IDENTIFICATION OF STUDENTS AT RISK OF LOW PERFORMANCE BY COMBINING RULE-BASED MODELS, ENHANCED MACHINE LEARNING, AND KNOWLEDGE GRAPH TECHNIQUES

Technologies and online learning platforms have changed the contemporary educational paradigm, giving institutions more alternatives in a complex and competitive environment. Online learning platforms, learning-based analytics, and data mining tools are increasingly complementing and replacing traditional education techniques. However, academic underachievement, graduation delays, and student dropouts remain common problems in educational institutions. One potential method of preventing these issues is by predicting student performance through the use of institution data and advanced technologies. However, to date, scholars have yet to develop a module that can accurately predict students’ academic achievement and commitment. This dissertation attempts to bridge that gap by presenting a framework that allows instructors to achieve four goals: (1) track and monitor the performance of each student on their course, (2) identify at-risk students during the earliest stages of the course progression (3), enhance the accuracy with which at-risk student performance is predicted, and (4) improve the accuracy of student ranking and development of personalized learning interventions. These goals are achieved via four objectives. Objective One proposes a rule-based strategy and risk factor flag to warn instructors about at-risk students. Objective Two classifies at-risk students using an explainable ML-based model and rule-based approach. It also offers remedial strategies for at-risk students at each checkpoint to address their weaknesses. Objective Three uses ML-based models, GCNs, and knowledge graphs to enhance the prediction results. Objective Four predicts students’ ranking using ML-based models and clustering-based KGEs with the aim of developing personalized learning interventions. It is anticipated that the solution presented in this dissertation will help educational institutions identify and analyze at-risk students on a course-by-course basis and, thereby, minimize course failure rates

Student Community Detection and Recommendation of Customized Paths to Reinforce Academic Success

Educational Data Mining (EDM) is a research area that analyzes educational data and extracts interesting and unique information to address education issues. EDM implements computational methods to explore data for the purpose of studying questions related to educational achievements. A common task in an educational environment is the grouping of students and the identification of communities that have common features. Then, these communities of students may be studied by a course developer to build a personalized learning system, promote effective group learning, provide adaptive contents, etc. The objective of this thesis is to find an approach to detect student communities and analyze students who do well academically with particular sequences of classes in each community. Then, we compute one or more sequences of courses that a student in a community may pursue to higher their chances of obtaining good academic performance

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented

Assessing and predicting the students’ systems thinking preference: multi-criteria decision making and machine learning

The 21st century is marked by a technological revolution that features digital implementation and high interconnectivity between systems across different domains, such as transportation, agriculture, education, and health. Although these technological changes resulted in modern systems capable of easing individuals’ lives, these systems are increasingly complex, and that increased complexity is only expected to continue. The increased system complexity is due to the rapid exchange of information between subsystems, which creates high interconnectivity and interdependence between the subsystems and their elements. Workforce skill sets, as a result, must be modified appropriately to ensure the systems’ success. Systems Thinking is an approach that helps individuals better understand and effectively solve modern complex systems problems by encouraging holistic thinking. Systems thinking consists of two approaches holistic and reductionist views. This dissertation aims to study college engineering and non-engineering students’ preference for holistic thinking versus reductionist thinking, their ranking to the systems thinking dimensions, and whether this preference varies depending on demographics and general factors. Additionally, this study investigates the possibility of predicting the students’ preference for holistic thinking. The study uses the multi-criteria decision-making method, the Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process to determine the student’s preferences, and uses statistical analysis such as independent sample t-test and ANOVA to evaluate the factors. Also, the study uses machine learning classification models such as Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Trees, voting classifiers, Bagging, and Random Forest to predict and evaluate the most predicting model. The results of the dissertation conclude that overall students prefer the reductionist approach and report the students’ preference towards dimensions of complexity, independence, uncertainty, systems worldview, and flexibility and the ranking difference based on some factors. Lastly, the results show that the students’ preference for holistic thinking can be predicted with a 77% accuracy using the Random Forest classifier

Evidence of Students’ Academic Performance at the Federal College of Education Asaba Nigeria: Mining Education Data

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining techniques in the context of higher education by offering a data mining model for the higher education system in the university. In this research, the classification task is used to evaluate student’s performance, and as many approaches are used for data classification, the decision tree method is used here. By this, we extract data that describes students’ summative performance at semester’s end, helps to identify the dropouts and students who need special attention, and allows the teacher to provide appropriate advising/counseling

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields