Gloriana Trujillo initially trained as a basic science researcher, having first earned a B.A. at Dartmouth College in Biology, followed by a Ph.D. in Biological Sciences from the University of California, San Diego. Gloriana became interested in teaching and learning through her graduate work as a developmental neurobiologist and was awarded a National Science Foundation GK-12 Fellowship. As an NSF GK-12 Fellow, Gloriana translated her graduate research into experiments for high school biology students, and simultaneously explored the field of science education. She became intrigued by pedagogical approaches and how these impact students in the biology classroom, which influenced her decision to pursue a research and teaching National Institutes of Health-funded IRACDA Postdoctoral Fellowship at the University of New Mexico.
Gloriana's interest in biology education research led her to San Francisco State University, where she worked with Dr. Kimberly Tanner on biology department-wide faculty professional development funded by the Howard Hughes Medical Institute. At SFSU, Gloriana's research sought to understand students' self-efficacy, sense of belonging, and science identity to ultimately affect change in undergraduate biology classrooms. Throughout her scientific career, Gloriana has been an advocate for science outreach and diversity efforts, in particular to underrepresented and underprivileged populations. In her current role at Stanford, Gloriana shares her passion for creating effective, inclusive, and equitable learning experiences with the STEM community.
- Collectively Improving Our Teaching: Attempting Biology Department-wide Professional Development in Scientific Teaching CBE-LIFE SCIENCES EDUCATION 2018; 17 (1)
Classroom sound can be used to classify teaching practices in college science courses
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2017; 114 (12): 3085-3090
Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.
View details for DOI 10.1073/pnas.1618693114
View details for Web of Science ID 000396893600054
View details for PubMedID 28265087
- Considering the Role of Affect in Learning: Monitoring Students' Self-Efficacy, Sense of Belonging, and Science Identity CBE-LIFE SCIENCES EDUCATION 2014; 13 (1): 6–15