Developing Information Literacy in the Malaysian Smart Schools: Resource-Based Learning as a Tool to Prepare Today's Students for Tomorrow's Society
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
Today's students are surrounded by more information coming from more sources than ever before. In order to deal with the vast amount of information they will encounter in school, life, and work, they must develop skills not required of previous generations. Since schools cannot teach all that students need to know, a better way is to teach them to manage the information resources. Although schools should still identify the basic information that students need to know, schools must also teach "information literacy", that is, the ability to find, interpret, use, and communicate information from a variety of sources. Resource-based learning is a tool to help students handle information. It is based on the belief that students learn best by interacting directly with learning resources instead of just listening to classroom lectures. The learning is in line with the Malaysian Smart School Concept in that it is more self-directed, self-paced, and self-accessed, and hopefully, more meaningful. Since the skills of information literacy cannot be taught in a content vacuum, resource-based learning integrates the classroom and the school resource centre or the school library. Students go through a problem-solving process that requires them to define the need for information, determine a search strategy, locate the needed resources, assess and understand the information they find, interpret the information, communicate the information, and finally, evaluate their conclusions in view of the original problem.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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