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Subject: search.filters.subject.Prediction of scholastic success

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    Prediction of students' performance through data mining : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Auckland, New Zealand
    (Massey University, 2020) Umer Baloch, Rahila
    Government funding to higher education providers is based upon graduate completions rather than on student enrollments. Therefore, unfinished degrees or delayed degree completions are major concerns for higher education providers since these problems impact their long-term financial security and overall cost-effectiveness. Therefore, providers need to develop strategies for improving the quality of their education to ensure increased enrollment and retention rates. This study uses predictive modeling techniques for assisting providers with real-time identification of struggling students in order to improve their course retention rates. Predictive models utilizing student demographic and other behavioral data gathered from an institutional learning platform have been developed to predict whether a student should be classed as at-risk of failing a course or not. Identification of at-risk students will help instructors take proactive measures, such as offering students extra help and other timely supports. The outcomes of this study will, therefore, provide a safety net for students as well as education providers in improving student engagement and retention rates. The computational approaches adopted in this study include machine learning techniques in combination with educational process mining methods. Results show that multi-purpose predictive models that were designed to operate across a variety of different courses could not be generalized due to the complexity and diversity of the courses. Instead, a meta-learning approach for recommending the best classification algorithms for predicting students’ performance is demonstrated. The study reveals how process-unaware learning platforms that do not accurately reflect ongoing learner interactions can enable the discovery of student learning practices. It holds value in reconsidering predictive modeling techniques by supplementing the analysis with contextually-relevant process models that can be extracted from stand-alone activities of process-unaware learning platforms. This provides a prescriptive approach for conducting empirical research on predictive modeling with educational data sets. The study contributes to the fields of learning analytics and education process mining by providing a distinctive use of predictive modeling techniques that can be effectively applied to real-world data sets.
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    Data mining techniques to improve predictions accuracy of students' academic performance : a case study with Xorro-Q : a thesis presented in partial fulfilment of the requirements for Master of Information Science (IT) at Massey University, Auckland, New Zealand in 2018
    (Massey University, 2018) Ramaswami, Gomathy Suganya
    Recent research in analytics has assisted policy makers capitalize on their ever-increasing data repositories and make data-driven predictions to create a vision for developing strategies to achieve their business targets. This is especially relevant in educational environments where data mining techniques can be applied to make predictions around students’ academic performance. This can help educators align a teaching strategy which encourages and assists students with their learning. Suit-able pedagogical support can be provided to enhance the overall student learning experience. This study is in the educational domain where student-related course data has been used to extract insights on student performances over the study period. Exten-sive data collected from an educational tool (Xorro-Q) used in an engineering course delivery has aided this investigation. Data collected from Xorro-Q comprised stu-dent scores from real-time and self-paced activities set by educators over a 12-week semester period along with students’ final Exam scores and scores from a compulsory prerequisite course. Popular data mining techniques have been applied to predict the academic performance of students based on data extracted from Xorro-Q. This is done by training the classifier using four different algorithms, namely, Naive Bayes, Logistic regression, K nearest neighbour and Random Forest. Process mining techniques have been applied along with the general features to find out the effectiveness, such as improvement in accuracy of predictions. The study has further implications in enhancing value of the role of analytics for predictive modelling by incorporating process mining features in the training set of data.
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    Emotional intelligence : a requisite for schools : a thesis presented in partial fulfilment of the requirements for the degree of Master of Education at Massey University
    (Massey University, 2005) Mapara, Leeloobhai Varajdas
    Emotional intelligence has been widely accepted in the world of work since 1995 and has had a tremendous impact on our understanding of the contribution that emotional reasoning makes to the quality and functioning of the workplace. Recently, the educational sector has made some timid attempts to study the impact of emotional intelligence. New theories of intelligence have been developed and studies now concentrate on establishing the contribution that emotional intelligence makes to overall educational achievement. The aim of this research was to study the relationship between emotional intelligence and academic achievement of sixty (60) sixteen to eighteen year old students in two Auckland schools. The Bar-On Emotional Quotient Inventory: Youth Version (Bar-On EO-i: YV) Short Form Questionnaire was be given to these students and matched with their end of year results. The working hypothesis was that there is a positive correlation between student's EQ score and their end of year results.