Professor Wei Li is the principal investigator of the NSF-funded project that aims to help researchers plan long-term studies in STEM education by making statistical advice, software and training materials available to educators and researchers.
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Despite the proliferation of video-based instruction and its benefits—such as promoting student autonomy and self-paced learning—the complexities of online teaching remain a challenge. To be effective, educators require extensive training in digital teaching methodologies. As such, there’s a pressing need to examine and comprehend the intricacies of instructors’ communication patterns within this context. This research addresses the pressing need to understand pedagogical discourse in online video lectures in Algebra classes by employing computational linguistic tools and natural language processing (NLP). Using transcripts from 125 Algebra 1 video lectures—comprising 4962 instances of pedagogical discourse—from five instructors at Math Nation, a virtual math learning environment, we analyzed the conveyance of linguistic, attitudinal, and emotional nuances. With the aid of 26 Coh-Metrix and SÉANCE features, we classified educators’ language choices, achieving an accuracy of 86.7%. Furthermore, variations in language choices, as signified by discourse markers, were examined through a K-means clustering approach. The resulting 17 clusters were grouped into interpersonal, structural, and cognitive pedagogic functions. Through this exploration, we demonstrate the promising potential of NLP in efficiently deciphering pedagogical communication patterns in video lectures. These insights open a new avenue for research, aimed at assessing the efficacy of digital instruction by scrutinizing pedagogical discourse characteristics in computer-based learning environments.
The purpose of this project is to develop the Revealing Special Educators’ Conditions for Teaching (ReSpECT) measure to evaluate special education teachers’ working conditions. Working conditions include teachers’ job demands as well as the available social, informational, and logistical resources to meet those demands. Working conditions are a crucial lever by which leaders may be able to improve teacher effectiveness, retention, and well-being and, as such, there is increased interest in researching and improving them. However, research on working conditions and practical efforts to improve them require well-validated measures, of which there are few. Therefore, the goal of this project is to develop a valid self-report measure of special education teacher working conditions that could be used for research purposes (such as evaluating how working conditions relate to teacher outcomes and how interventions affect working conditions) and for school and district leaders to identify strengths and weaknesses in how special education teachers are supported, which can inform how they allocate resources.
Learn about Yiqin Pan’s research and how she leverages quantitative methodologies, including artificial intelligence, statistical modeling, and psychometrics, to address applied issues in educational measurement and to optimize the learning process.
David Miller is currently developing assessments of diversity, equity, and inclusion (DEI) in higher education, including assessment standards for fair and equitable evaluation.
A quantitative researcher, Wei Li’s research centers on the development and application of experimental and quasi-experimental methods to address issues in education and policy studies. Read on to learn about Dr. Li’s various methodological and applied projects.
Find out how Walter Leite’s research and work aims to answer questions about strengthening causal inference in research using large, non-experimental datasets and detecting clusters of individuals in those datasets.
Explore Corinne Huggins-Manley’s goals and projects as she seeks to advance the field of educational measurement with research topics including item response theory and statistical model building to help practitioners overcome issues like non-response bias.
