Ipsative learning: a personal approach to a student's experience of PBL within an integrated engineering design cornerstone module
Bibliographic record
Abstract
The Faculty of Engineering Science atUniversity College London (UCL) has recently undergonea reform of the undergraduate curriculum, which resultedin the creation of a distinctive programme that connectedcurriculums from across seven engineering disciplines.The Integrated Engineering Programme is extensive,taking in nearly 700 students in its inaugural year at thestart of the autumn 2014 term. Its most significantcontributions are the experiential and authentic learningopportunities it provides students allowing them to applytheir technical knowledge and develop their professionalskills in engineering design modules year on year. Thefirst opportunity for students to do this is within thecornerstone Integrated Engineering Design module inyear I. This paper seeks to investigate the impact of anipsative learning approach (feedback and assessment thatis based on learner’s progress) within this multidisciplinaryproblem/project-based learning environment(PBL/PjBL), which focuses learning outcomes on thestudent’s ability to engage with the process of engineeringdesign. The methods of conducting the research includethe analysis of reflective writings by each studentthroughout the first of two 5-week ‘Challenge’ projects. Aset of reflections written by each student was associatedwith two formative assessment meetings, referred to asDesign Review Meetings, held with their academic leader.This data is also supplemented with verbal feedbackprovided by students and academics, which has beenprovided during follow up interviews and focus groups.Student self reflections written after each of the twomeetings support common theses of increasedunderstanding of the project aims and depth of studentresearch efforts. Surprisingly, however is the evidence,which implies that an ipsative PBL environmentempowers students to make critical personal andengineering decisions for effective progression within anengineering design project.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".