The Evaluation of an Integrated Growth & Goals Module to Better Equip Students with Learning Skills in Postsecondary Courses: Systematic, Scalable, and Explicit
Notice bibliographique
Résumé
In this dynamic and rapidly changing world, students need to be able to continually learn and adapt throughout their lives. However, most students spend years in formal education settings without being explicitly taught how to learn effectively. To reach our goal of explicitly and effectively equipping all students with learning skills, we developed and evaluated a Growth & Goals module. The module is an Open Education Resource for postsecondary students that educators integrate in their courses to teach core learning skills of metacognition, goal-setting, growth mindset, and mindfulness. Over 5000 students at ten institutions have now used the module. In the present study, we evaluated the module using a Practical Participatory Evaluation approach and the 4-level Kirkpatrick Evaluation model. To answer ten questions aligned with the Kirkpatrick model, we collected data from 1845 students and 5 educators from nine undergraduate courses in science, engineering, and mathematics, which were used to investigate ten research questions aligned with Kirkpatrick’s four evaluation levels. For Level 1 (Reaction), students and educators reported high satisfaction and gave constructive suggestions that centred on expanding the module. The training was new to 88% of students. Most completion rates were over 75% when professors provided an incentive (³ 1%). Students in some demographics used the module less than others: lower-achieving, first-generation university students, from outside the Ottawa-Gatineau area, male, and in certain programs. In Level 2 (Learning), students’ metacognitive skills increased throughout the semester. They could identify SMART goals (Specific, Measurable, Accountable, Reachable, and Time-specific) and differentiate growth/fixed mindset statements. At Level 3 (Behaviour), students applied the module within the originating course, indicated their intent to use the module in the future, and a survey of a subsample indicated that most students used or intended to use the module in a new course. Most educators created course-level learning outcomes for the first time to integrate with the module. As an Open Education Resource with a nearly complete “plug-and-play” format, using the module required little time and low technological skills of students and educators; however, greater support, incentives, and rewards could be provided. Research and development require sustained resources. Finally, in Level 4 (Results), educators have used the module in courses in a number of disciplines, including sciences, engineering, mathematics, education, and psychology. The module addresses institutional goals of transformational learning and agility, as well as two provincial degree level expectations that are rarely explicitly taught in courses. In summary, the Growth & Goals module explicitly teaches core learning skills in a way that is systematic, scalable, and explicit for science, engineering, and mathematics courses, with a potential to expand to any discipline.
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 | 0,022 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».