Institution
Diablo Valley College
Education•Pleasant Hill, California, United States•
About: Diablo Valley College is a education organization based out in Pleasant Hill, California, United States. It is known for research contribution in the topics: Metrology & Cyclotomic field. The organization has 43 authors who have published 52 publications receiving 444 citations. The organization is also known as: DVC.
Papers published on a yearly basis
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San Francisco State University1, Pacific Lutheran University2, San Jose State University3, De Anza College4, City College of San Francisco5, Laney College6, Foothill College7, Las Positas College8, University of California, Davis9, Diablo Valley College10, Portland Community College11, Cañada College12, College of San Mateo13, Chabot College14, Skyline College15, Palomar College16, Solano Community College17, California State University18, Los Medanos College19, Berkeley City College20, Contra Costa College21, Holy Names University22, University of Texas at Austin23, University of San Francisco24, Georgia Institute of Technology25, Stanford University26
TL;DR: 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 is described.
Abstract: 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.
71 citations
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Foothill College1, De Anza College2, Community College of Philadelphia3, Bellevue College4, New York University5, Arizona State University6, Volunteer State Community College7, University of Georgia8, St. Mary's College of Maryland9, Austin Community College District10, Santa Fe College11, Diablo Valley College12, Lane Community College13, Edmonds Community College14, Florida SouthWestern State College15, University of North Carolina at Chapel Hill16, Everett Community College17, Pacific Lutheran University18, Kapiolani Community College19, University of Illinois at Chicago20, University of Colorado Boulder21
TL;DR: The results of a meeting convened to identify affordances and constraints associated withCC BER are reported and support strategies for advancing CC BER going forward are described.
Abstract: Nearly half of all undergraduates are enrolled at community colleges (CCs), including the majority of U.S. students who represent groups underserved in the sciences. Yet only a small minority of studies published in discipline-based education research journals address CC biology students, faculty, courses, or authors. This marked underrepresentation of CC biology education research (BER) limits the availability of evidence that could be used to increase CC student success in biology programs. To address this issue, a diverse group of stakeholders convened at the Building Capacity for Biology Education Research at Community Colleges meeting to discuss how to increase the prevalence of CC BER and foster participation of CC faculty as BER collaborators and authors. The group identified characteristics of CCs that make them excellent environments for studying biology teaching and learning, including student diversity and institutional cultures that prioritize teaching, learning, and assessment. The group also identified constraints likely to impede BER at CCs: limited time, resources, support, and incentives, as well as misalignment between doing research and CC faculty identities as teachers. The meeting culminated with proposing strategies for faculty, administrators, journal editors, scientific societies, and funding agencies to better support CC BER.
63 citations
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31 citations
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TL;DR: With institutional commitment and an academic partnership, a safety net institution can integrate routine FV applications and oral health interventions into well-child visits to reduce ECC.
Abstract: BACKGROUND AND OBJECTIVE: Applying topical fluoride varnish (FV) to young children’s teeth is an effective therapeutic strategy for preventing early childhood caries (ECC). In 2008, the pediatricians at Contra Costa Regional Medical Center and Health Centers became concerned that our low-income pediatric patients had high rates of ECC and very limited access to dental care. We formed an interdisciplinary safety net-academic partnership with the University of California San Francisco to implement routine FV applications, along with oral health education, screening, and referral during well-child exams for children aged 1 to 5 years. METHODS: Over 3 years, the team developed clinical policies, educational materials, billing, and support systems to facilitate implementation in the primary care setting. A pilot study was performed in 2 health centers; improvements to the implementation plan were made. A team of local providers and academic partners performed system-wide didactic and hands-on trainings and spread this intervention to the remaining 6 health centers. Continued improvement strategies and provider feedback were pursued with each measurement cycle. RESULTS: In August 2012, 95% of all children aged 1 to 5 years who were seen for well-child checkups received a FV application and oral health education during their primary care well visit. Repeat measurement in April 2014 showed a sustained rate of 97% application of FV for children in this age group seen for well-child visits. CONCLUSIONS: With institutional commitment and an academic partnership, a safety net institution can integrate routine FV applications and oral health interventions into well-child visits to reduce ECC.
31 citations
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Georgia Institute of Technology1, San Francisco State University2, Pacific Lutheran University3, University of California, Los Angeles4, University of Southern California5, De Anza College6, City College of San Francisco7, Merritt College8, Foothill College9, Las Positas College10, University of California, San Diego11, University of California, Davis12, Diablo Valley College13, Mt. Hood Community College14, Cañada College15, College of San Mateo16, Chabot College17, Samuel Merritt University18, Palomar College19, California State University, Sacramento20, Los Medanos College21, Berkeley City College22, Contra Costa College23, Holy Names University24, University of Texas at Austin25, MCPHS University26, Stanford University27
TL;DR: The vast majority of Instructor Talk could be characterized using the originally published Instructor Talk framework, suggesting the robustness of this framework and a new form of Instructor talk—Negatively Phrased Instructor Talk, language that may discourage students or distract from the learning process—was detected in these novel course contexts.
Abstract: Instructor Talk-noncontent language used by instructors in classrooms-is a recently defined and promising variable for better understanding classroom dynamics. Having previously characterized the Instructor Talk framework within the context of a single course, we present here our results surrounding the applicability of the Instructor Talk framework to noncontent language used by instructors in novel course contexts. We analyzed Instructor Talk in eight additional biology courses in their entirety and in 61 biology courses using an emergent sampling strategy. We observed widespread use of Instructor Talk with variation in the amount and category type used. The vast majority of Instructor Talk could be characterized using the originally published Instructor Talk framework, suggesting the robustness of this framework. Additionally, a new form of Instructor Talk-Negatively Phrased Instructor Talk, language that may discourage students or distract from the learning process-was detected in these novel course contexts. Finally, the emergent sampling strategy described here may allow investigation of Instructor Talk in even larger numbers of courses across institutions and disciplines. Given its widespread use, potential influence on students in learning environments, and ability to be sampled, Instructor Talk may be a key variable to consider in future research on teaching and learning in higher education.
29 citations
Authors
Showing all 43 results
Name | H-index | Papers | Citations |
---|---|---|---|
Wayne R. McKinney | 7 | 17 | 191 |
Michael A. Chisar | 3 | 3 | 42 |
James J. Rawls | 3 | 6 | 22 |
Mary Ann Irwin | 3 | 7 | 31 |
Joseph J. Gorga | 3 | 3 | 111 |
Bruce Lerro | 3 | 4 | 48 |
Despina T. Prapavessi | 2 | 3 | 9 |
Raymond Wilson | 2 | 2 | 37 |
Julia K. Willsie | 2 | 2 | 71 |
Mickey Huff | 2 | 2 | 20 |
Robert Ovetz | 2 | 2 | 16 |
Nicolette M. Moultrie | 2 | 2 | 39 |
Bryan K. Clarkson | 2 | 2 | 71 |
Mark Akiyama | 2 | 2 | 26 |
Robert M. Kidd | 2 | 2 | 18 |