Enhancing E-learning Research Articles

Enhancing E-Learning Quality in Business Schools: Leveraging Communication Technologies in the Online Education Era

Introduction: This study examines the influence of Quality Management Systems (QMS), such as ISO 9001 and EFQM, on e-learning environments in business and science schools. Objectives: It highlights how combining QMS with modern e-learning standards, including SCORM, Learning Object Metadata (LOM), and tools like wikis and blogs, enhances the scalability, consistency, and compatibility of online learning systems. Methods: The research applies a systematic review methodology (2020–2025) using a logical filtering framework. Results: The findings show that implementing QMS frameworks leads to improved student satisfaction and better learning outcomes by supporting well-organized, accessible online platforms. The study also identifies challenges in faculty training and infrastructure, emphasizing the need for more in-depth research. These results underline the importance of QMS in transforming educational platforms to meet the needs of digital-native students and improving the overall quality of online education. Conclusions: implementing Quality Management Systems (QMS) in e-learning is not just an operational task but a strategic priority for improving the overall quality of education.

Enhancing E-learning satisfaction in digital education: a comprehensive analysis of influential factors in Punjab’s E-learning institutes

ABSTRACT This study explores the critical factors influencing E-learning satisfaction in digital education environments in Punjab, Pakistan, a region marked by significant disparities in educational accessibility. E-learning has emerged as a crucial tool for addressing educational gaps, particularly in areas with limited traditional infrastructure. A survey of 250 students across primary, secondary, and tertiary education levels from urban and rural areas, both public and private institutions, this research provides a comprehensive analysis of diverse E-learning experiences. The study employs a conceptual framework encompassing six dimensions: student characteristics, instructor behaviors, course content, technology use, design quality, and environmental influences. Using detailed statistical analysis through SPSS, key determinants of E-learning satisfaction were identified, with instructor responsiveness, course flexibility, and technological usability emerging as the most significant factors. Interestingly, student computer anxiety was found not to significantly affect satisfaction, indicating growing digital literacy among learners. The study also highlights disparities in technology integration based on gender, geographical location, and type of institution, emphasizing the need for tailored educational strategies. This research provides actionable insights for policymakers and educational institutions to enhance E-learning systems, ensuring they meet the diverse needs of students and contribute to reducing the digital divide in Punjab.

Enhancing E-learning effectiveness: a process mining approach for short-term tutorials

The rise of e-learning systems has revolutionized education, enabling the collection of valuable students’ activity data for continuous improvement. While existing studies have predominantly focused on prolonged learning paths, short-term tutorials offer a flexible and efficient alternative that is recently gaining increasing popularity. This article presents a methodology for investigating e-learning systems for short-term tutorials leveraging user behavior tracking and process mining techniques. A case study involving a web-based tutorial with approximately one hour of learning explores the learning processes of 250 students in Italy. The study analyzes learning outcomes and investigates the impact of different learning paths on student progress. The research questions concern i) the extraction of activity flows in short-term tutorials; ii) the prediction of outcomes in the early stages of short-term learning process. The proposed approach provides descriptive insights into the learning process which can also be used to offer prescriptive guidance.

Enhancing E-learning in higher education: Lessons learned from the pandemic

This study delves into the intricate realm of evaluating and refining e-learning practices, drawing upon insights from esteemed higher education professionals. Through a qualitative lens rooted in phenomenology, the research engages in semi-structured interviews with 30 faculty members from seven public universities in Tehran, ensuring theoretical saturation. Employing inductive content analysis, the data examination unveils three core categories of challenges: educational assessment, research evaluation, and university services. Emphasizing the significance of internal task performance, adaptability to dynamic environments, and external institutional promotion, the study advocates for the development of a comprehensive evaluation model and tools to uphold program quality, efficiency, and effectiveness. Against the backdrop of the COVID-19 pandemic, the research highlights the pressing need for Iranian higher education to bolster productivity through strategic resource allocation across education, research, services, and human capital development domains. Such efforts are pivotal in shaping a more prosperous future and bolstering societal scientific and cultural standing.

Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education

In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious and may not reflect their true preferences. Additionally, this approach assumes that learning styles are fixed, leading to a cold-start problem when automatically identifying styles based on e-learning platform behaviors. To address these challenges, we propose a novel approach that annotates unlabeled student feedback using multi-layer topic modeling and implements the Felder–Silverman Learning Style Model (FSLSM) to identify learning styles automatically. Our method involves learners answering four FSLSM-based questions upon logging into the e-learning platform and providing personal information like age, gender, and cognitive characteristics, which are weighted using fuzzy logic. We then analyze learners’ behaviors and activities using web usage mining techniques, classifying their learning sequences into specific styles with an advanced deep learning model. Additionally, we analyze textual feedback using latent Dirichlet allocation (LDA) for sentiment analysis to enhance the learning experience further. The experimental results demonstrate that our approach outperforms existing models in accurately detecting learning styles and improves the overall quality of personalized content delivery.

Enhancing E-Learning Platform for Higher Education

The use of e-learning platforms in higher education has grown in popularity in recent years. Systematic study into the efficiency and improvement of these platforms is still necessary, though. This report does a thorough analysis of e-learning websites for higher education in an effort to close this gap. Utilizing both qualitative and quantitative techniques, such as literature reviews, data analysis, and user surveys, we examine the main features of e-learning websites and how they affect students' educational experiences in higher education settings. The research's conclusions offer insightful information about the layout, operation, and usability of e-learning environments, with potential applications in raising student happiness, learning results, and engagement levels. Keywords—E-Learning, Higher Education, Educational Technology, Learning Management Systems, User Experience, E-Learning Platforms, Student Engagement, Online Learning

Enhancing E-learning in the Clinical Sciences Phase of the Medical Programme

The COVID-19 pandemic served to catalyse and enhance the digital transformation of the clinical medical curriculum at the International Medical University (IMU). This transformation was implemented in three stages; Stage 1: enhancement of the learning management system (LMS), including development of lesson plans and review of learning resources; Stage 2: quality assessment of lesson plans; and Stage 3: curation and creation of learning resources. This systematically implemented transformation received encouraging feedback from students.

Enhancing e-learning through AI: advanced techniques for optimizing student performance.

The integration of Artificial Intelligence (AI) into e-learning has transformed conventional educational approaches, improving the learning process and maximizing student achievement. This study offers a thorough examination of how AI can be utilized to enhance e-learning results by employing advanced predictive methods and performance optimization strategies. The main goals consist of creating an AI-based framework to monitor and analyze student interactions, evaluating the influence of online learning platforms on student understanding using advanced algorithms, and determining the most efficient methods for blended learning systems. AI algorithms, known for their cognitive ability and capacity to learn, adapt, and make decisions, are employed to analyze and forecast student performance, thereby improving educational quality and outcomes. The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. This research highlights the ability of AI to develop adaptable, effective, and successful e-learning environments, promoting enhanced academic achievement and customized learning experiences. The findings demonstrate that CNN outperformed other deep learning and machine learning algorithms in terms of accuracy during the prediction phase, showcasing the advanced capabilities of AI in educational contexts. Portions of this text were previously published as part of a preprint (https://doi.org/10.21203/rs.3.rs-4724603/v1).

Design and Analysis of Unlocking Student Emotions: Enhancing E-Learning with Facial Emotion Detection

Abstract: In all forms of learning, students are required to make the most of the time allotted to them during instruction. They will pay close attention to the material being taught to them. Students' performance will rise as a result. In order to provide a framework for the future, we are attempting to ascertain the feelings and behaviors of the students participating in an online course as well as to examine their distracted behavior. This study on students' tendency toward distraction can assist teachers in monitoring these students and providing assistance to those who choose to improvise their performances. To investigate the distracted behavior of students during virtual classes, one must possess knowledge in image processing, machine learning, and deep learning techniques.

Competency-Based Curriculum as a Catalyst for Enhancing E-Learning: An Empirical Review

Competency-Based Curriculum as a Catalyst for Enhancing E-Learning: An Empirical Review

Enhancing e-learning effectiveness: analyzing extrinsic and intrinsic factors influencing students’ use, learning, and performance in higher education

Enhancing e-learning effectiveness: analyzing extrinsic and intrinsic factors influencing students’ use, learning, and performance in higher education

Enhancing E-Learning with Blockchain: Characteristics, Projects, and Emerging Trends

Blockchain represents a decentralized and distributed ledger technology, ensuring transparent and secure transaction recording across networks. This innovative technology offers several benefits, including increased security, trust, and transparency, making it suitable for a wide range of applications. In the last few years, there has been a growing interest in investigating the potential of Blockchain technology to enhance diverse fields, such as e-learning. In this research, we undertook a systematic literature review to explore the potential of Blockchain technology in enhancing the e-learning domain. Our research focused on four main questions: (1) What potential characteristics of Blockchain can contribute to enhancing e-learning? (2) What are the existing Blockchain projects dedicated to e-learning? (3) What are the limitations of existing projects? (4) What are the future trends in Blockchain-related research that will impact e-learning? The results showed that Blockchain technology has several characteristics that could benefit e-learning. We also discussed immutability, transparency, decentralization, security, and traceability. We also identified several existing Blockchain projects dedicated to e-learning and discussed their potential to revolutionize learning by providing more transparency, security, and effectiveness. However, our research also revealed many limitations and challenges that could be addressed to achieve Blockchain technology’s potential in e-learning.

Integrating AI Applications into Learning Management Systems to Enhance e-Learning

Artificial intelligence technologies have the potential to greatly enhance the organization of learning management systems and online learning experiences. In particular, the public release of OpenAI's GPT-3 model in December 2022 has given society a chance to learn more about the current status of artificial intelligence. There have been much research on the use of artificial intelligence in e-learning processes, but very few of them demonstrate how to integrate these applications step-by-step and with examples into learning management systems. This study intends to close this gap by demonstrating how artificial intelligence technologies are integrated into learning management systems using real-world examples. This study provides examples of practical applications as well as integration steps. Finally, GPT-3 plugins for Moodle are examined and sample JavaScript code is included for integrating Chat GPT into Moodle. By presenting a useful guide for integrating artificial intelligence technology into learning management systems, this work adds to the body of literature.

Enhancing E-Learning Material Creation: Exploring the Practices, Challenges, and Opportunities of the School Learning Resource Quality Assurance Team (SLRQAT) towards TIMER's Information Manuel of Educational Resources Project


 
 
 This study assessed the relationship between the practices, challenges and opportunities among School Resource Quality Assurance Team (SLRQAT) members of City Schools Division of Cabuyao when crafting e-learning materials during the school year 2021-2022. Three hundred ninety-eight (398) public elementary and secondary teachers were the respondents of the study. The researcher utilized the descriptive-correlational research design and self- made questionnaire to assess and measure the variables under study. The statistical tools used to analyze the data gathered were Mean, Standard Deviation, Pearson r and Multiple Linear Regression Analysis. Findings revealed that the profile of the respondents moderate the practices, opportunities and challenges of the SLRQAT members. While, the extent of practices and challenges among SLRQAT were moderated by such profile of the respondents. To ensure a more efficient output, recommendations were suggested based from the findings of the study regarding crafting e-learning materials.
 
 

Mobile Cloud Computing Based Technologies for Enhancing E-learning Content Delivery and Sharing in Higher Learning Institutions in Tanzania using Learner-Centered Approach

Electronic learning (E-learning) in Higher Learning Institutions (HLIs) offers a cost-effective teaching and learning that support social interactivity, flexibility, context sensitivity, and active participation of learners in learning activities. The objective of this study was to investigate the challenges facing the traditional E-learning tools and leverage the advanced capacity of Mobile Cloud Computing (MCC) to enhance E-learning service delivery and sharing of learning resources focusing in learner-centered approach. Also, the evolvement of mobile computing devices such as smartphones, Personal Digital Assistance (PDA), and laptops owned by learners bring prospects in overcoming the inherent challenges facing HLIs in developing countries such as shortage of computer laboratories and network resources.Consequently, this study proposes MCC-based E-learning content delivery and sharing to augment higher learning institutions with limited resource setting in developing countries. The main benefits of MCC-based E-learning include, first, augment traditional LMS by provisioning abundant processing capacity and storage in the cloud that guarantee unlimited learning materials available for learners and instructors; Second, improves performance in local Learning Management System (LMS) servers by outsourcing execution and storage into the cloud especially when resource-intensive E-learning contents such as games, Virtual Reality (VR), and video streaming are used for learning; third, supports multi-platforms to execute the workload of various E-learning applications in the cloud which is potential for E-learning resource sharing; and fourth, guarantee cost-effective E-learning content delivery and sharing. Keywords: Mobile cloud computing, E-learning, content delivery, Learner-centered learning DOI: 10.7176/JIEA/13-2-03 Publication date: March 31 st 2023

Enhancing E-Learning System through Learning Management System (LMS) Technologies: Reshape the Learner Experience

Enhancing E-Learning System through Learning Management System (LMS) Technologies: Reshape the Learner Experience

Enhancing E-Learning Through Integration of Online Formative Assessment and Teaching Presence

The proliferation of information communication technologies (ICT) continues to increase opportunities for effective pedagogical approaches and online learning. This paper reports a study on integration of online formative assessment from a teaching presence perspective of the community of inquiry (CoI) framework. The effects of this integrative pedagogical approach on students' learning experiences are explored. The study was conducted in a post graduate online course. Case study research design was utilized. The study exemplified the core elements of formative assessment including integration of authentic assessment activities within teaching and learning processes, explicit learning goals, formative feedback, and documentation of evidence of learning. These elements were aligned to the functions of teaching presence, namely design, facilitation, and direct instruction. This approach enhanced meaningful engagement with critical learning experiences including interactive collaboration, critical thinking, reflective thinking, multi-dimensional perspectives, and self-regulation.

Enhancing e-learning effectivity through the discussion method combined with e-portfolio assignments for vocational education master students during a pandemic

Discussion is one of the effective methods to make the e-learning condition to be student-centered. However, the use of the discussion method only focuses on specific issues, so that it could not explore broader knowledge and obtain feedback from many sources. The study aims to get the impact of using the discussion method combined with e-portfolio assignments on e-learning during the Covid-19 pandemic in students' perceptions perspective. This research has been carried out with a simple procedure using a survey approach. The research subjects are students of the Vocational Education Master Degree who participated in the Vocational Learning Theory and Strategy course. The research instrument is a closed questionnaire to explore student perceptions, including 8-aspects, using statements with options on a 4-degree Likert scale. The average perception level for each aspect is the total score divide by the total maximum score of the research subjects. Perception is declared good if the ratio is above 80%. The results showed that e-portfolio assignments combined with the discussion method in e-learning gave positive student perceptions. Students feel that e-portfolio is a learning tool that can increase learning effectiveness, provide useful feedback, help carry out self-assessments, increase interaction between teacher and student. The results also show that the students gave positive perception to the e-portfolio as a tool that is easy to use, useful, systematic, motivating, and effectively to improve learning outcomes. In general, this result shows that e-portfolio tools can increase the effectiveness of e-learning.

Enhancing E-Learning Education with Interactive Software: A Summary Report

Enhancing E-Learning Education with Interactive Software: A Summary Report

Enhance e-learning system performance with a cloud and crowd-oriented approach

There are several e-learning systems focusing on the flipped classroom concept. The basic concept of flipped classroom is to have students learn by themselves. Once the background learning stage is performed outside of the class time, tutors have free time to lead students to participate in higher-order thinking. However, as shown in the report of Ash (2012), the performance of the flipped classroom method is arguable. Our survey shows that the contents offered by most modern e-learning systems are relatively static. Considering how fast new information appears on the web, creating content requires effort. The work load of our teachers is already heavy, so expecting teachers to update contents very frequently is not practical. Besides, crowd intelligence is usually considered helpful in enhancing learning performance. In this research, a system to utilise both cloud materials and crowd intelligence is proposed and an experiment to validate the system is included.