Bibliographic record
Abstract
This entry begins by discussing the history of computer-based learning (CBL), followed by a description of learning theories and instructional design models that are being used to design CBL materials. The chapter concludes by proposing a model for designing CBL materials. The model proposed is based on current instructional design models but goes beyond these models by suggesting the use of intelligent agents to capitalize on the power of the computer in CBL. Instructors and tutors working in CBL one-to-one environments claim that it takes more time to design, develop, and deliver instruction when compared to face-to-face delivery. The main reason for extra time is the lack of use of the power of the computer in CBL. The author is suggesting the use of intelligent agents in the design, development, and delivery of instructions in CBL. Intelligent agents can be used to conduct learner analysis after interacting with the learner, assemble the content, and prescribe instructional strategies for individual learners after forming a profile of the learner. Intelligent agents can also be used to manage learners’ interaction and participation in the CBL process, freeing the tutor to do other human-related tasks. Wooldridge and Jennings (1995) defined an intelligent agent as a computer system that is capable of flexible autonomous action in order to meet its design objectives
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".