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[DSC Europe 22] Machine learning algorithms as tools for student success prediction - Dijana Oreski

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[DSC Europe 22] Machine learning algorithms as tools for student success prediction - Dijana Oreski

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The goal of higher education institutions is to provide quality education to students. Predicting academic success and early intervention to help at-risk students is an important task for this purpose. This talk explores the possibilities of applying machine learning in developing predictive models of academic performance. What factors lead to success at university? Are there differences between students of different generations? Answers are given by applying machine learning algorithms to a data set of 400 students of three generations of IT studies. The results show differences between students with regard to student responsibility and regularity of class attendance and great potential of applying machine learning in developing predictive models.

The goal of higher education institutions is to provide quality education to students. Predicting academic success and early intervention to help at-risk students is an important task for this purpose. This talk explores the possibilities of applying machine learning in developing predictive models of academic performance. What factors lead to success at university? Are there differences between students of different generations? Answers are given by applying machine learning algorithms to a data set of 400 students of three generations of IT studies. The results show differences between students with regard to student responsibility and regularity of class attendance and great potential of applying machine learning in developing predictive models.

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[DSC Europe 22] Machine learning algorithms as tools for student success prediction - Dijana Oreski

  1. 1. Machine learning algorithms as tools for student success prediction Assoc. Prof. Dijana Oreški, PhD University of Zagreb Faculty of Organization and Informatics
  2. 2. Intro • This work has been supported by Croatian Science Foundation under the project UIP-2020-02-6312.  SIMON – Intelligent system for automatic selection of machine learning algorithm in social sciences • Lab Louise  Laboratory for data mining and intelligent systems  louise.foi.hr
  3. 3. Intro • “If you torture data long enough they will confess at the end.” Ronald Coase
  4. 4. Agenda • Introduction  Education data & WHY?  machine learning algorithms • Research motivation • Research methodology HOW?  CRISP DM standard • Research results WHAT?  Model evaluation  Model interpretation • Conclusion
  5. 5. Agenda • Introduction  Education data & WHY?  machine learning algorithms • Research motivation • Research methodology HOW?  CRISP DM standard • Research results WHAT?  Model evaluation  Model interpretation • Conclusion
  6. 6. Introduction • Huge number of machine learning algorithms applications in a broad spectrum of domains.  Crucial role in harnessing the power of the vast amount of data we produce daily in the digital age. • The application of algorithms is complex, iterative and time-consuming.  There is a need to automate the selection of algorithms for models development. • Which algorithm is best to used in a specific situation, in a particular domain, at a particular dataset?
  7. 7. Research motivation
  8. 8. Research motivation Education data Supervised machine learning algorithms
  9. 9. Agenda • Introduction  Education data & WHY?  machine learning algorithms • Research motivation • Research methodology HOW?  CRISP DM standard • Research results WHAT?  Model evaluation  Model interpretation • Conclusion
  10. 10. CRISP DM standard Data preparation Data understanding Evaluation Modelling Data Domain understanding Deployment
  11. 11. Research papers • Kliček, B.; Oreški, D.; Divjak, B., Determining individual learning strategies for students in higher education using neural networks, International Journal of Arts and Sciences. • Oreški, D; Konecki, M; Pihir, I: Predictive Modelling of Academic Performance by Means of Bayesian Networks, 47th International Scientific Conference on Economic and Social Development. • Oreški, D; Pihir, I; Konecki, M., CRISP-DM process model in educational setting, 20th International Scientific Conference on Economic and Social Development
  12. 12. Research papers • Oreški, D; Konecki, M; Milić, L., Estimating profile of successful IT student: data mining approach, MIPRO 2017 - 40th International Convention Proceedings • Kovač, R; Oreški, D., Educational Data Driven Decision Making: Early Identification of Students at Risk by Means of Machine Learning, Proceedings of CECIIS 2018 / • Oreški, D.; Hajdin, G., Exploring differences in predictors of academic success between different generations of students, EDULEARN19 Proceedings, 2019.
  13. 13. Research papers • Oreški, D., Hajdin, G. Development and comparison of predictive models based on learning management system data // WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS, 2019 • Oreški, D.; Hajdin, G., A Comparative Study of Machine Learning Approaches on Learning Management System Data, 2019 Proceedings - 3rd International Conference on Control, Artificial Intelligence, Robotics & Optimization • Filipović, D.; Balaban, I.; Oreški, D., Cluster analysis of students’ activities from logs and their success in self-assessment tests, Proceedings of CECIIS 2018 • Oreški, D.; Kadoić, N., Analysis of ICT students' LMS engagement and sucess, International Scientific Conference on Economic and Social Development, 2018.
  14. 14. Domain understanding • Research goals:  To determine whether data from different sources (surveys, e- learning systems...) can be a good basis for creating predictive models of academic success.  To determine which variables are the best predictors of academic success.  To determine whether predictors of success change over time.
  15. 15. Domain understanding • The improvement of the educational system and the achievement of students optimal learning requires the data collection and analysis.  Recent papers deal with this topic from the perspective of:  (i) the various academic and non-academic factors involved in the data,  (ii) the research methodology used in data analysis: previously focused on advanced statistical approaches, nowadays on machine learning approaches,  iii) accuracy and reliability of developed predictive models.
  16. 16. Data understanding • Data sources:  Survey,  Learning management system data,  YouTube analytics.
  17. 17. Data understanding • Data sources:  Survey,  Learning management system data,  YouTube analytics.
  18. 18. Data understanding Students through generations
  19. 19. Data understanding Students by entrance exam results Bottom 10%-30% Middle Top 10%
  20. 20. Data understanding First grade at the Faculty
  21. 21. Data understanding I manage time well Completely Disagree Neither agree Agree Completely disagree or disagree agree
  22. 22. Data understanding I see myself as responsible person Completely Disagree Neither agree Agree Completely disagree or disagree agree
  23. 23. Data understanding I find teamwork useful Completely Disagree Neither agree Agree Completely disagree or disagree agree
  24. 24. Data understanding I have prepared for the classes Completely Disagree Neither agree Agree Completely disagree or disagree agree
  25. 25. Data understanding • Data sources:  Survey,  Learning management system data,  YouTube analytics.
  26. 26. Data understanding
  27. 27. Data understanding
  28. 28. Data understanding
  29. 29. Data understanding
  30. 30. Data understanding • Data sources:  Survey,  Learning management system data,  YouTube analytics.
  31. 31. Data understanding Variable Variable Correlation Video duration Percentage of video views -0,78 Variable Variable Correlation Complexity Percentage of video views -0,35
  32. 32. Modelling • Information based machine learning  Decision tree • Similarity based machine learning  K-nearest neighbours  “When I see a bird that walks like a duck and swims like a duck and quacks like a duck, I call that bird a duck.” James W. Riley • Error based machine learning  Neural networks  “Success is stumbling from failure to failure with no loss of enthusiasm.” Winston Churchill • Probability based machine learning  Bayesian networks  “When my information changes, I alter my conclusions. What do you do, sir?” John Maynard Keynes
  33. 33. Agenda • Introduction  Education data & WHY?  machine learning algorithms • Research motivation • Research methodology HOW?  CRISP DM standard • Research results WHAT?  Model evaluation  Model interpretation • Conclusion
  34. 34. Research results rang A rang B rang C Highschool grade average 1 1 4 Lecture attendance 2 4 10 First grade at the Faculty 3 3 3 I manage my time well 4 15 13 Entrance exam results 5 2 1 I find myself as responsible person 8 10 5 Seminars attendance 9 11 7 I have prepared for the classes 12 13 12 I find teamwork useful 15 8 9 Gender 16 7 16
  35. 35. Research results • Strong positive correlation between predictors of academic success in Generations B and C(r=0.652941176, p<0,01). • Correlations in predictors between generations A and B (r=0.370588235, p<0,01), A and C(r=0.388235294, p<0,01) are smaller. • Conclusion:  Predictors of student success change over time, but only when there are significant changes in the education system.
  36. 36. Research results • Female students are more active on the e-learning system and complete the course more successfully than male colleagues. • Activity on the e-learning system is a significant predictor of student success.  Students were most active during the weeks of the colloquium, and especially the day before the colloquium.  Students can be characterized as "last minute" students, because they fulfill their obligations as late as possible in terms of the deadline.  They are active in the "late" hours.
  37. 37. Conclusion • Machine learning algorithms provide accurate and reliable predictive models. • However, results are not perfect:  Predicting students at risk will always suffer from classification errors - false positives and false negatives.  The error affects the allocation of available resources.  Potentially negative effects on the individual.
  38. 38. Conclusion • To determine whether data from different sources (surveys, e-learning systems...) can be a good basis for creating predictive models of academic success.  Yes! Integration contributes to successful prediction. • To determine which variables are the best predictors of academic success.  First grade at the faculty, previous knowledge..  LMS activity.. • To determine whether predictors of success change over time.  Change according to change in educational system.
  39. 39. Thank you!

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