Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

Improving Female Students’ Academic Achievements: Special Evidence from 2nd Year Management Department Students of Bonga University, Ethiopia

Action Research is a formative study of progress commonly practiced by teachers in schools. It enables a teacher to craft most appropriate strategy within its own teaching environment. Action research aims to contribute both to the practical concerns of people in an immediate problematic situation and to further the goals of social science simultaneously. This paper is aims to improve the academic performance of female students with special evidence from 2nd year management department of Bonga University. further, factors affecting the academic achievement of female students were examined. Finally, the role of teachers in improving female student academic performance were investigated. In doing so, the researcher adopted interview, focused group discussion and observations as data collection instruments. In addition, the researcher, prepared schedule composed of Proposed plan, action and evaluation for achieving the goals of this project. The collected data were analyzed using descriptive statistics such as mean and Paired sample T- test. The finding of this Action research project revealed that; Lack of proper Tutorial class has significant effects on female students’ achievement with average mean of 3.55, followed by lack of pear learning with mean score of 3.40. further, the overall Average Score of female students Before intervention was 3.98. but, After the researcher and course instructor made intervention which described in methodology parts the overall Average Score of female students has increased to 6.65. The researcher recommends the female students to give due consideration for their education and to read cooperatively with their colleagues. Further, Teachers should encourage female students through providing enough and timely tutorials. Finally, Bonga university shall establish female students club that actively serves all female students of the university through preparing training and conferences on which they exchange experiences with each other if possible with other universities female students.

Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network

Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.

Student Academic Performance Prediction under Various Machine Learning Classification Algorithms

Data Mining in Educational System has increased tremendously in the past and still increasing in present era. This study focusses on the academic stand point and the performance of the student is evaluated by various parameters such as Scholastic Features, Demographic Features and Emotional Features are carried out. Various Machine learning methodologies are adopted to extract the masked knowledge from the educational data set provided, which helps in identifying the features giving more impact to the student academic performance and there by knowing the impacting features, helps us to predict deeper insights about student performance in academics. Various Machine learning workflow starting from problem definition to Model Prediction has been carried out in this study. The supervised learning methodology has been adopted and various Feature engineering methods has been adopted to make the ML model appropriate for training and evaluation. It is a prediction problem and various Classification algorithms such as Logistic Regression, Random Forest, SVM, KNN, XGBOOST, Decision Tree modelling has been done to fit the student data appropriately. Keywords: Scholastic, Demographic, Emotional, Logistic Regression, Random Forest, SVM, KNN, XGBOOST, Decision Tree.

Evaluation of MirMe® Online Assessment for 21st Century Skills: Situational Intelligence (SQ) and online psychometric assessment of 21st century skills

We assess the Situational Intelligence (SQ) component of the MirMe assessment system for 21st century skills. MirMe is an online, game-based psychometric instrument developed by LogicMills. The SQ component measures skills related to decision-making and critical thinking. It does so via AI-driven heuristic engines that deploy analogs of “fast and frugal” heuristics. Approximately 7,000 participants (aged 5 to 92) and 25,000 gameplays were involved in the various validation studies. Results support the conclusion that MirMe’s SQ measures are internally consistent (Cronbach alpha ranging from .71 to .93). Results also suggest that MirMe may be a better predictor of student academic performance (measured by GPA) than A-levels (MirMe <i>r²</i> = .265, <i>p</i> < .001; A-levels <i>r²</i> = .209, <i>p</i> < .001). MirMe also appears to predict the kinds of co-curricular activities students participate in both prior to and during university.

Evaluation of MirMe® Online Assessment for 21st Century Skills: Situational Intelligence (SQ) and online psychometric assessment of 21st century skills

We assess the Situational Intelligence (SQ) component of the MirMe assessment system for 21st century skills. MirMe is an online, game-based psychometric instrument developed by LogicMills. The SQ component measures skills related to decision-making and critical thinking. It does so via AI-driven heuristic engines that deploy analogs of “fast and frugal” heuristics. Approximately 7,000 participants (aged 5 to 92) and 25,000 gameplays were involved in the various validation studies. Results support the conclusion that MirMe’s SQ measures are internally consistent (Cronbach alpha ranging from .71 to .93). Results also suggest that MirMe may be a better predictor of student academic performance (measured by GPA) than A-levels (MirMe <i>r²</i> = .265, <i>p</i> < .001; A-levels <i>r²</i> = .209, <i>p</i> < .001). MirMe also appears to predict the kinds of co-curricular activities students participate in both prior to and during university.