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


Content :14.10.2008 Ljubljana, 2007 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


Research objectives :14.10.2008 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.


Why ICT? :14.10.2008 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


Why Retention? :14.10.2008 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


Why 1st year? :14.10.2008 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.)


Mathematics for ICT. Why? :14.10.2008 Ljubljana, 2007 7 Mathematics for ICT. 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 )


What is the situation in Croatia? :14.10.2008 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


Secondary schools math... :14.10.2008 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 and teachers are the biggest in mathematics


Secondary schools math :14.10.2008 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


Gender issue in Croatia - legal :14.10.2008 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


Gender issue in Croatia - research :14.10.2008 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%)


Slide 13:14.10.2008 Ljubljana, 2007 13


Gender issue on FOI :14.10.2008 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)


Are there gender differences? :14.10.2008 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


What do you think? What is confirmed? :14.10.2008 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 ...


“Women and men skills” :14.10.2008 Ljubljana, 2007 17 “Women and men skills” 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


Contradicition :14.10.2008 Ljubljana, 2007 18 Contradicition 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 There is a contradiction, on the first glance, in the literature and research on the next issues:


Learning environment at FOI :Learning environment at FOI Mathematics


Enhancing Mathematics for Informatics and its correlation with student pass rates :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 -


Innovations to Enhance Retention: :14.10.2008 Ljubljana, 2007 21 Innovations to Enhance Retention: Institutional Management Curriculum & Instruction Academic & Social Supports


Gender vs. pedagogy :14.10.2008 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, 2006 P. 61


Quality in teaching mathematics :14.10.2008 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.


Learning outcomes - construction :14.10.2008 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


Classification of mathematical tasks and learning objectives :14.10.2008 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


Cox Taxonomy – MathKIT :14.10.2008 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


Slide 27:14.10.2008 Ljubljana, 2007 27 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


Slide 28:14.10.2008 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.


E-learning :14.10.2008 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 teachers to create online learning material. Learning outcomes Lectures – presentations and smartboards Homework, individualized homework with MathKIT Self-evaluations, quizzes Problem solving Chat, Forums, Glossary


Mathematics 1 :14.10.2008 Ljubljana, 2007 30 Mathematics 1


Monitoring :14.10.2008 Ljubljana, 2007 31 Monitoring


Radar chart classification of on-line course :14.10.2008 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


Research questions :14.10.2008 Ljubljana, 2007 33 Research questions Is the evaluation of innovative learning strategy in mathematics positive regarding 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?


Background :14.10.2008 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


Background :14.10.2008 Ljubljana, 2007 35 Background Axis x – N= numeric, V= verbal, G = graphic, P= problem Axis y = Results in tests (scores x 100)


Gender differences in pass rate :14.10.2008 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 low because of satisfactory support for student learning 6% in general 1,6% for female students


Questionnaire survey for students :14.10.2008 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


Survey results :14.10.2008 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)


Gender perspective in answers :14.10.2008 Ljubljana, 2007 39 Gender perspective in answers Women are slightly more satisfied with 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


Comparable research :14.10.2008 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.


Independency in work with technology in self-evaluations :14.10.2008 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


Gender perspective – verbal skills, independency :14.10.2008 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


How many hours a week you learn maths at home? :14.10.2008 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


Gender perspective – independent learning :14.10.2008 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


Some conclusions :14.10.2008 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


Further research :14.10.2008 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


Thank you :Thank you