A Mixed-methods Systematic Review of Online versus Face-to-face Problem-based Learning
Bibliographic record
Abstract
Problem-based learning (PBL) is an instructional strategy that is poised for widespread application in the current, growing, online digital learning environment. While enjoying a track-record of being a defensible strategy in face-to-face learning settings, the research evidence is not clear regarding PBL in online environments. A review of literature revealed that there are few research studies comparing online PBL (oPBL) to face-to-face PBL, and of these, findings have been mixed. This study is a systematic review of the existing research literature comparing oPBL to face-to-face PBL. The study’s aim is to: 1. Detect the presence and magnitude of the effectiveness of oPBL; 2. Uncover and identify the factors which contribute or explain the effectiveness of oPBL. This review used a mixed methods strategy, combining a meta-analysis with a qualitative analysis of the studies that met inclusion criteria. An overall effect size was found to be slightly in favour of oPBL in terms of student performance outcomes. The qualitative analysis revealed relationships between established concepts of learning. The observations in this systematic review help reduce uncertainty about the robustness of PBL as in instructional strategy delivered in the online environment. Resume L’apprentissage par problemes (APP), aussi denomme apprentissage par resolution de problemes (ARP), est une strategie pedagogique qui est appelee a se repandre dans l’environnement actuel et toujours croissant de l’apprentissage en ligne. Alors que les evaluations anterieures demontrent que cette strategie est defendable dans les situations d’apprentissage en face a face, les resultats d’etudes scientifiques ne sont pas clairs en ce qui a trait a l’APP dans les environnements en ligne. Une revue de la litterature a revele qu’il y a peu d’etudes qui comparent l’APP en ligne (eAPP) a l’APP en face a face (APP) et que celles qui ont ete realisees arrivent a des resultats mitiges. La presente etude est une revue systematique de la litterature scientifique comparant l’eAPP a l’APP en face a face. Les objectifs de l’etude sont de : Deceler la presence et l’ampleur de l’efficacite de l’eAPP; Decouvrir et identifier les facteurs qui contribuent ou qui expliquent l’efficacite de l’eAPP. Pour realiser cette revue, nous avons eu recours a une strategie de methodes mixtes, combinant une meta-analyse et une analyse qualitative des etudes qui satisfaisaient aux criteres d’inclusion. Nous avons observe, dans l’ensemble, que l’ampleur de l’effet penchait legerement en faveur de l’eAPP au niveau des resultats de la performance des eleves. L’analyse qualitative a revele des liens entre les concepts d’apprentissage bien etablis. Les remarques formulees dans cette revue systematique aident a reduire l’incertitude au niveau de la robustesse de l’APP en tant que strategie pedagogique utilisee dans un environnement en ligne.
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 | 0.039 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".