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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
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

Ng, G. S. – Computer Applications in Engineering Education, 1997
Describes a course teaching the use of computers in emulating human visual capability and image processing and proposes an interactive presentation using multimedia technology to capture and sustain student attention. Describes the three phase presentation: introduction of image processing equipment, presentation of lecture material, and…
Descriptors: Algorithms, Attention, Computer Assisted Instruction, Educational Technology

Eubank, Philip T.; Barrufet, Maria A. – Chemical Engineering Education, 1988
Describes an algorithm that provides more rapid convergence for more complicated forms of phase separation requiring the use of a digital computer. Demonstrates that this "inside-out" algorithm remains efficient for determination of the equilibrium states for any type of phase transition for a binary system. (CW)
Descriptors: Algorithms, Chemical Engineering, Chemistry, College Science

Joye, Donald D.; Koko, F. William Jr. – Chemical Engineering Education, 1988
Presents a new method to teach the subject of evaporators which is both simple enough to use in the classroom and accurate and flexible enough to be used as a design tool in practice. Gives an example using a triple evaporator series. Analyzes the effect of this method. (CW)
Descriptors: Algorithms, Chemical Engineering, Chemistry, College Science
Scanlan, David – Engineering Education, 1988
Notes that almost all computer engineering textbooks present algorithms using only verbal methods. Poses that engineering students' ability to handle graphic representation is crucial yet information is presented verbally. Summarizes the results of 12 replications on learner preference for graphic or verbal algorithmic techniques. (MVL)
Descriptors: Algorithms, Cognitive Processes, College Science, Curriculum Design