Background and context:

Many factors have been shown to be important for supporting effective learning and teaching – and thus progression and success – in formal educational contexts. While factors such as key introductory-level computer science knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. This preliminary work investigates the relationships between effective learning and success, and two measures of positive psychology, Grit (Duckworth’s 12-item Grit scale) [6] and the Nicolson McBride Resilience Quotient (NMRQ) [3], in success in first-year undergraduate computer science to provide insight into the factors that impact on the transition from secondary education into tertiary education

Crick, Tom

Harvey, Morgan

Prickett, Tom

Walters, Julie

Yang, Longzhi

English

Northumbria Research Link

Northumbria Research LinkCitation:  Prickett,  Tom,  Crick,  Tom,  Harvey,  Morgan,  Walters,  Julie  and  Yang,  Longzhi  (2020) Exploring resilience for effective learning in computer science education. In: Cambridge Computing Education Research Symposium, 1st April 2020, University of Cambridge, Cambridge, UK. URL: https://www.raspberrypi.org/cambridge-computing-ed... <https://www.raspberrypi.org/cambridge-computing-education-research-symposium/presentations/>This version was downloaded from Northumbria Research Link: http://nrl.northumbria.ac.uk/42892/Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University’s research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners.  Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any  format or medium without formal permission of the copyright holder.  The full policy is available online: http://nrl.northumbria.ac.uk/pol  i cies.html  This  document  may differ  from the  final,  published version of  the research  and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher’s website (a subscription may be required.)                        Exploring Resilience for Effective Learning in Computer Science Education  Tom Prickett1*, Tom Crick2, Morgan Harvey3, Julie Walters1 and Longzhi Yang1 1Department of Computer and Information Sciences, Northumbria University 2School of Education, Swansea University 3Information School, University of Sheffield   Background and context  Many factors have been shown to be important for supporting effective learning and teaching – and thus progression and success – in formal educational contexts. While factors such as key introductory-level computer science knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. This preliminary work investigates the relationships between effective learning and success, and two measures of positive psychology, Grit (Duckworth’s 12-item Grit scale) [6] and the Nicolson McBride Resilience Quotient (NMRQ) [3], in success in first-year undergraduate computer science to provide insight into the factors that impact on the transition from secondary education into tertiary education.  Research methods  This quantitative study was conducted by incorporating two survey-based measures of positive psychology – on Grit and Resilience – into the teaching of a first-year core subject as part of a UK computer science degree programme in February 2019. Students were asked to complete the surveys using the University’s electronic learning platform. The students were supported in the interpretation of their results and guidance was provided regarding strategies they could adopt to improve them in the context of their degree studies. The study was approved by the University’s ethics board and student consent was explicitly obtained to use their data for research. Data on student performance was obtained at the end of the teaching year and consists of the results from five different subjects over both semesters of the academic year as well as attendance data over the year.   The data was analysed by a combination of correlation analysis and logistic regression. The intention of the logistic regression was to explore the potential strength of relationship rather than to develop a model for predictive purposes.   Findings  Data was captured by related to Grit (N=58) and Resilience (N=50) questionnaires and related coaching. Analyses demonstrate that Resilience is statistically significantly (1% level) linked (correlation analysis and logistical regression) to attendance and performance for individual subjects and year average marks; however, this was not the case for Grit.   Conclusion and implications  * Corresponding author: Tom Prickett (tom.prickett@northumbria.ac.uk)  Promoting effective learning and student success remains a challenge in computer science, with high failure rates reported in foundation areas, such as introductory programming [1, 8]. The results of this preliminary study demonstrate that the 12-item resilience scale could be a factor in promoting success, but that the same was not true for the 12-item grit scale. The results of this single-institution study lead to a number of possibilities for future work analysing the transition from secondary education to tertiary education, providing insight into learner attitudes, behaviours and dispositions, especially how this links to the teaching and assessment of key curricula concepts in computer science. For example:  i) Initiatives related to the active development of student resilience can be deployed and their effectiveness evaluated ii) Replicating the study with larger cohorts and at other schools/colleges/universities to validate this study, increasing the sample size and strengthening the statistical basis. iii) Using resilience in predictive models alongside other key factors in order to further augment and enhance the prediction of student success.  Alongside substantial national curriculum and qualifications reform across the four nations of the UK [2], as well as a significant socio-economic push to produce more graduates with “high-value” digital, data and cyber skills [4, 5, 7], these changes are being monitored and replicated internationally. With changes to curricula, as well as rethinking programmes, pedagogies and practice, we thus recognise similar challenges and opportunities in a number of other jurisdictions, providing a platform for replicability, portability and extension of this work.   References  [1] J. Bennedsen and M. E. Caspersen (2019). Failure Rates in Introductory Programming: 12 Years Later. ACM Inroads 10(2), 30–36. [2] N. C. C. Brown, S. Sentance, T. Crick, and S. Humphreys (2014). Restart: The Resurgence of Computer Science in UK Schools. ACM Transactions on Computer Science Education 14, 2 (2014), 1–22 [3] J. Clarke (2010). Resilience: bounce back from whatever life throws at you. Crimson Publishing, USA. [4] T. Crick, J. H. Davenport, P. Hanna, A. Irons, and T. Prickett (2020). Computer Science Degree Accreditation in the UK: A Post-Shadbolt Review Update. In Proceedings of Computing Education Practice. ACM, Article 6, 1–4. [5] T. Crick, J. H. Davenport, A. Irons, and T. Prickett (2019). A UK Case Study on Cybersecurity Education and Accreditation. In Proceedings of 49th Annual Frontiers in Education Conference (FIE 2019), IEEE. [6] A. Duckworth, C. Peterson, Matthews M.D., and D.R. Kelly (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology 9(6), 1087–1101. [7] T. Tryfonas and T. Crick (2018). Public Policy and Skills for Smart Cities: The UK Outlook. Proceedings of 11th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA’18). ACM, 116–117. [8] C. Watson and F. W. B. Li (2014). Failure Rates in Introductory Programming Revisited. Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education (ITiCSE’14). ACM, 39–44. 

Exploring resilience for effective learning in computer science education

Northumbria Research Link
Citation:  Prickett,  Tom,  Crick,  Tom,  Harvey,  Morgan,  Walters,  Julie  and  Yang,  Longzhi  (2020) 
Exploring resilience for effective learning in computer science education. In: Cambridge Computing 
Education Research Symposium, 1st April 2020, University of Cambridge, Cambridge, UK. 
URL: https://www.raspberrypi.org/cambridge-computing-ed... <https://www.raspberrypi.org/cambridge-
computing-education-research-symposium/presentations/>
This version was downloaded from Northumbria Research Link: http://nrl.northumbria.ac.uk/42892/
Northumbria University has developed Northumbria Research Link (NRL) to enable users to access 
the University’s research output. Copyright © and moral rights for items on NRL are retained by the 
individual author(s) and/or other copyright owners.  Single copies of full items can be reproduced, 
displayed or performed, and given to third parties in any format or medium for personal research or 
study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, 
title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata 
page. The content must not be changed in any way. Full items must not be sold commercially in any  
format or medium without formal permission of the copyright holder.  The full policy is available online: 
http://nrl.northumbria.ac.uk/pol  i cies.html  
This  document  may differ  from the  final,  published version of  the research  and has been made 
available online in accordance with publisher policies. To read and/or cite from the published version 
of the research, please visit the publisher’s website (a subscription may be required.)
                        
Exploring Resilience for Effective Learning in Computer 
Science Education 
 
Tom Prickett1*, Tom Crick2, Morgan Harvey3, Julie Walters1 and Longzhi Yang1 
1Department of Computer and Information Sciences, Northumbria University 
2School of Education, Swansea University 
3Information School, University of Sheffield 
 
 
Background and context 
 
Many factors have been shown to be important for supporting effective learning and teaching – and 
thus progression and success – in formal educational contexts. While factors such as key 
introductory-level computer science knowledge and skills, as well as pre-university learning and 
qualifications, have been extensively explored, the impact of measures of positive psychology are 
less well understood for the discipline of computer science. This preliminary work investigates the 
relationships between effective learning and success, and two measures of positive psychology, Grit 
(Duckworth’s 12-item Grit scale) [6] and the Nicolson McBride Resilience Quotient (NMRQ) [3], in 
success in first-year undergraduate computer science to provide insight into the factors that impact 
on the transition from secondary education into tertiary education. 
 
Research methods 
 
This quantitative study was conducted by incorporating two survey-based measures of positive 
psychology – on Grit and Resilience – into the teaching of a first-year core subject as part of a UK 
computer science degree programme in February 2019. Students were asked to complete the 
surveys using the University’s electronic learning platform. The students were supported in the 
interpretation of their results and guidance was provided regarding strategies they could adopt to 
improve them in the context of their degree studies. The study was approved by the University’s 
ethics board and student consent was explicitly obtained to use their data for research. Data on 
student performance was obtained at the end of the teaching year and consists of the results from 
five different subjects over both semesters of the academic year as well as attendance data over the 
year.  
 
The data was analysed by a combination of correlation analysis and logistic regression. The intention 
of the logistic regression was to explore the potential strength of relationship rather than to develop 
a model for predictive purposes.  
 
Findings 
 
Data was captured by related to Grit (N=58) and Resilience (N=50) questionnaires and related 
coaching. Analyses demonstrate that Resilience is statistically significantly (1% level) linked 
(correlation analysis and logistical regression) to attendance and performance for individual subjects 
and year average marks; however, this was not the case for Grit.  
 
Conclusion and implications 
 
* Corresponding author: Tom Prickett (tom.prickett@northumbria.ac.uk) 
 
Promoting effective learning and student success remains a challenge in computer science, with high 
failure rates reported in foundation areas, such as introductory programming [1, 8]. The results of 
this preliminary study demonstrate that the 12-item resilience scale could be a factor in promoting 
success, but that the same was not true for the 12-item grit scale. The results of this single-institution 
study lead to a number of possibilities for future work analysing the transition from secondary 
education to tertiary education, providing insight into learner attitudes, behaviours and dispositions, 
especially how this links to the teaching and assessment of key curricula concepts in computer 
science. For example: 
 
i) Initiatives related to the active development of student resilience can be deployed and 
their effectiveness evaluated 
ii) Replicating the study with larger cohorts and at other schools/colleges/universities to 
validate this study, increasing the sample size and strengthening the statistical basis. 
iii) Using resilience in predictive models alongside other key factors in order to further 
augment and enhance the prediction of student success. 
 
Alongside substantial national curriculum and qualifications reform across the four nations of the 
UK [2], as well as a significant socio-economic push to produce more graduates with “high-value” 
digital, data and cyber skills [4, 5, 7], these changes are being monitored and replicated 
internationally. With changes to curricula, as well as rethinking programmes, pedagogies and 
practice, we thus recognise similar challenges and opportunities in a number of other jurisdictions, 
providing a platform for replicability, portability and extension of this work. 
 
 
References 
 
[1] J. Bennedsen and M. E. Caspersen (2019). Failure Rates in Introductory Programming: 12 Years Later. ACM 
Inroads 10(2), 30–36. 
[2] N. C. C. Brown, S. Sentance, T. Crick, and S. Humphreys (2014). Restart: The Resurgence of Computer 
Science in UK Schools. ACM Transactions on Computer Science Education 14, 2 (2014), 1–22 
[3] J. Clarke (2010). Resilience: bounce back from whatever life throws at you. Crimson Publishing, USA. 
[4] T. Crick, J. H. Davenport, P. Hanna, A. Irons, and T. Prickett (2020). Computer Science Degree Accreditation 
in the UK: A Post-Shadbolt Review Update. In Proceedings of Computing Education Practice. ACM, Article 6, 
1–4. 
[5] T. Crick, J. H. Davenport, A. Irons, and T. Prickett (2019). A UK Case Study on Cybersecurity Education and 
Accreditation. In Proceedings of 49th Annual Frontiers in Education Conference (FIE 2019), IEEE. 
[6] A. Duckworth, C. Peterson, Matthews M.D., and D.R. Kelly (2007). Grit: Perseverance and passion for long-
term goals. Journal of Personality and Social Psychology 9(6), 1087–1101. 
[7] T. Tryfonas and T. Crick (2018). Public Policy and Skills for Smart Cities: The UK Outlook. Proceedings of 
11th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA’18). ACM, 
116–117. 
[8] C. Watson and F. W. B. Li (2014). Failure Rates in Introductory Programming Revisited. Proceedings of the 
2014 Conference on Innovation & Technology in Computer Science Education (ITiCSE’14). ACM, 39–44. 


http://nrl.northumbria.ac.uk/42892/1/CCERS2020_abstract_FINAL.pdf

Exploring resilience for effective learning in computer science education

Abstract

Similar works

Full text

Available Versions

Northumbria Research Link

Northumbria Research Link

Northumbria Research Link