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An Insight Into the Relationship Between Confidence, Self-efficacy, Anxiety and Physiological Responses in a CS1 Exam-like Scenario

Published: 05 September 2019 Publication History

Abstract

Computer Science typically has one of the highest attrition rates in tertiary level education. Many reasons have been put forward as to the cause, including, for example, no prior formal experience of programming, high workloads and poor mental health of students. Recent advancements in wearable technology have made it possible to accurately and easily measure aspects of physiological response associated with emotional arousal which can be indicative of stress, such as heart rate variability and skin conductance. A novel opportunity now exists to monitor learners in real-time and gain an insight on their physiological responses during a learning task. Such information, perhaps coupled with known factors that influence learning success, could provide new insight to allow educators to better tailor module design, delivery, and assessment.
This paper builds on a study which concluded that there was no correlation between self-reported anxiety and Heart Rate and Electrodermal Activity. The goal of this paper is to investigate the relationship between measures such as self-reported anxiety, programming self-efficacy, confidence in responses and physiological responses during a controlled exam-like setting. An out-of-the-box psychological test was used to measure self-reported anxiety, a well-established questionnaire was used to measure self-efficacy and wearable sensors were used to measure physiological arousal, before and during the exam. Study design and methodology are described in detail in this paper. While no significant results were found, perhaps the most interesting finding is that students confidence in their answers weakly correlates with their physiological response when completing multiple choice programming questions. While the findings presented may not be major, they are novel and will provide direction for future research in the area.

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Cited By

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  • (2023)Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming CoursesProceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice10.1145/3633083.3633190(22-28)Online publication date: 14-Dec-2023
  • (2023)Proposal for Monitoring Students’ Self-Efficacy Using Neurophysiological Measures and Self-Report ScalesGeNeDis 202210.1007/978-3-031-31986-0_62(635-643)Online publication date: 16-Aug-2023
  • (2021)Developing an Open-Book Online Exam for Final Year StudentsProceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 110.1145/3430665.3456373(338-344)Online publication date: 26-Jun-2021

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  1. An Insight Into the Relationship Between Confidence, Self-efficacy, Anxiety and Physiological Responses in a CS1 Exam-like Scenario

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cover image ACM Other conferences
UKICER '19: Proceedings of the 2019 Conference on United Kingdom & Ireland Computing Education Research
September 2019
81 pages
ISBN:9781450372572
DOI:10.1145/3351287
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  • Univ of Kent at Canterbury: University of Kent at Canterbury

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Published: 05 September 2019

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Author Tags

  1. CS1
  2. Confidence
  3. anxiety
  4. physiological signals
  5. self-efficacy

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UKICER
UKICER: UK & Ireland Computing Education Research Conference
September 5 - 6, 2019
Canterbury, United Kingdom

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Cited By

View all
  • (2023)Evolving Towards a Trustworthy AIEd Model to Predict at Risk Students in Introductory Programming CoursesProceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice10.1145/3633083.3633190(22-28)Online publication date: 14-Dec-2023
  • (2023)Proposal for Monitoring Students’ Self-Efficacy Using Neurophysiological Measures and Self-Report ScalesGeNeDis 202210.1007/978-3-031-31986-0_62(635-643)Online publication date: 16-Aug-2023
  • (2021)Developing an Open-Book Online Exam for Final Year StudentsProceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 110.1145/3430665.3456373(338-344)Online publication date: 26-Jun-2021

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