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Articles 1 - 7 of 7
Full-Text Articles in Education
Thriving In College: International, First-Generation, And Transfer Students, Hannah Webb, Nikita Kulkarni, Dustin Grabsch
Thriving In College: International, First-Generation, And Transfer Students, Hannah Webb, Nikita Kulkarni, Dustin Grabsch
SMU Journal of Undergraduate Research
Underrepresented-student groups experience unique challenges throughout their college experience, the impacts of which can be assessed by measuring students’ levels of thriving. The purpose of this study was to understand the thriving of underrepresented college students—first-generation, international, and transfer students, specifically. To understand this, we sought to measure students’ thriving levels and determine the experiences contributing to or detracting from their perception of thriving. This study utilized a sequential exploratory design using the established 72-item thriving quotient survey to measure students’ overall thriving levels. In addition, the study utilized a qualitative content analysis on an open-ended question asking participants to …
Profiting From Dow Jones Industrial Index And Hang Seng Index Using Moving Average And Macd Optimization Model, Anthony Yeung, Joe Wailun Chung, Nibhrat Lohia, Onyeka Emmanuel
Profiting From Dow Jones Industrial Index And Hang Seng Index Using Moving Average And Macd Optimization Model, Anthony Yeung, Joe Wailun Chung, Nibhrat Lohia, Onyeka Emmanuel
SMU Data Science Review
Before the internet, high-speed laptop computers, and big data became accessible and popular, academia on stock market trading concentrated on Efficient Market Hypothesis (EMH). EMH hinges on the idea that the market is efficient and there is no extra return that could be generated. With the dynamic development of the internet, big-data and computing technology, many researchers started to pay attention to Technical Analysis and its usage. Numerous academic papers claimed that technical analysis can enhance returns by using various technical tools. This paper explores in-depth the simulation model of Moving Average and Moving Average Convergence/Divergence (MACD) to come up …
Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed
Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed
SMU Data Science Review
For English teachers and students who are dissatisfied with the one-size-fits-all approach of current Automated Essay Scoring (AES) systems, this research uses Natural Language Processing (NLP) techniques that provide a focus on configurability and interpretability. Unlike traditional AES models which are designed to provide an overall score based on pre-trained criteria, this tool allows teachers to tailor feedback based upon specific focus areas. The tool implements a user-interface that serves as a customizable rubric. Students’ essays are inputted into the tool either by the student or by the teacher via the application’s user-interface. Based on the rubric settings, the tool …
Accelerating Reinforcement Learning With Prioritized Experience Replay For Maze Game, Chaoshun Hu, Mehesh Kuklani, Paul Panek
Accelerating Reinforcement Learning With Prioritized Experience Replay For Maze Game, Chaoshun Hu, Mehesh Kuklani, Paul Panek
SMU Data Science Review
In this paper we implemented two ways of improving the performance of reinforcement learning algorithms. We proposed a new equation to prioritize transition samples to improve model accuracy, and by deploying a generalized solver of randomly-generated two-dimensional mazes on a distributed computing platform, our dual-network model is available to others for further research and development. Reinforcement Learning is concerned with identifying the optimal sequence of actions for an agent to take in order to reach an objective to achieve the highest score in the future. Complex situations can lead to computational challenges in terms of both finding the best answer …
Optimizing The Enrollment Funnel With Decision Trees And Rule Based List, Stephen Merritt, Anne Francomano, Martin Garcia
Optimizing The Enrollment Funnel With Decision Trees And Rule Based List, Stephen Merritt, Anne Francomano, Martin Garcia
SMU Data Science Review
In this paper, an analysis is presented of the enrollment funnel for prospective graduate school students, predicting application submission and enrollment. Efficient university outreach is critical to optimizing a positive interaction cadence for prospective students, reducing costs, and strengthening academic program revenue streams. Models employing rules, decision lists, and tree-based algorithms assessed the impact of a prospect's characterization and university outreach methods on application and enrollment probabilities. A novel two-stage modeling workflow, applied to each prediction problem, mirrored steps taken by a prospective candidate to become a future enrollee. This analysis could help a graduate school decide what communication mechanisms …
Tidying And Analysis Of The 2014 Texas English Ii End-Of-Course Exam, David Churchman, Abigail Morton Garland
Tidying And Analysis Of The 2014 Texas English Ii End-Of-Course Exam, David Churchman, Abigail Morton Garland
SMU Data Science Review
The state of Texas requires all public high school students to take End of Course (EOC) exams. The results of these exams are made nominally public, but in a shape and format that precludes ready analysis. To the extent possible, principles of tidy data will be applied to clean and analyze the publicly released data file for the 2014 English II EOC exam, providing insights into the EOC program and a case for better public data from the Texas Education Administration (TEA).
Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.
Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.
SMU Data Science Review
In this paper, we present an analysis of the predictive ability of machine learning on the success of students in college courses in a California Community College. The California Legislature passed assembly bill 705 in order to place students in non-remedial coursework, based on high school transcripts, to increase college completion. We utilize machine learning methods on de-identified student high school transcript data to create predictive algorithms on whether or not the student will be successful in college-level English and Mathematics coursework. To satisfy the bill’s requirements, we first use exploratory data analysis on applicable transcript variables. Then we use …