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Learning and study strategies: a learning analytics approach for feedback

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Presentation of a learning dashboard developed by KU Leuven within the STELA project (http://stela-project.eu//).
Learning dashboard supported by learning analytics, showing off the use of technology for learning in higher education, for the transition of secondary to higher education in particular. The dashboard provides feedback on the learning and study strategies, as measured by the LASSI questionnaire.

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Learning and study strategies: a learning analytics approach for feedback

  1. 1. LEARNING AND STUDY STRATEGIES A LEARNING ANALYTICS APPROACH FOR FEEDBACK Tinne De Laet KU Leuven, Belgium
  2. 2. OUTLINE 1. Who am I? Why I am here? 2. What is learning analytics? 3. Specific context & methodology 4. Results 5. Conclusion & reflections
  3. 3. WHO AM I? WHY I AM HERE?
  4. 4. WHO AM I? WHY I AM HERE? woman engineer Head Tutorial Services Engineering Science KU Leuven, Belgium . Tinne De Laet associate professor
  5. 5. WHO AM I? WHY I AM HERE? Head of Tutorial Services of Engineering Science KU Leuven • Daily experiences of challenges in transition from secondary to higher education • looking for opportunities for cross-fertilization between “first-year experience” and “engineering” •Forward-looking cooperation project: 562167- EPP-1-2015-1-BE-EPPKA3-PI-FORWARD •Successful Transition from secondary to higher Education using Learning Analytics •KU Leuven (Belgium), TU Delft (Netherlands), TU Graz (Austria), Nottingham Trent University (UK), European Society of Engineering Education (SEFI) • http://stela-project.eu/ Coordinator of STELA Erasmus+ forward-looking cooperation project
  6. 6. WHAT IS LEARNING ANALYTICS?
  7. 7. WHAT IS LEARNING ANALYTICS? no universally agreed definition 7 “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” [1] [1] Learning and Academic Analytics, Siemens, G., 5 August 2011, http://www.learninganalytics.net/?p=131 [2] What is Analytics? Definition and Essential Characteristics, Vol. 1, No. 5. CETIS Analytics Series, Cooper, A., http://publications.cetis.ac.uk/2012/521 “the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” [2]
  8. 8. WAT IS LEARNING ANALYTICS? 8 [3] Learning Analytics and Educational Data Mining, Erik Duval’s Weblog, 30 January 2012, https://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/ “learning analytics is about collecting traces that learners leave behind and using those traces to improve learning” [Erik Duval, 3] † 12 March 2016 no universally agreed definition
  9. 9. IS IT ABOUT INSTITUTIONAL DATA? •high-level figures: provide an overview for internal and external reports; used for organisational planning purposes. •academic analytics: figures on retention and success, used by the institution to assess performance. •educational data mining: searching for patterns in the data. •learning analytics: use of data, which may include ‘big data’, to provide actionable intelligence for learners and teachers. [4] Learning analytics FAQs, Rebecca Ferguson, Slideshare, http://www.slideshare.net/R3beccaF/learning-analytics-fa-qs
  10. 10. SPECIFIC CONTEXT & METHODOLOGY
  11. 11. SPECIFIC CONTEXT • open admission in the Flemish (Belgium) higher education system → a substantial part of first-year students enters without the right qualifications → first-year drop-out rate of around 30% in the Faculties of Science & Technology at KU Leuven. • university invests in advising students before and throughout the first-year • readySTEMgo Erasmus+ project • paper-and-pencil questionnaires with first-year students • five academic skills as measured in the LASSI (The Learning and Study Strategies Inventory) are important for STEM study success • concentration, performance anxiety, motivation, the use of test strategies, and time management (Pinxten et al. 2016). • Results were actively disseminated to the KU Leuven faculties’ student support services and the central study advice center, who in return adapted their coaching and training based on the results. • BUT students did not receive feedback
  12. 12. THE DASHBOARD HTTPS://LEARNINGANALYTICS.SET.KULEUVEN.BE/LASSI.STELA
  13. 13. THE DASHBOARD
  14. 14. THE DASHBOARD
  15. 15. THE INTERVENTION • 1406 first-year KU Leuven (STEM) students with full LASSI profile • 12 study programs, 4 faculties • Students received “personalized email” with invitation to the dashboard
  16. 16. DO STUDENTS ENTER THE DASHBOARD? 1135 studenten “clicked through” (80,7%)
  17. 17. STUDENTS WITH BETTER LEARNING SKILLS GO TO THE DASHBOARD MORE 17Kruskal-Wallis test, with multiple comparison according to Dunn with a Bonferroni correction
  18. 18. STUDENTS WITH WORSE LEARNING SKILLS VISIT THE CORRESPONDING TAB AND READ THE TIPS MORE 18Kruskal-Wallis test, with multiple comparison according to Dunn with a Bonferroni correction
  19. 19. STATISTICAL MODEL 19 Logistic regression
  20. 20. DO STUDENTS LIKE IT? The information is clear The information is useful I would like to receive more similar information
  21. 21. CONCLUSION AND REFLECTIONS
  22. 22. SOME SPECIFICS • students like “additional” feedback • challenge to attract “right” students to the platform • once students are on the platform, the “targeted” students interact more Future • repeat intervention and extend (KU Leuven and TU Delft) • study relation with study results • qualitative analysis using focus groups and structured interviews with students
  23. 23. LEARNING ANALYTICS & DATA FOCUS ON DATA THAT IS AVAILABLE • A lot of data COULD be available • What IS available?
  24. 24. LEARNING ANALYTICS AND ACTIONABLE INSIGHTS “Female students are more successful in higher education than male students” 70% successful 60% successful so ? FEEDBACK SHOULD ALWAYS BE ACTIONABLE
  25. 25. LEARNING ANALYTICS & ETHICS & ADOPTION & … • Integrate all available expertise DURING development • educational scientists • computer scientists & IT experts • visualization experts • PRACTITIONERS!!! (study advisors, tutors, etc.) • students & teachers •Be wary of out-of-the-box commercial solutions! • no one-size-fits-all solution • jeopardizes acceptance of students and staff • What is underlying the recommendation? → actionable! → transparency! •Ethics AND privacy are big issues! • Ethics: involve practitioners and experts • Privacy regulations can be hurdle might be opportunity for learning analytics → overview and insight in data that IS gathered! 25 USE ALL AVAILABLE EXPERTISE

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