Sustainable student retention: gender issues in maths for ICT

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Sustainable student retention gender issues in
maths for ICT

• Prof.dr.sc.Blaženka Divjak
• blazenka.divjak_at_foi.hr
• Faculty of Organization and Informatics
• University of Zagreb

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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

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.

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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

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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

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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.)

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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 MaccobyJacklin (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 )

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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?Pag
  eID4
• Expectations of students at the National exams
  for mathematics is very low

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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

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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
  dont like mathematics at all (it is at the last
  position).
• They dont recognize the value and applicability
  of mathematics in real life and learn mathematics
  because of the grade.
• In general teachers of mathematics dont use
  contemporary teaching methods

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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

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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)

Level of education Male Female Total of Women
PhD 3268 1926 5194 37.08
MSc 1583 1615 3198 50.50
University degree 1246 1082 2328 46.48
Total 6097 4623 10720 43.13
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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)

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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

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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 ...

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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

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Contradicition
There is a contradiction, on the first glance,
in the literature and research on the next
issues

• 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

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Learning environment at FOI

• Mathematics

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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 -

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Innovations to Enhance Retention

• Institutional Management
• Curriculum Instruction
• Academic Social Supports

Learning outcomes of the Study program Learning outcomes of the Course Assessment methods Development higher order learning skills
Learning outcomes of the Study program Learning outcomes of the Course Teaching methods Development higher order learning skills
Learning outcomes of the Study program Learning outcomes of the Course Curricula Development higher order learning skills
Learning outcomes of the Study program Taxonomy ECTS ECTS
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Gender vs. pedagogy

• Change pedagogy
• The argument for changing the content or the way
  ST 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

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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.

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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

Evaluation judges the value of information
Synthesis builds a pattern from diverse elements
Analysis separates information into part for better understanding
Application applying knowledge to a new situation
Comprehension understanding information
Knowledge recall of data
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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

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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

K Knowledge/routine skills and techniques (knowledge/remember)
I Interpretation/insight of these (understand, analysis)
T Transfer to new context and application (application, evaluation, synthesis/create)
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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

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Statistics and student pass rates
Academic year No. of students enrolled in math No. of students passed Mathematics No. of students passed Math 1 Math 2 Pass rate in regular time (during the ac. year of enrolment)
2002/2003 317 78 25
2003/2004 278 129 46
2004/2005 273 115 42
2005/2006 309 131 42
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.

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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

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Mathematics 1
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Monitoring
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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/web
  maths1.pdf

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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?

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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

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Background
Axis x N numeric, V verbal, G graphic, P
problem Axis y Results in tests (scores x 100)
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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

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Questionnaire survey for students

• Anonymous questionnaire survey
• At the end of 1st semester Mathematics 1
• Survey participants
• N130 participants
• 22.3 female students
• 77.7 male students
• 96.9 full time students,
• 3.1 part time students
• Five points Likert type scale

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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)
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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

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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, womens 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.

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Independency in work with technology in
self-evaluations

• First test optional Second test credits
  given

1male 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
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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

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How many hours a week you learn maths at home?
x axis categorymale students /female students
y axes average number of hours (weekly)
without hours at lecturs and exercises at the
faculty
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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

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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

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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

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Thank you