MétaCan
Menu
Back to cohort
Record W1930013869 · doi:10.24908/pceea.v0i0.5841

IMPLEMENTATION OF A BLENDED INSTRUCTION-BASED & PROBLEM-BASED LEARNING STRATEGY IN A SECOND-YEAR ENGINEERING CURRICULUM

2015· article· en· W1930013869 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBrainstormingCurriculumBlended learningComputer scienceClass (philosophy)Problem-based learningMathematics educationScope (computer science)Project-based learningAdaptation (eye)Teaching methodEducational technologyPedagogyArtificial intelligenceMathematicsPsychology

Abstract

fetched live from OpenAlex

This paper presents the implementation of a blended Instruction-Based Learning /Problem Based Learning (IBL/PBL) approach in an engineering technology curriculum. In a second year course “Thermodynamics and Heat Transfer”, students’ background knowledge is developed through IBL in the form of weekly lectures, and PBL in the form of labs and project. Eight weekly lab experiments are used to develop the students’ lab skills. Each one of the labs is scheduled such that it perfectly matches the material covered in the lectures. Through such a coordinated blended approach, students see in real-life how analytical solutions discussed in the textbook are applied and what the effect of altering design parameters is. This helps them develop problem solving skills. Also, they collect and analyze data to understand the limitations of the theory. Then in weeks 9-12, a PBL course project is introduced allowing students to implement the knowledge learned. In groups, they research the given topic, brainstorm solutions, build and test the prototypes, and present the results to the class. The benefits of such a blended approach include greater emphasis on important concepts, easier visualization of abstract ideas, higher adaptation of delivery method to the course content, broader scope of expected learning outcomes and increased student/professor contact time.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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