1. By Joseph Osunde & Anton Dil The Open University , United Kingdom
A secondary analysis of SEaM responses for programming and non- programming modules by gender
By Joseph Osunde & Anton Dil
The Open University , United Kingdom
SEaM : student experience on a module survey

2. Presentation Outline The background of the study Related work
Study plan
Initial results from the study
Conclusion and future work

3. The background of the study
Considerable evidence indicate that there is gender disparity in computer science higher education[1,3]
Reasons [6,8] to include the learning environments that convey gender stereotypes impact on interests and anticipated success [2,9,5]
Strategies to tackle issues – adjustment to teaching support and teaching contents
The context of Open Distance Learning (ODL), courses are delivered online and blended instructions [7]
Learner expectations, institution expectation, online and blended instructions as inputs and learner performance levels and satisfaction as outputs[4,7] -
online materials mostly as VLEs in the place of lectures in conjunction blended instructions such as computer-based activities, forums, television and radio programmes and student support provision such as face to face tutor sessions, tutor centres etc.

4. Study questions
2 key outputs for higher education – performance levels & satisfaction [4,7]
Considering that there are no significant differences in the achievements by gender for programming and non-programming modules
Are there differences in the learner satisfaction by gender for programming and non-programming modules?
Are there correlations between satisfaction and module content from the SEaM survey by gender for programming & non-programming modules from ?
“How does module content impact learner satisfaction for programming and non-programming modules by gender in the context of ODL”?
Pre 1980
As at 1960 , 65% of computer programmers in the US were women
Tech & entertainment post 1980s
-Home use of computers
-Niche market for entertainment games
-10-40% world wide
% in Asia and Eastern Europe, 10% in Western Europe

5. Objective 2- Data analysis Objective 3- Insights and evaluative report
Study plan - Outline
Aim 1:
Collect and organise SEaM data for programming & non-programming modules by gender
Objective 1 – Data collection
Collect Level 2 modules SEaM data for programming modules (M250 & M256) by gender between 2013 & 2016.
Collect Level 2 modules SEaM data for non- programming modules (T215 & T227) by gender between 2013 & 2016.
Aim 2:
Analysis of data by gender within modules and between modules by category i.e. programming and non-programming
Objective 2- Data analysis
-Analysis of data qualitatively – content analysis and trend lines
-Triangulation of SEaM data
-Analysis of data quantitatively- factor and correlation analysis
Aim 3:
Gain insight from data analysis
Objective 3- Insights and evaluative report
Data analysis to explore:
Insights into satisfaction in programming and non-programming modules by gender
Statistical significance of results

6. Programming modules (M250 & M257)
Study plan- Outline
Total sample size:
Programming modules (M250 & M257)
Object –oriented Java programming (M250) & Software development with Java (M256)
Non- programming modules (T215 & T227))
Communication and information technologies (T215) & Change, strategy and projects at work (T227)
Total sample
1891
Total male
1461
Total female
438
Total sample
1305
Total male
1054
Total female
250
programming modules – Software development with Java (M256), & Object –oriented Java programming (M250) and non- programming modules – Change, strategy and projects at work (T227), & Communication and information technologies (T215).
Total sample
586
Total male
407
Total female
188

7. Objective 2- Multi-level approach of analysis
2 strands of multi-dimensional variables in the SEaM questionnaire collection (The module overall and teaching, assessment & learning)
Content analysis indicate that the multi-dimensional variables could be separated into three strands – module content, teaching , assessment & learning(TAL) and satisfaction
Triangulation of module content and TAL as system input with satisfaction as an output

8. Objective 2- Initial insights
Trend line comparative of programming modules – M256 by gender (females/males)
Downward trend line for module content ( ) for females and males
TAL upward for 2014 & 2015 for females; all slightly upward except 2013 for males
Module satisfaction relatively better for males

9. Objective 2- Initial insights
Trend line comparative of programming modules – M250 by gender (females/males)
Downward trend lines for module content except 2014 for females & males
TAL upward except (almost flat) for females
Module satisfaction better for males

10. Objective 2- Initial insights
Trend line comparative of non-programming modules – T227 by gender (females/males)
Downward trend line for females and males mostly except 2014 for both genders
TAL upward trend lines more for females
Module satisfaction slightly better for males

11. Objective 2- Initial insights
Trend line comparative of non-programming modules – T215 by gender (females/males)
Downward trend lines mostly for females and males. Better for males
TAL trend line upwards mostly for both genders
Module satisfaction better for females

12. Objective 2- Initial insights
Programming modules by gender:
Module content trend lines indicate that there is work to be done for both genders
We do better at TAL relative to module content for both genders
Relatively males are more satisfied with M250 & M256 than females
Both module content and TAL appear to impact significantly on satisfaction for programming modules
Non- programming modules by gender:
Again we do better at TAL relative to module content for both genders
Better module satisfaction for both genders
The impact of both module content and TAL on satisfaction is not as obvious in non-programming modules as compared to programming courses

13. Conclusion and future work
So far…….
Satisfaction as an outcome is greater for males than females for programming modules in comparison to non-programming modules
We do better at TAL for both module types
A combination of both module content, and TAL is imperative for satisfaction for programming modules especially for females.
The significance of module content confirms conclusion from related studies.
Next step…..
Identify key drivers of module content and satisfaction applying Principal Axis Factoring (PAF) using Factor Analysis
Correlational analysis of key drivers between module content and satisfaction

14. References
1. Beede, Julian, Langdon, McKittrick, Khan & Doms (2011). Women in STEM: A gender gap to innovation.
2. Good, Rattan & Dweck(2012). Why do women opt out? Sense of belonging and women's representation in mathematics. Journal of personality and social psychology, 102(4), 700.
3. HESA, 2017: Available at:
4. Ilgaz, H., & Gülbahar, Y. (2015). A snapshot of online learners: e-Readiness, e-Satisfaction and expectations. The International Review of Research in Open and Distributed Learning, 16(2).
5. Lester, Yamanaka & Struthers (2016). Gender microaggressions and learning environments: The role of physical space in teaching pedagogy and communication. Community College Journal of Research and Practice, 40(11),
6. National Science Foundation (2015). IPEDS Completion Survey, 1987–2011, Integrated Science and Engineering Resources Data System. Available at:
7. Rienties & Toetenel (2016). The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules. Computers in Human Behavior, 60,
8. Sax, Lehman, Jacobs, Kanny, Lim, Monje-Paulson & Zimmerman (2017). Anatomy of an enduring gender gap: The evolution of women’s participation in computer science. The Journal of Higher Education, 88(2),
9. Smith, Lewis, Hawthorne & Hodges (2013). When trying hard isn’t natural: Women’s belonging with and motivation for male-dominated STEM fields as a function of effort expenditure concerns. Personality and Social Psychology Bulletin, 39(2),

15. Thank you!