Browsing by Author "Diaz, Noemi V. Mendoza"

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    Effective teaching in computational thinking: A bias-free alternative to the exclusive use of students' evaluations of teaching (SETs)
    (2023) Diaz, Noemi V. Mendoza; Sotomayor, Trinidad
    The tenure system in the United States places significant importance on teaching effectiveness. To date, students' evaluations of teaching (SETs) have been the reigning mechanism for assessing effective teaching. However, prior work has shown that SETs are often biased against underrepresented groups and minorities. The present study analyzes options for effective teaching assessments, which include evaluating final grades and measuring the differences between students' pre- and post-tests (normalized gain) using standard instruments. The content area and the instrument used in this study originated in the computational thinking field, which has a widespread presence in engineering, where minorities are at a disadvantage. This study obtained a total of 88 student participants from four sections of an introductory engineering course at a Southwestern institution. The study utilized a computational thinking diagnostic (CTD) to inform the course teaching approach (the intervention). Results show that (a) normalized learning gains correlated moderately with SETs, (b) final grades correlated strongly with SETs, (c) final grades correlated strongly with normalized learning gains, (d) the educational intervention based on the CTD significantly affected student learning, and (e) SET comments affect evaluations. The implications include the notion that standardized instrument-driven instruction and evaluations can increase the success of minorities on both sides of the classroom. The purpose of this manuscript is to invite the Heliyon readership to get involved in the development of related instruments and to incorporate these measures of learning into their instruction so biases are avoided or minimized.
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    Time to Graduate for Latinos/Hispanics in Comparison to Other Diverse Student Groups: A Multi-Institutional/Multilevel MIDFIELD Study
    (2021) Diaz, Noemi V. Mendoza; Sunny, Cijy Elizabeth; Sotomayor, Trinidad; Richard, Jacques
    Higher institutions of education represent the social mobility mechanisms that create more just societies. The STEM fields are particularly critical in the development of these modern, more just societies. In the United States, the social and racial justice debates are ever more relevant and present in academia. Studies focused on under-represented and under-served groups in education, especially STEM fields, are timely and of paramount importance. This is a study that analyzed student data of 19 institutions, concentrated in what is known as the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD). It utilized multilevel (HLM) analysis focused on the Time to Graduate outcome of under-represented populations, emphasizing the Latino/Hispanics group. Multilevel analysis is a powerful tool to evaluate differences in groups such as institutions and races/ethnicities, which is the type of data MIDFIELD affords. Results show that depending on the multilevel model, either fixed or random slope, there is a significant difference between the number of terms taken to graduate for under-represented groups, including Latinos/ Hispanics, compared to White groups and for Black compared to White groups. This suggests that Black students tend to be more impacted by their institution than other racial/ethnical groups. Since the emphasis was Latinos/Hispanics, the question remaining is if these results transfer to a sample with more Latino/Hispanic representation.