Design of an Integrated Project-Based Learning Curriculum: Analysis Through Fink’s Taxonomy of Significant Learning
Bibliographic record
Abstract
Contribution:In this article, integrated problem-based learning and critical reflection are shown to contribute to significant learning experiences, without needing to increase course hours and course assignments.Background:With the advances in technology, such as artificial intelligence, there is a shift in teaching and learning paradigms, where integration and critical reflection of one’s learning become as important as foundational knowledge and its application. In engineering education, the trend has been to increase the hours students spend in the classroom to compensate for this shift.Intended Outcomes:In this manuscript, the design and implementation of integrated project-based learning referred to as the integrated learning stream (ILS) is discussed. The aim is to show how ILS fosters significant learning through learning communities, critical reflection, and learning how to learn.Application Design:In ILS, significant learning experiences were created by taking a holistic view of the students and their communities. The curriculum moved from being content-centered to learner-centered, providing a classroom community where there is respect for individual voices and care for society.Findings:A qualitative content analysis of students’ reflections, comments, and course artifacts found the students were able to learn materials more efficiently and apply their learnings to solve real-world problems. The students developed better habits that improved their learning and their well-being. Students’ comments demonstrated that they were feeling enjoyment from their learning experiences while being challenged to learn more.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".