Online-Journals.org (International Association of Online Engineering)
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The Impact of Quiz Mode on Students’ Learning Achievement: A Gamified e-Quiz Study
Get PDFThis study investigated the impact of quiz mode on university students' academic achievement in an online ESOL theory course as well as their perceptions of the gamified e-quiz mode. Forty-two students at a women's university in Seoul participated in the study for 12 weeks. Utilizing a crossover design (AB/BA), this study compared the academic achievements of Group A (n=22) and Group B (n=20) after experiencing both gamified and conventional e-quiz interventions. Group A was treated with gamified e-quizzes for an initial three weeks, had a three-week break, and was then provided with conventional e-quizzes for three weeks. Group B received the same treatments in reverse order: taking the conventional e-quizzes first, having a washout break, and then taking gamified e-quizzes. Three tests—one pre-test and two post-tests—were developed and used to measure the participants’ academic achievement. Two parallel survey forms were used after each intervention to measure the students' perceptions. The data analyses of the test results indicated that the students' academic achievements after the gamified e-quizzes were not significantly different from those after the conventional e-quizzes. In addition, the survey data illustrated, except for some data sets indicating more favorable perceptions toward gamified e-quizzes, that the participants of both groups viewed the two modes of quizzes mostly positively. Moreover, more participants in both groups chose gamified e-quizzes over conventional ones, describing the gamified e-quizzes as enjoyable, motivating, and low-anxious experiences
High-Stakes Online Exams: Faculty Perceptions on Forced Digitization of Assessment During Corona at a Swiss Business School
Get PDFCOVID-19 has affected university assessment procedures on a large scale. This empirical study aims to understand the types of high-stakes exams delivered online at the Lucerne School of Business in Switzerland during the “Corona Semesters” of 2020 and 2021 and the decision-making factors that influenced their implementation. To do so, the authors conducted semi-structured interviews with eight faculty members across a variety of disciplines. Requirements from the exam workflow (preparation, proctoring, grading) were identified and analyzed by course type. Four factors emerged that significantly impacted design and delivery for high-stakes exams online: 1) Digital exam formats significantly impact the nature of exams for procedural subjects such as mathematics; 2) “Group Exams” are not the answer to preventing student collusion on online exams; 3) interrater reliability and low answer variance are considered a central factor for exam quality assurance; 4) second-order effects such as stable wifi and device compatibility will continue to hinder widescale adoption of digital exams. The findings suggest that online exam delivery significantly affects institutional exam practice beyond mere consideration of learning outcomes. The authors conclude by speculating that similar dynamics may have impacted other business schools during their Corona semesters and invite future research on whether the findings from this article can spark discussion and reflection for policy makers in other institutions on post-pandemic legacies
Overcoming Integration Thresholds for Augmented Reality
Get PDFThe advent of augmented reality (AR) is reshaping the way people experience physical and virtual environments, from observation to immersion. Growing interest in adopting AR provides opportunities for immersive learning, upskilling, and renewal. However, uncertainties exist in how to maneuver a transition toward making use of this technology through systematic integration. Due to the turmoil caused by the global pandemic health crisis, implementation of AR now faces urgency in minimizing adoption thresholds and establishing a more systematic escalation approach. This paper investigates the characteristics of such learning approaches and examines the integration of AR with customized progression. Two solution suppliers were investigated to uncover the integration process of AR, which, to the best of our knowledge, is scarcely explored in existing research. This study reveals that a balanced escalation of user-centric learning activities, i.e., an onboarding process, harmonizes anticipated cognition levels for a designated AR application tool
Design of IoT Based Remote Renewable Energy Laboratory
Get PDFWith continuous increase in the number of students enrolled in universities, e-learning has become an urgent necessity in modern education systems. However, applied programs face challenges in adopting this type of learning because of their need for laboratories and training workshops. With the advancement in computers and wireless communications technologies, and advent of Internet of Things technology, it has become possible to design real laboratories that can be accessed remotely. Remote labs are an advanced step that enables students to interact remotely with real laboratory devices using personal computers or smartphones from anywhere and at any time. This paper presents the use of Internet of Things technology in the design of the renewable energy laboratory, where the experiments are designed in a way that allows the student to choose the required experiment remotely, study its components, and measure many variables in order to obtain the necessary skills
Machine Learning Approach for an Adaptive E-Learning System Based on Kolb Learning Styles
Get PDFIn order to effectively implement adaptive learning within E-learning systems, it is crucial to accurately define thelearner's profile that reflects the characteristics necessary for optimal learning. Traditional methods of identifying profiles often relyon questionnaires to collect data from learners, which can be time-consuming and result in irrelevant data due to arbitrary responses.As a solution, we propose an intelligent and dynamic model for adaptive learning that takes into account the entire learning process,from diagnostic assessment to knowledge assimilation. Our approach utilizes the k-means classification algorithm to group learners based on similar characteristics, as defined by the KOLB model. To enhance the accuracy of our model, we also incorporate neural networks to automatically predict learning styles and using decision tree to propose a adaptative pedagogical content to learner. By doing so, we aim to improve the overall performance of our proposed model
Effectiveness of an Adaptive Learning Chatbot on Students’ Learning Outcomes Based on Learning Styles
Get PDFIntelligent learning systems provide relevant learning materials to students based on their individual pedagogical needs and preferences. However, providing personalized learning objects based on learners’ preferences, such as learning styles which are particularly important for the recommendation of learning objects, re-mains a challenge. Recommending the most appropriate learning objects for learners has always been a challenge in the field of e-learning. This challenge has driven educators and researchers to implement new ideas to help learners improve their learning experience and knowledge. New solutions use artificial intelligence (AI) techniques such as machine learning (ML) and natural language processing (NLP). In this paper, we propose and develop a new personalization approach for recommendation that implements the adaptation of learning objects according to the learners’ learning style mainly focused on the use of a chatbot, named LearningPartnerBot, which will be integrated into the Moodle platform. We use the Felder-Silverman Learning Styles Model to determine learners’ learning styles in order to recommend learning objects, and also to overcome the cold start problem. A chatbot is an automated communication tool that attempts to imitate a conversation by detecting the intentions of its user. The proposed LearningPartnerBot should be able to answer learners’ questions in real time and provide a relevant set of suggestions according to their needs
A Proposed Framework for Human-like Language Processing of ChatGPT in Academic Writing
Get PDFThe study proposed a framework for analyzing and measuring the ChatGPT capabilities as a generic language model. This study aims to examine the capabilities of the emerging technological Artificial Intelligence tool (ChatGPT) in generating effective academic writing. The proposed framework consists of six principles (Relatedness, Adequacy, Limitation, Authenticity, Cognition, and Redundancy) related to Artificial Language Processing which would explore the accuracy and proficiency of this algorithm-generated writing. The researchers used ChatGPT to obtain some academic texts and paragraphs in different genres as responses to some textbased academic queries. A critical analysis of the content of these academic texts was conducted based on the proposed framework principles. The results show that despite ChatGPT’s exceptional capabilities, its serious defects are evident, as many issues in academic writing are raised. The major issues include information repetition, nonfactual inferences, illogical reasoning, fake references, hallucination, and lack of pragmatic interpretation. The proposed framework would be a valuable guideline for researchers and practitioners interested in analyzing and evaluating recently emerging machine languages of AI language models
A Comparative Study on the Performance of 64-bit ARM Processors
Get PDFMobile devices are playing an important role in our daily lives. Nowadays, mobile devices are not only phones to call and text, but they are also smart devices that enable users to do almost any task that could be done on a regular PC. At the heart of the design of smartphones, there lies the processor to which almost all the development in the smartphone arena is attributed. Recently, ARM processors are among the most prominent processors used in mobile devices, smartphones, and embedded systems. This paper conducts an experimental comparative study of ARM 64-bit processors in terms of performance and their effect on power consumption, CPU temperature, and battery temperature. We use a number of well-known benchmarks to evaluate those characteristics of three smartphones, namely, Snapdragon 778G+, Exynos 1280 and HiSilicon Kirin 980. Those smartphones are all equipped with ARM 64-bit processors. Our results reveal that none of the three-selected smartphones was the best in all characteristics; each has superiority amongst others in certain characteristics and is dominated by others in other characteristics
Classification of Tweets Related to Natural Disasters Using Machine Learning Algorithms
Get PDFIdentifying and classifying text extracted from social networks, following the traditional method, is very complex. In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work aims to identify, classify and analyze tweets related to real natural disasters through tweets with the hashtag #NaturalDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geolocated tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed to gain an initial understanding of the data. Fourth, the training and testing process of the BNB, MNB, L, KNN, DT, and RF models was initiated, using tools and libraries for this type of task. The results of the trained models demonstrated optimal performance: BNB, MNB, and LR models achieved a performance rate of 87% accuracy; and KNN, DT, and RF models achieved performances of 82%, 75%, and 86%, respectively. However, the BNB, MNB, and LR models performed better with respect to performance on their respective metrics, such as processing time, test accuracy, precision, and F1 score. Demonstrating, for this context and with the trained dataset that they are the best in terms of text classifiers
The Digital Game for Curriculum Public Relations (PR) and Learning Using Unity3D
Get PDFIn the age of digital disruption, universities and educational institutions all over the world provide many bachelor’s, postgraduate, and non-degree curriculums. Information is mostly presented in different forms, i.e., PR on websites, mobile applications, or social media; including field PR at different sites, e.g., department stores and schools. However, the limitations and the problems are that presented information fails to attract or motivate perception, interest, and participation of the target groups. Also, traditional PR requires resources in terms of persons, time, and a large amount of expenditures. Thus, this research aimed to present the development of a game to present information for PR of bachelor’s curriculums using Unity3D, which could reduce the limitation of presentation or PR in the traditional forms. It could also save PR budget and could be done anywhere and anytime