Barriers Experienced by Undergraduate Students to Access Summer Research Positions in Musculoskeletal Science and Proposed Solutions

In this study, we identified factors influencing completion rates (graduation) of undergraduate students in science, technology, engineering, and mathematics (STEM). We used hierarchical, logistic, and linear regression models to analyze the data and found significant predictor variable interactions across various underrepresented versus well-represented student groups. We asked six research questions, three involving two- and three-way interactions between STEM completion, underrepresented groups, and predictor variables and three involving two- and three-way interactions among time spent for graduation, predictor variables, and underrepresented groups. We found that female STEM students completed their degree in significantly less time than their male counterparts ( p < .001) and that for every 1 point increase in second-year grade point average, length of time to complete the bachelor’s degree decreased by 1.68 years. These and other findings can be used by institutions to help focus retention intervention efforts on those critical and significant predictors of STEM degree completion of underrepresented groups.

The aim of the study was to identify the factors that have an influence on the completion rates of undergraduate students in the fields of science, technology, engineering and mathematics (STEM). Using Tinto's retention rate theory as the theoretical foundation, data were collected from freshman who were enrolled in the years 2005 to 2008. Results showed gender and first-generation status were significant predictors of STEM completion and time taken to complete the degree. Institutional bias played a role in race/ethnicity not being a factor affecting completion rates, as this study was conducted at a Predominantly White Institution. SAT scores and first and second-year college GPA showed to have the most prominent influence on both STEM completion rate and time taken to complete the degree. Females with higher first-year college GPA and higher high school rank finished faster. Similar results were found with first-generation students as well. Students belonging to ethnic minority groups with higher SAT scores and college GPA had greater success in STEM fields as well. The study results can be used to increase completion rates of underrepresented students in the STEM fields, given what we know about the interactions between underrepresented student groups and the most important predictors.

Appraisal of student records is an essential part of building a complete narrative of a university’s history. Within this process, it is important to capture the experiences of underrepresented student groups. A rich source of documentation of both student life and campus diversity comes from the records produced by university cultural student clubs, cultural houses, and sororities and fraternities with historically BIPOC membership. The formation, activities, and dissolution of cultural student organizations can help to shape an understanding of a university’s demographic, social, and political history. Working with and building relationships with organization advisors and student members is important for forming good appraisal decisions about the records they produce. This article examines two case studies of appraisal projects involving the Ethnic Student Center at Western Washington University and the Bruce D. Nesbitt African American Cultural Center at the University of Illinois at Urbana-Champaign. It also includes an analysis of the appraisal of records of cultural student organizations and discusses the importance of working with stakeholders and understanding ownership of records.

Increased enrollment has not resulted in more degrees earned by women, minorities, or students with disabilities in science, technology, engineering, or mathematics (STEM). Although there are numerous reasons, many students drop out or change majors due to a loss of interest in their field. This study focuses on the role that career certainty has on the persistence of interest in STEM. A criterion-group approach employing quasi-experimental survey research methods is used with randomly (n = 608) and non-randomly (n = 123) selected underrepresented student groups participating in two National Science Foundation collaborations. The psychometric attributes of the instrument created for the study are inspected using principle components analysis and Cronbach's measure of internal consistency. Although an analysis of covariance did not find covariation in career certainty, significant interactions were detected among the three groups of students. A discussion of the results of the 6-year study and conclusions are presented along with implications for research, policy, and practice for those interested in the retention of underrepresented student groups in STEM.

This case study describes a staff-student partnership project from the perspective of three staff members based across independent departments within a UK higher education institution (HEI) and its students’ union. The authors, drawing upon an intersecting passion for advancing student equality, diversity, inclusion, widening participation and student engagement, developed a cross-collaborative and student-centred partnership project to create a series of guides specifically for underrepresented student groups. The guides, which sought to provide appropriate information and guidance in order to actively enhance students’ overall experience whilst navigating university life, were developed and co-created through lived student experience. This case study critically reflects upon this form of partnership, along with its benefits and challenges, and considers its contribution to literature on staff-student partnership beyond the formal realm of learning and teaching.

Self-selection refers to the decision of qualified students to not pursue their highest possible educational degree, including higher education. In this systematic meta-review, we used the conceptual framework of college choice to identify resources or mechanisms for students’ self-selection from higher education in international reviews and meta-analyses. In addition, we investigate whether underrepresented student groups (i.e., first-generation and cultural minority) experience unique resources and mechanisms for self-selection. Our narrative synthesis of international reviews and meta-analyses indicated that self-selection is related to financial, informational, social, aspirational resources in all contextual layers (i.e. individual habitus, school and community, higher education, socio-political context) of the conceptual framework of college choice. Whereas the family can help prevent self-selection by providing all four types of resources, school and higher education institutions can provide important information and social support through counselling and mentoring activities. For underrepresented groups, vertical and horizontal transmissions of social capital and experienced personal fit with the higher education environment may be of special importance for preventing self-selection. We conclude by discussing the potential of self-selection as concept for future interventions and research on widening access to higher education.

An institutional self-study at a Hispanic Serving Institution (HSI) explored factors thought to impact students’ decisions to persist in science, technology, engineering and mathematics (STEM) fields of study. This self-study is presented as a model first step for Institutions of Higher Education interested in launching efforts to improve STEM education and STEM student success and persistence. A methodology combining qualitative and quantitative analysis approaches was used to examine different aspects of the overarching research question, “Why do undergraduate students leave college STEM fields of study?” A quantitative review of institutional data was used to identify four particular gaps in student persistence and success in STEM fields of study at Texas State University. An online survey and a focus group guide were developed based on existing but more broadly focused instruments and used to collect faculty insights regarding faculty and student experiences and possible reasons for student attrition in STEM. A review of retention theories was undertaken to better inform the process of generating remedies to the STEM persistence issue. Data were collected and analyzed separately for each component to produce two sets of findings. Findings indicated that there were retention issues in regard to STEM majors in general and underrepresented student groups in particular. Underrepresented students do not pursue STEM degrees percentage wise in comparison to other degrees. The self-study and retention theories suggest the following remedial measures: supporting faculty to transform their teaching, providing supplemental instruction in math and science and including discipline specific introductory courses and early internship experiences.

This research presents a novel multi-scale paramorphic kernel learning framework (MPKLA) that is designed to enable autonomous, context-adaptive STEM learning for electrical and renewable energy topics. By employing distributed multi-agent kernel cores, recursive kernel reweighting, and entropy-guided abstraction modulation, this system dynamically adapts instructional content and sequencing based on the specific cognitive state of individual learners. Concept learning history is maintained in persistent memory buffers to facilitate individualized reinforcement and remediation in asynchronous, informal environments. Grounded in a physics-informed knowledge graph, the system offers epistemic coherence and domain consistency at multiple levels of abstraction. Evaluated across multiple, underrepresented student groups in community and laboratory implementations, MPKLA demonstrated a 45% rate increase in concept recall, a 3.2× improvement in student-led project completion, and sustained 68% learner interest over 12 weeks. These results emphasize the effectiveness of this architecture in delivering scalable, culturally sensitive, and high-fidelity STEM education without human interaction. The paper also discusses system deployment, statistical validation, and longitudinal deployment settings, informal education problem-solving, cultural adaptation, and learning assessment. MPKLA provides an extensible blueprint for inclusive, technology-driven workforce development in clean energy sectors, fueling inclusive participation and expertise in advanced technical fields.

When the needs of learners are misunderstood or ignored, traditional teaching strategies prove ineffective and the needs of the 21st century learner are not met. The purpose of this qualitative research is to describe the lived experiences of teachers as they use customized teaching strategies to support student learning in their classrooms. Specific issues relative to technology and underperforming and underrepresented student groups were explored to gain a deeper understanding of how gender, disabilities, culture, and socio-economic status impact students' leaning and inform the effective use of customized teaching strategies that are supported by technology. From a social constructivist's perspective, this phenomenological research addressed the following research questions: How do teachers in K-12, public schools in Pennsylvania describe their experience with implementing customized practices in a complex, dynamic, and technologically-enhanced educational environment? How do teachers characterize their efforts to use technology and customization to address the needs of all learners, especially those who do not benefit from a 'one-size-fits-all' instructional approach? How do teachers describe the impact of their personal experiences on their customized practices? The conceptual framework examined how technology has impacted the mindset of the 21st century student, the characteristics of effective customization, and teacher adoption of technology and customized teaching practices. The findings of this phenomenological study include descriptions offered by seven middle school teachers relative to their customized teaching strategies. Seven findings were synthesized into five results: the dynamic differences between a customized and traditional instructional approach, components that are critical to customization, enhanced relationships and learning experiences, support of 21st century skills, and the lack of explicit consideration of needs based on gender, culture, and socio-economic status. Recommendations for future research, a consideration of the needs of specific demographic groups, professional development, increasing collaboration, and re-designing school systems are discussed.

In Australia and elsewhere, curriculum improvements are needed to improve the development of Science, Technology and Mathematics (STM) graduates for future work and careers. Several studies document significant STM student attrition, poor graduate preparedness for work, relatively weak employment rates post-graduation, and mismatched expectations of graduate capabilities by employers. The inclusion of work-integrated learning (WIL) activities within curricula can better prepare students for relevant employment by bridging discipline-specific teaching with work practices. Traditionally, access to WIL has been limited for STM Bachelor students and a better understanding of WIL’s benefits and impact is needed to refine and extend STM WIL curricula for different disciplines and cohorts. Through the lens of personal capital resources, this article reports on the perspectives of 34,000 recent Bachelor graduates of Australian STM degrees on their participation in WIL and its impact on their perceived employability and transition to work. Findings indicated differences in engagement in WIL across STM disciplines and underrepresented student groups, yet consistent benefits of WIL on preparedness for work and graduate employment. The study provides valuable insights on gaps in WIL provision and potential ways to improve WIL offerings that support the development of future-capable STM graduates with positive labour market experiences.

Florida International University has undergone a reform in the introductory physics classes by focusing on the laboratory component of these classes. We present results from the secondary implementation of two research-based instructional strategies: the implementation of the Learning Assistant model as developed by the University of Colorado at Boulder and the Open Source Tutorial curriculum developed at the University of Maryland, College Park. We examine the results of the Force Concept Inventory (FCI) for introductory students over five years (n=872) and find that the mean raw gain of students in transformed lab sections was 0.243, while the mean raw gain of the traditional labs was 0.159, with a Cohen’s d effect size of 0.59. Average raw gains on the FCI were 0.243 for Hispanic students and 0.213 for women in the transformed labs, indicating that these reforms are not widening the gaps between underrepresented student groups and majority groups. Our results illustrate how research-based instructional strategies can be successfully implemented in a physics department with minimal department engagement and in a sustainable manner.

We report a program for a summer science academy in chemistry, a section of the Chicago Academic Achievement Program (CAAP) at the University of Chicago, as a gateway to STEM for incoming first-generation students and other underrepresented student groups. Our program emphasized diversity in classrooms, students’ psychological safety and a sense of belonging to STEM, increased knowledge base, and overall college readiness. On the basis of feedback from participating students, the reported summer science academy in chemistry successfully prepared and motivated the CAAP scholars toward a STEM career direction.

Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used for this purpose. However, ethical concerns have emerged about whether including protected attributes in the prediction models discriminates against underrepresented student groups and exacerbates existing inequities. We examine this issue in the context of a large U.S. research university with both residential and fully online degree-seeking students. Based on comprehensive institutional records for this entire student population across multiple years, we build machine learning models to predict student dropout after one academic year of study, and compare the overall performance and fairness of model predictions with or without four protected attributes (gender, URM, first-generation student, and high financial need). We find that including protected attributes does not impact the overall prediction performance and it only marginally improves algorithmic fairness of predictions. While these findings suggest that including protected attributes is preferred, our analysis also offers guidance on how to evaluate the impact in a local context, where institutional stakeholders seek to leverage predictive analytics to support student success.

Kingdom, Finland, and the United States and highlight the ongoing challenges with meeting the needs of the rising number of 'non-traditional' students in the sector.Collectively, the articles develop our understanding of the performance (in its broadest sense) and experiences of these students at and beyond university, and in relation to their more traditional peers.The articles cover a diversity of non-traditional students from various countries and assess a range of outcomes, including retention at university, degree completion, student experience and graduate employment outcomes.They also give hope by identifying various ways we can better support higher education students of different backgrounds to improve their university experience, better succeed in their learning and attain positive outcomes at and beyond university.The issue commences with an article from O'Shea on the lived experience of students who are first-in-family to attend university in Australia.As O'Shea notes, over one-half of Australian students are first-in-family at university, and often belong to one or more equity groups.In Australia, the government has formally classified the following student equity groups: students with disability, those from non-English speaking backgrounds, who are Indigenous, of low socio-economic status, reside in regional and remote areas, and women in STEM (Science, Technology, Engineering, Mathematics) fields of study (Dawkins, 1990).O'Shea found that reflections on their opportunities, experiences and challenges when seeking employment post-graduation showed being the first to university was important.Students, for example, often felt unprepared and unable to access adequate support to understand and navigate the complexities of the labour market.Rather than draw on institutional structures, they often relied on their own capabilities to secure graduate roles, such as personal agency, resilience, work ethic and flexible attitude.The article highlights the need for policy and practice that encourages and enables the development of social and cultural capital resources among first-in-family students for career purposes.In the next article, Li, Jackson and Carroll share their insights on how different entry pathways to universities in Australia can impact on students' academic performance.They highlight how widening participation policy has led to a growth in alternative entry routes, including vocational education training, pathway provider colleges, and enabling programs (or preparation/bridging programs).Their study of over 80,000 students from 16 Australian universities found these students generally had poorer academic outcomes than secondary school entrants, although not the case for most undertaking enabling programs.The article also clarifies how certain under-represented student groups are outperformed academically than their more privileged peers, illuminating policy responses for greater, nuanced academic support for both non-traditional student groups and those entering via alternative pathways.

Nowadays, online learning is preferable as it allows learners to learn anytime and anywhere, especially in pandemic crisis. However, this type of learning suffers from high dropout and low completion rates. Identifying at-risk students can improve students’ success and institutes effectiveness. Therefore, this paper proposes an at-risk predictive model by exploiting students behavioral and assessment data using data from a large distance-based university, Open University in UK. Unlike the tradition way of training and testing a model using one course, the proposed model is trained based on three historical course data and tested on the last offering. The prediction models show high performance values like 0.91 in accuracy. At same time, the paper examines if including protected feature like gender make prediction models discriminates against underrepresented student groups and aggravate present inequalities. To achieve this, different fairness metrics are used to compare models predictions with and without demographic characteristics. We find including of demographic characteristics does affect prediction performance of the models. In terms of fairness, there is no bias towards male and female students’ groups, but models can not be called fair for other features as there is bias in at least one metric towards other attributes.