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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Dei: Exploring Academic Reflections Using Natural Language Processing To Create A Roadmap Of Student Success And Foster Inclusive Engineering Education, Rajvir H. Vyas, Nidhi Raviprasad
Dei: Exploring Academic Reflections Using Natural Language Processing To Create A Roadmap Of Student Success And Foster Inclusive Engineering Education, Rajvir H. Vyas, Nidhi Raviprasad
College of Engineering Summer Undergraduate Research Program
Every year, the College of Engineering (CENG) students and faculty reach out to admitted students through “Text-a-Thon” programs to answer their questions about being a student at Cal Poly. In order to improve CENG outreach efforts, we analyzed these text conversations to predict the likelihood of an admitted student accepting an offer of admission from Cal Poly. Through our research, we discovered key factors that play a role in a student committing to Cal Poly through data-based insights. Additionally, we successfully used a human-on-the-loop system to help create Machine Learning (ML) models that predict satisfaction of response by way of …
Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim
Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
Programming courses provide students with the skills to develop complex business applications. Teaching and learning programming is challenging, and collaborative learning is proposed to help with this challenge. Online discussion forums promote networking with other learners such that they can build knowledge collaboratively. It aids students open their horizons of thought processes to acquire cognitive skills. Cognitive analysis of discussion is critical to understand students' learning process. In this paper, we propose Bloom's taxonomy based cognitive model for programming discussion forums. We present machine learning (ML) based solution to extract students' cognitive skills. Our evaluations on compupting courses show that …
Understanding Learners' Motivation Through Machine Learning Analysis On Reflection Writing, Elizabeth Pluskwik, Yuezhou Wang, Lauren Singelmann
Understanding Learners' Motivation Through Machine Learning Analysis On Reflection Writing, Elizabeth Pluskwik, Yuezhou Wang, Lauren Singelmann
Integrated Engineering Department Publications
Educational data mining (EDM) is an emerging interdisciplinary field that utilizes a machine learning (ML) algorithm to collect and analyze educational data, aiming to better predict students' performance and retention. In this WIP paper, we report our methodology and preliminary results from utilizing a ML program to assess students’ motivation through their upper-division years in the XYZ project-based learning (PBL) program. ML, or more specifically, the clustering algorithm, opens the door to processing large amounts of student-written artifacts, such as reflection journals, project reports, and written assignments, and then identifies keywords that signal their levels of motivation (i.e., extrinsic vs. …
A Comparison Of Machine Learning Techniques For Validating Students’ Proficiency In Mathematics, Alexander Avdeev
A Comparison Of Machine Learning Techniques For Validating Students’ Proficiency In Mathematics, Alexander Avdeev
Dissertations, Theses, and Capstone Projects
A principal goal of this project was to compare several machine learning (ML) algorithms to explore and validate math proficiency classifications based on standardized test scores. The data used in these analyses came from the 6th-grade students’ mathematics assessment records of the New York State Education Department’s Testing Program (NYSTP). Our approach was to test a number of competing machine learning (ML) algorithms for classifying students’ as proficient based on their test scores and other demographic information. Our samples were drawn from the 2016 test-taking cohort of 6th-grade students (N=156,800). Five classifiers including multinominal logistic regression (MLR), XGBoost, Tree-As, Lagrangian …
Using Data Analytics To Predict Students Score, Nang Laik Ma, Gim Hong Chua
Using Data Analytics To Predict Students Score, Nang Laik Ma, Gim Hong Chua
Research Collection School Of Computing and Information Systems
Education is very important to Singapore, and the government has continued to invest heavily in our education system to become one of the world-class systems today. A strong foundation of Science, Technology, Engineering, and Mathematics (STEM) was what underpinned Singapore's development over the past 50 years. PISA is a triennial international survey that evaluates education systems worldwide by testing the skills and knowledge of 15-year-old students who are nearing the end of compulsory education. In this paper, the authors used the PISA data from 2012 and 2015 and developed machine learning techniques to predictive the students' scores and understand the …
Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley
Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley
Articles
This paper examines impressive new applications of legal text analytics in automated contract review, litigation support, conceptual legal information retrieval, and legal question answering against the backdrop of some pressing technological constraints. First, artificial intelligence (Al) programs cannot read legal texts like lawyers can. Using statistical methods, Al can only extract some semantic information from legal texts. For example, it can use the extracted meanings to improve retrieval and ranking, but it cannot yet extract legal rules in logical form from statutory texts. Second, machine learning (ML) may yield answers, but it cannot explain its answers to legal questions or …
Teaching Law And Digital Age Legal Practice With An Ai And Law Seminar: Justice, Lawyering And Legal Education In The Digital Age, Kevin D. Ashley
Teaching Law And Digital Age Legal Practice With An Ai And Law Seminar: Justice, Lawyering And Legal Education In The Digital Age, Kevin D. Ashley
Articles
A seminar on Artificial Intelligence ("Al") and Law can teach law students lessons about legal reasoning and legal practice in the digital age. Al and Law is a subfield of Al/computer science research that focuses on designing computer programs—computational models—that perform legal reasoning. These computational models are used in building tools to assist in legal practice and pedagogy and in studying legal reasoning in order to contribute to cognitive science and jurisprudence. Today, subject to a number of qualifications, computer programs can reason with legal rules, apply legal precedents, and even argue like a legal advocate.
This article provides a …
Empirical Methods For Predicting Student Retention- A Summary From The Literature, Matt Bogard
Empirical Methods For Predicting Student Retention- A Summary From The Literature, Matt Bogard
Economics Faculty Publications
The vast majority of the literature related to the empirical estimation of retention models includes a discussion of the theoretical retention framework established by Bean, Braxton, Tinto, Pascarella, Terenzini and others (see Bean, 1980; Bean, 2000; Braxton, 2000; Braxton et al, 2004; Chapman and Pascarella, 1983; Pascarell and Ternzini, 1978; St. John and Cabrera, 2000; Tinto, 1975) This body of research provides a starting point for the consideration of which explanatory variables to include in any model specification, as well as identifying possible data sources. The literature separates itself into two major camps including research related to the hypothesis testing …