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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
Mike, Koby; Hazzan, Orit – Statistics Education Research Journal, 2022
Data science is a new field of research that has attracted growing interest in recent years as it focuses on turning raw data into understanding, insight, knowledge, and value. New data science education programs, which are being launched at an increasing rate, are designed for multiple education levels and populations. Machine learning (ML) is an…
Descriptors: Teaching Methods, Nonmajors, Statistics Education, Artificial Intelligence
Ragonis, Noa; Hazzan, Orit; Har-Shai, Gadi – Journal of Information Technology Education: Innovations in Practice, 2020
Aim/Purpose: This paper presents a study about changes in computer science and software engineering students' perceptions of their soft skills during their progress through the Computer Science Soft Skills course. Background: Soft skills are often associated with a person's social, emotional and cognitive capabilities. Soft skills are increasingly…
Descriptors: Soft Skills, Teamwork, Student Attitudes, Assignments