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  1. An Interac Learning

  2. tive and Personalized Management System Jing Zhao, BabakForouraghi Computer Science Department Saint Joseph’s University

  3. Abstract. Virtual learning environments (VLE) provide up-to-date education and training for individuals, and in some cases they can generate personalized feedback based on the learner’s performance. Unlike other VLE’s available to-date, the learning system developed in this work extends the basic instructional and assessment capabilities of a typical VLE by dynamically creating a cloud-based computer laboratory that is needed by computer science students. Specifically, the basic capabilities of Moodle are enhanced by developing two new modules: a virtual lab module (VLM) and a study progress module (SPM). VLM utilizes Amazon’s EC2 cloud-based web services technology in order to create a personalized experimental environment especially suited for Computer Science students although the same concept can easily be applied to other disciplines. SPM, on the other hand, evaluates student progress in terms of specific learning objectives and offers personalized guidance using the Apriori data mining technique. An implemented cloud-based Web Technologies laboratory highlights the advantages of the proposed approach.

  4. Open-source web application • Widely used to manage online courses • Basic functions are delivered in Standard Moodle Package • Extensible by developing customized modules Moodle System

  5. Amazon Web Services (AWS) is a widely used cloud computing platform which enables users to virtually run everything in the cloud • Elastic Compute Cloud (EC2) and Simple Storage Service (S3) are the most well-known services in AWS • EC2 offers computing resources as a service • S3 is a scalable storage service in the cloud Amazon Cloud

  6. VLM Integrates Amazon EC2 cloud to offer virtual experimental environments (VM) for learners. • Students can perform the assigned work on their personalized VM and Elastic Block System (EBS), which is used to store students’ persistent data. • All operations against VM can be done within VLM. Virtual Lab Module

  7. Cloud-based virtual lab

  8. Virtual Lab Module Workflow

  9. Extensively used in market-basket data analysis to discover association rules or relationships between various items • An association rule is denoted by such that and where is a set of items. • The support for is defined as the percentage of the transactions that contain Apriori Data Mining

  10. The confidence level for rule is defined as the ratio of the number of transactions that contain to the number of transactions that contain : • For example, rule {bread, milk}{cheese} means that the customers who buy bread and milk are highly likely buy cheese together. Aprior Data Mining (2)

  11. SPM evaluates student progress in terms of specific learning objectives and offers personalized guidance. • The core component of SPM is Apriori Data Mining which uses students’ grades of all gradable activities to reveal the mistakes that students always make together (association rules) • Rules are an invaluable tool for instructors to better understand and predict student performance Study Progress Module

  12. Study Progress Module Architecture

  13. Table 1. Students’ section grades Table 2. Transactions in Apriori algorithm

  14. Table 3. Itemset support Table 4. Association rules

  15. Virtual Lab and the Student Progress modules were developed and incorporated in the Moodle course management system • VLM allows students to have access to a virtual environment built on top of the Amazon EC2 technology • SPM can monitor the learner’s progress using the Apriori data mining approach Conclusion