Research Phases of University Data Mining Project Development

Educational Data Mining becomes one of the challenging new research fields where data mining methods and tools could help universities in taking timely and data analysis based management decisions, thus contributing to gaining competitive advantages in their successful policy introduction. This paper presents the research activities performed for the implementation of a data mining project initiated in one of the most prestigious Bulgarian universities. The project main goal is to reveal the high potential of data mining applications for university management, referring to the optimal usage of data mining methods and techniques to deeply analyze the collected historical data. That will lead to better understanding the student behavior and building well structured educational process that meets the university policy and supports the management decision making process

Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade

As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of educational data mining (EDM) to understand students\u27 learning experiences is a critical step forward. The adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to exploit the untapped data generated by various student information systems (SIS) and learning management systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live video streaming (LVS) students\u27 online learning behaviours and their performance in their courses. Students\u27 participation and login frequency, as well as the number of chat messages and questions that they submit to their instructors, were analysed, along with students\u27 final grades. Results of the study show a considerable variability in students\u27 questions and chat messages. Unlike previous studies, this study suggests no correlation between students\u27 number of questions/chat messages/login times and students\u27 success. However, our case study reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical framework capable of enabling a deeper and richer understanding of students\u27 learning behaviours and experiences

Process mining online assessment data

Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for generating recommendations and advice to students, for improving management of learning objects, etc. However, most of the traditional data mining techniques focus on data dependencies or simple patterns and do not provide a visual representation of the complete educational (assessment) process ready to be analyzed. To allow for these types of analysis (in which the process plays the central role), a new line of data-mining research, called process mining, has been initiated. Process mining focuses on the development of a set of intelligent tools and techniques aimed at extracting process-related knowledge from event logs recorded by an information system. In this paper we demonstrate the applicability of process mining, and the ProM framework in particular, to educational data mining context. We analyze assessment data from recently organized online multiple choice tests and demonstrate the use of process discovery, conformance checking and performance analysis techniques

Big data processing on educational data mining using pyspark with jupyter notebook

The rapid advancement of the information technology brings new challenges and put new demands on our education system. The process of teaching and learning have moved from classroom to Computer Aided Learning (CAL) system. Big data technology and machine learning plays an important role in Computer Aided Learning (CAL) system due to the massive information or data generated by the system. This leads to the rapid development of data mining in education denote as Educational Data Mining (EDM). The abundance of data collected by the system can be used to analyse, predict and solve many societal issues in the education field such as improve the quality of education, predict as well as monitor educational outcomes. Effective analysing or predicting the future growth of students’ performance can make the Computer Aided Learning (CAL) system a better platform for learning compared to traditional learning. Machine learning techniques were used to get reliable and accurate prediction on students’ performance. Apache Hadoop has been the backbone for big data technology until the emergence of Apache Spark. However, only several researches are done on EDM using Apache Spark. In this dissertation, PySpark was be integrated with Jupyter Notebook to perform EDM on Educational Process Mining (EPM) data set. The Spark MLlib was used to compare four classification algorithms such as Logistic Regression, Naïve Bayes, Decision Tree and Random Forest to deal with EPM data set. Random Forest classifier outperformed other classifiers in Accuracy, Area Under the Precision-Recall(PR) and Area Under the Receiver Operating Characteristic (ROC) although with slightly slower Execution Time in this study. Random Forest classifier are the best classifier when dealing with EDM

Predicting students drop out : a case study

The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%. Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students

Artificial System to Compare Energy Status in the Context of Europe and Middle East

Now-a-days Global economy depends on the supply of energy and proper use of it. Energy is very compelling and critical issues all over the world. But the price of energy especially oil is increasing day by day. It is an obvious duty for all government throughout the world that estimation of cost of Oil for future development. The main purpose of this research is to develop a dynamic future and instant oil price prediction model for Business organization, Ministry of Finance, Ministry of Economic, Oil Company, Think Tank of the Government, Prime-Minister, World Bank Policy Maker, International Monetary Fund (IMF) etc. In this work, we first apply chi square test to separate factors such as demand of Oil and Gas, over population, Increasing rate Industry, completion of Development and etc. We then make a automate comparison of the production and export rate of the Oil and Gas in various countries among Middle East and Europe. The main purpose of applying it is feature selection to data. Degree of freedom is used to P-value (Probability value) for best predicators of dependent variable. After being separation of factors we have had examined the desired outcome using Bayes2019; Networks (BN). The BN helps to determine the actual result based on our input factors. We should bear in mind that our activities for this work are dynamic and our system can inspect dynamically irrespective of any volume of dataset

Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation

Artificial System for Prediction of Studentas Academic Success from Tertiary Level in Bangladesh

Every year a large scale of students in Bangladesh enrol in different Universities in order to pursue higher studies With the aim to build up a prosperous career these students begin their academic phase at the University with great expectation and enthusiasm However among all these enthusiastic and hopeful bright students many seem to become successful in their academic career and found to pursue the higher education beyond the undergraduate level The main purpose of this research is to develop a dynamic academic success prediction model for universities institutes and colleges In this work we first apply chi square test to separate factors such as gender financial condition and dropping year to classify the successful from unsuccessful students The main purpose of applying it is feature selection to data Degree of freedom is used to P-value Probability value for best predicators of dependent variable Then we have classify the data using the latest data mining technique Support Vector Machines SVM SVM helped the data set to be properly design and manipulated After being processed data we used the MATH LAB for depiction of resultant data into figure After being separation of factors we have had examined by using data mining techniques Classification and Regression Tree CART and Bayes theorem using knowledge base Proposition logic is used for designing knowledge base Bayes theorem will perform the prediction by collecting the information from knowledge Base Here we have considered most important factors to classify the successful students over unsuccessful students are gender financial condition and dropping year We also consider the sociodemographic variables such as age gender ethnicity education work status and disability and study environment that may inflounce persistence or academic success of students at university level We have collected real data from Chittagong University Bangladesh from numerous students Finally by mining th