A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

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

Evaluation of Machine Learning Techniques for Early Identification of At-Risk Students

Student attrition is one of the long-standing problems facing higher education institutions despite the extensive research that has been undertaken to address it. To increase students’ success and retention rates, there is a need for early alert systems that facilitate the identification of at-risk students so that remedial measures may be taken in time to reduce the risk. However, incorporating ML predictive models into early warning systems face two main challenges: improving the accuracy of timely predictions and the generalizability of predictive models across on-campus and online courses. The goal of this study was to develop and evaluate predictive models that can be applied to on-campus and online courses to predict at-risk students based on data collected from different stages of a course: start of the course, 4th week, 8th week, and 12th week. In this research, several supervised machine learning algorithms were trained and evaluated on their performance. This study compared the performance of single classifiers (Logistic Regression, Decision Trees, Naïve Bayes, and Artificial Neural Networks) and ensemble classifiers (using bagging and boosting techniques). Their performance was evaluated in term of sensitivity, specificity, and Area Under Curve (AUC). A total of four experiments were conducted based on data collected from different stages of the course. In the first experiment, the classification algorithms were trained and evaluated based on data collected before the beginning of the semester. In the second experiment, the classification algorithms were trained and evaluated based on week-four data. Similarly, in the third and fourth experiments, the classification algorithms were trained and evaluated based on week-eight and week-12 data. The results demonstrated that ensemble classifiers were able to achieve the highest classification performance in all experiments. Additionally, the results of the generalizability analysis showed that the predictive models were able to attain a similar performance when used to classify on-campus and online students. Moreover, the Extreme Gradient Boosting (XGBoost) classifier was found to be the best performing classifier suited for the at-risk students’ classification problem and was able to achieve an AUC of ≈ 0.89, a sensitivity of ≈ 0.81, and specificity of ≈ 0.81 using data available at the start of a course. Finally, the XGBoost classifier was able to improve by 1% for each subsequent four weeks dataset reaching an AUC of ≈ 0.92, a sensitivity of ≈ 0.84, and specificity of ≈ 0.84 by week 12. While the additional learning management system\u27s (LMS) data helped in improving the prediction accuracy consistently as the course progresses, the improvement was marginal. Such findings suggest that the predictive models can be used to identify at-risk students even in courses that do not make significant use of LMS. The results of this research demonstrated the usefulness and effectiveness of ML techniques for early identification of at-risk students. Interestingly, it was found that fairly reliable predictions can be made at the start of the semester, which is significant in that help can be provided to at-risk students even before the course starts. Finally, it is hoped that the results of this study advance the understanding of the appropriateness and effectiveness of ML techniques when used for early identification of at-risk students

Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Design and Implementation of Real-time Student Performance Evaluation and Feedback System

Undergraduate education is challenged by high dropout rates and by delayed student graduation due to dropping courses or having to repeat courses due to low academic performance. In this context, an early prediction of student-performance may help students to understand where they stand amongst their peers and to change the attitude with about the course they are taking. Moreover, it is important to identify students in time who need special attention and providing appropriate interventions, such as mentoring and conducting review sessions. The goal of this thesis is the design and implementation of real-time student-performance evaluation and feedback system (RSPEF) to improve graduation rates. RSPEF is an interactive, web-based system consisting of a Predictive Analysis System (PAS) that uses machine-learning techniques to interpolate past student-performance into future, and the development of an Emergency Warning System (EWS) that identifies poor-performing students in courses. Moreover, a unified representation of student-background and student-performance data is provided in form of a relational database schema that is suitable to be used to assess student’s performance across multiple courses, which is critical for the generalizability of RSPEF system. The system design includes core machine-learning & data-analysis engine, a relational database that is reusable across courses and an interactive web-based interface to continuously collect data and create dashboards for users.Computer Science, Department o

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward