Pathways and Outcomes of Rural Students in Engineering

Ebrahiminejad, Hossein; Yagoub, Hassan Ali Al; Ricco, George D.; Ohland, Matthew W.; Zahedi, Leila

doi:10.1109/fie43999.2019.9028380

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…Different approaches are used to evaluate students' performance and persistence in higher education systems, among which machine learning algorithms and stochastic models are the most common [11][12][13][14][15][16][17]. Markov models have been used in many educational studies to analyze students' academic progress and behaviors [3,[18][19][20][21][22].…”

Section: Absorbing Markov Chainmentioning

confidence: 99%

Improving Graduation Rate Estimates Using Regularly Updating Multi-Level Absorbing Markov Chains

Boumi

Vela

2020

Education Sciences

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American universities use a procedure based on a rolling six-year graduation rate to calculate statistics regarding their students’ final educational outcomes (graduating or not graduating). As an alternative to the six-year graduation rate method, many studies have applied absorbing Markov chains for estimating graduation rates. In both cases, a frequentist approach is used. For the standard six-year graduation rate method, the frequentist approach corresponds to counting the number of students who finished their program within six years and dividing by the number of students who entered that year. In the case of absorbing Markov chains, the frequentist approach is used to compute the underlying transition matrix, which is then used to estimate the graduation rate. In this paper, we apply a sensitivity analysis to compare the performance of the standard six-year graduation rate method with that of absorbing Markov chains. Through the analysis, we highlight significant limitations with regards to the estimation accuracy of both approaches when applied to small sample sizes or cohorts at a university. Additionally, we note that the Absorbing Markov chain method introduces a significant bias, which leads to an underestimation of the true graduation rate. To overcome both these challenges, we propose and evaluate the use of a regularly updating multi-level absorbing Markov chain (RUML-AMC) in which the transition matrix is updated year to year. We empirically demonstrate that the proposed RUML-AMC approach nearly eliminates estimation bias while reducing the estimation variation by more than 40%, especially for populations with small sample sizes.

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Section: Absorbing Markov Chainmentioning

confidence: 99%

Improving Graduation Rate Estimates Using Regularly Updating Multi-Level Absorbing Markov Chains

Boumi

Vela

2020

Education Sciences

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“…Instead, low academic performance can be viewed as an underlying factor, predictor, or leading indicator of halting [1]. Along these lines, many studies have recognized GPA as a fair predictor or leading indicator of students persisting or halting their academic career [8], [16], [18], [20], [24]. Zahedi et al [24] investigated the relationship between graduation rate and numerous variables, including gender, race, transfer status, major, number of terms registered, and cumulative GPA.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Students Academic Performance Trajectories on Final Academic Success

Boumi¹,

Vela²

2022

Preprint

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Many studies in the field of education analytics have identified student grade point averages (GPA) as an important indicator and predictor of students' final academic outcomes (graduate or halt). And while semester-to-semester fluctuations in GPA are considered normal, significant changes in academic performance may warrant more thorough investigation and consideration, particularly with regards to final academic outcomes. However, such an approach is challenging due to the difficulties of representing complex academic trajectories over an academic career. In this study, we apply a Hidden Markov Model (HMM) to provide a standard and intuitive classification over students' academic-performance levels, which leads to a compact representation of academic-performance trajectories. Next, we explore the relationship between different academic-performance trajectories and their correspondence to final academic success. Based on student transcript data from University of Central Florida, our proposed HMM is trained using sequences of students' course grades for each semester. Through the HMM, our analysis follows the expected finding that higher academic performance levels correlate with lower halt rates. However, in this paper, we identify that there exist many scenarios in which both improving or worsening academic-performance trajectories actually correlate to higher graduation rates. This counter-intuitive finding is made possible through the proposed and developed HMM model.

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“…Two papers were presented at the FYEE conference: an expansion of the academic policy trends poster [23] and a comparison of first year course requirements across institutions [24]. Results of a study of outcomes for rural students in engineering education were presented at the 2019 FIE Conference [25]. We have a paper accepted to appear in the International Journal of Engineering Education in 2020 comparing MIDFIELD and the ASEE dataset [26].…”

Section: Disseminationmentioning

confidence: 99%

Expanding Access to and Participation in Midfield (Year 4)

Ohland¹,

Lord²,

Layton³

et al.

2020 ASEE Virtual Annual Conference Content Access Proceedings

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Susan M. Lord received a B.S. from Cornell University in Materials Science and Electrical Engineering (EE) and the M.S. and Ph.D. in EE from Stanford University. She is currently Professor and Chair of Integrated Engineering at the University of San Diego. Her research focuses on the study and promotion of diversity in engineering including student pathways and inclusive teaching. She is Co-Director of the National Effective Teaching Institute (NETI). Her research has been sponsored by the National Science Foundation (NSF). Dr. Lord is among the first to study Latinos in engineering and coauthored The Borderlands of Education: Latinas in Engineering. Dr. Lord is a Fellow of the IEEE and ASEE and is active in the engineering education community including serving as General Co-Chair of the Frontiers in Education Conference, President of the IEEE Education Society, and Associate Editor of the IEEE Transactions on Education (ToE) and the Journal of Engineering Education (JEE). She and her coauthors received the 2011 Wickenden Award for the best paper in JEE and the 2011 and 2015 Best Paper Awards for the IEEE ToE. In Spring 2012, Dr. Lord spent a sabbatical at Southeast University in Nanjing, China teaching and doing research. She is on the USD team implementing "Developing Changemaking Engineers", an NSF-sponsored Revolutionizing Engineering Education (RED) project. Dr. Lord is the 2018 recipient of the IEEE Undergraduate Teaching Award.

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Pathways and Outcomes of Rural Students in Engineering

Cited by 11 publications

References 4 publications

Improving Graduation Rate Estimates Using Regularly Updating Multi-Level Absorbing Markov Chains

Improving Graduation Rate Estimates Using Regularly Updating Multi-Level Absorbing Markov Chains

Impacts of Students Academic Performance Trajectories on Final Academic Success

Expanding Access to and Participation in Midfield (Year 4)

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