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Record W2345526790 · doi:10.1177/1475725716645961

Learner Perspectives of Online Problem-Based Learning and Applications from Cognitive Load Theory

2016· article· en· W2345526790 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychology Learning & Teaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProblem-based learningCurriculumCognitive loadCognitionMathematics educationPsychologyOnline learningClass (philosophy)Computer scienceMultimediaPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

Problem-based learning (PBL) courses have historically been situated in physical classrooms involving in-person interactions. As online learning is embraced in higher education, programs that use PBL can integrate online platforms to support curriculum delivery and facilitate student engagement. This report describes student perspectives of the online PBL experience, interpreted through the lens of Cognitive Load Theory. Fifty-two undergraduate health professional students participated in this descriptive survey. The responses revealed that, overall, learners perceived the platform to be suitable for conducting online PBL, that distractions in the online environment were no greater than those experienced in physical classroom, and that online PBL was as effective as in-class PBL for learning. In online PBL, the technological capabilities and limitations of the online platform were identified by students as the key sources of hindrances and facilitators to learning. Suggestions for implementing online PBL using instructional principles from Cognitive Load Theory and multimedia learning are offered.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.364
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it