Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/135384
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Type: Conference paper
Title: Using Assignment Design as an Instrument to Collect Student Voice
Author: Garcia, R.
Alexander, B.
Citation: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE, 2022), 2022, vol.1, pp.223-229
Publisher: Association for Computing Machinery
Issue Date: 2022
ISBN: 9781450390705
Conference Name: Computer Science Education (SIGCSE) (3 Mar 2022 - 5 Mar 2022 : Providence, RI, USA)
Statement of
Responsibility: 
Rita Garcia, Bradley Alexander
Abstract: Students might have preconceptions about programming when enrolling in an Introductory Programming (CS1) course. These preconceptions might influence their expectations about programming assignments. Understanding these preconceptions could help give students a voice in their learning experience. This paper reports on a study for CS1 programming assignments. This study uses an assignment design activity as an instrument to collect student voice, asking students to design a programming assignment they expect to accomplish at the end of a CS1 course. A mixed-methods approach was used to analyse the subject matter and course learning outcomes of the students’ assignment designs. The results show students applying prior knowledge, a process known as transfer of training, to design their assignments, predominately focused on math and gaming. The results also show that students with no prior programming experience had lower expectations from the programming assignments, which might have influenced their study effort in the course. We discuss integrating the results into CS1 assignments, helping students transition to new roles as programmers, and adjust their study expectations early to recognise when more effort is needed to successfully complete a course.
Keywords: Assignment Design; Student Voice; Transfer of Training
Rights: © 2022 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.
DOI: 10.1145/3478431.3499271
Published version: https://www.acm.org/
Appears in Collections:Computer Science publications

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