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Learning gains of international students in South African distance education

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The proportion of students studying in international distance education programmes has risen dramatically in
the last decade, particularly in developing countries like South Africa. Although there has been some research
in UK distance education, there is little known about academic trajectories of distance education students
in other countries. One promising approach of assessing students’ learning through the use of learning
analytics is through measuring students’ learning gains and learning trajectories. Longitudinal data was
collected for 69,935 undergraduate Science students from across 30 different qualifications at UNISA. Our
multilevel modelling indicated that students made positive learning gains over time, whereby most variance
(78.9%) was within-student, followed by the variance due to individual differences (18.3%). Theoretical
implications and practical applications for these findings in the context of distance education as well as the
appropriateness of the selected method in the context of Africa will be discussed.

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Learning gains of international students in South African distance education

  1. 1. Learning gains of international students in South African distance education. http://ideaspartnership.org/ @ESRC_IDEAS #ESRCIDEAS Jekaterina Rogaten Jenna Mittelmeier Dion Van Zyl Melis Cin Dianne Long Bart Rienties
  2. 2. Who are we? Parvati Raghuram (UK) Principal Investigator Ashley Gunter (South Africa) Principal Investigator Clare Madge Co-Investigator Paul Prinsloo Co-Investigator Katharine Reedy Learning Design specialist Bart Rienties Co-Investigator Jenna Mittelmeier Post-Doc Melis Cin Post-Doc Dianne Long Post-Doc Jekaterina Rogaten Post-Doc Adelino Chissale Post-Doc
  3. 3. Project aims: 1. To examine how far IDE in South Africa offers equitable access to students in Africa through both supply side and demand side analysis. 2. To assess and improve the quality of IDE and see how it varies among students. 3. To advance theoretical understandings of IDE though a postcolonial framework and produce impactful findings that contribute towards Sustainable Development Goal 4 regarding equal access to quality education.
  4. 4. Assessing trajectories  Using assessment results for estimating learning trajectories has number of advantages:  Assessment data readily available  Widely recognized as appropriate measure of learning  Relatively free from self-reported biases  Allows a direct comparison of research finding with the results of other studies
  5. 5. Data exploration Total 241,090 students enrolled into 216 different qualifications % of exams that student PASSED % of exams that student FAILED % of exams not attended Mean Mean Mean SA living in SA 49.4% 44.8% 5.9% SA living in another country 67.3% 24.9% 7.8% International living in SA 61.1% 34.3% 4.6% International living in another country 58.3% 34.6% 7.1% Average Grade for the module SA living in SA 65 SA living in another country 71 International living in SA 67 International living in another country 66 South African Other Nationalities Female 109,634 93.4% 7,715 6.6% Male 111,794 90.3% 11,935 9.6% SA living in SA SA living in another country 91.1% 0.7% International living in SA International living in another country 5.1% 3%
  6. 6. Sample Characteristics  69,935 student studying in the faculty of Science  60% Male students  40% Female students  90% South African students  10% International students  67% Black students  21% Part-time students  21% Other ethnic backgrounds  30 Undergraduate degree programmes  Between years 2005 and 2016
  7. 7. Regression Intercept = 63.56 Slope = -0.115** **p<0.01 48 54 60 66 72 78 84 90 5 10 15 20
  8. 8. Estimating learning trajectories Level 1 Level 2 Level 3 Grade1 Student1 Grade3 Grade1Grade2Grade3Grade1Grade2Grade3Grade2 Student2 Student3 Course1 Course2 Grade1Grade2Grade3 Student4 Grade1Grade2Grade3 Student5 Course3
  9. 9. Comparison of the 1-, 2- and 3-level structures Regression S.E. 2-level S.E. 3-level S.E. Intercept B0 63.558 0.032 63.519 0.036 63.166 0.361 Slope B1 -0.115** 0.009 -0.143** 0.01 0.393** 0.188 Deviance 1817144.002 1807253.05 1804441.08 X2 change 9890.95** 2811.97** ** p<0.01
  10. 10. Course trajectories 48 54 60 66 72 78 84 90 5 10 15 20 mean.pred RNtime.pred 48 54 60 66 72 78 84 90 5 10 15 20 mean.pred RNtime.pred National Diploma: Safety Management Bachelor of Science in Computing
  11. 11. Gender differences in learning trajectories Intercept 63.166 0.361 Slope 0.393** 0.188 Female -0.506** 0.064 Female*time -0.698** 0.078 Grade SE **p<0.01 63.6 64.8 66.0 67.2 2.3 4.6 6.9 9.2 mean.pred RNtime.pred Female Male
  12. 12. Differences between Home and International students’ learning trajectories 63.6 64.8 66.0 67.2 2.3 4.6 6.9 9.2 mean.pred RNtime.pred NNationalityGR_1 NNationalityGR_2 South African International Intercept 63.105 0.358 Slope 0.393* 0.188 International 0.646** 0.095 International*time -0.107** 0.031 Grade SE **p<0.01
  13. 13. Key message  The largest portion of variance 79% was within-students and additional 18% between students.  This means that changes should be done at the degree programme (between modules) level like changes to the assessment, module structure and learning design can.
  14. 14. Benefits of knowing students’ learning trajectories: We can make a positive change Identify students and modules that progress well Identify students and modules that do not progress well Predict how certain students will do in certain modules Implement data informed interventions Evaluate effectiveness of interventions Identify most effective routes towards qualification
  15. 15. Jekaterina Rogaten Jekaterina.rogaten@open.ac.uk http://ideaspartnership.org/ @ESRC_IDEAS #ESRCIDEAS

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