Dissertations (IT & Engineering)

Permanent URI for this community

https://bspace.buid.ac.ae/handle/1234/487

Browse

Browsing Dissertations (IT & Engineering) by Subject "academic performance"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Data mining approach to predict student's selection of program majors
    (The British University in Dubai (BUiD), 2019-06) SIDDARTHA, SHARMILA
    Students in higher education do not have access to sufficient information when selecting their program major. Program administrators cannot easily predict majors that will be undersubscribed early enough to take corrective actions. At the same time, institutional databases have large volumes of data relating to student demographic profiles, course grades and academic performance. There is an opportunity to apply data mining to arrive at a model to predict student selection of a major. The nature of academic data relating to student majors is multi class and imbalanced – there is always a niche major with few students enrolled. Hence this needs special considerations within the area of data mining. The purpose of this study is to develop a data mining approach for predicting student's selection of program majors. The approach includes a methodology to manage data mining projects, sampling techniques to handle imbalanced data and multiclass data, a set of classification algorithms to predict and measures to evaluate performance of models. The methodology used in this study is the systematic literature review to source, evaluate and synthesize current information in this domain and the CRISP-DM to deploy data mining activities. Several data mining techniques such as data exploration, visualization, sampling and evaluation are presented and applied to the academic data. Datamining experiments are deployed in RapidMiner using Decision Trees, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Networks and Gradient Boosted Trees. Balanced sampling, SMOTE – oversampling of minority classes is used to compare results using the confusion matrix, F1-score and the balanced accuracy. Cross validation is applied to train and test performance of models. Naïve Bayes, Decision Trees offered the best predictions across the different sampling techniques. This study presents an approach to design and deploy a data mining project that can be used as a basis for developing systems to enable the selection of student majors.
  • Thumbnail Image
    Item
    Mining Student Information System Records to Predict Students’ Academic Performance
    (The British University in Dubai (BUiD), 2018-11) ABU SAA, AMJED TARIQ MOHAMMAD
    An increasing interest has arisen during the past decade to identify the most important factors influencing students’ performance in higher education, especially by using predictive data mining techniques. This field of research is usually identified as educational data mining. Educational Data Mining (EDM) is the field of study that is concerned about mining useful patterns and predicting student’s academic performance in the field of education. Researchers in this field tend to study different types of students’ factors and attributes that affect their performance and learning outcomes. In this dissertation, 36 research articles out of a total of 420 from 2009 to 2018 were critically reviewed and analyzed by applying a systematic literature review approach. As well as, this dissertation provides a predictive data mining study targeted towards the use of student information systems’ data to predict students’ academic performance. A gap between student information systems and data mining was identified and addressed in this study, which suggests connecting both worlds together creating an intelligent system that is capable to predict student’s failures and low academic performance according to relevant students’ attributes. The main aim of this study is to identify the most commonly studied factors that affect the students’ performance, as well as, the most common data mining techniques applied to identify these factors. Accordingly, this dissertation generated a dataset from a student information system from a local university in the United Arab Emirates. The dataset included 34 attributes of student’s related information, and was having a data size of more than 56,000 records. Empirical results showed that four types of student attributes are responsible for academic performance prediction, including, students’ demographics, students’ previous performance information, course and instructor information as well as some student general information. Additionally, the results also indicated that the most common data mining techniques used to predict and classify students’ factors are decision trees, Naïve Bayes, and artificial neural networks. Finally, a set of data-mining models was compared in order to identify the most suitable one for predicting students’ academic performance from student information systems. Keywords: Educational Data Mining; students’ factors; students’ academic performance; systematic review; data mining techniques; student information systems.