Predictive Analysis of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study of Feature Selection Methods

The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient Boost for the regression part of this study had the least Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of 12.93 and 18.28, respectively, in the case of the Boruta selection method. In contrast, RFI was found to be the superior classification method, yielding an accuracy rate of 78% in the classification part. This research emphasized the significance of employing appropriate feature engineering and selection methodologies to enhance the efficacy of machine learning algorithms. Using a diverse set of machine learning techniques, this study analyzed the OULA dataset, focusing on both feature engineering and selection. Our approach was to systematically compare the performance of different models, leading to insights about the most effective strategies for predicting student success

Prediction of students' performance in e-learning environment using random forest

The need for advancement in e-learning technology causes educational data to become very huge and increase rapidly. The data is generated on daily basis as a result of students’ interaction with learning management systems. The data contains hidden information about participation of students in various activities of e-learning which when revealed can be used to associate with the students’ performance. Predicting the performance of students based on the use of e-learning system in educational institutions is a major concern and has become very important for education managements to better understand why so many students perform poorly or even fail in their studies. However, it is difficult to do the prediction due to the diverse factors or characteristics that influence their performance. This paper is aimed at predicting students’ performance by considering the students interaction in e-learning environment, their assessment marks and prerequisite knowledge as prediction features. Random Forest algorithm has been used for the prediction. Results show that the algorithm outperforms the popular decision tree and K-Nearest Neighbor algorithms. In addition to the performance prediction, the research findings also revealed most significant attributes that influences students’ performance

Data Mining for Detecting E-learning Courses Anomalies: An Application of Decision Tree Algorithm

E-learning adaptation has become the most important method that facilitates access to the appropriate content. Adaptive approaches consist of reducing the problems of incompatibilities between learner’s cognitive abilities and educational content’s difficulties. In some cases, the adapted curriculum cannot meet learner's skills completely seen its incoherent structure, its unsuitable methodologies and sometimes its complexity. Therefore, we need to measure the convenience of the content material to improve it and ensure learners’ satisfaction. In other words, it is necessary to estimate its appropriateness to each learner. That is why; we have proceeded by using decision tree (DT) algorithm which is a supervised data mining method. It helps to predict the convenience of the proposed content material for learners. Our system consists of classifying learning material into two classes: “good” if it is convenient, and “anomaly” if not. To achieve that, we have used an intelligent agent called Classifier Agent (CLA). It tracks learner’s behavior by collecting a set of attributes like score, learning time, and number of attempts, feedback and interactions with the tutor. Then, he calculates the predictive attribute by using the (DT) algorithm. The finding algorithm shows that the score is the most crucial indicator gives us more information about the conformity of curriculum to learners, followed by learning time, feedback and number of attempts

The Global Information Educational Resources: Methodological Issues

AbstractThis article investigates trends in the global educational community. Studied issues of the growing importance of intellectual work, based on the use of information resources on a global scale and the need for the implementation of operational communications between individual professionals, social and professional groups, communities, individuals, community organizations and states. Considers the role of information and communication in the development of society and the mechanisms for creating a single global information space within the world of education. Substantiates the competence to find information and use it in their work, to give a professional assessment of information requirements to the expert any profile

Problems and Decision in the Field of Distance Education

AbstractArticle is devoted to actual problem of modern education - to distance learning. The main objective of article - to prove and reason distance learning as new form of education. The article gives analysis of forms of education, gives main directions of development of distance learning are, and shows differences of distance learning from the traditional. It allocates problems solved by students and teachers in distance learning. It is shown that efficiency of distance learning is defined by use of pedagogical technologies which underlie design and implementation of remote courses. The conclusion that distance learning can be considered as independent form of education because possesses essential differences which can’t be implemented in a traditional form is draw

The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe prediction of academic success is a major topic in higher education, especially among the academic community. In this dissertation, we are going to present a data mining approach taking into consideration the features that are the most relevant in terms of successful academic achievement of the Bachelors’ programs at Nova School of Business and Economics (Nova SBE). Initially, we are going to perform a literature review in order to understand the framework of academic success and also to make a summary of previous research on the field of educational data mining when used to assess student success. Subsequently, the empirical approach will start being developed with the extraction of socio-economic, socio-demographic, and academic data of students, which will result in our main dataset. Later, and after the data discovery, data cleansing, and transformation activities, a set of features are going to be taken into consideration according to their relevance for the subject. Based on the dataset containing these features, several predictive data-driven techniques are going to be applied, resulting in models which are going to be assessed in order to understand if the selected features are relevant enough to answer our problem or if there is a need to substitute them by other attributes. This process will result in several iterations that will confer credibility and robustness to the model that demonstrates the best performance in classifying students’ academic success. In the end, it is intended that the insights extracted from the model will provide the school key stakeholders with enough knowledge to capacitate them to take actions that will result in the maximization of the students learning success

A Theoretical Analysis of Why Hybrid Ensembles Work

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles

Detecting Students At-Risk Using Learning Analytics

The issue of supporting struggling tertiary students has been a long-standing concern in academia. Universities are increasingly devoting resources to supporting underperforming students, to enhance each student’s ability to achieve better academic performance, alongside boosting retention rates. However, identifying such students represents a heavy workload for educators, given the significant increases in tertiary student numbers over the past decade. Utilising the power of learning analytic approaches can help to address this problem by analysing diverse students' characteristics in order to identify underperforming students. Automated, early detection of students who are at potential risk of failing or dropping out of academic courses enhances the lecturers' capacity to supply timely and proactive interventions with minimal effort, and thereby ultimately improve university outcomes. This thesis focuses on the early detection of struggling students in blended learning settings, based on their online learning activities. Online learning data were used to extract a wide range of online learning characteristics using diverse quantitative, social and qualitative analysis approaches, including developing an automated mechanism to weight sentiments expressed in post messages, using combinations of adverbs, strengths. The extracted variables are used to predict academic performance in timely manner. The particular interest of this thesis is on providing accurate, early predictions of students’ academic risk. Hence, we proposed a novel Grey Zone design to enhance the quality of binary predictive instruments, where the experimental results illustrate its positive overall impact on the predictive models, performances. The experimental results indicate that utilising the Grey Zone design improves prediction-accuracy by up to 25 percent when compared with other commonly-used prediction strategies. Furthermore, this thesis involves developing an exemplar multi-course early warning framework for academically at-risk students on a weekly basis. The predictive framework relies on online learning characteristics to detect struggling students, from which was developed the Grey Zone design. In addition, the multi-course framework was evaluated using a set of unseen datasets drawn from four diverse courses (N = 319) to determine its performance in a real-life situation, alongside identifying the optimal time to start the student interventions. The experimental results show the framework’s ability to provide early, quality predictions, where it achieved over 0.92 AUC points across most of the evaluated courses. The framework's predictivity analysis indicates that week 3 is the optimal week to establish support interventions. Moreover, within this thesis, an adaptive framework and algorithms were developed to allow the underlying predictive instrument to cope with any changes that may occur due to dynamic changes in the prediction concept. The adaptive framework and algorithms are designed to be applied with a predictive instrument developed for the multi-course framework. The developed adaptive strategy was evaluated over two adaptive scenarios, with and without utilising a forgetting mechanism for historical instances. The results show the ability of the proposed adaptive strategy to enhance the performance of updated predictive instruments when compared with the performance of an unupdated, static baseline model. Utilising a forgetting mechanism for historical data instances led the system to achieve significantly faster and better adaptation outcomes.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201