Effects of an interdisciplinary STEM Ph.D. program on innovation capacity development

This five-year longitudinal study explores the potential influence of interdisciplinary science, technology, engineering, and mathematics (STEM) training on the development of innovation capacities among Ph.D. graduate students. Guided by Kegan’s (2009) constructive-developmental theory the research assesses how participation in an interdisciplinary program predicted the growth rate of student’s cognitive, social, and intrapersonal capabilities to innovate and develop contextually beneficial new ideas. The study uses a longitudinal design and hierarchical blocked regression using both a large control group with covariate-adjusted analyses and a smaller propensity score matched control group to evaluate these differential rates of innovation capacity development. Results demonstrate that five years of Ph.D. interdisciplinary training predicts an acceleration in the development of innovation capacities by well over one quarter of a standard deviation (β = .40-.41) – a very substantial increase. Stronger innovation capacities are observable in participating students’ creativity, proactivity, and teamwork across diverse fields. These findings highlight the potential of interdisciplinary STEM programs to meet modern scientific and industrial demands for innovative, adaptive researchers, while also underscoring challenges in scaling such programs within traditional academic structures. Implications for program design, student engagement, and the effectiveness of interdisciplinary approaches in higher education are discussed.

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Full text: https://link.springer.com/article/10.1007/s10755-025-09848-3

Where to behave, when to think, and how to feel: The ABC+ model of learning engagement and its relationship to the components of academic performance

Background

This study breaks new ground by presenting a new, more sophisticated model of learning engagement that goes beyond the current state of the art embodied in the widely used Affective-Behavioral-Cognitive (ABC) model. This work synthesizes and builds upon neglected lines of research in the structure of affective engagement. It also integrates entirely novel theoretical considerations in the form of activity spaces—the different learning spaces associated with different course-defined activities, which in turn afford different forms of cognitive and behavioral engagement. To empirically test these theoretical notions, data from a sample of 655 students across multiple sections of a course were analyzed using structural equation modeling that linked different elements of learning engagement to academic performance outcomes like exam grades, quizzes, participation, and assignment performance. The model fit and practical implications of the traditional ABC structure was compared to that of the Revised Affective-Behavioral-Cognitive model (ABC +) proposed here.

Results

The traditional ABC model evinced substantially more misfit to the data (CFI = 0.97, TLI = 0.94, RMSEA = 0.037, [90% CI 0.017, 0.056, p RMSEA ≤ 0.05 = 0.86], CD = 0.57) than the revised ABC + model, which fit the data well (CFI = 0.99, TLI = 0.98, RMSEA = 0.022, [90% CI 0.000, 0.041, p RMSEA ≤ 0.05 = 0.99], CD = 0.68). As predicted, in the ABC + model affective learning engagement separated into positive and negative components, and cognitive and behavioral engagement further separated by activity spaces. Crucially, each of these engagement factors contribute to different academic outcomes in a manner masked in the ABC model. The ABC + components are not interchangeable, and cannot be conflated with one another, but have distinct functions in supporting student performance.

Conclusions

The more sophisticated engagement model presented here reveals deeper patterns of student engagement that can better guide the research community and can be used by faculty members and policymakers to improve student engagement and performance.

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Full text: https://stemeducationjournal.springeropen.com/articles/10.1186/s40594-025-00555-1

Gender and race/ethnicity differences in predictors of course grade in a first-year engineering course and continued enrollment in engineering

Background

Engineering requires new solutions to improve undergraduate performance outcomes, including course grades and continued enrollment in engineering pathways. Belonging and engineering role identity have long been associated with successful outcomes in engineering, including academic success, retention, and well-being.

Purpose

We measure the relationships between belonging and role identity at the beginning of a first-year engineering course with course grade and continued enrollment in engineering courses. We test the effect of an ecological belonging intervention on student belonging, course grade, and persistence.

Method

Students (n = 834) reported their sense of belonging in engineering, cross-racial experiences, engineering performance/competence, interest in engineering, and engineering recognition before and after an in-class intervention to improve classroom belonging ecology. Through a series of longitudinal multigroup path analyses, a form of structural equation modeling, we tested the predictive relationships of the measured constructs with engineering identity and investigated differences in these relationships by student gender and race/ethnicity.

Findings

The proposed model predicts course grades and continued enrollment, providing insight into the potential for interventions to support first-year engineering students. Group analysis results demonstrate the difference in the function of these psychosocial measures for women and Black, Latino/a/x, and Indigenous (BLI) students, providing insights into the potential importance of sociocultural interventions within engineering classrooms to improve the engineering climate, engagement, and retention of students.

Implications

The results highlight the need for more specific, nuanced theoretical investigations of how marginalized students experience the engineering environment and develop social belonging and engineering role identity.

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Full text: https://onlinelibrary.wiley.com/doi/10.1002/jee.70007