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This study examined the impact of a National Science Foundation-funded support program for academically promising STEM students from low-income backgrounds. The program operates as a collaborative consortium, bringing together three public community colleges and one private university, to support the retention and graduation of program scholars. Using propensity score matching, we compared 169 program scholars to 169 matched non-scholars with similar demographic, academic, and financial characteristics ( M_age= 24.82 years; 41% female; 60% students of color). Participation in the program was associated with favorable academic outcomes, with large to moderate effect sizes. Specifically, program scholars had significantly higher cumulative and STEM-specific grade point averages than their non-scholar counterparts. They also completed significantly more STEM courses and were less likely to withdraw from college than non-scholars. These findings underscore the potential of comprehensive and equity-oriented approaches to STEM education in facilitating academic success for STEM students, particularly those from low-income backgrounds. 

Course-based undergraduate research experiences (CUREs) are laboratory courses that integrate broadly relevant problems, discovery, use of the scientific process, collaboration, and iteration to provide more students with research experiences than is possible in individually mentored faculty laboratories. Members of the national Malate dehydrogenase CUREs Community (MCC) investigated the differences in student impacts between traditional laboratory courses (control), a short module CURE within traditional laboratory courses (mCURE), and CUREs lasting the entire course (cCURE). The sample included approximately 1,500 students taught by 22 faculty at 19 institutions. We investigated course structures for elements of a CURE and student outcomes including student knowledge, student learning, student attitudes, interest in future research, overall experience, future GPA, and retention in STEM. We also disaggregated the data to investigate whether underrepresented minority (URM) outcomes were different from White and Asian students. We found that the less time students spent in the CURE the less the course was reported to contain experiences indicative of a CURE. The cCURE imparted the largest impacts for experimental design, career interests, and plans to conduct future research, while the remaining outcomes were similar between the three conditions. The mCURE student outcomes were similar to control courses for most outcomes measured in this study. However, for experimental design, the mCURE was not significantly different than either the control or cCURE. Comparing URM and White/Asian student outcomes indicated no difference for condition, except for interest in future research. Notably, the URM students in the mCURE condition had significantly higher interest in conducting research in the future than White/Asian students. 

Challenges persist in creating a diverse pipeline of STEM professionals. This study aims to understand the multifaceted experiences and needs of Underrepresented Minority (URM) college students as they navigate STEM environments and career choices. Utilizing social cognitive career theory (SCCT), this qualitative, multi-institutional study explored the varied experiences and barriers that 44 URM STEM students negotiated at two Predominantly White Institutions (PWIs). Implications for practice, research, and policy focus on interventions aimed at increasing persistence and fostering STEM career decision-making. 

In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.