RESUMEN
Unlike virtual classrooms that have received extensive research attention in both academic and practical contexts because of their ability to improve students' outcomes, the use of advance organizers are still in need for more research to prove their efficacy in fulfilling expected learning outcomes in these virtual classrooms. Hence, the present study aims to identify the impact of using such organizers in virtual classrooms on the development of students' integrated science process skills. The present study was applied to 64 students who were studying for their Master's degree in the vocational education techniques in the "Research Project" course. Participants were randomly divided into two equal experimental groups with 32 students in each. An assessment card of five main domains was used to evaluate students' skills in research procedural definition, identification and control of the research variables, questions and/or hypotheses, procedures and experimentation besides research interpretation of the results. Data analysis showed that the use of advance organizers in virtual classes was of great effect on the development of participants' integrated science process skills because skills of students in the first experimental group were better mastered than the skills of their peers in the second experimental group in accordance with the subskills in the five domains.
RESUMEN
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.
RESUMEN
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.
RESUMEN
In many nations affected by the COVID-19 pandemic, the situation in higher education institutions has changed. During the pandemic, these institutions have introduced numerous e-solutions to continue the process of education. Besides, research has shown many benefits in the last years of MOOCs. Yet, to date there are few studies to explore some individual characteristics, such as learners' metacognitive skills, that might have an impact on learning outcomes in MOOCs. Furthermore, promotion of deep learning is a serious challenge for online courses including MOOCs. Therefore, the purpose of this research was to explore the role of metacognition in promoting deep learning in MOOCs during COVID-19 pandemic. The participants were students at the department of home economics who were all at the seventh academic level. Based on their scores on the metacognition awareness inventory (MAI), they were divided into two experimental groups, i.e. high metacognition students and low metacognition students. A three- aspect assessment card of deep learning namely connecting concepts, creating new concepts, and critical thinking was used to collect data. The results showed that MOOC was more effective in fostering the deep learning aspects of high metacognition skills, and deep learning as a whole. With regard to backward seeking and slow watching events, results showed significant differences in favor of high metacognition students (HMs). Nevertheless, there were no statistically significant differences between students in both groups regarding the pausing event.
