Kalvi: An Adaptive Tamil m-Learning SystemDocument Transcript
Kalvi: An Adaptive Tamil mLearning System Keshava Rangarajan, Chief Architect, Landmark (Halliburton) Corporation Jayaradha Natarajan, Software consultant at TIBCO Arivoli Tirouvingadame, Principal Member of Technical Staff, Oracle America, Inc.Abstract: Learning Management Systems (LMS) in Tamil are at an early stage today.They typically model users as a homogenous group with content representing a staticstructural organization of the course material determined by the educator. In most cases,LMS users who are the primary consumers, represent heterogeneous groups with diverselearning characteristics, needs and goals. This lack of sophistication in LMS isparticularly noticeable when they are used to deliver course content that are typicallytaught in languages other than Tamil. The adaptation of e/m-education systems to anindividual or to a group based on their characteristics, expectations, knowledge,background and preferences of the students is understood as critical but is under servedtoday. Emphasis is moving slowly towards learner-oriented platforms and putting thelearner’s expectations, motivations, habits, learning styles, needs, etc. as the focus ofinterest. This paper delves into an LMS for Tamil language education. It also exploresthe mobility aspect of the LMS systems, which makes the e-learning systems availablevia mobile devices like iPad, iPhone, Android based devices, etc., thus making the LMSmuch more accessible and in line with current computing trends.Keywords: Tamil, learning management systems, LMS, Data mining, Machine learning,Analytics, e-learning, m-learning, adaptive learning, mobile devicesIntroductionThis paper proposes Kalvi, an adaptive Tamil mLearning System, which is based on theSakai project. Sakai is a community of academic institutions, commercial organizationsand individuals who work together to develop a common Collaboration and LearningEnvironment (CLE). The Sakai CLE is used for teaching, research and collaboration. Itis a free, community source, educational software platform distributed under theEducational Community License. Sakai is a Java-based, service-oriented application suitethat is designed to be scalable, reliable, interoperable and extensible.Anatomy of an academic courseBefore we begin, let’s first take a closer look at the structure of a typical educationalcourse. A course typically consists of an ordered sequence of learning modules. Each ofthese learning modules consists of a sequence of topics/lessons that introduce andillustrate concepts. The lessons could include quizzes at various points that test thelearner’s grasp of concepts and reinforces previously learned concepts. Such a coursecan be viewed as a directed, typically acyclic graph where modules and concepts (andpotentially quizzes) form the nodes. These nodes are connected by links that represent thelearner’s transitions from topic to topic. Educators and students progress via links acrossthe vertices (modules and concepts) from start to end during the life cycle of a course.
Non-adaptive and adaptive coursesIn a non-adaptive course, the connecting links/arcs are static, pre-determined globallyand follow a pre-determined path. In an adaptive course, firstly, the links are initiallyconfigured 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 overa given period. Additional nodes/topics may be brought in dynamically based on adynamic evaluation of the learner’s level of knowledge as she/he progresses through thecourse. The topics introduced are driven by analytical insight gained from communityuse.Existing problemsTo summarize, existing problems in Tamil LMSs are as follows: 1. There are very few modern Learning Management Systems for education via Tamil language especially ones that deliver content typically taught in other languages (like English) 2. 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 activityData mining, Machine learning and Analytics in LMSData mining, Machine learning and Analytics forms the core of LMS systems. • Data mining and Machine learning: Learning management systems and Learning Content management systems 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 • Analytics: Analytics plays a big role in LMSs. 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. At a high level, the following are the types of analytics of interest here: • Learning Analytics: Wikipedia defines Learning Analytics as 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.
• Academic Analytics: Wikipedia describes Academic analytics as 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.Adaptive e-learning systemsNow let’s take a look at adaptive e-learning systems. An e-learning system should bedesigned to match students’ needs and desires as closely as possible, and adapt duringcourse progression. It is considered to be adaptive if it is capable of: 1. Modeling users, monitoring the activities of its users; 2. Interpreting these on the basis of domain-specific models; 3. Inferring user requirements and preferences out of the interpreted activities, appropriately representing these in associated models; and 4. Acting upon the available knowledge on its users and the subject matter at hand, to dynamically facilitate the learning process.Thus, adaptive e-learning system can be described as a personalized system, which isable to: 1. Perform content discovery and assembly, 2. Provide an adaptive course delivery, an adaptive interaction, and adaptive collaboration supportArchitecture of KALVI systemThe Kalvi system proposed in the paper is built on Sakai LMS platform.At a high level, the Kalvi system has two parts to it: 1. Kalvi server 2. Kalvi clientKalvi serverThe Kalvi server is the backend module. It supports all the full-fledged features of atypical LMS. There is a central repository of the offered Course list. Educators can buildand publish new courses via the publishing site. The students can search the course listand select their courses of interest and take them via the community site. The server hasthe Adaptive Learning system as well, which is responsible for making the LMSadaptive. All data is persisted in a central backend database.
Here is the architecture diagram of Kalvi:Kalvi clientKalvi supports both web based and mobile clients. Students can take a course via mobiledevices like iPad, iPhone, Android based devices, etc. The mobile client downloads thecourse from the server and saves it locally. Along with the course, the client piece of theAdaptive 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 themobile device, it is not required to stay connected to the server. That is, courses can betaken from the mobile devices both in online and offline modes. All the data obtained bymonitoring and recording student activities during the course life cycle are persisted in alocal database in the mobile device. When they are connected, the Kalvi server and clientcan sync up periodically.
Here is proposed sample screen shot of a typical Tamil course taken from iPad:
Here is proposed sample screen shot of a typical Tamil course taken from iPhone:Concluding thoughts and future workThe key barrier here is not the veracity of the concept or the implementation of the LMSbut it is their incorporation into the current educational processes and culture which is arather static. This is more so in Tamil LMSs and Tamil educational systems today. Thisrequires evangelization as well as a high level of engagement from all participants in theeducation process to effect a change. But this is the clear trend forward. Irrespective ofthe subjects and courses offered, the demography served, and the medium of languagesdelivered to, the learning methodologies and techniques are the same as they broadly relyon data mining, machine learning and analytics to deliver adaptive learner-centric contentin mobile form factors for the current and next generation of learners. The promisingaspect of this paper is that the proposed adaptive LMS system could be appliedubiquitously.
AcknowledgementThe proposed Kalvi LMS is based on the Sakai project. A free trial hosted instance of theSakai CLE from Longsight (https://trysakai.longsight.com/portal) was used during thisresearch. The authors would like to acknowledge the contributions of all the peopleinvolved in Sakai project and Longsight, and their numerous colleagues.References • U.S. Department of Education - "Enhancing Teaching and Learning Through Educational 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 - "Open Learning Analytics: an integrated & modularized platform", July 28, 2011 http://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