TY - GEN
T1 - Student misconceptions about finite state machines
T2 - 4th International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, EASEAI 2022, co-located with ESEC/FSE 2022
AU - Henry, Julie
AU - Dumas, Bruno
AU - Vescan, Andreea
AU - Pasca, Alexandra Maria
N1 - Funding Information:
This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI - UEFISCDI, project number PN-III-CEI-BIM-PBE-2020-0021/14BM/2021 within PNCDI III.
Publisher Copyright:
© 2022 ACM.
PY - 2022/11/7
Y1 - 2022/11/7
N2 - A concept inventory (CI) is a standardized assessment tool designed to evaluate a student's understanding of the fundamental concepts of a topic. To create a CI, it is necessary to accurately identify these concepts, but also to identify how poorly students understand them. The aim of this paper is to present an approach used to identify misconceptions related to the concept of Finite State Machine (FSM). In the learning process, identifying the students' misconceptions, i.e., when they appear and how to efficiently correct them, are important aspects of the best learning outcome. Rather than measuring understanding at a specific point in the learning timeline, the CI can be administered to students several times over the course of the learning period to measure how students' understanding of concepts changes. This preliminary study is composed of two main steps. In the first step, four misconceptions were identified about FSM based on multi-year observations and teacher experiences. From these misconceptions, seven statements about FSM are specified. In the second step, a Likert scale questionnaire (composed of seven statements) was administered five times to students according to a specific schedule, allowing to measure the evolution of FSM understanding. A pre-questionnaire is used to determine the students' misconceptions about the FSM concept, based on their learning (self-learning or from previous courses). This measure, which is the starting point of this preliminary study, makes it possible to highlight the changes in the students' positioning in relation to the statements provided and to link these changes to the teaching interventions. Thus, changes are clearly observable after the two theoretical classes, and stabilization is devoted after the delivery of the lab work.
AB - A concept inventory (CI) is a standardized assessment tool designed to evaluate a student's understanding of the fundamental concepts of a topic. To create a CI, it is necessary to accurately identify these concepts, but also to identify how poorly students understand them. The aim of this paper is to present an approach used to identify misconceptions related to the concept of Finite State Machine (FSM). In the learning process, identifying the students' misconceptions, i.e., when they appear and how to efficiently correct them, are important aspects of the best learning outcome. Rather than measuring understanding at a specific point in the learning timeline, the CI can be administered to students several times over the course of the learning period to measure how students' understanding of concepts changes. This preliminary study is composed of two main steps. In the first step, four misconceptions were identified about FSM based on multi-year observations and teacher experiences. From these misconceptions, seven statements about FSM are specified. In the second step, a Likert scale questionnaire (composed of seven statements) was administered five times to students according to a specific schedule, allowing to measure the evolution of FSM understanding. A pre-questionnaire is used to determine the students' misconceptions about the FSM concept, based on their learning (self-learning or from previous courses). This measure, which is the starting point of this preliminary study, makes it possible to highlight the changes in the students' positioning in relation to the statements provided and to link these changes to the teaching interventions. Thus, changes are clearly observable after the two theoretical classes, and stabilization is devoted after the delivery of the lab work.
KW - Computer science education
KW - concept inventory
KW - distributed systems
KW - learning outcomes assessment process
KW - misconceptions
KW - monitoring learning progress assessment
KW - software engineering
UR - http://www.scopus.com/inward/record.url?scp=85142924926&partnerID=8YFLogxK
U2 - 10.1145/3548660.3561330
DO - 10.1145/3548660.3561330
M3 - Conference contribution
AN - SCOPUS:85142924926
T3 - EASEAI 2022 - Proceedings of the 4th International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, co-located with ESEC/FSE 2022
SP - 2
EP - 9
BT - EASEAI 2022
A2 - Vescan, Andreea
A2 - Serban, Camelia
A2 - Henry, Julie
A2 - Praphamontripong, Upsorn
PB - ACM Press
Y2 - 18 November 2022
ER -