Investigating Technological Pedagogical Content Knowledge Competencies among Trainee Teachers in the Context of ICT Course
Bibliographic record
Abstract
The Technological Pedagogical Content Knowledge (TPACK) framework developed over a decade ago is still valid and applicable in educational contexts when dealing with the use of technology in teaching and learning. With widespread availability of devices and prolific use of technology among students, teachers need to be conversant with various technologies that can be integrated and enhance the teaching and learning process. Most teacher education programmes equip trainee teachers with the integration of technology in the lessons and introduce them to instructional design that would align to the curriculum and make their teaching attractive and effective. It is important to establish the level of TPACK among trainee teachers and prepare them appropriately with necessary domain of knowledge to enable them to function well in future classrooms. This study was conducted with trainee teachers to determine the validity and reliability of the TPACK questionnaire and to identify trainee teachers’ perceived pathways to TPACK. Data were analysed using the maximum likelihood estimation (MLE) procedure, and the measurement model was assessed using confirmatory factor analysis (CFA). The structural model was developed and the path coefficients and their statistical significance were tested to determine the correlations between TPACK competencies. Le modèle TPACK portant sur les connaissances technologiques, pédagogiques et de contenu, développé il y a plus de dix ans, demeure valide et applicable dans les contextes pédagogiques où l’enseignement et l’apprentissage sont appuyés par la technologie. La grande disponibilité des appareils technologiques et leur emploi généralisé par les élèves exigent que les enseignants soient à l’aise avec les technologies qui peuvent être intégrées pour améliorer l’enseignement et l’apprentissage. La plupart des programmes de formation des enseignants les prépare à intégrer la technologie dans leurs leçons et en aligner la conception pédagogique avec les programmes d’études pour augmenter l’efficacité de leur enseignement. Il est important d’établir le niveau de TPACK chez les enseignants stagiaires et de les préparer en conséquence en leur communiquant les connaissances nécessaires pour bien fonctionner dans les salles de classe de l’avenir. Cette étude s’est déroulée auprès d’enseignants stagiaires, de sorte à déterminer la validité et la fiabilité du questionnaire TPACK et pour identifier ce que les enseignants stagiaires perçoivent comme étant les moyens d’acquérir les connaissances liées au TPACK. Les données ont été analysées par la méthode d’estimation du maximum de vraisemblance et le modèle de mesure a été évalué par une analyse factorielle confirmatoire. Le modèle structurel a été élaboré, et les chemins et la signification statistique des coefficients ont été testés, de sorte à établir les corrélations entre les compétences du modèle TPACK. Mots clés: connaissances technologiques, pédagogiques et du contenu; enseignants stagiaires; validation; cours sur les TIC; formation des enseignants
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".