ePEARL: Electronic Portfolio Encouraging Active Reflection Learning
Notice bibliographique
Résumé
In Québec, like many other places, more than 20 percent of primary-school students have to repeat a grade before going on to secondary school and 70 percent of those drop out of high school (Statistics Canada, 2001). Currently, school is too often a place that disengages learners, which fails to encourage honest self-assessment, and where learning and evaluation are not meaningful acts of improvement but detached and punitive symbols of failure. Over the past several years, the Québec Ministère de l’Education du Loisir et du Sport (MELS) has been phasing in the Québec Education Program (QEP)-a complete reform of the curriculum favoring an integrated, comprehensive learner-centred approach to education based partly on a co-constructed, inquiry-based curriculum that responds to individual student needs and interests. The cross-curricular competencies, which have become central to the reform, are designed to ensure that the skills and knowledge being taught in our schools meet the changing demands of the 21st century workforce (Conference Board of Canada, 2001; MEQ, 2001). One way to meet this challenge appears to lie in the use of electronic portfolios which can be designed to support the process of students’ self-regulated learning. The value of portfolios for exhibiting evidence of learning has been well established and while the research and debate continue over the best vehicles or formats for portfolios, their use has become mandate in Canadian provinces such as Quebec as a means for capturing students’ metacognitive processes and evidence of learning. Social cognitive theorists like Bandura (1986) identify personal, behavioral and environmental factors as triadic processes which influence student performance. These processes underlie the self-regulatory processes which Zimmerman (2000) defines as forethought, performance or volitional control and self-reflection. The importance of developing self-regulating ability within students has been extensively researched for the past two decades and is believed to be essential to successful learning within schools and extending self-directed learning into adulthood (Boekaerts, 1999; Corno & Randi, 1999). Concordia University’s Center for the Study of Learning and Performance (CSLP) has identified the potential for portfolios to provide evidence of self-regulation as well as the potential for a an electronic portfolio tool to support and scaffold self-regulation (Wade, Abrami, & Sclater, 2005). As the research continues regarding the effects of portfolios in their various formats, the development of a tool which not only supports the development of a student’s portfolio but also of their self-regulative abilities provides opportunities for researching student outcomes in both arenas. This presentation will provide the theoretical background that guided the redesign of the CSLP’s bilingual, web-based electronic portfolio, now called ePEARL, along with some of the key features within the software.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,016 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».