Online-Journals.org (International Association of Online Engineering)
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The Relationship Between Learning Motivation and Online Learning Performance: The Mediating Role of Academic Self-Efficacy and Flow Experience
Get PDFLearning motivation is one of the key factors influencing students’ engagement in online learning. This study aims to explore the relationship between learning motivation and online learning performance and to delve into the mediating roles of academic self-efficacy and flow experience in this relationship. A questionnaire survey was conducted with 427 online learners, and structural equation modeling was employed for analysis. The results indicate that learning motivation has a positive effect on online learning performance. Academic self-efficacy and flow experience play a mediating role in the relationship between learning motivation and online learning performance. When students possess higher levels of academic self-efficacy, they are more likely to actively engage in learning, thereby improving their learning performance. Simultaneously, flow experience plays a significant role during the learning process. When students experience a state of flow, learning becomes more enjoyable and efficient, consequently enhancing academic achievement. Therefore, educators and educational institutions can take measures to cultivate students’ academic self-efficacy, such as providing positive feedback and support and encouraging students to face challenges. Additionally, creating a positive learning environment that fosters flow experiences can help improve students’ academic performance
A Comprehensive Model for Recommending Personalized Learning Resources for the Development of Linguistic Competence
Get PDFWith the continuous advancement of globalization and informatization, the linguistic competence of college students has become a key index for evaluating their comprehensive quality. Faced with diverse needs of students and educational environments, it is increasingly important and complex to accurately locate the linguistic competence goals of college students. Although existing research methods, such as standardized testing and teacher assessment, provide certain convenience, they rely on single data sources and have a certain degree of subjectivity, which limits their universality and accuracy. This study aimed to solve this problem by doing comprehensive research on two aspects: first, curriculum analysis based on relation extraction. A relation extraction model, such as Casrel, was used for advanced text analysis, which provided educators with more in-depth insights; second, personalized learning material recommendation based on text recommendation. Personalized learning paths were provided for students of different levels using the abstractness-based text recommendation algorithm. This study not only filled the gaps in existing research methods, but also provided a new, scientific and efficient solution, helping improve the quality of education and promote the formulation of scientific education policies
Effectiveness of Team-Based Project Integrated E-books in Improving Student Self-Directed Learning and Creativity
Get PDFStudent self-directed learning and creativity are needed to support the hybrid learning currently being implemented in higher education. This research aims to develop an integrated team-based project e-book to increase students’ self-directed learning and creativity. Products are designed according to the stages of the ADDIE model. The product is then tested for feasibility by material experts and evaluated by users. The results of the feasibility test conducted by experts show that the e-book is highly feasible for use. Meanwhile, user testing is conducted through pre- and post-tests. Furthermore, in-depth interviews were conducted with several students to enhance the depth of the obtained data. The results of user trials were analyzed using inferential statistical data analysis methods, specifically the paired sample t-test. The results of the analysis show that e-books can improve learning outcomes, students’ selfdirected learning abilities, and student creativity in cultural anthropology courses. Therefore, the results of this research can be used with students to improve learning outcomes, creativity, and students’ self-directed learning abilities among students with similar characteristics
Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram
Get PDFThis research article presents a novel methodology for effectively extracting the fetal electrocardiogram (FECG) from a single-channel signal acquired on the maternal abdomen. The signal comprises a mixture of the FECG, maternal electrocardiogram (MECG), and ambient noise. The central concept involves projecting the signal into higher-dimensional spaces and leveraging the assumption of statistical independence among the constituent components to achieve their separation from the mixture. To accomplish this, singular value decomposition (SVD) is initially applied to the spectrogram, followed by an iterative application of independent component analysis (ICA) on the principal components. The SVD technique contributes to the enhanced separability of each individual component, while ICA facilitates the promotion of statistical independence between the fetal and maternal ECGs. Furthermore, we refine and customize the aforementioned approach specifically for ECG signals by incorporating knowledge of the frequency distribution of the MECG and other inherent ECG characteristics. The effectiveness of the proposed methodology is validated through comprehensive experimental studies, demonstrating its superior accuracy and performance compared to existing techniques
Artificial Intelligence Empowers Gamification: Optimizing Student Engagement and Learning Outcomes in E-learning and MOOCs
Get PDFIn this era of Artificial Intelligence (AI) growth, characterized by advances in the Large Language Models (LLMs) used by ChatGPT and Bard, this study examines the effects of gamification and Automatic Question Generation (AQG) on student engagement and learning outcomes in the context of a Massive Open Online Course (MOOC). AQG, implemented via a Moodle plugin, transforms conventional assessments into an interactive, gamified experience, leveraging the “test effect” to improve learning outcomes. Research with 100 fifth-graders in a primary and secondary school shows that gamified assessments significantly boost student motivation and learning outcomes compared with traditional methods. The custom Moodle plugin facilitates the AQG process, generating contextually relevant and grammatically correct Multiple-Choice Questions (MCQs) from course content. The result is a dynamic, personalized assessment experience aimed at optimizing student retention. This paper concludes by discussing the implications of the study for educators and highlighting potential directions for future research
Creating a Supportive and Effective Learning Environment for Engineering Students: Pedagogical Strategies, Engagement, and Enhanced Outcomes
Get PDFEngineering education requires a strong emphasis on problem-solving, critical thinking, and practical application of knowledge. To achieve the highest quality of teaching, educators must create a trusting environment that allows students to feel comfortable asking questions and performing to the best of their abilities. This paper outlines the teaching philosophy and practices of an engineering lecturer who has adapted his pedagogical approach across several universities in Sweden and abroad. The author emphasizes the importance of being flexible and responsive to student needs, offering early and constructive feedback, and providing students a safe and supportive learning environment with opportunities to develop programming skills. The paper also includes comments from students that reflect the author’s effectiveness as an educator in creating a supportive and challenging learning environment for engineering students
Faculties Behavioural Intention Toward the Use of the Fourth Industrial Revolution Related-Technologies in Higher Education Institutions
Get PDFThe Fourth Industrial Revolution (4IR) has affected every aspect of our life, including education. Studies related to acceptance and adoption of 4IR related technologies in Higher education Institutions (HEIs) are limited. Therefore, this study aimed to investigate the acceptance of 4IR related-technologies by faculty members in HEIs. An online questionnaire was implemented based on the Unified Theory of Acceptance and Use of Technology (UTAUT) in a sample of 275 faculty members aged between 21 and 60 years old. The results showed that all the five constructs of the UTAUT model have significant impacts on the behavior intention with different degrees of influence: Performance Expectancy (43%), Facilitation Condition (27%), Effort Expectancy (21.5%), Social Influence, and Attitude toward using the technology are equally the same with (15.4%). Gender has only significant effects on Social Influence and Facilitation Condition, while age has only a significant impact on Social Influence. Furthermore, faculty members' acceptance of using 4IR technologies in teaching and learning is significantly higher among the younger group (less than 46 years old) than their counterparts (46 years old and above). However, there was no significant difference in the behavioral intention between male and female lecturers. The obtained results have added scientific evidence to the literature about faculty readiness to adopt IR-related technologies and lead to a better practical understanding of the factors that may incite or discourage them to use 4IR related-technologies in HEIs
An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance
Get PDFEvery academic institution's goal is to identify students who require additional assistance and take appropriate actions to improve their performance. As such, various research studies have focused on developing prediction models that can detect correlated patterns influencing students' performance, dropout, collaboration, and engagement. Among the influential predictive models available, the bagging ensemble has captured the interest of researchers seeking to improve prediction accuracy over single classifiers. However, prior work in this area has focused mainly on selecting single classifiers as the base classifier of the bagging ensemble, with little to no further optimization of the proposed framework. This study aims to fill this gap by providing a bagging ensemble framework to optimize its hyperparameters and achieve improved prediction accuracy. The proposed model used the Weka BESTrees data mining tool and Math language course student dataset from UCI Machine Learning Repository. Based on the experiments performed, the proposed bagging optimization technique can effectively increase the accuracy of a traditional bagging ensemble method. It reveals further that the proposed BESTrees framework can achieve an optimized performance when trained with the appropriate hyperparameters and hill climb metrics.
 
A Hybrid Approach to Measure Students’ Satisfaction on YouTube Educational Videos
Get PDFEvery aspect of life has undergone innovation in recent years. Information Technology has revolutionized the concept of teaching and learning. Both educators and students are now utilizing contemporary resources for learning and teaching. One of the primary sources of contemporary educational resources is educational videos posted on various channels on YouTube. This study intends to investigate the opinions voiced in YouTube comments for educational videos with the help of Sentiment Analysis. In this research, three techniques namely SentiStrength, TextBlob, and Naive Bayes Classifier were utilized to analyze the sentiments. According to the results, the majority of the remarks made about the videos have positive sentiments that concluded that students are satisfied with YouTube educational videos and consider YouTube as a beneficial learning tool
Exploring the Relationship between Participant Role and Collaborative Quality in Online Collaborative Discussions
Get PDFThe exploration of the role concept has become an important perspective for analyzing and promoting computer-supported collaborative learning (CSCL). Understanding the relationships between individual participation roles and collaborative performance is of great significance to the research of collaborative learning theory, pedagogy and technology. However, few empirical studies investigated the individual participation roles in collaborative discussions and the impact of participation role configuration on group performance. Based on the interactive content of learners in collaborative discussions, this research uses machine learning methods to automatically identify learners’ participating roles. Through cluster analysis, five different roles are identified: leader, problem solver, coordinator, marginal learners and learners with difficulties. Furthermore, this research explores the relationships between individual participation roles and group collaboration quality. The results show that groups with different collaboration performances have different role compositions, and the roles of leader, problem solver and coordinator have significant positive effects on collaboration performance. Learners with difficulties have a negative impact on collaboration performance. Combining the research results with the discussion content of the learners, this research conducted an in-depth discussion and analysis of the characteristics of each role, and proposed implications for teaching guidance and researchers