Data driven blended learning

Going from a heterogeneous classroom to personalised learning

Dr. Sara Hershkovitzsarah@cet.ac.il

Mr. Ernest Lyubchikernest@selflab.com

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.

SelflabA 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.

Selflab

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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

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Goals

The Center for Educational Technology (CET)

achievement and academic excellence in the 21st century

Promote

Createequal opportunities to all children

Activities

CET’s Core Competencies

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Technology & Pedagogy

Assessment & Evaluation

Content Development

Professional Development

21st Century Education

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

Fractions in the 4th grade

Computerised tools

Computerised instruction works!

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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.

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

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!

What are the students solving?

Check my answer

Write < or > or =

Difficulty scaleLaboratory Back

Hello Ernest [exit]

The shape is a single unit.

Write a fraction and a mixed number that match the illustration

Fraction Mixed number

Check my answer

Pizzas at the pizza stand are divided to 3 equal slices. Make an order for 6 slices of pizza

Check my answer

Difficulty scale

This is 2/3 of a shape:

Draw a complete shape

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 receivedreports, while the systemitself did not activate adaptive personalisation.

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.

Students analytics

Real time data

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.

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?”

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.

Results – correct answersFrontal Focus

Means: 75% 86%Std: 11% 6%

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).

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?

Some questions are hard to guess but easy to solve if you

know the material1

While other questions might be easy to guess but difficult to solve

2 3

Success rate is not identical to difficulty!

Success rate Difficulty0%

50%

100%

150%

200%

250%

300%

350%

Question 1Question 2Question 3

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)

THANK YOU