Abstract

Managers and policymakers in higher education institutions try to improve graduation rates and decrease halt rates. To achieve this goal, it is important to understand academic and demographic factors that correlate with academic performance. Many studies in the field of education analytics have identified student grade point averages (GPA) as an important indicator and predictor of final academic outcomes (graduating or halting their studies). While semester-to-semester fluctuations in GPA are considered normal, significant changes in academic performance may warrant more thorough investigation and consideration, particularly with regard to final academic outcomes. However, it is challenging to represent complex academic trajectories over an academic career. To overcome this challenge, in this dissertation two different Hidden Markov Models (HMMs) are developed to provide a standard and intuitive classification over students' academic-performance levels. This leads to a compact representation of academic-performance trajectories. Next, the relationship between different academic-performance trajectories and their correspondence to final academic success are explored. Based on student transcript data from the University of Central Florida, the proposed HMMs are trained using sequences of students' course grades for each semester to estimate the students' academic-performance levels. Through the HMMs, the analysis follows the expected finding that higher academic performance levels correlate with lower halt rates. However, in this dissertation, we identify 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 two proposed HMMs.

Notes

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Graduation Date

2022

Semester

Spring

Advisor

Vela, Adan

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0008951; DP0026284

URL

https://purls.library.ucf.edu/go/DP0026284

Language

English

Release Date

May 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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