Online-Journals.org (International Association of Online Engineering)
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Toward an Effective Model for Evaluating Distance Education
Get PDFDistance education has become popular due to their ability to provide learning from almost anywhere and anytime. COVID-19 forced educational institutions to urgently introduce distance education to ensure pedagogical continuity, so all stakeholders were invited to adapt to this new paradigm. In order to identify strengths and weaknesses, the research focuses on the need to create an effective mechanism for evaluating distance education. The aims of this research were to explore and evaluate the use of digital media in general and official platforms in particular in distance education practices. To this end, we have developed and validated a questionnaire before administering it to a sample of 431 teachers in Morocco. Teachers reported lower knowledge and skills in the didactic use of ICT in the distance education process. In addition, although age and educative experience of the teachers continue to modulate the level of instrumental skills. Therefore, resources (digital resources and infrastructure) and the teachers’ ICT training present serious limitations, which require a training more focused on the distance educational paradigm and educational environments that allow teachers to create educational activities able to promote and facilitate the distance learning process
AuthOMath: Combining the Strengths of STACK and GeoGebra for School and Academic Mathematics
Get PDFGeoGebra has its strength in creating multimodal dynamic and interactive math applets and is widely used in secondary school math teaching. STACK is particularly strong in generating randomized task with adaptive feedback and is mostly used in academic math teaching. The Erasmus+ project AuthOMath (2022 – 2024) aims to combine the strengths of both systems in an authoring tool with a transformative digitization potential in mathematics teaching and learning
The Effect of STEM Autonomous Learning City Map Application on Students’ Critical Thinking Skills
Get PDFThis research aims to discover the effect of STEM autonomous learning city map application on the students’ critical thinking skills. The design of the research is quasi-experimental with pre-test and post-test control groups. The population of the research is the fourth graders of an elementary school with a total of 60 students involved as the research samples. To collect the data, the research instruments used are a test and an interview. The test is to gain the data of the students’ critical thinking skills while the interview is to gain information regarding the students’ experiences with STEM autonomous learning city map. The findings revealed that there is a significant difference of the critical thinking skills between the students of both groups (Asymp. Sig. 2-tailed = 0.015, p<0.05). The critical thinking skills of the students in the experiment class were increased by 23.7%. In addition, compared to the students in the control group, those in the experimental group showed a more positive attitude towards their experiences of learning using STEM autonomous city map application. Owing to this, it can be concluded that learning with the STEM autonomous learning city map application can affect students’ critical thinking skills positively
Space Travel: Game Development to Promote the Learning of Science Subjects about Solar Systems for 4th-Grade Students
Get PDFThe objectives of this research are to develop a game to promote the learning of the solar system in science subjects of 4th grade students, to compare the academic results of pre- and posttest of students after learning with the space travel game and to determine the satisfaction level of students with the space travel game. The sample group used in the experiment was 4th grade students from school T, and classes were randomized using the stratified random sampling method. Students were categorized into 2 groups: those with average scores and those with high scores in science subjects. The final results were compared between the control group, 27 students learning normally using the conventional method, and the experimental group, 27 students learning using the space travel game. There were 52 students in total. The tools used in this experiment are a space travel game, an achievement assessment test, a pre- and posttest, and a questionnaire to determine the satisfaction level of students. The statistical tools used are the mean, percentage, and standard deviation, paired t test, and t test independent. The results of this experiment showed that the experimental group learning from the game obtained a higher score than that of the control group by 1.19, on average. The scores of both groups were shown to have statistically significant differences at a 95% confidence level
Distributed Communicative Language Training Platform Using Automatic Speech Recognition Technology for Smart University
Get PDFThe purpose of this research is to achieve the following objectives: 1) Synthesize documents and international research on the characteristics of a smart university. 2) Synthesize the processes of distributed communicative language training (DCLT). 3) Design the system architecture of a DCLT platform that utilizes automatic speech recognition (ASR) technology for a smart university. 4) Evaluate the appropriateness of a DCLT platform that utilizes ASR technology for a smart university. Nine experts were selected for this research. They were required to have more than five years of relevant experience in the field, including expertise in system architecture, distributed enterprise, language teaching, and ASR. The research instruments included a suitable assessment form for evaluating the system architecture of a DCLT platform that utilizes ASR technology for a smart university. The results of this research indicate that the DCLT platform, which utilizes ASR technology, was considered suitable for a smart university
Development of the Creative Potential of Future Teachers Based on Smart Education
Get PDFThe purpose of this research is to examine the impact of smart education on the development of the creative potential of future teachers. If we consider the research as a model, we can see that it was formed using the mixed method. The study group consisted of 172 teacher candidates who are pursuing their education at various universities in Almaty, Kazakhstan. It is noted that the data collection tools used in the research included a semi-structured interview form that was developed by the researchers and supported by experts in the field. The analysis of the quantitative data from the research was conducted using a statistical programme. In addition to calculating percentages, frequencies, and means, independent variables t-tests and one-way analyses of variance were applied. For the analysis of the qualitative data from the studies, they were grouped and analysed. As a result of the research, it has been determined that teacher candidates who use smart education have high creative potential
Students’ Decisions in the Context of Social Network Learning Interaction
Get PDFIn the context of globalization and technology-driven advancements in the 21st century, learning methods have undergone significant changes. The development of informatization education and social networking technology has facilitated the integration of learning into every aspect of daily life, extending beyond the confines of traditional classrooms. Students interact and cooperate with other learners on social media platforms, which provide them with rich learning resources. However, making the best choice from them has become a core issue. Although the evaluation and selection of learning resources on social networks have been extensively studied in the academic community, most of the studies have focused on the observable attributes of resources while neglecting the subjective perception and experiential attributes of students. This study aimed to investigate how students make learning decisions based on their expectations of learning energy efficiency in social network learning environments. The study also considered observable and experiential attributes of learning resources to provide more comprehensive and accurate references for learning decisions
Assessing the Effectiveness of Financial Literacy Mobile Apps Using the Content Analysis Approach
Get PDFThis study aimed to assess the effectiveness of financial literacy mobile apps for learning purposes using a content analysis approach. For this purpose, 200 mobile apps from Google Play Store and Apple App Store were identified and extracted that were about financial literacy and matched the search criteria using a structured sheet. Using the purposeful sampling technique and selection criteria, 163 apps were shortlisted for the analysis. By means of content analysis method, these apps were examined using 13 different features related to learning design from the cognitive theory of multimedia learning’s (CTML) principles, Jakob Nielsen’s usability heuristics, and other valuable features of mobile apps from the literature. The results of the assessed features were found to be in the following number of the selected apps: export of data (34/163, 21%), gamification (80/163, 49%), plans or orders (91/163, 56%), reminder (102/163, 63%), community forum (34/163, 21%), social media (33/163, 20%), tailored education (134/163, 82%), tracker (53/163, 33%), free of cost (117/163, 72%), usability (149/163, 91%), multimedia principle (100/163, 61%), pre-training principle (106/163, 65%), and personalization principle (117/163, 72%). These results provide hints regarding the quality of existing financial literacy apps on the two most popular marketplaces of mobile apps
Model Development of Android-Based Learning in Vocational High School
Get PDFThis study aimed to develop an Android-based learning model for subjects of Creative Products and Entrepreneurship for catering students at Vocational Schools. The study analyzed the validity of Android- based learning models and analyzed practicality and the effectiveness of Android-based learning models for Creative Product and Entrepreneurship subjects for catering students at SMK Negeri 9 Padang. Based on the validity assessment of material experts, media experts, linguists, and practitioners, the average score of material aspects is 4.78 (Very Eligible), the media aspect is 4.22 (Very Eligible), and the language aspect is 4.23 (Very decent). The results of practicality tests for teachers and students, as well as the effectiveness test of the use of Android-based learning media on students of Creative Product and Entrepreneurship subjects conducted using a questionnaire, obtained a practicality value of 4.55 (feasible) and effectiveness of 4.62 (feasible)
Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification
Get PDFArtificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs