Faceted search of open educational resources using the desirability index / Ishan Sudeera Abeywardena
Bibliographic record
Abstract
The open educational resources (OER) movement has gained considerable momentum in the past few years. According to the Paris OER Declaration, OER can be defined as “teaching, learning and research materials in any medium, digital or otherwise, that reside in the public domain or have been released under an open license that permits no-cost access, use, adaptation and redistribution by others with no or limited restrictions. Open licensing is built within the existing framework of intellectual property rights as defined by relevant international conventions and respects the authorship of the work”. With this drive towards making knowledge open and accessible, a large number of OER repositories have been established and made available online throughout the world. However, the limitation of existing search engines such as Google, Yahoo!, and Bing to effectively search for useful OER that are useful or fit for teaching purposes is a major factor contributing to the slow uptake of the movement. As a major step to solve this issue, the researcher has designed, developed and tested OERScout, a technology framework based on text mining solutions. Utilizing the concept of faceted search, the system allows academics to search heterogeneous OER repositories for useful resources from a central location. Furthermore, the desirability framework has been conceptualized to parametrically measure the usefulness of an OER with respect to openness, accessibility and relevance attributes. The objectives of the project are: (i) to identify user difficulties in searching OER for academic purposes; (ii) to identify the limitations of existing OER search methodologies with respect to locating fit-for-purpose resources from heterogeneous repositories; (iii) to conceptualize a framework for parametrically measuring the suitability of OER for academic use; and (iv) to design a technology framework to facilitate the accurate centralized search of OER from heterogeneous repositories. The major contributions of this research work are twofold: The first contribution is a conceptual framework which can be used by search engines to parametrically measure the usefulness of an OER, taking into consideration the openness, accessibility and relevance attributes. The advantage of this framework is that, using the well-established four R’s and ALMS frameworks, it can restructure search results to prioritize the resources which are the easiest to reuse, redistribute, revise and remix. As a result, academics practicing the Open and Distance Learning (ODL) mode of delivery can locate resources which can be readily used in their teaching and learning. The second contribution is a search mechanism which uses text mining techniques and a faceted search interface to provide a centralized OER search tool to locate useful resources from the heterogeneous repositories for academic purposes. One of the key advantages of this search mechanism is its ability to autonomously identify and annotate OER with domain specific keywords. As a result, this search mechanism provides a central search tool which can effectively search for OER from any repository regardless of the technology platforms or metadata standards used. Another major advantage is the utilization of the conceptual framework which can parametrically measure the usefulness of an OER in terms of fit-for-purpose. As a result, academics are able to easily locate high quality OER from around the world which best fit their academic needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".