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  • As many believes, its not as easy to learn Hadoop by own. Its must for anyone to have proper training on Hadoop. If you feel otherwise, take a look at this blog to know how essential is Hadoop Training http://www.edureka.in/blog/how-essential-is-hadoop-training
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    Hadoop in Education Hadoop in Education Presentation Transcript

    • Hadoop In Education: The advent of data-drivenapplications © 2010 Apollo Group – Confidential & Proprietary
    • Online Learning is in high demand Adults learn at the online University of Phoenix on their own schedules of available time and numbers who prefer that modality more than the ground (“traditional”) equivalent is on the rise. Online students and faculty do not have to be geographically co-located as in the traditional settings, allowing for richer and diverse interactions across geographical boundaries and time-differences. As people spend more time online, it is only natural to expect that the learners will want their education online as well. Recent press on huge enrolment in MOOCs (Massive Open Online Course) again proves that there is a great latent demand for online courses. © 2010 Apollo Group – Confidential & Proprietary 2
    • What should online learning look like? © 2010 Apollo Group – Confidential & Proprietary 3
    • Online Education challenges Every learner is unique in aptitude, preparation, and motivation. A good teacher is continuously observing and intervening appropriately to keep the learners engaged and learning. –If we just take the traditional classroom online, all the visual and audio feedback are taken away from the trained teacher! © 2010 Apollo Group – Confidential & Proprietary 4
    • Online Education Opportunities What if, instead, –We collect detailed interaction data-sets and converted them into actionable insights for the teacher so that (s)he can focus only where (s)he is needed and not exhaust her/himself by being the filter? –With algorithms we harness the best practices that are working for student and teacher and recommend them in appropriate contexts and take away unnecessary and inefficient guessing? Wait, would that not be Web 2.0 in Education?With top-name universities, start-up companies, learningplatform or learning content companies … this innovationrace is already on! © 2010 Apollo Group – Confidential & Proprietary 5
    • Data driven learner guidance Data Driven Apps: Assignments, discussions, Faculty Guidance, Recommendations Faculty Processed Content UsageStudent Logs Interaction Student/Faculty InteractionStudent Assessment Logs Logs Data Driven Apps for Effectiveness/Reco mmendation of Content & Instruction Designer Faculty Assessments © 2010 Apollo Group – Confidential & Proprietary 6
    • System Architecture © 2010 Apollo Group – Confidential & Proprietary 7
    • New Learning System Architecture Browser Mobile Client Client RESTful ServicesLog data Curriculum Class Curriculum Quizzes & Curriculum Log data Curriculum Discussions Content Data Collection and Log Processing Pipeline © 2010 Apollo Group – Confidential & Proprietary 8
    • Considerations Enable logging • Built GWT/JavaScript framework to automatically enable client side logging.without much effort • Automatically enable server-side logging from developers using servlet filters.Common pipelines • Used canonical log records with Avro as the serialization format.for processing log • Service specific information logged as JSON data and processed using Hive UDFs. Time-sync clients • Server responds with its timestamp on every call. and server to • Client includes this information in the nextsimplify log ordering call. © 2010 Apollo Group – Confidential & Proprietary 9
    • Client/Server – Built for Log Collection View Controller Model Event Bus API Calls Instrumentation Filter Log Data RESTfulLog and Data Canonical Services Processing Log File Pipeline (Avro) © 2010 Apollo Group – Confidential & Proprietary 10
    • Connecting the Data and Processing Pipeline S3 LogApplication & Server Processing ServerLog Collection Pipeline Servers ~7 TB/Week/Class Oozie Workflows HBase Hive Tables Tables Services, Dashboards, ~700 GB /Week/Class M/L Tools RDBMS Traditional BI Tools © 2010 Apollo Group – Confidential & Proprietary 11
    • ConsiderationsUser session • User in a discussion forum in a browsersplit across • User receives grade notifications on multiple mobile phone • User views notification devicesMerging and • Only partial ordering of events possible without application specific informationordering of • Full ordering required to extract features events from logs © 2010 Apollo Group – Confidential & Proprietary 12
    • Feature Extraction after Joins – Some challenges View Question Get Question Request QuestionUser Interaction Partial Event Order Reordered Events Select Answer Submit Answer Get Question View Hint Request Question Display Question Select Another Display Question Select Answer Answer Select Answer View Hint Submit Answer View Hint Select Answer Receive Feedback Select Answer Submit Answer Submit Answer Submit Answer Question Feedback Question Feedback  Exploring generic alignment algorithms that use declared application semantics © 2010 Apollo Group – Confidential & Proprietary 13
    • Data DrivenApplications © 2010 Apollo Group – Confidential & Proprietary 14
    • A Data Driven Application: The Faculty Dashboard for Action © 2010 Apollo Group – Confidential & Proprietary 15
    • A Data Driven Application: The Faculty Dashboard for Action © 2010 Apollo Group – Confidential & Proprietary 16
    • Story 1: How detailed logs help © 2010 Apollo Group – Confidential & Proprietary 17
    • Story 1: How detailed logs help © 2010 Apollo Group – Confidential & Proprietary 18
    • Story 2: The Carnegie Learning Math Tutor Enhanced Activities: Adaptive CL’s Cognitive Tutor provides adaptive online curriculum in high school and middle school math. – Interactive lessons – Practice problems – Response-sensitive feedback and support (e.g. hints, examples) – Intelligent guidance through curricular units, with detailed tracking of skill proficiency – Personalized preferences © 2010 Apollo Group – Confidential & Proprietary 19
    • Example Features from Detailed Logs from the Math Tutor Baker, et.al: Towards Sensor Free Affect Detection in Cognitive Tutor Algebra, retrieved from http://users.wpi.edu/~rsbaker/publications.htmlFrustration Engaged ConcentrationThe percent of past actions on The minimum number of previous incorrectthe skills involved in the clip that actions and help requests for any skill in thewere incorrect. clip.Were there any actions in the clip Among the skills involved in the clip, thewhere the student made a wrong minimum value for previous incorrect actionsanswer rather than requesting and help requests for that skill.help when their probability of The duration (in seconds) of the fastest actionknowing the skill was under 0.7? in the clip. The percentage of clip actions involving a hint followed by an error. © 2010 Apollo Group – Confidential & Proprietary 20
    • Why the features matterFrom Stephen Fancsali, Variable Construction and Causal Discovery forCognitive Tutor Log Data: Initial Results, Educational Data Mining 2012 Helps design “intervention” features in the data driven math product to help the learner © 2010 Apollo Group – Confidential & Proprietary 21
    • Questions? © 2010 Apollo Group – Confidential & Proprietary 22
    • Sessions will resume at 4:30pm Page 23