Outcomes of a Faculty Development Program Promoting Scholarly Teaching and Student Engagement at a Large Research-Intensive University
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".