Sustainable student retention: gender issues in maths for ICT

1. Sustainable student retention: gender issues inmaths for ICT Prof.dr.sc.Blaženka Divjak blazenka.divjak@foi.hr Faculty of Organization and Informatics University of Zagreb

2. Content • Overall and specific objective • Explanation why that topic is chosen • from the faculty and national perspective • in the light of research reported in the literature • Innovative teaching methodology • Gender differences in retention • Interpretation of results • Conclusions and possible further research Ljubljana, 2007

3. Research objectives Overall objective: • Improve the student recruitment, retention and advancement in ICT study by means of improving teaching methods and support services, with special attention given to underrepresented groups. Specific objective for the pilot project: • Improve the student retention in mathematics at FOI (ICT study) on 1st study year, by means of improving teaching methods, with special attention given to gender issue. Ljubljana, 2007

4. Why ICT? • “The number of women choosing careers in IT continues to decline”, “only 16% of tech workers are women, and even that meager number is a drop from 18% a couple of years ago”Source:http://www.silicon.com • “despite female predominance in undergraduate enrolments (over 50% in many EU countries, 55% America, 59% Australia), women are reluctant to pursue ICT study at tertiary level.” Source: Rees, T. (2001), Mainstreaming gender equality in science in the EU: the “ETAN” report, Gender and Education, 13(3), 243-260 Ljubljana, 2007

5. Why Retention? • Three issues concerning underrepresented groups of students: • Recruitment • Retention – pilot project – easy to handle and research on • Advancement • Def: “Retention is continued student participation in a learning event to completion, which in HE could be a course, program, institution, or system” • Source: A Model for Sustainable Student Retention: A Holistic Perspective on the Student Dropout Problem with Special Attention to e-Learning, Zane L. Berge, Yi-Ping Huang Ljubljana, 2007

6. Why 1st year? • “Freshman year is the most crucial period for student retention, with 21% dropping out during, or at the end of, their first year” • (Source: CSRDE (Consortium for Student Retention Data Exchange). 2000.-2001- CSRDE Report: The retention and graduation rates in 344 colleges and universities. Available at: http://tel.occe.ou.edu/csrde/execsum.pdf) • FOI – 40% dropp out at 1st year – before Bologna reform 60% of dropp out • “.. the percentage of students drop out in HE has held constant at between 40-45% for the past 100 years” (Source: Tinto, V. (1982). Limits of theory and practice in student attrition. Journal of Higher Education, 53(6), p.687-700.) Ljubljana, 2007

7. Mathematics forICT. Why? • “Because mathematics is often viewed as a critical enabling course in science and engineering (ICT), it is important that women develop their mathematical skills prior to or early on in college.” • (Source: To recruit and advance, NRC, USA, 2006, p. 51) • Notes on undergraduate recruitment: • Female students are less likely to concentrate on mathematics in secondary schools • Female students have a less positive view of mathematics • Gender differences are well established in mathematical ability • (Source: Maccoby&Jacklin (1974) -Hyde, J. S. (2005). The Gender Similarities Hypothesis. American Psychologist, Vol. 60, No. 6. Available at: http://www.apa.org/journals/releases/amp606581.pdf ) Ljubljana, 2007

8. What is the situation in Croatia? • Results of National exams for secondary school students confirm that students have less possitive view of mathematics • Source: Državna matura u hrvatskim srednjm školama: http://www.drzavnamatura.hr/Home.aspx?PageID=4 • Expectations of students at the National exams for mathematics is very low Ljubljana, 2007

9. Secondary schools math... The gaps in opinion on • the test difficulty • if the test was interesting • if there were unclear questions in the test • if the test were in accordance with expectations between students andteachers are the biggest in mathematics Ljubljana, 2007

10. Secondary schools math • Working methods in mathematics in secondary schools • Students are used to ex cathedra approach and lack of communication between teachers and students. • Statistically, students in secondary schools don’t like mathematics at all (it is at the last position). • They don’t recognize the value and applicability of mathematics in real life and learn mathematics because of the grade. • In general teachers of mathematics don’t use contemporary teaching methods Ljubljana, 2007

11. Gender issue in Croatia - legal • Legal basis • Gender Equality Act (OG 116/03) • promoting gender equality and gender mainstreaming in all activities • gender balance in science and research is not subject to regulation • Labour Act (OG 137/04) • National Policy for Gender Equality (2001 – 2005; policy 2006-2010 under preparation) • Institutional structure • Office for Gender Equality • Gender Equality Ombudsman • Parliamentary Committee for Gender Equality Ljubljana, 2007

12. Gender issue in Croatia - research • Percentage of women researchers close to average in new EU member states, but • Percentage of women in science is higher at lower level positions; relatively high proportion of young researchers • Women are underrepresented in top positions (9%) Ljubljana, 2007

13. Ljubljana, 2007

14. Gender issue on FOI • Reflects the situation at the national level • Less women professors at higher positions (only 2 women professors – associate and full professorship) • Among assistants and young researchers almost equal number of men and women • Math professors & assistants: 4 men +3 women • Female students – around 20% on the first study year • Comparable to other studies (Source: Miliszewska et all, The Issue of Gender Equality in Computer Science – What Students Say, J. of Information Technology Education, Vol 5, 2006) Ljubljana, 2007

15. Are there gender differences? “Men and women behave, think and operate differently. To pretend otherwise – for example, to ignore there are two sexes in the workplace -- is to ignore a fruitful and provocative input into IT team-building, leadership, talent management, global projects and innovation. The subject of gender differences remains behind closed doors. In this session we expose the conversation, analysis and myths of how behavioral differences of men and women – and how our cultural treatment of men and women -- can influence business and IT outcomes and work practices.” Source: Women and Men in IT: Breaking Through Sexual Stereotypes; Syposium Nov 2006, Gartner Ljubljana, 2007

16. What do you think? What is confirmed? • Girls have better verbal abilities • Girls are more “social” than boys • Boys have better spacial abilities • Girls are more suggestible • Boys have higher self-esteem • Girls are better at higher level cognitive processing • Girls lack achievement motivation • Girls likes technology less than boys do • Boys are better in math ... Ljubljana, 2007

17. Gender differences well established in Verbal ability Visual-spatial ability Mathematical ability Aggression Sources: Hyde, J. S. (2005). The Gender Similarities Hypothesis. American Psychologist, Vol. 60, No. 6. Beller, M., Gafni N. (1996) The 1991 International Assessment of Educational Progress in Mathematics and Science: The Gender Difference Perspective. Journal of Educational Psychology, 88, 365-377. Popular beliefs not confirmed in majority of cases Girls are more “social” than boys Girls are more suggestible Girls have lower self-esteem Girls are better at higher level cognitive processing Girls lack achievement motivation “Women and men skills” Ljubljana, 2007

18. Women are less able to solve problems involving certain typically men skills (like graphics, spacial abilities etc.) Female students are less likely to concentrate on mathematics in secondary schools Female students have a less positive view of mathematics “Retention and graduation rates were consistently higher for women” Source: CSRDE report “In most subjects (except mathematics at some levels), the average performance of girls exceeds that of boys at all levels of education” Source: Gender and Student Achievment in English Schools, UK, Feb 2006 Contradicition There is a contradiction, on the first glance, in the literature and research on the next issues: Ljubljana, 2007

19. Learning environment at FOI Mathematics

20. Enhancing Mathematics for Informatics and its correlation with student pass rates Blaženka Divjak, Zlatko Erjavec Accepted for publishing in International Journal of Mathematical Education in Science and Technology, August, 2006 - Copies available -

21. Innovations to Enhance Retention: • Institutional Management • Curriculum & Instruction • Academic & Social Supports Ljubljana, 2007

22. Gender vs. pedagogy • Change pedagogy • The argument for changing the content or the way S&T is taught to promote diversity rests on the assumption that men and women learn differently or appreciate content differently. Source: P.60 • … efforts to change pedagogy and course content can diminish learning outcomes.Source: To recruit and advance, NRC, USA, 2006P. 61 Ljubljana, 2007

23. Quality in teaching mathematics • Long history • 20th century – from Withehead and Russell through Polya to Smith etc. • Different activities in teaching and learning corresponding to the study programme and learning outcomes on the programme and course level • Depending on position of mathematics in study programme • Studying mathematics • Using mathematics in studying engineering, social sciences etc. Ljubljana, 2007

24. Learning outcomes - construction • Bloome Taxonomy (1956): • skills are arranged into six • hierarchical levels • categories are arranged on • scale of difficulty • learner who is able to • perform at higher levels • of the taxonomy, • is demonstrating a more • complex level of cognitive • thinking Ljubljana, 2007

25. Classification of mathematical tasks and learning objectives • Polya (1981) – shift from authorative teacher to facilitator • Galbraith & Haines (2001) – 3 tasks: • mechanical, interpretative, constructive • Smith et al. (1996) – MATH taxonomy • Mathematical Assessment Task Hierarchy • TIMSS (2003) • Trends in International Mathematics and Science Study • http://timss.be.edu • Cox (2003)– MATH-KIT • practitioner friendly taxonomy of learning objectives for mathematics Ljubljana, 2007

26. Cox Taxonomy – MathKIT • Practitioner-friendly taxonomy of learning objectives for math • Enables to design teaching, learning and assessment strategy according to LO of study programme • Simple to use for classifying depth of knowledge and assessment questions • Appropriate for web-based teaching assessment • Link to ECTS Ljubljana, 2007

27. Activity Approx.no. hours Lectures + seminars 60 Peergroup tutorials – max. 30 h 15 Monthly tests 3*2 + home study for tests 40 Weekly homework 30 Essays/problems 15 Otherlearning acti. 25 TOTAL 185 ECTS:7 Student workload – Problem based learning • Mode of assessment is • a factor explaining the • differential performance of boys and girls: • Boys tend to be favored by multiple choice questions and girls by essays and coursework • Females do less well in times examinations due to higher levels of anxiety • Source: Gender and Student • Achievement in English Schools, • Feb 2006 Ljubljana, 2007

28. Statistics and student pass rates Though we can list some other possible factors which might have influenced the pass rate, we thought that the changes described above were the primary factor. Ljubljana, 2007

29. E-learning • Technology innovation – use of blended (hybrid) learning • LMS - Moodle (Modular Object-Oriented Dynamic Learning Environment) is free learning management system that enables teachersto create online learning material. • Learning outcomes • Lectures – presentations and smartboards • Homework, individualized homework with MathKIT • Self-evaluations, quizzes • Problem solving • Chat, Forums, • Glossary Ljubljana, 2007

30. Mathematics 1 Ljubljana, 2007

31. Monitoring Ljubljana, 2007

32. Radar chart classification of on-line course INTERACTION: • A: Dynamics and access • B: Assessement • C: Communicaton MATERIAL: • D: Content • E: Richness • F: Independence Source: Engelbrecht, J. & Harding, A. (2005), Teaching Undergraduate Mathematics on the Internet Part 1: Technologies and Taxonomy. Educational Studies in Mathematics (58)2, 235 - 252.Avalable at: http://ridcully.up.ac.za/muti/webmaths1.pdf Ljubljana, 2007

33. Research questions • Is the evaluation of innovative learning strategy in mathematics positiveregarding gender and retention? • Are female students underperforming in “typically men areas” when studying ICT? • Are there gender differences in students’ perspective related to the learning environment? Ljubljana, 2007

34. Background • There is no significant gender difference respecting number of hours of mathematics a week in secondary schools • Naturally there is a correlation between number of hours (and grades in secondary school) and success on math tests on 1st year at the faculty • No significant difference in knowledge of mathematics measured with the enterence test • Neither in rage nor in depth • Around 70% of students have Internet connection and computer at the place they live during study period. • Mayority of students come from small cities and villages Ljubljana, 2007

35. Background Axis x – N= numeric, V= verbal, G = graphic, P= problem Axis y = Results in tests (scores x 100) Ljubljana, 2007

36. Gender differences in pass rate • Pass rate (completion rate):  • For female students for Math 1: 62.79% • For male students for Math 1 : 39.9 % • Student attrition rate (decline in the number of students from the beginning to the end of the course – drop out during the course) is lowbecause of satisfactory support for student learning • 6% in general • 1,6% for female students Ljubljana, 2007

37. Questionnaire survey for students • Anonymous questionnaire survey • At the end of 1st semester – Mathematics 1 • Survey participants: • N=130 participants • 22.3% female students • 77.7 % male students • 96.9% full time students, • 3.1% part time students • Five points Likert – type scale Ljubljana, 2007

38. Survey results Axis x: 1- Satisfaction with the content, 2 - Satisfaction with teaching methods 3- Satisfaction with communication, 4= Availability of computers at the faculty Axis y: average grade On the Likert scale (1 - 5) Ljubljana, 2007

39. Women are slightly more satisfiedwith content, teaching methods, computers available at the faculty, literature availabe at faculty library but less satisfied with (compared to men) Level of communication with teachers☺ Satisfaction with Moodle (e-learning system) Women have slightly lower expectations than men Gender perspective in answers Ljubljana, 2007

40. Comparable research • Comparable with other studies and reports of research for example Australian report • (Source: Miliszewska et all, The Issue of Gender Equality in Computer Science • – What Students Say, J. of Information Technology Education, Vol 5, 2006) • UK and Chinese male students are also less likely to express positive views towards use of technology • (Source: Nai L., Kirkup, (2007) Gender and Cultural differences in Internet Use: A study of China and the UK, Computers & Education, 48, 301-317 • Despite having generally positive attitudes towards computers, women’s attitudes are more negative than those of men, and they have higher computer anxiety than men (Source: Kirkpatrick, H., Cuban, L. (1998), GShould we be worried. What the research says about gender differences in access, use, attitudes and achievement with computers, Educational Technology (July-August), 56-61. Ljubljana, 2007

41. Independency in work with technology in self-evaluations First test – optional ; Second test – credits given 1=male students, 2= female students; y axes – mean (1..3), 1= using a lot of help from others, 2= using little help from others, 3= doing alone Ljubljana, 2007

42. Gender perspective – verbal skills, independency • Essays • verbal and presentation skills, • data retrieval and • problem solving (not so much in Math 1) • Students in general are doing their essays on their own • Female average: 7.4/10 • Male average: 6.6/10 • Confirmation of gender difference in verbal and presentation skills Ljubljana, 2007

43. How many hours a week you learn maths at home? x axis – category:male students /female students; y axes – average number of hours (weekly) without hours at lecturs and exercises at the faculty Ljubljana, 2007

44. Gender perspective – independent learning • Female students learn independently (at home) 1,46 more than males • Despite more independent work female students expect worse grade on the exam than male students • Consequences: • There is no significant difference on the first monthly test • but it is on the second and the third – influence of more learning is visible Ljubljana, 2007

45. Some conclusions • Female students are underrepresented in ICT study • Enhancing retention in mathematics by use of different teaching methods respecting different learning styles and gender differences helps • Students pass rate considerably higher than before the course reconstruction, due to the learning environment • Female students have significantly higher pass and significantly lower attrition rate than mail students • Factor with the highest gender difference: female students learn 1.5 h weekly more than men • Females are not underperforming in “typical men” area • There are different gender perspectives about learning environment but not significant Ljubljana, 2007

46. Further research • Research on mathematics in 2nd semester and higher study years • Accent on graphics and spatial abilities, problem solving etc. • Research on retention of other courses and the program as a whole • Recruitment and retention phase • Underrepresented: students from rural areas, digital gap • Influence of technology enhance learning • Comparable research with other institutions • Open to European projects Ljubljana, 2007

47. Thank you