A critical examination of Machine Learning as a tool to predict performance of students in CS1
Paper in proceeding, 2024
of student performance in introductory computer science courses (CS1)? The focus is on how knowledge from educational science is incorporated alongside with ethical and gender considerations.
Only peer-reviewed articles, published in journals or conference proceedings between 2017 and mid 2020, reporting on empirical studies that used data on more than 30 students are included. This study addresses prevalent shortcomings in empirical CS education research, noting often inadequate descriptions of data selection, processing, and the representation and diversity of sample sizes that can limit the utility of results. It underscores the
frequent omission of ethical considerations regarding students’ data consent and the potential negative impacts on students’ educational trajectories. Additionally, many studies fail to incorporate the educational context or address gender-related issues adequately, disconnecting the models from established knowledge about women in computer science.
Ethical considerations
Machine learning
Gender considerations
Introductory programming courses
Author
Kristina von Hausswolff
Malardalen University
Christina Björkman
Malardalen University
Gordana Dodig Crnkovic
Mälardalens högskola
Proceedings - Frontiers in Education Conference, FIE
15394565 (ISSN)
Vol. 2024Washington, USA,
Areas of Advance
Information and Communication Technology
Subject Categories (SSIF 2011)
Educational Sciences
Computer and Information Science
Driving Forces
Sustainable development
Learning and teaching
Pedagogical work