Higher Arts and Design students’ attitudes towards learning computer programming

A presentation of an ongoing investigation of the attitudes of arts and design students towards learning computer programming.

END 2019 International Conference on Education and New Developments.
Porto, June 24, 2019.

Transcript

1. Higher Arts and Design students’ attitudes towards learning computer programming Eduardo Morais  eduardo.morais@fe.up.pt With Carla Morais & João C. Paiva  cmorais | jcpaiva@fc.up.pt END 2019 – International Conference on Education and New Developments Porto, June 22-24 This work was supported by the UT Austin | Portugal Program and the Foundation of Science and Technology (FCT) doctoral scholarship PD/BD/128416/2017.
2. My story University of Porto Digital Media PhD scholarship (2016-); Experience teaching technological subjects to arts students (ESAP 2005 – 2015, U.Porto 2014 – 2017): • Digital video editing and processing (2005 – 2017); • Computer programming / ‘creative code’ (2010 – 2017).
3. How to make learning to code work for everyone?
4. Programming in Higher Arts Education There were ~15500 students in 105 public art and design* undergraduate programs in Portugal in 2017;1 * Visual art, graphic / product design, multimedia, music, or architecture.
5. Programming in Higher Arts Education There were ~15500 students in 105 public art and design* undergraduate programs in Portugal in 2017;1 * Visual art, graphic / product design, multimedia, music, or architecture. Curricular analysis: Approx. 6400 students in 43 programs are required to learn to code; Another ~1250 students in 7 programs have access to elective courses;
6. Programming in Higher Arts Education There were ~15500 students in 105 public art and design* undergraduate programs in Portugal in 2017;1 * Visual art, graphic / product design, multimedia, music, or architecture. Curricular analysis: Approx. 6400 students in 43 programs are required to learn to code; Another ~1250 students in 7 programs have access to elective courses; = ~7650 students in 50 public undergraduate programs.
7. Programming in Higher Arts Education This suggests Portuguese Higher Arts Education institutions support the notion that computer programming is part of digital literacy.2
8. How do these students regard programming?
9. Survey by questionnaire Our research employed an acceptance framework based on the Unified Theory of Acceptance and Use of Technology.3, 4
10. UTAUT – Unified theory of acceptance and use of technology3 Venkatesh, Morris, Davis & Davis (2003) Technology acceptance model synthesized from finding the superpositions of constructs included in eight existing models: Theory of Reasoned Action (Ajzen & Fishbein); Theory of Planned Behaviour (Ajzen); Technology Acceptance Model – TAM & TAM2 (Davis); Motivational Model (Davis); Model of PC Utilization (Thompson); Innovation Difusion Theory (Rogers; Moore & Bensabat); Social Cognitive Theory (Compeau & Higgins).
11. UTAUT – Unified theory of acceptance and use of technology3 Actual Use Intention Performance Expectancy Effortlessness Expectancy Social Influence Attitude towards Use Facilitating Conditions Self-Efficacy Anxiety UTAUT constructs UTAUT (speculative) Relationships between constructs are moderated by Age, Gender, Experience, and users’ perception of their Voluntariness of computer programming use.
12. Method and participants Method • Online questionnaire based on the Unified Theory of Technology Acceptance and Use3 survey instrument; • Questionnaire adapted to the Portuguese language and to the specific theme of computer programming, validated through retroversion and a focus group;
13. Method and participants Method • Online questionnaire based on the Unified Theory of Technology Acceptance and Use3 survey instrument; • Questionnaire adapted to the Portuguese language and to the specific theme of computer programming, validated through retroversion and a focus group; • Opportunity sample of students resulting from the willingness of contacted institutions to pass the survey along.
14. Institutional contacts are difficult! You are asking for a favour. Privacy policies, GDPR, etc. People receive too many requests to fill out surveys already.
15. Method and participants Data collection • Mid-March to mid-April and mid-September to mid-October 2018, depending on when institutions forwarded the surveys to students.
16. Method and participants Participants • Opportunity sample of participants from eighteen (18) public higher education institutions. • 270 responses were validated out of 344 total; • 43% of validated participants were male and 57% female.
17. Participants Demographic characteristics and their level of programming experience No knowledge (n = 72) Learning (n = 77) Prior knowledge (n = 121) Total (n = 270) Gender n % n % n % n Female 46 29.9% 51 33.1% 57 37.0% 154 Male 26 22.4% 26 22.4% 64 55.2% 116 Age 18 – 20 35 25.5% 51 37.2% 51 37.2% 137 21 – 24 29 28.7% 19 18.8% 53 52.5% 101 25 – 34 6 25.0% 5 20.8% 13 54.2% 24 35 + 2 25.0% 2 25.0% 4 50.0% 8 Year 1st year 20 27.0% 34 45.9% 20 27.0% 74 2nd year 21 29.6% 19 26.9% 31 43.7% 71 3rd year 22 23.7% 21 22.6% 50 53.8% 93 4th year + 9 28.1% 3 9.4% 20 62.5% 32
18. Participants Female Male Can code Can’t code Learning for the first time 29.9% 37.0% 33.1% 22.4% 55.2% 22.4%
19. Method and participants Analysis • Item groups checked for internal reliability (Cronbach α > .7); • Factor analysis used to corroborate constructs’ consistency.
20. Findings Acceptance and use factors / students’ programming experience (range -3 to 3). No knowledge (n = 72) Learning (n = 77) Prior knowledge (n = 121) mean Std. Dev. mean Std. Dev. mean Std. Dev Performance Expectancy 0.15 1.43 0.68 1.43 1.07 1.43 Effortlessness Expectancy -0.95 1.24 -0.69 1.21 0.10 1.48 Attitude Towards Use -0.35 1.64 0.25 1.59 0.91 1.74 Social Influence - - - - 0.22 1.51 (Subjective Norm) -1.49 1.52 -0.55 1.57 0.06 1.73 Facilitating Conditions -0.62 1.29 0.16 1.56 0.72 1.50 Self-Efficacy -0.80 1.17 0.26 1.31 0.75 1.27 Anxiety -0.22 1.47 0.13 1.28 -0.58 1.52 Behavioral Intention -1.99 1.21 -0.08 2.04 0.25 2.11
21. Findings Most compelling findings • Prior experience strongly correlates with positive views of computer programming;
22. Findings Most compelling findings • Gender differences in students’ responses (across all groups). Female Male Performance expectancy Effortlessness Attitude towards use Subjective norm Facilitating conditions Self-efficacy Anxiety Intention
23. Findings Most compelling findings • Prior experience strongly correlates with positive views of computer programming; • Negative views more prevalent among female students;
24. Findings Most compelling findings • Prior experience strongly correlates with positive views of computer programming; • Negative views more prevalent among female students; • More senior students won’t be as interested in learning to code;
25. Findings Most compelling findings • Prior experience strongly correlates with positive views of computer programming; • Negative views more prevalent among female students; • More senior students won’t be as interested in learning to code; • Voluntariness of computer programming correlates with positive views of programming – and with positive intention to code;
26. Findings Most compelling findings • Prior experience strongly correlates with positive views of computer programming; • Negative views more prevalent among female students; • More senior students won’t be as interested in learning to code; • Voluntariness of computer programming correlates with positive views of programming – and with positive intention to code; • Anxiety is the great enemy.
27. Findings Questions raised • How can art and design educators bridge the perception and intention gap between students of different genders?
28. Findings Questions raised • How can art and design educators bridge the perception and intention gap between students of different genders? • Should programming courses be taught earlier in the curriculum?
29. Findings Questions raised • How can art and design educators bridge the perception and intention gap between students of different genders? • Should programming courses be taught earlier in the curriculum? • Should educators approach individual programming as optional (through group activities, for instance)?
30. Findings Questions raised • How can art and design educators bridge the perception and intention gap between students of different genders? • Should programming courses be taught earlier in the curriculum? • Should educators approach individual programming as optional (through group activities, for instance)? • What pedagogical approaches can reduce anxiety and boost self-efficacy while learning to code?
31. Future & ongoing work • Continued interrogation of the UTAUT model: • Statistical methods (SEM, etc.); • Theoretical development – for instance, readdressing the gap between intention and expectation 5 in predicting programming use by students;
32. Future & ongoing work • Continued interrogation of the UTAUT model: • Statistical methods (SEM, etc.); • Theoretical development – for instance, readdressing the gap between intention and expectation 5 in predicting programming use by students; • Practice characterization case-studies, with the addition of qualitative methods.
33. References 1. DGEEC, “Infocursos 2017,” 2017. [Online]. Available: http://infocursos.mec.pt. 2. VV. AA., “The Future of Learning: Education in the Era of Partners in Code.” KnowledgeWorks, 2015. 3. V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly, 27 (3), 2003. 4. Y. Dwivedi, N. Rana, A. Jeyaraj, M. Clement, and M. Williams, “Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model,” Information Systems Frontiers, pp. 1–16, 2017. 5. I. Ajzen, “From Intentions to Actions: A Theory of Planned Behavior,” Action Control, pp. 11– 39, 1985.
34. Thank you! Eduardo Morais  eduardo.morais@fe.up.pt END 2019 – International Conference on Education and New Developments Porto, June 22-24 This work was supported by the UT Austin | Portugal Program and the Foundation of Science and Technology (FCT) doctoral scholarship PD/BD/128416/2017.