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Khalid, Mohammad; Akanmu, Abiola; Murzi, Homero; Lee, Sang Won; Awolusi, Ibukun; Manesh, Daniel; Okonkwo, Chinedu – Journal of Civil Engineering Education, 2024
Construction, one of the largest industries in the world, consistently underperforms and faces barriers in leveraging the full potential of applying analytics to sensor data due to a lack of a skilled workforce. The prospects for data-driven solutions to address emerging construction challenges and enhance performance across project life cycles…
Descriptors: Construction Industry, Engineering, Job Skills, Measurement Equipment
Mohammad Heshmati; W. David Purvis – Chemical Engineering Education, 2024
Three real-world datasets are introduced for a petroleum engineering capstone design course, providing detailed problem statements, assessment criteria, team-building practices, and required software packages. Two surveys reveal that students prefer real datasets over synthetic ones, and despite initial challenges, students feel proud of their…
Descriptors: Chemical Engineering, Engineering Education, Fuels, Capstone Experiences
Jamal Kay B. Rogers; Tamara Cher R. Mercado; Ronald S. Decano – Journal of Education and Learning (EduLearn), 2025
Poor academic performance remains among the most concerning educational issues, especially in higher education and online learning. To address the concern, institutions like the University of Southeastern Philippines (USeP) leverage educational data mining (EDM) techniques to generate relevant information from learning management systems (LMS)…
Descriptors: Foreign Countries, Learning Management Systems, Academic Achievement, Data Analysis
Avital Binah-Pollak; Orit Hazzan; Koby Mike; Ronit Lis Hacohen – Education and Information Technologies, 2024
The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses…
Descriptors: Data Science, Anthropology, Ethics, Context Effect
Mark W. Isken – INFORMS Transactions on Education, 2025
A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and…
Descriptors: Spreadsheets, Models, Programming Languages, Monte Carlo Methods
Harun Cigdem; Semiral Oncu – TechTrends: Linking Research and Practice to Improve Learning, 2025
Despite efforts to implement innovative approaches such as flipped learning leveraging computer technology, the challenge of student failure persists. Understanding the factors that contribute to student success in flipped engineering courses remains a critical issue. This study addresses this issue by investigating the impact of student…
Descriptors: Gamification, Flipped Classroom, Learner Engagement, Learning Readiness
Diana Adela Martin; Gunter Bombaerts – European Journal of Engineering Education, 2025
Challenge Based Learning (CBL) is an educational approach that has gained popularity in response to the need for authentic learning environments. While the CBL literature is predominantly focused on cases of pedagogical implementations, the actual processes by which students develop CBL projects remain under-investigated. This shortitudinal study…
Descriptors: Student Projects, Active Learning, Difficulty Level, Student Behavior
Andrew J. Collins; Brandon M. Butler; James F. Leathrum; Christopher J. Lynch – Studying Teacher Education, 2024
As the COVID-19 pandemic caused severe disruption to education enterprises throughout the world, the main response by educational institutions was to move to online learning environments. The purpose of this study was to understand better how instructors could improve online learning for a professional-level week-long short course in a highly…
Descriptors: COVID-19, Pandemics, Engineering Education, Electronic Learning
Xiner Liu; Andres Felipe Zambrano; Ryan S. Baker; Amanda Barany; Jaclyn Ocumpaugh; Jiayi Zhang; Maciej Pankiewicz; Nidhi Nasiar; Zhanlan Wei – Journal of Learning Analytics, 2025
This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…
Descriptors: Coding, Artificial Intelligence, Automation, Data Analysis
David P. Reid; Timothy D. Drysdale – IEEE Transactions on Learning Technologies, 2024
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment…
Descriptors: Learning Analytics, Laboratory Experiments, Electronic Learning, Feedback (Response)
Syahrul Amin; Karen E. Rambo-Hernandez; Blaine A. Pedersen; Camille S. Burnett; Bimal P. Nepal; Noemi V. Mendoza Diaz – Cogent Education, 2024
This study examined the persistence of first-year engineering students at a Hispanic-Serving Institution (HSI) and a Historically Black College and University (HBCU) pre- and mid-COVID-19 interruptions and whether their characteristics (race/ethnicity, financial need status, first-generation status, SAT scores) predicted their persistence. Using…
Descriptors: College Freshmen, Engineering Education, Academic Persistence, COVID-19
Wonkyung Choi; Jun Jo; Geraldine Torrisi-Steele – International Journal of Adult Education and Technology, 2024
Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of…
Descriptors: College Students, Data Analysis, Data Collection, Learning Analytics