Board # 65 : Retention and Persistence among STEM Students: A Comparison of Direct Admit and Transfer Students across Engineering and Science

Johri, Aditya; Rangwala, Huzefa; Lester, Jaime; Almatrafi, Omaima

doi:10.18260/1-2--27899

Cited by 10 publications

(10 citation statements)

References 5 publications

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“…And comparing this common problem between STEM majors, Almatrafi et al (2017) and Godfrey et al (2010) found out even though the engineering retention is high and an issue to be worked on due to multiple reasons, like difficulties on understanding academic concepts, self-esteem or time issues, the persistence rates for this major is higher compared to college of sciences, highlighting the importance of studies related not only to engineering but, also, focused on the STEM science fields in general.…”

Section: The Engineering Retention Problemmentioning

confidence: 99%

Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

Gil¹,

Albuquerque²,

de³

2021

EUROPEAN J ED RES

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<p style="text-align: justify;">The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.</p>

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Section: The Engineering Retention Problemmentioning

confidence: 99%

Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

Gil¹,

Albuquerque²,

de³

2021

EUROPEAN J ED RES

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“…Improving student retention rates among science, technology, engineering, and math (STEM) university majors has been an enduring issue in higher education (Almatrafi et al, 2017;Findley-Van Nostrand & Pollenz, 2017). Only ~40% of university undergraduate students enrolled in STEM degree programs in the United States complete their degree (Waldrop, 2015), yet within the next 10-20 years a projected one million STEM-related jobs will need to be filled by qualified individuals (Findley- Van Nostrand & Pollenz, 2017).…”

Section: Introductionmentioning

confidence: 99%

Task-based attentional and default mode connectivity associated with STEM anxiety profiles among university physics students

Smith

Meca

Bottenhorn

et al. 2022

Preprint

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Attentional control theory (ACT) posits that elevated anxiety increases the probability of re-allocating cognitive resources needed to complete a task to processing anxiety-related stimuli. This process impairs processing efficiency and can lead to reduced performance effectiveness. Science, technology, engineering, and math (STEM) students frequently experience STEM-related anxiety, which can interfere with learning and performance and negatively impact student retention and graduation rates. The objective of this study was to extend the ACT framework to investigate the neurobiological associations between STEM-related anxiety and cognitive performance among 123 physics undergraduate students. Latent profile analysis (LPA) identified four profiles of student STEM-related anxiety, including two profiles that represented the majority of the sample (Low STEM Anxiety; 59.3% and High Math Anxiety; 21.9%) and two additional profiles that were not well represented (High STEM Anxiety; 6.5% and High Science Anxiety; 4.1%). Students underwent a functional magnetic resonance imaging (fMRI) session in which they performed two tasks involving physics cognition: the Force Concept Inventory (FCI) task and the Physics Knowledge (PK) task. No significant differences were observed in FCI or PK task performance between High Math Anxiety and Low STEM Anxiety students. During the three phases of the FCI task, we found no significant brain connectivity differences during scenario and question presentation, yet we observed significant differences during answer selection within and between the dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN). Further, we found significant group differences during the PK task were limited to the DAN, including DAN-VAN and within-DAN connectivity. These results highlight the different cognitive processes required for physics conceptual reasoning compared to physics knowledge retrieval, provide new insight into the underlying brain dynamics associated with anxiety and physics cognition, and confirm the relevance of ACT theory for STEM-related anxiety.

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“…This can be accomplished, in part, by retaining more individuals who enter college intending to pursue a STEM degree. The highest attrition among these students occurs early in the college experience (Tinto, 1988(Tinto, , 2006Almatrafi et al, 2017). Further, this attrition is disproportionately higher among students who are first-generation (first in their families to attend college) or are people of color (including Latinx, Black, Indigenous or Native American, and Pacific Islander).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Academic Performance and Retention of First-Year Biology Students through a Scalable Peer Mentorship Program

Wilton

Katz

Clairmont

et al. 2021

LSE

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Board # 65 : Retention and Persistence among STEM Students: A Comparison of Direct Admit and Transfer Students across Engineering and Science

Cited by 10 publications

References 5 publications

Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

Task-based attentional and default mode connectivity associated with STEM anxiety profiles among university physics students

Improving Academic Performance and Retention of First-Year Biology Students through a Scalable Peer Mentorship Program

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