Dataset Information

The Classroom Discourse Observation Protocol (CDOP): A quantitative method for characterizing teacher discourse moves in undergraduate STEM learning environments.

ABSTRACT: We describe the development and validation of a new instrument, the Classroom Discourse Observation Protocol (CDOP), which quantifies teacher discourse moves (TDMs) from observational data in undergraduate STEM classrooms. TDMs can be conceptualized as epistemic tools that can mediate classroom discussions. Through an inductive-deductive coding process, we identified commonly occurring TDMs among a group of biology instructors (n = 13, 37 class session) teaching in Active Learning Environments. We describe the CDOP coding scheme and its associated matrix that allows observers to reliably characterize TDMs in 2-min time intervals over the course of a class period. We present the protocol, discuss how it differs from existing classroom observation protocols, and describe the process by which it was developed and validated. Also, we show how this protocol is able to discriminate the discursive practices of instructors teaching in undergraduate STEM learning environments with sample qualitative and quantitative results that illustrate its utility for assessing and improving STEM instructional practices.

SUBMITTER: Kranzfelder P

PROVIDER: S-EPMC6636728 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Classroom Discourse Observation Protocol (CDOP): A quantitative method for characterizing teacher discourse moves in undergraduate STEM learning environments.

Kranzfelder Petra P Bankers-Fulbright Jennifer L JL García-Ojeda Marcos E ME Melloy Marin M Mohammed Sagal S Warfa Abdi-Rizak M AM

PloS one 20190717 7

We describe the development and validation of a new instrument, the Classroom Discourse Observation Protocol (CDOP), which quantifies teacher discourse moves (TDMs) from observational data in undergraduate STEM classrooms. TDMs can be conceptualized as epistemic tools that can mediate classroom discussions. Through an inductive-deductive coding process, we identified commonly occurring TDMs among a group of biology instructors (n = 13, 37 class session) teaching in Active Learning Environments. ...[more]

PMID: 31314756

Similar Datasets

Project description:BackgroundIt is possible that tailoring dietary approaches to an individual's genomic profile could provide optimal dietary inputs for biological functioning and support adherence to dietary management protocols. The science required for such nutrigenetic and nutrigenomic profiling is not yet considered ready for broad application by the scientific and medical communities; however, many personalized nutrition products are available in the marketplace, creating the potential for hype and misleading information on social media. Twitter provides a unique big data source that provides real-time information. Therefore, it has the potential to disseminate evidence-based health information, as well as misinformation.ObjectiveWe sought to characterize the landscape of precision nutrition content on Twitter, with a specific focus on nutrigenetics and nutrigenomics. We focused on tweet authors, types of content, and presence of misinformation.MethodsTwitter Archiver was used to capture tweets from September 1, 2020, to December 1, 2020, using keywords related to nutrition and genetics. A random sample of tweets was coded using quantitative content analysis by 4 trained coders. Codebook-driven, quantified information about tweet authors, content details, information quality, and engagement metrics were compiled and analyzed.ResultsThe most common categories of tweets were precision nutrition products and nutrigenomic concepts. About a quarter (132/504, 26.2%) of tweet authors presented themselves as science experts, medicine experts, or both. Nutrigenetics concepts most frequently came from authors with science and medicine expertise, and tweets about the influence of genes on weight were more likely to come from authors with neither type of expertise. A total of 14.9% (75/504) of the tweets were noted to contain untrue information; these were most likely to occur in the nutrigenomics concepts topic category.ConclusionsBy evaluating social media discourse on precision nutrition on Twitter, we made several observations about the content available in the information environment through which individuals can learn about related concepts and products. Tweet content was consistent with the indicators of medical hype, and the inclusion of potentially misleading and untrue information was common. We identified a contingent of users with scientific and medical expertise who were active in discussing nutrigenomics concepts and products and who may be encouraged to share credible expert advice on precision nutrition and tackle false information as this technology develops.

Project description:IntroductionPrevious reviews on active learning in dental education have not comprehensibly summarized the research activity on this topic as they have largely focused on specific active learning strategies. This scoping review aimed to map the breadth and depth of the research activity on active learning strategies in undergraduate classroom dental education.MethodsThe review was guided by Arksey & O'Malley's multi-step framework and followed the PRISMA Extension Scoping Reviews guidelines. MEDLINE, ERIC, EMBASE, and Scopus databases were searched from January 2005 to October 2022. Peer-reviewed, primary research articles published in English were selected. Reference lists of relevant studies were verified to improve the search. Two trained researchers independently screened titles, abstracts, and full-texts articles for eligibility and extracted the relevant data.ResultsIn total, 93 studies were included in the review. All studies performed outcome evaluations, including reaction evaluation alone (n = 32; 34.4%), learning evaluation alone (n = 19; 20.4%), and reaction and learning evaluations combined (n = 42; 45.1%). Most studies used quantitative approaches (n = 85; 91.3%), performed post-intervention evaluations (n = 70; 75.3%), and measured student satisfaction (n = 73; 78.5%) and knowledge acquisition (n = 61; 65.6%) using direct and indirect (self-report) measures. Only 4 studies (4.3%) reported faculty data in addition to student data. Flipped learning, group discussion, problem-based learning, and team-based learning were the active learning strategies most frequently evaluated (≥6 studies). Overall, most studies found that active learning improved satisfaction and knowledge acquisition and was superior to traditional lectures based on direct and indirect outcome measures.ConclusionActive learning has the potential to enhance student learning in undergraduate classroom dental education; however, robust process and outcome evaluation designs are needed to demonstrate its effectiveness in this educational context. Further research is warranted to evaluate the impact of active learning strategies on skill development and behavioral change in order to support the competency-based approach in dental education.

Dataset Information

The Classroom Discourse Observation Protocol (CDOP): A quantitative method for characterizing teacher discourse moves in undergraduate STEM learning environments.

Publications

The Classroom Discourse Observation Protocol (CDOP): A quantitative method for characterizing teacher discourse moves in undergraduate STEM learning environments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets