International Research Symposium on Pure and Applied Sciences (IRSPAS)
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Item E-marker: Moodle plugin tool to grade essay type questions(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Jayakody, J.R.K.C.Moodle is one of well-known Learning Management Systems (LMS) that helps academics to create varied assessment types such as Multiple Choice Question (MCQ), tutorials, short question and assignments etc. Typically, MCQ questions and small essay type questions are used as formative assessment techniques to evaluate students’ performance. MCQ question marking is automated and straight forward in Moodle whereas short essay type questions are marked manually by academics. Subsequently sizes of the class and diversity of courses and assessments are increasing day by day. Therefore, it is a challenging practice to evaluate and grade short type questions on time. Hence the present research was conducted to build a Moodle plug-in to mark essay type questions automatically. Two hundred short essay type questions of the Software Engineering course of the Department of Computing and Information System at University of Wayamba were used as the initial dataset. Initially, the research was conducted in a few steps. Statistical features were derived with Natural Language Processing (NLP) techniques such as number of word used in the answer, number of name entities, number of distinct words, correct words and incorrect words. In addition, several chunking rules were developed to identify the correct usage of the languages. Next, semantic mapper module was developed to extract the semantic features based on provided answers. Finally, several experiment were done to identify the most appropriate feature set to develop a logistic regression model with scikit learning machine learning package. The final model showed an accuracy of 82%.Item Natural language processing framework: WordNet based sentimental analyzer(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Jayakody, J.R.K.C.Sentimental analysis is a technique which is used to classify different types of documents as positive, negative or neutral. Hand written form, mails, telephone surveys or online feedback forms are used to get customer feedbacks about products and services. In fact, sentimental analysis is the technique which is used to mine online and offline customer feedback data to gain insight of product and services automatically. Since business types are different, it is quite challenging to develop a generic sentimental analyzer. Therefore, this ongoing research focused on developing a generic framework that can be extended further in future to develop the best generic sentiment analyzer. Several online customer feedback forms were used as the dataset. Webpage scraping module was developed to extract the reviews from web pages and chunk and chink rules were developed to extract the comparative and superlative adverbs to build the knowledge base. The web site (Thesaurus.com) was used to build the test data with synonyms of good, bad and neutrals. Next WordNet database was used with different semantic similarity algorithms such as path similarity, Leacock-Chodorow-similarity, Wu- Palmer-Similarity and Jiang-conrath similarity to test the sentiments. Accuracy of this framework was improved further with the vector model built with natural language processing techniques. Label dataset of amazon product reviews provided by University of Pennsylvania were used to test the accuracy. Framework was developed to change the multiplied value based on the domain. The accuracy of the final sentiment value was given as a percentage of the positive or negative type. This framework gave fairly accurate results which are useful to generate good insights with user reviews.Item Plagiarism detection educational tool: A student’s assessments similarity checker(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Jayakody, J.R.K.C.Plagiarism is very common among students in higher education institutes due to many reasons such as lack of knowledge about the subject, poor academic writing skills or difficulty in meeting a given deadline. The most popular method of plagiarism is to use the online web pages or e-books as it is an easy effort to get the contents from internet, change it and to submit as an original work. Hence, there are bunch of online software tools as well as offline tools exists to detect the plagiarism. However, there are less software tools to identify the copied works among students. Therefore, in this research I developed a plagiarism detection tool to identify the plagiarized assignments or tutorial submitted. Individual assignments and tutorials which had been given to software engineering courses of the Department of Computing and Information System of Wayamba University were used as the dataset. Natural language processing algorithms were developed to derive the statistical features from the assignments such as bag of words, most frequent words, number of words, name entities and paragraphs etc. Moreover, Term Frequency and Inverted Document Frequency (TF-IDF) module was developed to generate a similarity index value among assignments. In addition, Latent semantic analysis module was developed with the word dictionary and vector corpus. Features that were generated and extracted from every module were used to identify the clusters of similar assignments. K-mean clustering algorithms in rapid minor were used to identify the clusters. Most of the submitted assignments were identified with number of clusters. Once the clustering results were verified with the students, it was evident that fairly good results were the given by the automatic cluster classification.