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Sourajit Ghosh; Md. Sarwar Kamal; Linkon Chowdhury; Biswarup Neogi; Nilanjan Dey; Robert Simon Sherratt – Education and Information Technologies, 2024
Students are the future of a nation. Personalizing student interests in higher education courses is one of the biggest challenges in higher education. Various AI and ML approaches have been used to study student behaviour. Existing AI and ML algorithms are used to identify features for various fields, such as behavioural analysis, economic…
Descriptors: Engineering Education, Artificial Intelligence, College Students, Student Interests
Shernoff, David J. – AERA Online Paper Repository, 2023
In this paper, we report the results of a 3-year, quasi-experimental study comparing students' engagement and deep learning of course materials between students who took an undergraduate engineering course that used a video game approach to a control group. The video game, EduTorcs, provided challenges in which students devised control algorithms…
Descriptors: Learner Engagement, Undergraduate Students, Engineering Education, Video Games
Singelmann, Lauren Nichole – ProQuest LLC, 2022
To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where…
Descriptors: Engineering Education, Design, Educational Innovation, Models
Saxena, Nitin Kumar; Chauhan, Bhavesh Kumar; Gouri, Sonia; Kumar, Ashwani; Gupta, Anmol – IEEE Transactions on Education, 2023
Contribution: The proposed work carries out the training and testing of the available data through an artificial neural network and develops a model to allocate the subject for maximum outcome. The system also provides percentagewise correlation among all the possible subjects of best fit to allocate among the faculty members. Background: Data…
Descriptors: Knowledge Management, Artificial Intelligence, Higher Education, Information Technology
Mike, Koby; Hazzan, Orit – IEEE Transactions on Education, 2023
Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect…
Descriptors: Engineering Education, Computer Science Education, Data Science, Introductory Courses
Peter Hu; Yangqiuting Li; Chandralekha Singh – Physical Review Physics Education Research, 2024
Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name…
Descriptors: Quantum Mechanics, Computer Science Education, Inquiry, Teaching Methods
Xing, Wanli; Pei, Bo; Li, Shan; Chen, Guanhua; Xie, Charles – Interactive Learning Environments, 2023
Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…
Descriptors: Learning Analytics, Engineering Education, Prediction, Design
Taylor V. Williams – ProQuest LLC, 2022
Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…
Descriptors: Multivariate Analysis, Data Analysis, Student Evaluation, Large Group Instruction
de Carvalho, Walisson Ferreira; Zárate, Luis Enrique – International Journal of Information and Learning Technology, 2021
Purpose: The paper aims to present a new two stage local causal learning algorithm -- HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm,…
Descriptors: Causal Models, Algorithms, Learning Analytics, Correlation

Jacquot, Raymond G.; And Others – CoED, 1985
Presents a technique for the numerical inversion of Laplace Transforms and several examples employing this technique. Limitations of the method in terms of available computer word length and the effects of these limitations on approximate inverse functions are also discussed. (JN)
Descriptors: Algorithms, Computer Software, Engineering, Engineering Education
Case, Jennifer; Gunstone, Richard; Lewis, Alison – 2000
Previous findings from the study within which this research is located had uncovered students' approaches to learning in the context of a second year chemical engineering course. Using an analysis of students' reflections on their experience, the study has shown the existence of three approaches to learning in this context: an 'information-based'…
Descriptors: Algorithms, Chemistry, Engineering Education, Higher Education

Harden, Richard C.; Simons, Fred O., Jr. – CoED, 1983
A previously developed program for the HP-41C programmable calculator is extended to handle models of differential and difference equations with multiple eigenvalues. How to obtain difference equation solutions via the Z transform is described. (MNS)
Descriptors: Algorithms, Calculators, Engineering Education, Higher Education

Technology Teacher, 1991
This learning activity develops algorithms to ensure that the process of selecting materials is well defined and sound. These procedures require the use of many databases to provide the designer with information such as physical, mechanical, and chemical properties of the materials under consideration. A design brief, student quiz, and five…
Descriptors: Algorithms, Engineering Education, Evaluation Criteria, Media Selection
Carlsen, William S.; Single, Peg Boyle – 2000
This paper reports findings from a comprehensive evaluation of the first national electronic mentoring program that matches female engineering students with mentors working in industry. The program being evaluated--MentorNet--uses a combination of on-line tools, computer databases, mentoring specialists, and campus and industrial contacts to…
Descriptors: Algorithms, College Students, Electronic Mail, Engineering Education
Kletsky, E. J. – 1975
This paper describes a student project in digital simulation techniques that is part of a graduate systems analysis course entitled Biosimulation. The students chose different simulation techniques to solve a problem related to the neuron model. (MLH)
Descriptors: Algorithms, Biophysics, Computers, Engineering