Owner: Terry Anderson
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The Canadian Initiative for Distance Education Research (CIDER) is a research initiative of the International Review of Research in Open and Distributed Learning (IRRODL) and Centre for Distance Education (CDE), Canada's largest graduate and professional distance education programming provider, at Athabasca University, Canada's Open University.
CIDER sponsors a variety of professional development activities designed to increase the quantity and quality of distance education research. CIDER's professional development scope is broad, ranging from learning and teaching application, issues of finance and access, the strategic use of technology in distance education settings, and other factors that influence distance education in Canada.
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CIDER receives support from Athabasca University and UNESCO.
Although educational practitioners have adopted social media to their online or mobile communities, little attention has been paid to investigate the social media messages related to online or mobile learning. The purpose of this research is to identify social media influencers and trends by mining Twitter posts related to online learning and mobile learning. We identified the influencers on Twitter by three different measures: the number of tweets posted by each user, the number of mentions by other users for each user, and the number of retweets for each user. We also analyzed the trends of online learning and mobile learning by the following perspectives: the descriptive statistics of the related tweets, the monthly and hourly line charts of the related tweets, the descriptive statistics of the related retweets, the volume trends of the most retweeted tweets, and the top 10 hashtags of the related tweets. The results of this study can provide educational practitioners different ways of understanding and explaining the public opinions toward online learning and mobile learning.
The quality of Education in Chile is a controversial topic that has been in the public debate in the last several years. To ensure quality in graduate programs, accreditation is compulsory. The current article presents a model to improve the process of self-regulation. The main objective was to design a Model of Quality Assurance for Postgraduate Programs in order to constitute a theoretical, mathematical, and informatics reference that would optimize the processes of self-regulation, self-evaluation, and accreditation of master and doctorate programs from the Universidad Católica de la Santísima Concepción, Chile. This descriptive research is based on a mixed methods approach. The proposal was intended through theoretical and empirical references related to the accreditation systems. The analysis process was conducted with key informants, and the informatics instrument was created and validated through expert judgment. After the analysis, the model was optimized considering the expert’s suggestions. As a result of the optimization process, a matrix of eight dimensions was obtained and it is available online in order to be used by the heads of postgraduate programs. Finally, a model with four main stages was achieved in order to install a self-regulation and a self-evaluated culture that leads to accreditation as evidence of the quality of postgraduate programs.
This paper presents We-Share, a social annotation application that enables educators to publish and retrieve information about educational ICT tools. As a distinctive characteristic, We-Share provides educators data about educational tools already available on the Web of Data while allowing them to enrich such data with their experience using technology in the classroom. We-Share evaluation entails an empirical study where 23 educators enriched tool descriptions available on the Web of Data out of their own experience. The results suggest that experiential annotations published by educators using We-Share improve the satisfaction and confidence of other educators when discovering and selecting ICT tools. Further, most educators found We-Share an easy-to-use application suitable to share and retrieve information about educational ICT tools.
Massive Open Online Courses and e-learning represent the future of the teaching-learning processes through the development of Information and Communication Technologies. They are the response to the new education needs of society. However, this future also presents many challenges such as the processing of online forums when a huge number of messages are generated. These forums provide an excellent platform for learning and connecting students of the subject, but the difficulties in following and searching the vast volume of information that they generate may produce the opposite effect. In this paper, we propose a computational method for enabling the educational process in huge online learning communities. This method analyses the forum information through Natural Language Processing techniques and extract the main topics discussed. The results generated improves the management of the forums, increases the effectiveness of the teachers’ explanations and reduces the time spent by students to follow the course. The proposal has been complemented with a real case study that shows promising results.
The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies —both in the Learning Objects Metadata (LOM) format— into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art.
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