A rule‐based system for hybrid search and delivery of learning objects to learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Presently, searching the internet for learning material relevant to ones own interest continues to be a time‐consuming task. Systems that can suggest learning material (learning objects) to a learner would reduce time spent searching for material, and enable the learner to spend more time for actual learning. The purpose of this paper is to present a system of “hybrid search and delivery of learning objects to learners”. Design/methodology/approach This paper presents a system of “hybrid search and delivery of learning objects to learners” that combines the use of WordNet for semantic query expansion and an approach to personalized learning object delivery by suggesting relevant learning objects based on attributes specified in the learner's profile. The learning objects are related to the learner's attributes using the IEEE LOM and IMS LIP standards. The system includes a web crawler to collect learning objects from existing learning object repositories, such as NEEDS or SMETE. Findings The presented HSDLO system has the ability to accurately search and deliver learning objects of interest to a learner as well as adjust the learner's profile over time by evaluating the learner's preferences implicitly through the learning object selections. Research limitations/implications Since real LOM's from SMETE are not much populated, the system is tested with a limited set of attributes. The system is evaluated using a test bench rather than real learners. Originality/value The paper proposes a combination of three search techniques in one system as well an architectural solution which can be used for other types of online search engines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it