International Symposium on ICT for Sustainable Development (ICTSD 2016)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/13967

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Author: search.filters.author.Jayakody, J.R.K.C.

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    Question paper analysis with Natural Language Processing
    (Department of Zoology and Environmental Management, University of Kelaniya, Kelaniya, Sri Lanka., 2016) Jayakody, J.R.K.C.; Perera, P.L.M.
    “Art of Paper Setting” is very popular terminology when it is come to education examination process. As it is an “Art”, teachers should passionate enough to prepare a better question paper which will reflect the educational objectives. There are few steps involved in the process of paper setting and analysis of the paper is the most important element among those steps as it is only indicator of the alignment of questions with intended objectives. When it comes to the analysis process, human intelligence can analyze questions more easily. But implementing similar intelligent systems with computer intelligence is a real challenge. Therefore the purpose of this research is to build a computer intelligent system which can analyze and classify questions. When it is come to classification standards, Bloom’s Taxonomy is a world recognized cognitive skills classification standard. Therefore this standard was used as the guide for the questions categorization of question papers. In the analysis phase, natural language processing techniques were used to analyze the raw text. With these techniques, first the row texts were processed and then the meaningful features of the questions such as verb similarity stem pattern similarity and stem meaning similarity were extracted. Next with machine learning techniques, a model (the brain of the system) was trained by feeding extracted question features. For the model training, several classification algorithms such as Multinomial Naive Bayes Classifier, Bernoulli Naive Bayes Classifier, Logistic Regression Classifier, Stochastic Gradient Descent Classifier, C-Support Vector Classifier and Linear Support Vector Classifier were used. Accuracy levels of each and every classification algorithms were measured with changing the size of the training data set and the optimum algorithm was selected for model training. Finally the model was trained with the optimum algorithm and that model was used to classify the unseen questions. The ultimate model was fine tuned to gain 80% classification accuracy.
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    Identification of varying standard of student based on Moodle Pattern Identification Business Intelligence Tool
    (Department of Zoology and Environmental Management, University of Kelaniya, Kelaniya, Sri Lanka., 2016) Jayakody, J.R.K.C.; Allagalla, W.H.P.
    Learning Management Systems (LMS) takes place as an interaction in the internet environment, with different methods for retrieving class content, materials, subject related information, resources and student teacher interactions. Since the interaction details of the LMS such as Moodle is stored in its database as log files, those logs can be used to analyze and understand the weak and good students. Discussions, Forums, Assignments, wikis and the course are the main categories of the logs which are resided in the logs. This research was done to develop a Business intelligence (BI) tool to identify the able students and less able students log patterns with Moodle which is immensely helpful to identify the less able students very early and find remedies to improve their educational standard. Moodle dataset of MSc Business Management students of University of Moratuwa was used for the research. Store procedures were written in java to extract the xml format data to store the log details to mysql server. BI capabilities such as organizational memory, information integration, insight creation and visualization were covered. Sql server 2012 was used as the main database to develop the data warehouse . Dimensions were created to generate the necessary cubes. Apart from that sql server integration services were used to enhance the Extract-Transform-Load (ETL) process. Data cubes were analyzed with Multidimensional Expressions (MDX) queries. finally dashboards were built using power BI too. Power Pivot graph and the power table were used to present interactive details to the end users. Number of patterns was realized to identify the less able students. Based on assignment submission, number of time a user used the system, number of times pages and resources were accessed, new patterns were identified and presented to the users to get the decision which is immensely helpful to the academics and the students.
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    Moodle system performance analysis of Wayamba University
    (Department of Zoology and Environmental Management, University of Kelaniya, Kelaniya, Sri Lanka., 2016) Jayakody, J.R.K.C.; Ekanayaka, S.M.E.W.P.M.; Ubesinghe, U.W.A.C.C.
    Moodle is an online management system which was built for online learning. Moodle has already become a term of its own synonymous with a software package designed to help educators create quality online education. Most of the higher educational courses are conducted based on Moodle system. Due to different factors Moodle system performance degrade which makes a difficult task to academics to conduct the Moodle based courses. Research was done mostly with the Moodle resources of Wayamba University to check the response time of the pages for the users requests such as login page, video resource page, forum pages and discussion pages. System starts response and system finishes response was used as the performance matrices for Moodle resources. Several factors such as computer parameters (Speed of the CPU, Number of cores, Capacity of the disk, Main memory available capacity), network parameters (Network structure, Types of switches, routers ), Moodle services (login , view image ,view video files) were considered to evaluate the Moodle system performances. Apache JMeter was used as the testing tool. JMeter was used to simulate a heavy load on a server to test its strength or to analyze overall performance under different load types. Thread groups, config elements, timers, samplers and listeners were used extensively to check the performance. Testing data were collected during 20 working days. According to the analysis, number of users, size of the resources, and speed of the CUP with response time showcase a significant negative linear relationship.