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In Focus presentation: Data driven blended learning: going from a heterogeneous classroom to personalized learning


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Data driven blended learning: going from a heterogeneous classroom to personalized learning.

Presentation from 'In Focus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by Ernest Lyubchik (Founder, Head of Data and Algorithm Development, and Dr Sara Hershkovitz (Head of Mathematics Dept, Center for Educational Technology, Israel).

Audio of the session and more details can be found at

Published in: Education, Technology
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In Focus presentation: Data driven blended learning: going from a heterogeneous classroom to personalized learning

  1. 1. Mr. Ernest Lyubchik Dr. Sara Hershkovitz Data driven blended learning Going from a heterogeneous classroom to personalised learning
  2. 2. Agenda • Selflab & CET are conducting an adaptive learning experiment. • Goal: validate Selflab’s adaptive technology. • First stage introduced Selflab’s analytics. • Presenting new instruction methods that became possible using the analytics. • First stage results. • Future research.
  3. 3. Selflab A paradigm shift in adaptive learning. • Selflab's software-as-a-service platform automatically serves any educational content based on individual users' unique learning needs. • Selflab’s easy integration gives publishers extensive data and analytics about their content and their students.
  4. 4. Selflab
  5. 5. The Center for Educational Technology (CET) Goals Promote achievement and academic excellence in the 21st century Create equal opportunities to all children Activities In its 42 years of activity, CET, the leading educational NGO in Israel, has established its expertise and reputation by: • Developing state-of-the-art printed and digital content • Developing interactive learning objects, virtual labs and simulations in all subjects • Paving new ways in online and blended learning programs for students and teachers (PD) • Creating online systems and tools for assessment and evaluation
  6. 6. CET’s Core Competencies
  7. 7. Mathematical Proficiency Kilpatrick J., Swafford J. & Findell B. (2001): Adding It Up: Helping children learn mathematics. Washington, DC: National Academy Press. Common core state standards for mathematics, 2012
  8. 8. Fractions in the th 4 grade
  9. 9. Computerised tools
  10. 10. Computerised instruction works!
  11. 11. The aim is: To focus on students’ readiness to learn, scaffolding and building on his current knowledge and move the student along an efficient trajectory according to their own skills abilities and knowledge to the next stage.
  12. 12. Pierce R., Stacey K. (2010): “Mapping Pedagogical Opportunities Provided by Mathematics analysis Software” International journal of computers for Mathematical Learning, 15(1), pp. 1-20
  13. 13. Analytics – the first step for adaptive • Current classroom instruction is limited by the teachers knowledge of the students’ abilities. • To fit the needs of the student, first we must know what the student needs. • A student making a multiplication mistake in an Algebra question, needs help with multiplication, not Algebra!
  14. 14. What are the students solving? Hello Ernest [exit] Difficulty scale Laboratory Write < or > or = Check my answer Back
  15. 15. The shape is a single unit. Write a fraction and a mixed number that match the illustration Fraction Check my answer Mixed number
  16. 16. Pizzas at the pizza stand are divided to 3 equal slices. Make an order for 6 slices of pizza Check my answer
  17. 17. Difficulty scale This is 2/3 of a shape: Draw a complete shape
  18. 18. Experiment • 6 classes of 4th grade, from 2 different schools, were selected for the first phase. • The students began learning fractions without prior instruction, receiving computerised instruction and exercises. • All data was monitored, and the teachers received reports, while the system itself did not activate adaptive personalisation.
  19. 19. Reporting and Analytics • CET was provided with content reports, and the content on the platform was supervised. • The teachers received both custom requested reports of the student’s performance, and a real time tracking system to oversee the progress of the students.
  20. 20. Students analytics
  21. 21. Real time data
  22. 22. New Instructional Methods • Students receive instruction from the program. However, some students still require instruction from the teacher. • Instead of presenting the class with frontal instruction, the teacher can work directly with those who require assistance, helping them with targeted sessions.
  23. 23. Feedback • Students’ feedback: “This is right where I am now, how did you know I had a problem with that?” • Teachers’ feedback: “The class is more organized” “I know what they really understood” “The students love the program” “Can we allow sixth graders to use it?”
  24. 24. Big impact for low achievers • The teachers pushed for adding sixth graders who had problems with the material to the experiment. • The sixth graders who joined were low achievers, and unmotivated students. In just a few lessons, the students reported increased motivation, confidence and interest. • The previously low achievers improved their performance, they achieved similar ability grades as the rest.
  25. 25. Results – correct answers Means: Std: Frontal 75% 11% Focus 86% 6%
  26. 26. Results • Personalised learning students made on average %14 mistakes compared to 25% of frontal instruction groups. 44% less mistakes. • The measured performance gain of the personalised learning students was found significant by T-Test, ALPHA=0.01. • Personalised learning led to higher levels of motivation and lower levels of class disruptions and stress. (precise data still being gathered).
  27. 27. Future plans • Going adaptive – individual learning pathways. • Understanding student’s abilities rather than student’s score. • Data driven analysis of content. But what is the difference between success rate and ability?
  28. 28. Some questions are hard to guess but easy to solve if you know the material
  29. 29. While other questions might be easy to guess but difficult to solve
  30. 30. Success rate is not identical to difficulty! 350% 300% 250% 200% Question 1 Question 2 150% Question 3 100% 50% 0% Success rate Difficulty
  31. 31. Fully adaptive – it’s about to begin Educate the student according to his way (Book of Proverbs) Lead the man on the way he walks (Talmud)
  32. 32. THANK YOU