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Enregistrement W2992854906

Outcomes of a Faculty Development Program Promoting Scholarly Teaching and Student Engagement at a Large Research-Intensive University

2015· article· en· W2992854906 sur OpenAlexaboutno aff
Leslie Reid, Julie Sexton, Rebecca Orsi

Notice bibliographique

Revue˜The œjournal of faculty development · 2015
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueInnovative Teaching Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésActive learning (machine learning)Student engagementAttendancePsychologyMathematics educationExperiential learningHigher educationCooperative learningTeam-based learningClass (philosophy)PedagogyTeaching methodMedical educationComputer scienceMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The links between student engagement and student learning, motivation, and satisfaction have been the focus of research in higher education for the last three decades. Kinzie (2010) provides a comprehensive summary of the research on engagement practices and student learning and development. Research has also shown that instructors who develop and communicate high expectations for learning and design learning experiences that support those expectations promote deep approaches to learning in their students (Baeten, Kyndt, Struyven & Dochy, 2010; Entwistle & McClune, 2004; Fyrenius, Wirell & Silen, 2007). Specific engagement practices within learning experiences such as active and collaborative learning, peer instruction, feedback and practice have been found to improve learning and motivation to learn (Cavanagh, 2011; Cherney, 2008; Kuh, 2009; Prince, 2004; Pascarella & Terenzini, 2005; Yoder & Hochevar, 2005). Recent research on active and collaborative learning in high-enrollment science classes has found increased learning for students when compared to traditional lecture classes. Deslauriers, Schelew and Weiman (2011) found that students in an introductory physics course who received instruction that was designed using active learning strategies performed significantly better on concept tests than students who had received traditional lectures on the same content. Increases in student attendance and participation were also noted in the active learning class. The metaanalysis by Freeman, Eddy, McDonough, Smith, Okoroafor, Jordt and Wenderoth (2014) looked at 225 published studies that compared student outcomes in traditional lecture courses to those in active learning courses. They found active learning courses produced significantly better results for student learning with student performance on exams and concept tests increasing by an average of 0.47 SDs in active learning courses. They also found the odds of failing were reduced by 1.95 SDs in active learning courses. Contrary to these findings, research by Andrews, Leonard, Colgrove and Kalinowski (2011), found that active learning strategies were not associated with improved learning gains. They interpret their findings to be related to the level of teaching expertise of the instructors; when instructors who lack deep and nuanced understanding of the engagement research implement active learning strategies, they will not produce the same results. This work highlights the need for instructors to develop some expertise in the learning theory behind these engagement strategies as well as how to design and implement them in their courses.Project Engage (PE) was a teaching enhancement program implemented at a large, doctoral-granting university in Canada. The program was initiated in response to institutional results on the National Survey of Student Engagement (NSSE) (Kuh, 2001). Of concern were NSSE responses from students at the institution, who indicated lower levels of engagement in first-year courses relative to comparator institutions. In particular, first-year students who completed the NSSE survey identified 'quality of instruction' as a key area to improve their experience in first-year courses. PE was developed to better understand students' experiences and perceptions of engagement in first-year classes and to support faculty members who were teaching these classes.Guided by the institutional concern about firstyear student engagement in introductory courses and the teaching skills of faculty members teaching those courses, we developed PE with three goals: faculty teaching first-year students will (1) increase their knowledge of best teaching practices and research on student learning and engagement; (2) redesign their first-year course and change their teaching practice to incorporate what they have learned; and (3) see an increase in students' perceptions of engagement in their classes.In this paper we report the following: (1) a description of the PE program; (2) program evaluation results; and (3) recommendations for changes to the PE program model to promote scholarly teaching. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,056

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,017
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0030,001
Communication savante0,0020,001
Science ouverte0,0020,005
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,287
Tête enseignante GPT0,487
Écart entre enseignants0,200 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2015
Routes d'admission1
Résumé présentoui

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