3,295 research outputs found
A heuristic approach for new-item cold start problem in recommendation of micro open education resources
© Springer International Publishing AG, part of Springer Nature 2018. The recommendation of micro Open Education Resources (OERs) suffers from the new-item cold start problem because little is known about the continuously published micro OERs. This paper provides a heuristic approach to inserting newly published micro OERs into established learning paths, to enhance the possibilities of new items to be discovered and appear in the recommendation lists. It considers the accumulation and attenuation of user interests and conform with the demand of fast response in online computation. Performance of this approach has been proved by empirical studies
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
In this paper, we suggest a novel method to aid lifelong learners to access
relevant OER based learning content to master skills demanded on the labour
market. Our software prototype 1) applies Text Classification and Text Mining
methods on vacancy announcements to decompose jobs into meaningful skills
components, which lifelong learners should target; and 2) creates a hybrid OER
Recommender System to suggest personalized learning content for learners to
progress towards their skill targets. For the first evaluation of this
prototype we focused on two job areas: Data Scientist, and Mechanical Engineer.
We applied our skill extractor approach and provided OER recommendations for
learners targeting these jobs. We conducted in-depth, semi-structured
interviews with 12 subject matter experts to learn how our prototype performs
in terms of its objectives, logic, and contribution to learning. More than 150
recommendations were generated, and 76.9% of these recommendations were treated
as useful by the interviewees. Interviews revealed that a personalized OER
recommender system, based on skills demanded by labour market, has the
potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of
CSEDU 2020 by SciTePres
OER Recommendations to Support Career Development
This Work in Progress Research paper departs from the recent, turbulent
changes in global societies, forcing many citizens to re-skill themselves to
(re)gain employment. Learners therefore need to be equipped with skills to be
autonomous and strategic about their own skill development. Subsequently,
high-quality, on-line, personalized educational content and services are also
essential to serve this high demand for learning content. Open Educational
Resources (OERs) have high potential to contribute to the mitigation of these
problems, as they are available in a wide range of learning and occupational
contexts globally. However, their applicability has been limited, due to low
metadata quality and complex quality control. These issues resulted in a lack
of personalised OER functions, like recommendation and search. Therefore, we
suggest a novel, personalised OER recommendation method to match skill
development targets with open learning content. This is done by: 1) using an
OER quality prediction model based on metadata, OER properties, and content; 2)
supporting learners to set individual skill targets based on actual labour
market information, and 3) building a personalized OER recommender to help
learners to master their skill targets. Accordingly, we built a prototype
focusing on Data Science related jobs, and evaluated this prototype with 23
data scientists in different expertise levels. Pilot participants used our
prototype for at least 30 minutes and commented on each of the recommended
OERs. As a result, more than 400 recommendations were generated and 80.9% of
the recommendations were reported as useful.Comment: This paper has been accepted to be published in the proceedings of
IEEE Frontiers In Education (FIE) 2020 by IEEE Xplor
Computational Intelligence for the Micro Learning
The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis.
The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace
Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach
Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200
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