Mining feature-opinion in educational data for course improvement

In academic institutions, student comments about courses can be considered as a significant informative resource to improve teaching effectiveness. This paper proposes a model that extracts knowledge from students' opinions to improve and to measure the performance of courses. Our task is to use user-generated contents of students to study the performance of a certain course and to compare the performance of some courses with each others. To do that, we propose a model that consists of two main components: Feature extraction to extract features, such as teacher, exams and resources, from the user-generated content for a specific course. And classifier to give a sentiment to each feature. Then we group and visualize the features of the courses graphically. In this way, we can also compare the performance of one or more courses

Arabic opinion mining using combined classification approach

In this paper, we present a combined approach that automatically extracts opinions from Arabic documents. Most research efforts in the area of opinion mining deal with English texts and little work with Arabic text. Unlike English, from our experiments, we found that using only one method on Arabic opinioned documents produce a poor performance. So, we used a combined approach that consists of three methods. At the beginning, lexicon based method is used to classify as much documents as possible. The resultant classified documents used as training set for maximum entropy method which subsequently classifies some other documents. Finally, k-nearest method used the classified documents from lexicon based method and maximum entropy as training set and classifies the rest of the documents. Our experiments showed that in average, the accuracy moved (almost) from 50% when using only lexicon based method to 60% when used lexicon based method and maximum entropy together, to 80% when using the three combined methods

Mining students data to analyze e-Learning behavior: A Case Study

Educational data mining concerns with developing methods for discovering knowledge from data that come from educational environment. In this paper we used educational data mining to analyze learning behavior. In our case study, we collected students' data from DataBase course. After preprocessing the data, we applied data mining techniques to discover association, classification, clustering and outlier detection rules. In each of these four tasks, we extracted knowledge that describes students' behavior

Mining opinions in user-generated contents to improve course evaluation

The purpose of this paper is to show how opinion mining may offer an alternative way to improve course evaluation using students’ attitudes posted on Internet forums, discussion groups and/or blogs, which are collectively called user-generated content. We propose a model to mine knowledge from students’ opinions to improve teaching effectiveness in academic institutes. Opinion mining is used to evaluate course quality in two steps: opinion classification and opinion extraction. In opinion classification, machine learning methods have been applied to classify an opinion as positive or negative for each student’s posts. Then, we used opinion extraction to extract features, such as teacher, exams and resources, from the user-generated content for a specific course. Then we grouped and assigned orientations for each feature

Mining Changes of Opinions Expressed by Students to Improve Course Evaluation

Opinion mining can be used in many applications. In universities, students' opinions about courses can be considered as a significant informative resource to improve the effectiveness of education. Past works in this area focused on direct mining of students' opinions in regard to the courses. The aim of this paper is to develop a system which detects changes of students' opinions. Understanding such changes can help the management improve course evaluation in academic institutions. For course evaluation, knowing what is changing and how it has changed is crucial as they allow the management to provide the right course features such as teachers, contents, teaching materials and exams which need to satisfy the students' needs. In our work, we present a strategy for mining opinion changes based on the associative classification approach. Firstly, we collect opinions from students in two different semesters in regard to a specific course. Then, we extract rules using association rules. For this purpose, we detect and measure students' change of opinion from one semester to another. We describe types of opinions which can be detected by the students. Finally, we shed light on some of the examples which we have spotted from each type of opinion change

Filtering Spam E-Mail from Mixed Arabic and English Messages: A Comparison of Machine Learning Techniques.

Spam is one of the main problems in emails communications. As the volume of non-english language spam increases, little work is done in this area. For example, in Arab world users receive spam written mostly in arabic, english or mixed Arabic and english. To filter this kind of messages, this research applied several machine learning techniques. Many researchers have used machine learning techniques to filter spam email messages. This study compared six supervised machine learning classifiers which are maximum entropy, decision trees, artificial neural nets, naïve bayes, support system machines and k-nearest neighbor. The experiments suggested that words in Arabic messages should be stemmed before applying classifier. In addition, in most cases, experiments showed that classifiers using feature selection techniques can achieve comparable or better performance than filters do not used them

Arabic Text Genre Classification

Text genre is a type of written text. Arabic text genre classification predicts genre of specific text document written in Arabic independent of its topic. In this paper, an approach was proposed that takes an Arabic document and classify it into one of four genres which are advertisements, news, subjective and scientific documents. Since the frequency of words approach produces a low performance when used in the genre, an attempted was made to generate attributes based on the style of the text. This approach evaluated using corpus collected for this purpose. Using four machine learning methods, our approach compared with the word frequency approach, and it found that our approach is better than this mainstream approach. It, also, found that predicting subjectivity and scientific genre is more accurate than predicting advertisements and news

Improving Teacher Performance using Data Mining

This study examines the factors associated with the assessment of teacher's performance. To improve the teacher performance, good prediction of training course that will be obtained by teacher in one way to reach the highest level of quality in Teacher performance, but there is no certainty if there are accurately determine Teacher advantage and increase its efficiency through this session. In this case the real data is collected for teachers from the Ministry of Education and Higher Education in Gaza City. It contains data from the academic qualifications for teachers as well as their experience and courses. The data includes three years and questionnaire contains many questions about the course and length of service in the ministry. We propose a model to evaluate their performance through the use of techniques of data mining like association, classification rules (Decision Tree, Rule Induction, K-NN, Naïve Bayesian (Kernel)) to determine ways that can help them to better serve the educational process and hopefully improve their performance and thus reflect it on the performance of teachers in the classroom. In each tasks, we present the extracted knowledge and describe its importance in teacher performance domain

Blood tumor prediction using data mining techniques

Healthcare systems generate a huge data collected from medical tests. Data mining is the computing process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an exception; there are many test data can be collected from their patients. In this paper, we applied data mining techniques to discover the relations between blood test characteristics and blood tumor in order to predict the disease in an early stage, which can be used to enhance the curing ability. We conducted experiments in our blood test dataset using three different data mining techniques which are association rules, rule induction and deep learning. The goal of our experiments is to generate models that can distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our results using different metrics applied on real data collected from Gaza European hospital in Palestine. The final results showed that association rules could give us the relationship between blood test characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an explanation of rules that describes both tumor in blood and normal hematology

Automated recognition of urinary microscopic solid particles

Urine analysis reveals the presence of many problems and diseases in the human body. Manual microscopic urine analysis is time-consuming, subjective to human observation and causes mistakes. Computer aided automatic microscopic analysis can help to overcome these problems. This paper introduces a comprehensive approach for automating procedures for detecting and recognition of microscopic urine particles. Samples of red blood cells (RBC), white blood cells (WBC), calcium oxalate, triple phosphate and other undefined images were used in experiments. Image processing functions and segmentation were applied, shape and textural features were extracted and five classifiers were tested to get the best results. Repeated experiments were done for adjusting factors to produce the best evaluation results. A good performance was achieved compared with many related works