What do we mean by web‐based learning? A systematic review of the variability of interventions
Bibliographic record
Abstract
OBJECTIVES: Educators often speak of web-based learning (WBL) as a single entity or a cluster of similar activities with homogeneous effects. Yet a recent systematic review demonstrated large heterogeneity among results from individual studies. Our purpose is to describe the variation in configurations, instructional methods and presentation formats in WBL. METHODS: We systematically searched MEDLINE, EMBASE, ERIC, CINAHL and other databases (last search November 2008) for studies comparing a WBL intervention with no intervention or another educational activity. From eligible studies we abstracted information on course participants, topic, configuration and instructional methods. We summarised this information and then purposively selected and described several WBL interventions that illustrate specific technologies and design features. RESULTS: We identified 266 eligible studies. Nearly all courses (89%) used written text and most (55%) used multimedia. A total of 32% used online communication via e-mail, threaded discussion, chat or videoconferencing, and 9% implemented synchronous components. Overall, 24% blended web-based and non-computer-based instruction. Most web-based courses (77%) employed specific instructional methods, other than text alone, to enhance the learning process. The most common instructional methods (each used in nearly 50% of courses) were patient cases, self-assessment questions and feedback. We describe several studies to illustrate the range of instructional designs. CONCLUSIONS: Educators and researchers cannot treat WBL as a single entity. Many different configurations and instructional methods are available for WBL instructors. Researchers should study when to use specific WBL designs and how to use them effectively.
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 | 0.064 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".