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Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
Kalvi: An Adaptive Tamil m-Learning System
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Kalvi: An Adaptive Tamil m-Learning System

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  • 1. KALVI: An Adaptive Tamil m-Learning System Keshava Rangarajan Chief Architect, Landmark (Halliburton) Corporation Jayaradha Natarajan Software consultant, TIBCO Arivoli Tirouvingadame Principal Member of Technical Staff, Oracle America, Inc.
  • 2. AcknowledgementsThe proposed Kalvi Learning Management System isbased on the Sakai project. A free trial hosted instance ofthe Sakai CLE from Longsight(https://trysakai.longsight.com/portal) was used duringthis research. The authors would like to acknowledge thecontributions of all the people involved in Sakai projectand Longsight, and their numerous colleagues.
  • 3. கல்வி
  • 4. What is Learning Management System ?•Wikipedia definition: A Learning Management System (LMS) is a softwareapplication for the administration, documentation, tracking, reporting anddelivery of education courses or training programs. Delivery of online materials and Tracking and Registration for courses reporting of instructor lead e- learning learning courses LMS
  • 5. What are the characteristics of LMS ?•Systems are web-based to facilitate “ anytime, any place,any pace” access to training content and administration•A robust LMS should be able to do the following: •centralize and automate administration •use self-service and self-guided services •assemble and deliver learning content rapidly •consolidate training initiatives on a scalable web-based platform •support portability and standards Delivery of online materials and Tracking and Registration for •personalize content and enable instructor lead e- courses reporting of learning knowledge reuse learning courses •deliver online training and webinars LMS
  • 6. What is Content Management System ?•Wikipedia definition: A content management system (CMS) is a computerprogram that allows publishing, editing and modifying content as well asmaintenance from a central interface. கல்விக் களஞசியம் (CMS)
  • 7. What are the characteristics of CMS ?•CMS systems provide procedures to manage workflow in a collaborativeenvironment. These procedures can be manual steps or an automatedcascade.•CMS platforms allow users to centralize data editing, publishing andmodification on a single back-end interface. கல்விக் களஞசியம் (CMS)
  • 8. Anatomy of an academic course … CourseModule 1 Module 2 Module 3 (பகுதி) (பகுதி) (பகுதி)
  • 9. Anatomy of an academic course … CourseModule 1 Module 2 Module 3 (பகுதி) (பகுதி) (பகுதி) Lesson 1 (பாடம்) Module 1 (பகுதி) Lesson 2 (பாடம்)
  • 10. Anatomy of an academic course … CourseModule 1 Module 2 Module 3 (பகுதி) (பகுதி) (பகுதி) Concept 1 (கருத்த) Lesson 1 (பாடம்) Concept 2 Module 1 (கருத்த) (பகுதி) Lesson 2 Concept 1 (பாடம்) (கருத்த)
  • 11. A course is nothing but a directed cyclic graphReinforcement Reinforcement Reinforcementlearning learning learning Learner’s Learner’s Modules/ transition transition Modules/ Modules/ Concepts/ Concepts/ Concepts/ Quizzes Quizzes Quizzes
  • 12. What are the types of Academic courses ? ACADEMIC COURSE Non- adaptive Adaptive
  • 13. Non-adaptive course Module n Module 2 Module 1•Connecting links/arcs are static, pre-determined globally and follow a pre-determined path
  • 14. Adaptive course• Links are initially configured based on the information (descriptive attributes) available about the learner.• Additionally, there are many possible link flow paths.• These paths are conditional, i.e. based on an ongoing evaluation/scoring of the learner’s progress through the topics over a given period.• Additional nodes/topics may be brought in dynamically based on a dynamic evaluation of the learner’s level of knowledge as she/he progresses through the course.• The topics introduced are driven by analytical insight gained from community use.
  • 15. Adaptive course: An adaptive directed cyclic graph 1 2 3 4 5 6 7 8 9 10 23 26 11 27 25 2415 16 17 18 30 12 28 32 29 19 21 22 13 20 31 33 34 35 14
  • 16. What are the existing problems in Tamil LMS ?• Very few modern Learning Management Systems for education via Tamil language especially ones that deliver content typically taught in other languages (like English)• Even if they do exist, these LMS systems deliver content in a static fashion; they do not take into account the user’s preferences, level of skill, learning goals and other factors explicitly into account and use this as the basis for learning content delivery and learn from user activity
  • 17. Data MiningMachine Learning Analytics What are the core pillars of LMS ?
  • 18. Role of Data mining and Machine Learning in LMS • Learning management systems (LMS) and Learning Content management systems (LCMS) deal with volumes of data. • Users consuming the course material leave a trail of data while performing their activities. • These data can and needs to be mined to extract insight into learning patterns, learner groupings, Topic classifications (eg: easy, difficult, etc.). • Machine learning techniques like Dynamic Regression, Support Vector Machines (SVM), Neural Net engines, etc. can be employed to mine the data to extract insight 10101 01111 11100 01010 10100 0010Data Mining Machine Learning
  • 19. What are the tools used in LMS? LMS Machine Learning Intelligence Automation
  • 20. Where does Machine Learning fit in? Descriptive attributes + Knowledge level + Ongoing evaluation/scoring Analytic insight Linguistics (UIMA) + Neural Net Engine/ Text SVM Reinforced-knowledge based (dynamic) course path
  • 21. Role of Analytics in LMS• The broad promise of analytics is that new insights can be gained from in-depth analysis of the data trails left by individuals in their interactions with others, with information, with technology, and with organizations.
  • 22. What are the types of Analytics in LMS ? LMS ANALYTICS Learning Analytics Academic Analytics
  • 23. Learning Analytics• Wikipedia 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. Learning analytics are largely concerned with improving learner success.
  • 24. Academic Analytics• Wikipedia definition: The term for Business Intelligence used in an academic setting. Academic analytics is the improvement of organizational processes, workflows, resource allocation, and institutional measurement through the use of learner, academic, and institutional data. Academic analytics, akin to business analytics, are concerned with improving organizational effectiveness.
  • 25. ADAPTIVE e-LEARNING SYSTEMS
  • 26. Adaptive e-Learning system• An e-learning system should be designed to match students’ needs and desires as closely as possible, and adapt during course progression. It is considered to be adaptive if it is capable of: – Modeling users, monitoring the activities of its users; – Interpreting these on the basis of domain-specific models; – Inferring user requirements and preferences out of the interpreted activities, appropriately representing these in associated models; and – Acting upon the available knowledge on its users and the subject matter at hand, to dynamically facilitate the learning process.
  • 27. Adaptive e-Learning system (Contd.)• Adaptive e-learning system can be described as a personalized system, which is able to: – Perform content discovery and assembly – Provide an adaptive course delivery, an adaptive interaction, and adaptive collaboration support
  • 28. KALVI: AN ADAPTIVE TAMIL m-LEARNING SYSTEM
  • 29. Sakai project• Sakai is a community of academic institutions, commercial organizations and individuals who work together to develop a common Collaboration and Learning Environment (CLE).• The Sakai CLE is used for teaching, research and collaboration.• It is a free, community source, educational software platform distributed under the Educational Community License.• Sakai is a Java-based, service-oriented application suite that is designed to be scalable, reliable, interoperable and extensible.• http://www.sakaiproject.org• Kalvi LMS is based on the Sakai project.
  • 30. What are the components of Kalvi LMS ? Kalvi LMS Kalvi Server Kalvi Client
  • 31. KALVI architecture
  • 32. Kalvi Server• Supports all the full-fledged features of a typical LMS.• There is a central repository of the offered Course list.• Adaptive Learning system is responsible for making the LMS adaptive.• All data is persisted in a central backend database.• Educators: Build and publish new courses via the publishing site.• Students: Search the course list and select their courses of interest and take them via the community site.
  • 33. Kalvi Client• Supports both web based and mobile clients.• Students can take a course via mobile devices like iPad, iPhone, Android based devices, etc.• The mobile client downloads the course from the server and saves it locally. Along with the course, the client piece of the Adaptive learning system pertinent to the course is also downloaded to the mobile device.• The student then takes the course in the mobile device.• While taking a course from the mobile device, it is not required to stay connected to the server. That is, courses can be taken from the mobile devices both in online and offline modes.• All the data obtained by monitoring and recording student activities during the course life cycle are persisted in a local database in the mobile device.• When they are connected, the Kalvi server and client can sync up periodically.
  • 34. A typical course taken from iPad
  • 35. A typical course taken from iPhone
  • 36. Ubiquitous application of Adaptive LMS• Irrespective of the subjects and courses offered, the demography served, and the medium of languages delivered to, the learning methodologies and techniques are the same as they broadly rely on data mining, machine learning and analytics to deliver adaptive learner-centric content in mobile form factors for the current and next generation of learners.• The promising aspect is that the proposed adaptive LMS system could be applied ubiquitously !
  • 37. Conclusion and future work …• The key barrier here is not the veracity of the concept or the implementation of the LMS but it is their incorporation into the current educational processes and culture which is a rather static.• This requires evangelization as well as a high level of engagement from all participants in the education process to effect a change.
  • 38. Software Tech stack1.Sakai project2.Android App development3.iOS App development
  • 39. References•U.S. Department of Education - "Enhancing Teaching and Learning ThroughEducational Data Mining and Learning Analytics", an Issue brief.http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf•George Siemens & Dragan Gasevic, Caroline Haythornthwaite & Shane Dawson,Simon Buckingham Shum & Rebecca Ferguson, Erik Duval & Katrien Verbert,Ryan S. J. d. Baker - Society for Learning and Analytics Research - "OpenLearning Analytics: an integrated & modularized platform", July 28, 2011http://solaresearch.org/OpenLearningAnalytics.pdf•http://www.sakaiproject.org/•http://en.wikipedia.org/wiki/Sakai_Project•http://en.wikipedia.org/wiki/Machine_learning•http://www.longsight.com/•https://trysakai.longsight.com/portal•https://moodle.org/•www.apple.com/ipad•www.apple.com/iphone

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