Online-Journals.org (International Association of Online Engineering)
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Exploring the Academic Preparation and Performance to the Comprehensive Examination of First-Year Engineering Students
Get PDFA comprehensive examination is an academic exercise that aims to determine students’ performance relevant to their achievements in the previous years of their studies. In the old curriculum of the College of Engineering at Camarines Sur Polytechnic Colleges, Philippines, a comprehensive examination is given to second-year students of all engineering programs. With the change in curriculum due to the K-12 implementation, this comprehensive examination is being planned to be given to first-year engineering students. This study aims to explore the influence of students’ academic preparation and academic performance on the comprehensive examination of first-year engineering students. The researcher analyzed the data from 468 first-year engineering students for the academic year 2018–2019 to determine the degree of correlation between their academic preparation, academic performance, and comprehensive examination. Findings showed that senior high school (SHS) tracks have no relationship with the academic performance of civil, electronics, and mechanical engineering students. However, for electrical engineering students, there is a relationship between the SHS tracks and their academic performance. Also, the college entrance test (CET) and academic performance showed a relationship with the comprehensive examination. Findings revealed that higher ratings in the CET and academic performance tend to go with higher ratings in the comprehensive examination and vice versa. Thus, a comprehensive examination is an effective way to determine the extent of achievement of students in engineering mathematics and sciences. This study did not consider any other factors that may affect the result of the comprehensive examination
Examining the Factors Influencing Students’ Satisfaction with Hackathon for IT Projects
Get PDFEducation-related hackathons provide a dynamic and participatory method of teaching where students take an active role and become inventors and problem solvers. It encourages teamwork, critical thinking, and the use of technology, preparing students for the demands of the workforce of the twenty-first century. Although it has played a significant role in changing enterprises and society at large, its impact on academia has not yet been adequately examined. Studying the factors influencing student satisfaction with the use of hackathons for teaching and learning is crucial and pertinent given that South African educational institutions are still in the early phases of employing hackathons. The study sought to determine the factors that contributed to students’ satisfaction with hackathons for IT project. To achieve the goal, a model incorporating the self-efficacy and satisfaction constructs with TAM variables (perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI)) was developed. To test the model, an online questionnaire-based survey was carried out on 180 IT students who had taken part in a hackathon. Exploratory factor analysis was performed to assess the validity of the results, while Cronbach’s alpha was used to assess reliability. The hypothesis in the suggested model was verified by using correlation to assess relationships between the constructs, while linear regressions were utilized to assess influences between variables. The Pearson correlation result showed that self-efficacy and behavioral intention have a moderately positive and statistically significant relationship with student satisfaction when using hackathon for IT projects, and the regression analysis revealed that students’ use of hackathons for IT projects is positively influenced by perceived usefulness and self-efficacy. The study identified factors that were positively correlated with and impacted students’ satisfaction with the use of hackathon for IT projects, which forms the primary contribution of this research work. By enabling a form of social learning where knowledge is created among students, the use of hackathon for IT projects will help change the learning environment from a teacher-centered to a learner-centered one
A Combined E-Learning Course Recommender System
Get PDFIn this paper, we propose learners course recommender system of the E-Dirassa platform. This system, which distinguishes between new and active learners, adopts three complementary recommendation approaches: content-based recommendation as well as course-based collaborative filtering for active learners, and static profile-based collaborative filtering for new learners. Let us also note the use of multi-criteria decision aid (MDA) techniques for the choice of the textual similarity measure to be used in the first approach of content-based recommendation
The Use of Knowledge Correlation for Classification and Evaluation of a Distance Education Teaching Resource Database
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Teaching information resource database is an important part of distance education in schools. Understanding the classification and characteristics of teaching resources in teaching information resource database can help teachers to search teaching resources more pertinently and make better use of these resources in actual teaching. Therefore, this article studies the classification and evaluation of distance education teaching resource database based on knowledge network. Combining the LSTM model and the conditional random field model, the resource entities that the target resource design knowledge points refer to are extracted from the resource text string of the teaching resource database. The resource entities that the extracted knowledge point refer to are matched and correlated with other resource entities related to the knowledge point in the teaching resource database, so as to realize the link of resource entities in the teaching resource database based on the concept of knowledge points. Starting with the feature extraction of resource text, this article innovates the resource classification algorithm of teaching resource database, and shows the new algorithm flow of feature extraction of resource text. Experimental results verify the effectiveness of the model
An IoT Based Real-Time Weather Monitoring System Using Telegram Bot and Thingsboard Platform
Get PDFIoT technology's rapid enlargement is implemented in developing automated devices that monitor real-time data. The device design uses the ESP32 Microcontroller, SHTC3 Sensor, BMP180, Windspeed Anemometer, Wind Direction Anemometer, and Tipping Bucket Rain Ombrometer to acquire the current data on weather parameters such as humidity, temperature, altitude, wind speed and direction, air pressure, and precipitation. Using the MQTT, WebSocket, and HTTP Request communication protocols, data transfer from these sensors visualize on the things board dashboard and chatbot telegram. The design of this real-time weather parameter monitoring system integrates with tomorrow's forecasts from BMKG's National Digital Forecast API data. This research carries an axiological approach that contributes to the benefits of the available methods and knowledge to provide more help or value. This research can contribute to the innovation and the development of real-time data observation system automation at BMKG Indonesia as an alternative recommendation to support observation density. In addition, implementing multi-protocol communication in the system can be a scientific reference for the benefits of communication in various outputs
Digital Tool for Film Promotion Through the Use of Augmented Reality
Get PDFAfter the isolation caused by the pandemic, the entertainment sector has been forced to change the way it markets its products and services, and users have been adapting to the change. Companies dedicated to the film industry have had to bet on digital marketing to attract customers through the promotion and advertising of movies, thus attracting a larger audience to their theaters. The objective of this research article is to provide added value and innovation to the advertising promotions of movies in the premiere, through the development of a mobile application based on augmented reality to improve customers’ shopping experience. For which the Agile Mobile-D Methodology was used for the development of the application and the results were studied using 5 indicators: Time to choose a movie, where an improvement of 38.26% was obtained compared to the Pre-Test; regarding the indicator time to acquire information about the movie, an improvement of 81.33% was obtained. 33%; concerning the third indicator, time to initiate the purchase, an improvement of 88% was obtained; concerning the fourth indicator, time to obtain a reminder, an improvement of 83.33% was obtained, making the time with the use of the application shorter; finally, concerning the customer satisfaction indicator, 73% of the users who used the application rated their experience as between satisfied and very satisfied
Improved Methods for Automatic Facial Expression Recognition
Get PDFFacial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems. In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK. To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing
Skill Scanner: An AI-Based Recommendation System for Employers, Job Seekers and Educational Institutions
Get PDFSkills are the common ground between employers, job seekers and educational institutions which can be analyzed with the help of artificial intelligence (AI), specifically natural language processing (NLP) techniques. In this paper we explore a state-of-the-art pipeline that extracts, vectorizes, clusters, and compares skills to provide recommendations for all three players—thereby bridging the gap between employers, job seekers and educational institutions. As companies hiring data scientists report that it is increasingly difficult to find a so-called "unicorn data scientist" [1], we conduct our experiments and analysis using companies’ job postings for a data scientist position, job seekers’ CVs for that position, and a curriculum from a master's program in data science. However, our investigated methods and our final recommendation system can be applied to other job positions as well. Our best system combines Sentence-BERT [2], UMAP [3], DBSCAN [4], and K-means clustering [5]. To also evaluate feedback from potential users, we conducted a survey, in which the majority of employers’, job seekers’ and educational institutions’ representatives state that with the help of our automatic recommendations, processes related to skills are more effective, faster, fairer, more explainable, more autonomous and more supported
Global Publication Trends in Augmented Reality and Virtual Reality for Learning: The Last Twenty-One Years
Get PDFAugmented reality (AR) and virtual reality (VR) provide new experiences in the digital and simulation worlds. While they are intended to stimulate users’ perception and senses in general, the two differ significantly. This study aimed to seek and analyze international publications on the trends, similarities, differences, advantages, and disadvantages of AR and VR for learning over the period 2001-2021. In this study, bibliometric analysis using VOSViewer with the help of MS Excel was utilized to visualize metadata obtained from Scopus. Publish or Perish software supported the data collection of this study, which included retrieving and analyzing academic citations. The findings show that the AR/VR has trend continued to rise over the years. The results showed that Denmark and India (based on country bibliographic pairs), Journal of Physics Conference Series (based on journal bibliographic pairs), Y. Zhou publications (2018) (based on bibliographic pairs of publications), and Archana M. (India) with Lars K. (Denmark) (based on author bibliographic pairs) have become the most influential in the field of augmented reality and virtual reality for learning. AR and VR are two technologies changing how people use screens, thus creating new and exciting interactive experiences. In the future, it is expected that further research related to AR/VR and even Mixed Reality (MR) will continue to develop along with technological advancement. However, pedagogical competence remains an essential key to learning
Self-Attention-Based Bi-LSTM Model for Sentiment Analysis on Tweets about Distance Learning in Higher Education
Get PDFFor limiting the COVID-19 spread, countries around the world have implemented prevention measures such as lockdowns, social distancing, and the closers of educational institutions. Therefore, most academic activities are shifted to distance learning. This study proposes a deep learning approach for analyzing people’s sentiments (positive, negative, and neutral) from Twitter regarding distance learning in higher education. We collected and pre-processed 24642 English tweets about distance learning posted between July 20, 2022, and November 06, 2022. Then, a self-attention-based Bi-LSTM model with GloVe word embedding was used for sentiment classification. The proposed model performance was compared to LSTM (Long Short Term Memory), Bi-LSTM (Bidirectional-LSTM), and CNN-Bi-LSTM (Convolutional Neural Network-Bi-LSTM). Our proposed model obtains the best test accuracy of 95% on a stratified 90:10 split ratio. The results reveal generally neutral sentiments about distance learning for higher education, followed by positive sentiments, particularly in psychology and computer science, and negative sentiments in biology and chemistry. According to the obtained results, the proposed approach outperformed the state-of-art methods