Commentary on: Simon, B., Dolog., P., Miklós, Z., Olmedilla, D. and Sintek, M. (2004). Conceptualising Smart Spaces for Learning
Bibliographic record
Abstract
A Sm a rt Space for Learning (SSL) is a proposed software application system s u p p o rting access to individualized learning materials and experiences using Pe r s o n a l Learning Assistants (PLAs).PLAs are intelligent agent software applications that aid the learner in searching for re l e vant content across a distributed network.They hold the promise of harnessing the power of the network to break the cost barriers that h a ve restricted learning environments to time-and location-dependent cohort g roups.Using SSL, individual learners should be able to navigate their way thro u g h the large mass of materials available on the World Wide Web and build themselves a c o h e rent and cohesive learning plan that is independent of time and place.These personal learning services re p resent the "Holy Gr a i l" in education.For the first time, thanks to the Semantic We b, large masses of learners using online learning applications and services will be able to access "p e r s o n a l i ze d" learning opport u n i t i e s that are at the same time appropriate and re l e vant to their individual or gro u p learning needs.Since the dawn of mass education in the nineteenth century, the only economically rational means of mass education has been the cohort model in which students have been "herd e d" and grouped by age or academic interest into education factories or schools (Thornburg, 1992).Howe ver from the time of Bathe (1611) and Comenius (1631), enlightened educators have been extolling the virtues of learning materials, activities, and e n v i ronments made re l e vant to the experiences of the individual learner.Up to the p resent it has not been possible to support this learning model in a cost-effective w a y, and so cohort-based learning continues to pre d o m i n a t e .For centuries traditional classroom-based courses and more recently industrialize d distance education models have dominated the educational landscape.Pa s s i ve i n s t ruction through correspondence courses, which emerged in the nineteenth c e n t u ry re q u i red self-study skills and self-discipline to ove rcome the lack of support and mentoring available in the classroom.Twentieth century video/televised courses, aimed at large numbers of learners individually, or assembled in groups, incorporated C o m m e n t a ry on: Simon, B., Dolog.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.049 | 0.056 |
| Insufficient payload (model declined to judge) | 0.027 | 0.029 |
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".