IMPLEMENTATION OF A BLENDED INSTRUCTION-BASED & PROBLEM-BASED LEARNING STRATEGY IN A SECOND-YEAR ENGINEERING CURRICULUM
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".