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Record W4312703347 · doi:10.4236/ce.2022.1311226

Construction Cycle and Quality Controls for Training Transfer Evaluations in Livelong Learning Programs in Quebec and Switzerland

2022· article· en· W4312703347 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCreative Education · 2022
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsElaborationQuality (philosophy)Test (biology)Table (database)Computer scienceTransfer of trainingData collectionPlan (archaeology)Transfer of learningMacroTransfer (computing)Relation (database)Training (meteorology)Product (mathematics)Process managementKnowledge managementArtificial intelligenceEngineeringData miningMathematicsStatistics

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.121
GPT teacher head0.407
Teacher spread0.286 · 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