Little studies have been conducted on the question of the use of augmented reality technology in the education field. The effects of augmented reality technology on the educational environment of tertiary students need to be further explored. Therefore, the present study aimed to investigate the effectiveness of augmented reality technology in developing students’ fashion design skills. The participants were 54 tertiary students. They were randomly divided into two groups, each group involved 27 students. Data collected from a three-aspect questionnaire namely, the functional, aesthetic and creative were analyzed to address the research issues. Findings showed that fashion products of students who were taught via the use of augmented reality technology achieved higher success and acceptance in all aspects (the functional, aesthetic, creative), and the fashion design skills as a whole ones than the products of students who learnt via the traditional teaching method supported by educational videos provided by the learning management system. The findings provide important evidence for the augmented reality benefits in research and development of educational technology.

The Effect of Mobile Learning on Students' Achievement and Conversational Skills

The present study aimed to examine the effect of Personal Learning Environments on participants’ higher-order thinking skills and satisfaction. Two main instruments, namely a test for higher-order thinking skills and a scale for participants’ satisfaction with the course content of educational techniques were developed to achieve this aim. I-Google Portal and Blackboard System were also used. The experimental approach was used to detect the relationship between the dependent variable, i.e. Personal Learning Environments versus Blackboard and the dependent variables mainly participants' higher-order thinking skills and satisfaction. Fifty students took part in the present study. Findings showed that there was a statistically significant difference between the modified gain ratio of participants’ degrees in the first experimental group that was taught through the Personal Learning Environments and second experimental group that received learning via Learning Management System in accordance to participants’ higher-order thinking and satisfaction in favor of the first experimental group.

The learning management system is a digital environment that enables the tracking of learner activities, allowing special forms of data from the academic context to be explored and used to enhance the learning process. This study aims to identify the effect of using big data technology in digital environments on the development of electronic social competence among optimal investment diploma students. An experimental method was used to explore the effect of big data technologies usage on social competence. The sample for this study consisted of (120) students in the Department of Curriculum and Teaching Methods, divided into two equal groups through random selection. The first group studied the course through a digital environment with the use of big data technology, while the second group studied the course through the digital environment without using big data technology. The electronic social competence scale was further utilized as a tool to meet the study’s goal. The experimental findings showed that big data technologies in the used digital environment significantly improved the electronic social competence of Optimal Investment Diploma students (personal skills, self-management skills, and academic skills). The results provide significant proof of the advantages of big data technology in social competence studies and development.

Modern approaches in education technology, which make use of advanced resources such as electronic books, infographics, and mobile applications, are progressing to improve education quality and learning levels, especially during the spread of the coronavirus, which resulted in the closure of schools, universities, and all educational facilities. To adapt to new developments, students’ performance must be tracked in order to closely monitor all unfavorable barriers that may affect their academic progress. Educational data mining (EDM) is one of the most popular methods for predicting a student’s performance. It helps monitoring and improving students’ results. Therefore, in the current study, a model has been developed so that students can be informed about the results of the computer networks course in the middle of the second semester and 11 machine algorithms (out of five classes). A questionnaire was used to determine the effectiveness of using infographics for teaching a computer networks course, as the results proved the effectiveness of infographics as a technique for teaching computer networks. The Moodle (Modular Object-Oriented Dynamic Learning Environment) educational platform was used to present the course because of its distinctive characteristics that allow interaction between the student and the teacher, especially during the COVID-19 pandemic. In addition, the different methods of classification in data mining were used to determine the best practices used to predict students’ performance using the weka program, where the results proved the effectiveness of the true positive direction of functions, multilayer perceptron, random forest trees, random tree and supplied test set, f-measure algorithms are the best ways to categories.