Link between learning profile and school achievement in primary school; a transversal study
Bibliographic record
Abstract
School achievement plays an essential role in terms of professional and social adaptation. However, all the students do not always perform as the system requires them to. In Belgium, at the end of compulsory education, one student out of two lags behind in their school education (Indicators from the Fédération Wallonie-Bruxelles [Walloon-Brussels Federation], 2012). In this context, we do not consider that there are “bad” learners. Instead, there are those students whose learning profile is more or less in line with the context’s requirements and those whose profile is not. In many works, the student’s learning profile is operationalised by measures on 3 levels: cognitive, metacognitive and psycho-affective. These psychological variables, which are major components of self-regulated learning, play a central role in the explanation of school performance (for more details, see Frenkel, in press; Frenkel & Deforge, in press). This profile is not fixed once and for all. It evolves over time and it represents the result of the interaction between these three variables with other factors, notably bio-medical, socio-demographical ones, factors related to family environment (Pourtois, Desmet & Lahaye, 2004; Trudel, Puentes-Neuman & Ntebutse, 2002) and the too often forgotten factors linked to class management and social interactions between students and teachers (see Wang, Haertel & Walberg, 1994). Determining the students’ learning profile enables us to identify their strengths and work on their weaknesses. Therefore, it is essential that psycho-educational teams have validated tools at their disposal in order to carry out this task. Our aim is to propose new tools which will complement the already-existing ones. In this presentation we propose to introduce and discuss our methodology and the results obtained. The starting target population was made up of 198 primary school students divided equitably into three levels (second year of primary school, fourth year of primary school and sixth year of primary school). A problem-solving task based on the DELF (Büchel & Büchel, 1995) was created. It was completed by two questionnaires to fill in before and after the task. The learning profile defined on this basis was analysed according to several variables (age, gender, socio-professional category, school results). Group testing is still in progress. We will also illustrate our presentation with the first results of individual testing which will begin in May 2014. References - Büchel, F.P. & Büchel, P. (1995). Découvrez vos capacités, rEalisez vos possibilités, pLanifiez votre démarche, soyez créatiFs. Le programme DELF. Russin, Switzerland: Centre d’éducation cognitive - Frenkel, S. (in press). Metacognitive components in learning to learn approaches. International Journal of Psychology: A Biopsychosocial Approach - Frenkel, S., & Deforge, H. (in press). Métacognition et réussite scolaire: Perspectives théoriques. In C. Giraudeau & G. Chasseigne (Eds.), Psychologie, Education et Vie scolaire. Tours, France: Editions Publibook Université - Pourtois, J.-P., Desmet, H., & Lahaye, W. (2004). Connaissances et pratiques en éducation familiale et parentale. Enfances, Familles, Générations, 1, 22-35 - Trudel, M., Puentes-Neuman, G., & Ntebutse J.G. (2002). Les conceptions contemporaines de l’enfant à risque et la valeur heuristique du construit de résilience en éducation. Revue Canadienne de l’Education, 27 (2 & 3), 153-173 - Wang, M.C., Haertel, G.D., & Walberg, H.J. (1994). What helps students learn? Educational Leadership, 51 (4), 74-79
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".