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Successful transition from secondary to higher education using learning analytics

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Slides of the SEFI 2016 workshop of the STELA project .

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Successful transition from secondary to higher education using learning analytics

  1. 1. Successful transition from secondary to higher education using learning analytics TINNE DE LAET, TOM BROOS (KU LEUVEN) JAN-PAUL VAN STAALDUINEN (TU DELFT) PHILIPP LEITNER, MARTIN EBNER (TU GRAZ) 1 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  2. 2. Welcome! Who are we?  Tinne De Laet ( KU Leuven)  Head of Tutorial Services of Engineering Science promotor STELA background = mechanical engineer  Tom Broos (KU Leuven)  Doctoral researcher STELA  Philipp Leitner (TU Graz)  Doctoral researcher STELA 2 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  3. 3. Welcome  Would you please introduce yourself to the people on your table  Who are you?  How familiar are you with learning analytics?  Is your institute applying learning analytics? 3 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  4. 4. Agenda for workshop  Welcome  What is learning analytics? (10 minutes)  STELA project:  Presentation of project goal & challenges (2 minutes)  Group discussion: how could learning analytics help? (10 minutes)  STELA project:  Presentation of project ideas (10 minutes)  Group discussion on project ideas (10 minutes)  Learning analytics technology (10 minutes)  Groups present their remarks, suggestions, … (10 minutes)  Wrap up, goodbye (3 minutes) 4 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  5. 5. Agenda for workshop …. so what we expect from you  participate in discussion in groups  each group gathers 3 ideas, thoughts, remarks, suggestions, ..  present them to each other at end of the workshop 5 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  6. 6. What is learning analytics? 6 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  7. 7. What is Learning Analytics?  no universally agreed definition “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] 7 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  8. 8. What is Learning Analytics?  no universally agreed definition [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 8
  9. 9. What is Learning Analytics? How is learning analytics different from institutional data? [4]  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 9 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  10. 10. What is Learning Analytics? Different level of analytics adapted from http://www.slideshare.net/gsiemens/learning-analytics-educause level beneficiaries course-level learners, teachers, faculties aggregate learners, teachers, tutors, counsellors, faculties institutional administrators, funders, marketing regional administrators, funders, policy makers national and international national and international governments, policy makers 10 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  11. 11. What is Learning Analytics? data visualization versus predictive analytics  is showing data enough?  how to show data to create sense-making and impact?  is predicting study success/drop out the only thing that matters?  can both be combined? 11 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  12. 12. What is Learning Analytics? learning analytics process model [Verbert et al. 2013] Verbert K, Duval E, Klerkx J; Govaerts S, Santos JL (2013) Learning analytics dashboard applications. American Behavioural Scientist, 10 pages. Published online February 201, doi: 10.1177/0002764213479363 12 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  13. 13. What is Learning Analytics? how to evaluate learning analytics?  is perceived usefulness enough?  is increased self-awareness enough? How will you measure this?  is increased sense-making enough? How will you measure this?  how could impact be measured? 13 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  14. 14. What is Learning Analytics? six critical dimensions of learning analytics [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] 14 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  15. 15. What is Learning Analytics? six critical dimensions of learning analytics [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] Tutors Policy makers 15
  16. 16. What is Learning Analytics?  data subjects  here: students (could also be teachers)  data clients  students  tutors  academic administrators  policy makers [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] 16 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  17. 17. What is Learning Analytics?  what are the objectives?  is it about reflection or prediction?  is showing data enough?  how to show data to create self-awareness, sense-making and impact? [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] 17 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  18. 18. What is Learning Analytics?  which data is available?  is it open? protected?  is it ethical to use the data? [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] 18 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  19. 19. What is Learning Analytics?  Technology, algorithms, theories are at the basis of learning analytics  open source or as close as possible to (proprietary) university systems?  pedagogic theories for supporting students [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] 19 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  20. 20. What is Learning Analytics? [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] • conventions: ethics, personal privacy, and similar socially motivated limitations • norms: restricted by laws or specific mandated policies or standard ethics and privacy IS a big issue 20 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  21. 21. What is Learning Analytics? [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57. http://ifets.info/journals/15_3/4.pdf ] • competences: application of learning analytics requires new higher-order competences to enable fruitful exploitation in learning and teaching • acceptance: acceptance factors can further influence the application or decision making that follows an analytics process 21 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  22. 22. Agenda for workshop  Welcome  What is learning analytics? (10 minutes)  STELA project:  Presentation of project goal & challenges (2 minutes)  Group discussion: how could learning analytics help? (10 minutes):  Presentation of project ideas (10 minutes)  Group discussion on project ideas (10 minutes)  Learning analytics technology (10 minutes)  Groups present their remarks, suggestions, … (10 minutes)  Wrap up, goodbye (3 minutes) 22 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  23. 23. STELA project Project goal & challenges 23 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  24. 24. STELA project  focus: transition from secondary to higher education (summer before entering university + 1st year experience)  goal: applying learning analytics to support transition  student-centered  additional focus to tutors, student counsellors 1. beyond identifying at-risk students  inclusive approach: feedback for every student 2. beyond single course  entire program 3. provide actionable feedback  ability to remediate based on feedback 24 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  25. 25. STELA project Group discussion: how could learning analytics help? (10 minutes) 25 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  26. 26. Group discussion: how could learning analytics help? (10 minutes)  What are challenges in the transition from secondary to higher education?  Which actionable feedback could support students in the transition?  How could learning analytics help in this matter? 26 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  27. 27. Agenda for workshop  Welcome  What is learning analytics? (10 minutes)  STELA project:  Presentation of project goal & challenges (2 minutes)  Group discussion: how could learning analytics help? (10 minutes):  Presentation of project ideas (10 minutes)  Group discussion on project ideas (10 minutes)  Learning analytics technology (10 minutes)  Groups present their remarks, suggestions, … (10 minutes)  Wrap up, goodbye (3 minutes) 27 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  28. 28. STELA project: project ideas four areas of focus 1. academic performance 2. academic engagement 3. academic skills 4. academic well-being How?  position students with respect to peers  show impact on future study pathway → show how students with similar profile did in the past 28 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  29. 29. STELA project: project ideas (1/4) academic performance 29 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD data (often) easily available present at all universities after the facts strong relation performanceT-1 ~ performance Source: ABLE project (2015-1-UK01-KA203-013767)
  30. 30. STELA project: project ideas (2/4) academic engagement 30 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD depends on context • e.g. card swipes at Nottingham Trent University, • learning platform activity at KU Leuven. relation engagement ~ performance not always straightforward. data not always available
  31. 31. STELA project: project ideas (3/4) academic skills 31 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD Number of peers: Motivation Score Very high PerformanceAnxiety High Average Low Very Low Very weak Weak Average High Very High Recommendations First aid for stress workshop at Student Health Center Talk to a student counselor. often self-reported tests and questionnaires available form pedagogy and psychology • Time management, motivation, persistence, … opportunity to move from information to recommendation
  32. 32. STELA project: project ideas (4/4) academic well-being 32 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD How do you feel about this course now? Last week, others felt like this: opportunity to complement with continuous, bi- directional feedback tests and questionnaires available form pedagogy and psychology
  33. 33. Agenda for workshop  Welcome  What is learning analytics? (10 minutes)  STELA project:  Presentation of project goal & challenges (2 minutes)  Group discussion: how could learning analytics help? (10 minutes):  Presentation of project ideas (10 minutes)  Group discussion on project ideas (10 minutes)  Learning analytics technology (10 minutes)  Groups present their remarks, suggestions, … (10 minutes)  Wrap up, goodbye (3 minutes) 33 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  34. 34. Learning analytics technology 34 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  35. 35. Learning Analytics technology Challenges  multiple and different kind of data-sources  lots of data  need to be combined and analyzed  scalable  real-time?  visualized  ... 35 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  36. 36. Learning Analytics technology 36 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD Concept
  37. 37. Learning Analytics technology (1/3) Data Collection: Logstash¹ 37 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD  flexbile and open source (Apache 2 Open Source License)  data collection, enrichtment and transportation pipeline  efficiently process log, event and unstructured data sources ¹) https://www.elastic.co/products/logstash
  38. 38. Learning Analytics technology (2/3) Search and Analytics Engine: Elasticsearch² 38 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD  distributed, open source search and analytics engine  scalability, reliability, and easy management  developer-friendly query language covering structured, unstructured, and time-series data ²) https://www.elastic.co/de/products/elasticsearch
  39. 39. Learning Analytics technology (3/3) Visualization: Kibana³ or Grafana4 39 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD 3 ) https://www.elastic.co/products/kibana 4 ) http://grafana.org/  open source data visualization platform  interact with data through powerful graphics  easy extensibility and a variety of panels
  40. 40. Agenda for workshop  Welcome  What is learning analytics? (10 minutes)  STELA project:  Presentation of project goal & challenges (2 minutes)  Group discussion: how could learning analytics help? (10 minutes):  Presentation of project ideas (10 minutes)  Group discussion on project ideas (10 minutes)  Learning analytics technology (10 minutes)  Groups present their remarks, suggestions, … (10 minutes)  Wrap up, goodbye (3 minutes) 40 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  41. 41. Groups present their remarks, suggestions, … 41 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  42. 42. Wrap up, goodbye 42 STELA - Erasmus+ Project 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD

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