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Record W6997546717

What practices are used by pre-service teachers to support the psychological needs of students? Validation of an observation grid for high school education

2022· article· en· W6997546717 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2022
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsInternshipCompetence (human resources)AutonomyNegotiationBurnoutVariety (cybernetics)ReciprocalProfessional development
DOInot available

Abstract

fetched live from OpenAlex

Internships are considered an essential part of the teacher education program (Kervick et al., 2020). In Québec and Belgium, pre-service teachers are expected to do a minimum of 700 hours of practical training (ME, 2020; FWB, 2000). While internships are perceived by trainees as the most important part of their training (Malo, 2010), high dropout rates among young Canadian and Belgian teachers, along with the burnout experienced by many of them (Hugues et al., 2014; Kamanzi et al., 2017), suggest they are not sufficiently prepared to face the profession’s demands. This raises a crucial question with regard to pre-service training: how can trainees be encouraged to reach their full potential, with the support of their trainers (associate teachers and university supervisors), to help them effectively negotiate their entry into the teaching profession? According to self-determination theory (SDT; Ryan & Deci, 2020), every individual has three basic psychological needs (BPNs) whose fulfillment is necessary for motivation, well-being and optimal functioning. These needs are autonomy (the individual perceives that they act voluntarily and have control over events), competence (they perceive that they interact effectively with their environment and that their actions generate desired consequences), and relatedness (they perceive that they are meaningfully connected to others, and that they have warm, reciprocal relationships with people they deem important). Many studies have documented the benefits of meeting these needs in a variety of professional fields, including teaching (Fernet et al., 2008; Reeve et al., 2003). In general, practicing teachers who perceive that their BPNs are met are more self-motivated (teaching is enjoyable and important), more engaged and more likely to exhibit behaviors that support their students' motivation and engagement (e.g., Pelletier et al., 2002). Fulfillment of BPNs, however, appears to depend on three factors found in the environment: autonomy support (the person feels that their perspective is recognized and that they are provided with opportunities to make their own choices), structure (they perceive that their environment is predictable, that expectations are clear and that they receive feedback that helps them meet expectations) and involvement (they perceive that they are cared about, given time and encouraged; Ryan et al., 2008). The purpose of our research is to validate an observation grid of the practices employed by pre-service teachers in the classroom in order to identify those that support their students’ psychological needs. High school pre-service teachers (mathematics, French, social sciences) shared with the researchers a video of themselves filmed during a complete teaching period. Three researchers coded each video according to a grid developed from the work of Reeve et al. (2003), and this cross-coding allowed for the triangulation of data (Hussein, 2009). The observation grid will be presented along with the potential support it could provide to trainee trainers (associate teachers and university supervisors).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.099
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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