Quality Rating and Recommendation of Learning Objects

Kumar, Vive; Nesbit, John C.; Winne, Philip H.; Hadwin, Allyson F.; Jamieson-Noel, Dianne; Han, Kate

doi:10.1007/978-1-84628-758-9_12

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Historical TEL datasets that fulfil all requirements for an evaluation are however hard to find [27]. An early solution to this problem was to create synthetic datasets and use these for evaluating TEL recommender systems as in [28], [51], [109], [150], [194], [214], [216] and proposed in [208]. Since then, attempts have been made to generate datasets for TEL, for example in [27] where a dataset is generated from a real life testing evaluation.…”

Section: Offline Experimentsmentioning

confidence: 99%

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Erdt

Fernández

Rensing

2015

IEEE Trans. Learning Technol.

118

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The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.

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Section: Offline Experimentsmentioning

confidence: 99%

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Erdt

Fernández

Rensing

2015

IEEE Trans. Learning Technol.

118

View full text Add to dashboard Cite

show abstract

“…An interesting idea is put to practice in [32], where authors propose that the users with greater knowledge have greater weight when computing recommendations than the ones with less knowledge. A totally different approach of obtaining the quality rating of learning objects is proposed in [33]. It contains a hybrid approach that includes content-based and collaborative filtering, and that implements a Markov model to verify the quality evaluation of the learning objects.…”

Section: Related Workmentioning

confidence: 99%

“…One of these works takes into account very few quality aspects when make recommendations [33], while other considers briefly the pedagogical value and other educational aspects of the resources [30]. The majority of them approach closed repositories that contain very specific instructional resources.…”

Section: Related Workmentioning

confidence: 99%

QORECT – A Case-Based Framework for Quality-Based Recommending Open Courseware and Open Educational Resources

Vlădoiu

Constantinescu

Moise

2013

Computational Collective Intelligence. Technologies and Applications

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Abstract. More than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, better findability is still expected for open educational resources and open courseware, so online guidance and services that support users to locate the appropriate such resources are most welcome. Recommender systems have a very valuable role in this direction. We propose here a hybrid architecture that combines enhanced casebased recommending (driven by a quality model tenet) with (collaborative) feedback from users to recommend open courseware and educational resources. IntroductionMore than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, despite the huge opportunities offered by open education, traditional textbooks and readings, and intranet educational resources are still here, dominating the majority of teaching and learning venues of Higher Education institutions even though all students are effectively online. Greater adoption of OERs both within formal and informal education seems to be impeded by four issues: discoverability, quality assurance, bridging the last mile, and acquisition [1]. Modern search engines generally do an ill job when searching for educational content because they are not tailored with this purpose, focusing mainly on content and metadata, and, moreover, they lack what it takes to locate the proper educational resource that is suited for a specific user's goal, that builds up on her prerequisites (for example, learner's previous knowledge), and that provide for making the next step towards her goal (e. g. mastering of a certain concept). For the time being, there is no quality assurance mechanism that could provide support for (1) learners and instructors in their quest for reaching the most appropriate educational resources for their specific educational needs in any particular context, neither for (2) faculty or institutions that are or want to become involved in this movement, and they may be concerned about the challenges or interested in the gains of this process, nor for (3) developers who need guidelines for designing and building such educational resources, nor for (4) educational resources' evaluators [2,3,4]. In many OCW/OER repositories educational content exists only immersed in context and without a significant effort this content cannot be both sorted out from its initial environment (becoming truly reusable and remixable) and entangled within a new educational context, bridging the last mile. Acquisition is also difficult, taking into account all the fears of OCW/OERs providers (faculty, teachers, educational resources designers etc.): lack of credit, of copyright control over derivative works, and so on. Therefore, better findability is expected for open courseware and OERs, so online guidance and services that su...

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MASECO: A Multi-agent System for Evaluation and Classification of OERs and OCW Based on Quality Criteria

Moise

Vlădoiu

Constantinescu

2013

E-Learning Paradigms and Applications

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Quality Rating and Recommendation of Learning Objects

Cited by 3 publications

References 18 publications

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

QORECT – A Case-Based Framework for Quality-Based Recommending Open Courseware and Open Educational Resources

MASECO: A Multi-agent System for Evaluation and Classification of OERs and OCW Based on Quality Criteria

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