Transformation of research in all biological fields necessitates the design, analysis and, interpretation of large data sets. Preparing students with the requisite skills in experimental design, statistical analysis, and interpretation, and mathematical reasoning will require both curricular reform and faculty who are willing and able to integrate mathematical and statistical concepts into their life science courses. A new Faculty Learning Community (FLC) was constituted each year for four years to assist in the transformation of the life sciences curriculum and faculty at a large, Midwestern research university. Participants were interviewed after participation and surveyed before and after participation to assess the impact of the FLC on their attitudes toward teaching, perceived pedagogical skills, and planned teaching practice. Overall, the FLC had a meaningful positive impact on participants' attitudes toward teaching, knowledge about teaching, and perceived pedagogical skills. Interestingly, confidence for viewing the classroom as a site for research about teaching declined. Implications for the creation and development of FLCs for science faculty are discussed. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(6):517-525, 2016.

Download full-text PDF

Source
http://dx.doi.org/10.1002/bmb.20974DOI Listing

Publication Analysis

Top Keywords

faculty learning
8
learning community
8
life science
8
analysis interpretation
8
attitudes teaching
8
teaching perceived
8
perceived pedagogical
8
pedagogical skills
8
faculty
5
teaching
5

Similar Publications

The present study investigated the neuromodulatory substrates of salience processing and its impact on memory encoding and behaviour, with a specific focus on two distinct types of salience: reward and contextual unexpectedness. 46 Participants performed a novel task paradigm modulating these two aspects independently and allowing for investigating their distinct and interactive effects on memory encoding while undergoing high-resolution fMRI. By using advanced image processing techniques tailored to examine midbrain and brainstem nuclei with high precision, our study additionally aimed to elucidate differential activation patterns in subcortical nuclei in response to reward-associated and contextually unexpected stimuli, including distinct pathways involving in particular dopaminergic modulation.

View Article and Find Full Text PDF

The ability of environmental cues to trigger alcohol-seeking behaviours is thought to facilitate problematic alcohol use. Individuals' tendency to attribute incentive salience to cues may increase the risk of addiction. We sought to study the relationship between incentive salience and alcohol addiction using non-preferring rats to model the heterogeneity of human alcohol consumption, investigating both males and females.

View Article and Find Full Text PDF

Rethinking polypharmacy: empowering junior doctors to tackle a chronic condition in modern practice.

Postgrad Med J

January 2025

Department of Primary Care and Public Health, Faculty of Medicine, Imperial College London, 86 Wood Lane, London, W12 0BZ, United Kingdom.

View Article and Find Full Text PDF

Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?

Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.

What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.

View Article and Find Full Text PDF

Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!