IMPACT INTERNATIONAL JOURNALS OF ART AND SOCIAL SCIENCES
Impact International Journals and Publications remains dedicated to fostering a collaborative, inclusive, and ethically sound environment for scholarly communication worldwide.
IMPACT INTERNATIONAL JOURNALS OF EDUCATION
Impact International Journals and Publications remains dedicated to fostering a collaborative, inclusive, and ethically sound environment for scholarly communication worldwide.
IMPACT INTERNATIONAL JOURNALS OF SCIENCES
IMPACT INTERNATIONAL JOURNALS OF SCIENCES AllSCIENCES ASSESSING THE RELEVANCE OF ARTIFICIAL INTELLIGENCE IN ENHANCING ACADEMIC STAFF PRODUCTIVITY IN PUBLIC UNIVERSITIES IN NORTH EAST NIGERIASCIENCES BRIDGING CLASSICAL AND QUANTUM MODELS: PEDAGOGICAL APPROACHES IN...Predicting Students’ Academic Performance Using Artificial Neural Network
University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university’s e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students’ performance and then use artificial neural networks (ANNs) to predict students’ performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this study generated a dataset from SEU’s Blackboard learning management system. The proposed model’s performance was evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests. The student’s performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students’ performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs.
INFLUENCE OF AUTOCAD ON STUDENTS’ MOTIVATION AND ACHIEVEMENT IN TECHNICAL DRAWING IN PLATEAU STATE, NIGERIA
This study investigated the influence of AutoCAD on students’ motivation and achievement in Technical Drawing in Plateau State, Nigeria. This study aimed to fill that gap by exploring awareness levels, types, frequency, and utilization of AutoCAD among teachers and students, and how these factors relate to student motivation and academic achievement in Technical Drawing. The study adopted a descriptive survey design with a sample of 242 respondents, comprising both teachers and students, selected through stratified random sampling to ensure representation across schools. Data were collected using a self-constructed questionnaire titled Students’ Perception of AutoCAD Questionnaire (SPAQ). Descriptive and inferential statistical tools were used to analyze the data and test four hypotheses at the 0.05 level of significance. The findings showed that over 75% of teachers and students were aware of AutoCAD and recognized its relevance in enhancing motivation and achievement in Technical Drawing. The study further found that frequent use of AutoCAD correlated positively with increased student motivation and improved academic performance. Various versions and applications of AutoCAD were found to influence engagement, especially when integrated into hands-on teaching and project-based learning. Based on these findings, the study recommends that professional development programs prioritize practical training in AutoCAD for Technical Drawing teachers. Integrating AutoCAD into the curriculum with consistent access to digital resources is also crucial for optimizing student outcomes.