Framework for Strengthening Research in ICT-Mediated Learning
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
ICT-mediated learning appears to hold great promise for achieving the goal of education for all such as, reducing the long-existing disparity between north and south, reducing poverty and promoting social inclusion. However, the integration of ICTs in education requires considerable investment in time and resources. Consequently, when planning to integrate ICT in education and training policy makers should be able to use evidence-based information for making sound decisions. In spite of the critical importance of sound research to guide policy and practice, it appears that there is a lack of valid and reliable evidence-based information in the field of learning technology. Many studies conducted during the past 70 years have failed to establish a significant difference in effectiveness between learning technology and traditional methods. While these findings tend to suggest that learning technology does not considerably improve learning, the fundamental question that remains unanswered is, were the researchers assessing the effectiveness of ICTs or were they simply assessing the effectiveness of instructional treatments that were initially less than perfect? If the instructional treatment is weak or flawed it may lead the researcher to reach false conclusions. The purpose of this paper is to propose a framework for establishing, a priori, the effectiveness of ICT-mediated instructional treatments used in educational research.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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