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Record W2992854906

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

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

Bibliographic record

Venue˜The œjournal of faculty development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsActive learning (machine learning)Student engagementAttendancePsychologyMathematics educationExperiential learningHigher educationCooperative learningTeam-based learningClass (philosophy)PedagogyTeaching methodMedical educationComputer scienceMedicine
DOInot available

Abstract

fetched live from 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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.287
GPT teacher head0.487
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

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