencing a massive digitisation process that has been ongoing for the past decade. The role played by distance learning and Video-Based Learning, which is even more reinforced by the pandemic crisis, has become an established reality. However, the typical features of video consumption, such as sequential viewing and viewing time proportional to duration, often
lead to sub-optimal conditions for the use of video lessons in the process of acquisition, retrieval and consolidation of
learning contents.
Video augmentation can prove to be an effective support to learners, allowing a more flexible exploration of contents, a better understanding of concepts and relationships between concepts and an optimization of time required for video consumption at different stages of the learning process.
This thesis focuses therefore on the study of
methods for: 1) enhancing video capabilities through video augmentation features; 2) extracting concept and relationships from video materials; 3) developing intelligent user interfaces based on the knowledge extracted.
The main research goal is to understand to what extent video augmentation can improve the learning experience.
This research goal inspired the design of EDURELL Framework, within which two applications were developed to enable the testing of augmented methods and their provision. The novelty of this work lies in using the knowledge within the video, without exploiting external materials, to exploit its educational potential. The enhancement of the user interface takes place through various support features among which in particular a map that progressively highlights the prerequisite relationships between the concepts as they are explained, i.e., following the advancement of the video.
The proposed approach has been designed following a user-centered iterative approach and the results in terms of effect and impact on video comprehension and learning experience make a contribution to the research in this field
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