Construction Cycle and Quality Controls for Training Transfer Evaluations in Livelong Learning Programs in Quebec and Switzerland
Bibliographic record
Abstract
The Construction Cycle and Quality Controls for Training Transfer Evaluations (CCQCTTE) is an assessment method that results of collaboration between University of Quebec in Montreal (UQAM) Canada and the University of Teacher Education of State of Vaud (HEP Vaud) Switzerland. The main objective of CCQCTTE project is to design and field test a method for building high quality training transfer assessments (level 3 of Kirkpatrick’s model). In relation with this goal, we defined five sub-objectives: easy way of use; implementation of best practices; cyclical quality approach; taking into account of transfer factors; diagnostic feedbacks. CCQCTTE consists of eight steps: 1) analysis of the training objectives and of the factors influencing the transfer; 2) assessment design; 3) items writing; 4) information about the assessment; 5) collection of transfer data; 6) processing of results; 7) feedbacks and 8) macro-regulation. The end product of the first step is a table of specifications and a list of transfer factors. Once the evaluation plan is defined in step two, we can move on to step three of item development. During the fourth step, all the stakeholders are informed. During step five data collection takes place in the training environment and in the workplace. Data are processed to extract information during step six. The seventh step concerns elaboration and sending of personalized feedback to the trainees and the stakeholders. Finally, the eighth and final step is a “macro-regulation” that consists of learning from all the previous steps in order to improve future transfer assessments cycles. During the first-year, we made a preliminary field testing and the second-year, a series of main field tests of the CCQCTTE. During the third year, the method was implemented in Montreal and in Lausanne. The three years international CCQCTTE project has made it possible to develop the method during construction of several transfer assessments for lifelong training programs. We highlighted a real added value of step 8 that transforms the cycle in a kind of spiral of quality. In terms of limitation, we note that step 1 “Analysis” remains time-consuming and that it is difficult to start without the accompaniment of an experienced expert of the CCQCTTE. As part of this paper, we will describe the CCQCTTE method and its quality approach, the circumstances in which it was developed and field tests results.
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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 | 0.126 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".