Journal of Online Engineering Education

Usage of E-Resources in Academic Libraries

2023-12-18T09:10:16+00:00

E-resources have become indispensable tools in academic libraries for acquiring current and up-to-date information. The use of e-resources has become increasingly prevalent in research and academic settings, providing a wide range of information in a convenient and accessible format. In this study of literature on e-resources, the use of e-resources in academic libraries was examined. It highlights their importance in providing easy access to a vast range of material for academicians and researchers, as well as their impact on the academic community through modifications to collections and services. This review study also emphasises the need for more investigation into the use of e-resources, particularly in identifying obstacles that library users face and offering suggestions to get through them. To better understand the changing demands of library users and to make sure that academic libraries are successfully serving those needs, the assessment also asks for more thorough investigations on the administration and use of electronic resources. Additionally, it claims that the way information is accessed, saved, and conserved in academic libraries has been profoundly changed by e-resources.

Augmented Reality-based Mobile Learning System for Electrical Fundamentals

2022-08-13T08:53:59+00:00

With the popularity of using mobile devices nowadays, students were not limited to use desktop or laptop computers for their learning. They used tablets or even smartphone to review lecture materials, complete assignments and attend online classes. This new learning habit allowed teachers to adopt latest technology in these mobile devices to facilitate teaching and learning, and the commonly adopted one was Augmented Reality. In this paper, an augmented reality-based mobile learning system was developed to improve students learning effectiveness and efficiency of electrical fundamentals. In the mobile learning system, students were able to understand the 3-dimensional electric field and potential of different charged objects, phasor diagrams of AC circuits, circuit analysis with different resistors and batteries arrangement, and experiment settings. Results showed that students were appreciate using the mobile learning system to ease their understanding of abstract concepts in electricity.

A Novel Approach to Analyzing Twitter Sentiments: Integrating Machine Learning Methodologies

2024-03-26T17:26:52+00:00

In the digital era, Twitter has emerged as a vital platform for gauging real-time public sentiment, making sentiment analysis of its posts essential for informed decision-making in various sectors. This study introduces an innovative hybrid machine learning strategy for analyzing Twitter sentiments, enhancing traditional methods that often fall short in interpreting the platform's brief and informal content. Our methodology synergizes deep learning techniques, like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), with conventional algorithms, including Support Vector Machines (SVMs) and Random Forests, to enrich feature learning and model interpretability. The process entails meticulous data preprocessing to eliminate noise and tokenize text, alongside feature extraction that employs word embeddings and linguistic attributes. The hybrid model, trained on a diverse sentiment-labeled Twitter dataset, is evaluated using metrics such as accuracy and F1-score. Results reveal our approach's superiority over traditional and single-model deep learning methods, showcasing its proficiency in capturing Twitter's sentiment nuances. This hybrid model offers a robust tool for analyzing brand sentiment, monitoring political trends, and assessing customer feedback, thus providing valuable insights for businesses, policymakers, and researchers. The study not only advances sentiment analysis techniques but also demonstrates the potential of a hybrid machine learning framework in extracting meaningful sentiment data from Twitter's dynamic environment, offering a comprehensive tool for navigating the complexities of social media analytics.