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Scaling up a Web-Based Intelligent Tutoring System Jozsef Patvarczki, Shane Almeida, and Neil Heffernan Computer Science Department Our research team has.

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Presentation on theme: "Scaling up a Web-Based Intelligent Tutoring System Jozsef Patvarczki, Shane Almeida, and Neil Heffernan Computer Science Department Our research team has."— Presentation transcript:

1 Scaling up a Web-Based Intelligent Tutoring System Jozsef Patvarczki, Shane Almeida, and Neil Heffernan Computer Science Department Our research team has built a web-based tutor, located at www.ASSISTment.org [1], that is used by hundreds of students a day in Worcester and surrounding towns The system’s focus is to teach 8th and 10th grad mathematics and MCAS preparation.www.ASSISTment.org Accessibility is an important concern for tutoring systems. Students, teachers, and content creators all must have access to the system. Because of widespread Internet access, Web-based tutoring systems have the potential to provide access to many more users than can be reached with client-based software. We will present how the Assistment system can improve performance and reliability with a fault-tolerant scalable architecture. Introduction Web-based systems virtually eliminate much of time and cost of installing software on individual client machines. We have greater control over content distribution. Software updates and configuration changes are easily manageable Data collection is simplified by a centralized system and reports can be available immediately. The disadvantage of server-based systems is scalability as centralization of resources can create bottlenecks. In order to server thousands of users, we must achieve high reliability and scalability at different levels. Two concerns when running the Intelligent Tutor on a central server are: 1) building a scalable server architecture; 2) providing reliable service to researchers, teachers, and students. We will answer several research questions: 1) can we reduce the cost of authoring ITS; 2) how can we improve performance and reliability with a better server architecture. System Scalability and Reliability Results Reference 1.Razzaq, L, Feng, M., Nuzzo-Jones, G., Heffernan, N.T. et. al (2005). The Assistment Project: Blending Assessment and Assisting. 12th Annual Conference on Artificial Intelligence in Education 2005, Amsterdam 2.Chunqiang Tang, et. al. (2007). A Scalable Application Placement Controller for Enterprise Data Centers. Proceedings of the 16 th international conference on World Wide Web 2007, Canada Ruby on Rails, a free Web application framework based on the open-source Ruby programming language, was used to create mockups of new interface components. We reduced the lines of code from roughly 20,000 to around 5,000. Mongrel, a single-threaded web server for Ruby applications, is used to serve content. Because it is single- threaded, multiple Mongrel application servers are used concurrently in a cluster. During peak times, the database server was constantly at maximum capacity while the application servers remained nearly idle waiting for data According to log data, the tutoring portion of our system spends an average of 2.27 seconds processing a request (standard deviation of 3.39) We calculated the average response time of the most expensive operations in the tutor. Most expensive ones: account creation (\Signup"), the welcome page (\Account"), class assignment page (\Assignment"), and loading the first problem of an assignment (\First Problem"). We captured the average response time of the created test curriculum (\Average Response Time") and the total number of users for that particular day (indicated above the each cluster). Fourth Generation: Rails with Thicker Client Architecture The lightweight interface means our software can be used in schools with limited budgets for computing resources. Beyond a reasonably modern web browser with support for JavaScript, no special software or third- party applications are required to use our system. With a server-based architecture, all changes in content and software happen on our systems and we do not need to push updates to clients. In initial testing of the JavaScript implementation of the tutor, our system spends an average of 0.08 seconds processing a request (standard deviation of 1.43). With just six Mongrel servers, our system is now posed to handle 75 requests per second. Contact: Neil Heffernan, nth@wpi.edunth@wpi.edu Horizontal scaled configuration -Scalable, Fault-tolerant, and Dynamically configurable Architecture 157


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