Self-Regulation of E-learning and Students’ Divided Experiences
Bibliographic record
Abstract
A mixed methods design was employed to study students’ self-regulation of e-learning to understand the phenomenon of the digital divide. Quantitative data consisting of the perceptions of comprehensive school students (N=29,863) on self-regulated learning (SRL) and equal access to digital devices were analyzed to identify subgroups. Qualitative data on e-learning experiences (n=13,310) were then analyzed according to their subgroups. The results indicated equal access to devices but strongly divided e-learning experiences between students. Those assessed as having the highest SRL (31%) provided remarkably detailed descriptions of how they developed new learning strategies, metacognitive, and digital skills during e-learning. In contrast, students belonging to the lowest SRL group (21%) expressed divided experiences; half of them claimed not to have learned anything. These students were often left without parental support. The current study provides empirical evidence of the digital divide and its realization during the pandemic, leading to deviant poor learning experiences for students with low SRL skills. Therefore, in the future, schools should create structures to recognize students who require support and ensure equal opportunities for meaningful e-learning. Keywords: e-learning, self-regulated learning, digital divide, mixed methods, compulsory education Une méthode mixte a été utilisée pour étudier l’autorégulation de l’apprentissage en ligne parmi les élèves d’établissements d’enseignement secondaire général, afin de comprendre le phénomène de la fracture numérique. Les données quantitatives concernant les perceptions des élèves (N=29,863) sur l’apprentissage autorégulé (AAR) et l’égalité d’accès aux appareils numériques ont été analysées pour identifier trois sous-groupes. Les données qualitatives des expériences d’apprentissage en ligne (n=13,310) ont ensuite été analysées selon des sous-groupes. Les résultats ont indiqué un accès égal aux appareils, mais des expériences d’apprentissage en ligne toujours fortement contrastées. Les élèves ayant le taux d’AAR le plus élevé (31 %) ont fourni des descriptions remarquablement détaillées de la manière dont ils ont acquis de nouvelles stratégies d’apprentissage, des compétences métacognitives et numériques dans le cadre de l’apprentissage en ligne, tandis que ceux ayant le taux d’AAR le plus bas (21 %) ont exprimé des expériences contrastées, et la moitié d’entre eux ont même affirmé n’avoir rien appris. Ces élèves étaient aussi souvent laissés sans soutien parental. La présente étude fournit des preuves empiriques sur la fracture numérique et sa réalisation pendant la pandémie, menant à des expériences d’apprentissage limitées, voire décalées, pour les élèves ayant de faibles compétences en AAR. Par conséquent, à l’avenir, les écoles devraient créer des structures permettant d’identifier les élèves qui ont besoin de soutien et d’assurer l’égalité des chances pour un apprentissage en ligne optimal. Mots-clés : apprentissage en ligne, apprentissage autorégulé, fracture numérique, méthode mixte, enseignement obligatoire
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".