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Adam Cuppy
Ahmed Omran
Alan Ridlehoover
Amit Zur
Andrew Mason
Andrew Nesbitt
Andy Andrea
Andy Croll
Asia Hoe
Avdi Grimm
Ben Greenberg
Bhavani Ravi
Brandon Carlson
Brittany Martin
Caleb Thompson
Caren Chang
Chiu-Ki Chan
Christine Seeman
Cody Norman
Devon Estes
Eileen Uchitelle
Emily Giurleo
Emily Samp
Enrico Grillo
Espartaco Palma
Fito von Zastrow
Frances Coronel
Hilary Stohs-Krause
Jalem Raj Rohit
Jemma Issroff
Jenny Shih
Joel Chippindale
Justin Searls
Katrina Owen
Kevin Murphy
Kudakwashe Paradzayi
Kylie Stradley
Maeve Revels
Maryann Bell
Matt Bee
Mayra Lucia Navarro
Molly Struve
Nadia Odunayo
Nickolas Means
Noah Gibbs
Olivier Lacan
Ramón Huidobro
Richard Schneeman
Rizky Ariestiyansyah
Saron Yitbarek
Sean Moran-Richards
Shem Magnezi
Srushith Repakula
Stefanni Brasil
Sweta Sanghavi
Syed Faraaz Ahmad
Tekin Suleyman
Thomas Carr
Tom Stuart
Ufuk Kayserilioglu
Valentino Stoll
Victoria Gonda
Vladimir Dementyev
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## Category Education ## Objectives - Design and Implementing Education Technology and Blended Learning - Extending EdTech with Python - Data Mining in Blended Learning ## Abstract I've been doing research in edtech and blended learning for two years and find out python is great super power in this area, in this talk I want to share how to design and implementing blended learning and how we can use python for data mining. Educational Technology is growing and going to disrupt online business nowadays. To be effective, educational technologies must be designed based on what we know about how people learn and of course development under open source umbrella will be more effective. Through this session, participants will learn the problem and concept how to design and develop the blended learning technology by using open source technologies, the idea of collaborative learning and active learning. In the last decades, the power of data mining and analytics has transformed instruction in blended learning. Increasingly, large-scale data is available on student learning and interaction online. Much of this data represents student behavior. This has allowed researchers to model and track many elements of student learning that were not previously feasible at scale: engagement, affect, collaborative skill, and robust learning. In turn, these models can be used in prediction of long-term student outcomes, and to analyze the factors driving long-term success of students, I will share how we use python as the super power in this field.
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