Online-Journals.org (International Association of Online Engineering)
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Can AI Function As A New Mode of Sketching: A Teaching Experiment with Freshman
Get PDFThis study investigates how artificial intelligence (AI) can be integrated into first-year design education. The goal of the study is to examine how students combine orthographic projections with AI-generated images. To answer this question, a design representation course called Visual Communication I (VC-I), taught at Istanbul Technical University’s Faculty of Architecture, was selected. A three-step assignment called “ISO-meets-AI” was planned and implemented in the VC-I. The steps of the assignment were as follows: (i) producing orthographic projections of created physical compositions; (ii) generating image output from text input using an AI program (Midjourney); and (iii) combining the AI-generated images with the orthographic projections. The assignment was completed by 50 students from the departments of architecture, interior architecture, and industrial design. Tutors defined the evaluation categories and sub-criteria based on an initial analysis. While tutors evaluated every submission based on the established categories and sub-criteria, students only assessed their own work through a survey. Lastly, a second survey was given to the students in order to better understand their willingness to employ AI in their future studies. The first survey’s comparative evaluation results of the tutors and students, as well as the results of the second survey, are presented
A Model to Predict and Analyze Students' Learning Preferences and their Cognitive Development through Educational Big Data
Get PDFUnderpinned by the accelerated progression of information technology, the role of educational big data in information gathering and analysis has been underscored, particularly so in finance, a discipline embedded in logic and analysis. Patterns in student learning and behavioral data, when examined, can afford educators invaluable insights to shape efficacious teaching strategies. Contemporary research probing into the dynamics of student learning preference evolution and cognitive advancement appears to over-depend on static data, often falling short of effectively addressing the intricate data structures in educational big data. In this light, it becomes imperative to delve into the temporal shifts in student learning preferences and their link to cognitive advancement. In this context, a novel dynamic trustaware preference evolution model is brought to the fore, with the potential to precisely track variations in learning preferences of finance students and elucidate their correlation with cognitive advancement. A correlation model is erected, laying bare the reciprocal interaction between the metamorphosis of student learning preferences and cognitive progression. This pioneering approach eclipses the constraints inherent in extant research methodologies, rendering deeper comprehension to educators. Findings from regression analysis divulge the association between the transformative journey of learning preferences and cognitive advancement, holding far-reaching implications for educational practices. These revelations can capacitate educators to fine-tune their teaching approaches in line with student development, fostering personalized learning ecosystems. This research further holds significant merits for addressing complexities within finance education, aiding in the cultivation of adept professionals capable of navigating the fluid landscape of modern finance
Effectiveness of Online Collaborative Learning in Gamified Environments
Get PDFOnline education, supported by emerging technologies, such as the Internet, big data, and industrial intelligence, is becoming a growing trend. Gamified learning is gaining popularity within educational environments. Educational games serve as the medium for this type of learning and as tools to support learning. They create real-world situations for students to solve tasks, effectively cultivating problem-solving, critical thinking, and creativity skills. In gamified teaching, collaborative learning helps players in completing game tasks by focusing on cooperation and learning essential knowledge or skills. This study selected 300 students from Harbin University of Science and Technology as research subjects. A single-factor, two-level between-subjects design was used to examine the influence of online collaborative learning (independent variable) on flow experience, intrinsic motivation, and academic performance (dependent variables). The participants were divided into an experimental group (two-player collaborative game group, N = 150) and a control group (single-player game group, N = 150). After completing their assigned tasks, the participants independently completed questionnaires and tests. Data were then analyzed using SPSS 25.0 software. Results show that there is no significant difference in prior knowledge between the two groups. However, notable differences are found in flow experience, intrinsic motivation, and academic performance. The experimental group demonstrates significantly better academic performance than the control group, indicating the effectiveness of collaborative learning within gamified learning. Flow experience has a partial mediating role in collaborative learning, whereas intrinsic motivation has a full mediating role. These findings have critical implications for understanding the impact mechanism of collaborative learning on academic performance in gamified learning, promoting the effective utilization of educational games, and accelerating the exploration and application of gamified learning models
Analysis of the Sentiment in the Evaluation Texts of University Students by Means of the Concept of Flexible Management
Get PDFWith the development of information technology (IT) and the popularity of the Internet, it is easier to obtain college students’ teaching evaluation text data. However, it is still challenging to deeply understand and effectively analyze these texts. Based on the flexible management concept in universities, this study aimed to understand and analyze the teaching evaluation texts of college students more accurately, thereby improving the teaching quality. The implicit features of those texts were recognized first in this study, which aimed to supplement and improve the implicit features neglected by existing sentiment analysis methods. A sentiment analysis method based on Bidirectional Encoder Representations from Transformers (BERT) was adopted to explore the deep semantic information of texts. It was done by using deep learning technology and to improve the accuracy of sentiment analysis. This in turn provides more valuable reference information for university teaching management and further promotes the practical application of the flexible management concept in university teaching management
An Evolutionary Study of the Impact of Artificial Intelligence Technology on Foreign Language Education
Get PDFThis study investigates the evolutionary impact of applying artificial intelligence (AI) technology to foreign language education. By employing complex systems thinking, the relationship between foreign language education and AI technology is explored, and dynamic models are employed to analyze the evolutionary patterns of AI technology in foreign language education. Through model analysis and numerical simulations, the interactive effects between foreign language education and AI technology in different modes are revealed. The findings demonstrate that, under different coupling modes, foreign language education and AI technology can achieve self-organizing evolution. When the interaction coefficient between foreign language education and AI technology is appropriately set, AI technology exhibits emergent properties for foreign language education. Lastly, suggestions are presented to promote the sound development of foreign language education and AI technology
A Novel Approach to Improving Distributed Deep Neural Networks over Cloud Computing
Get PDFIn recent years, deep distributed neural networks (DDNNs) and neural networks (NN) have excelled in an extensive list of applications. For example, deep convolutional neural networks (DCNNs) are constantly gaining new features in various tasks in computer vision. At the same time, the number of end devices, including Internet of Things (IoT) devices has increased prominently. These devices are attractive targets for machine learning applications because they are often directly connected to sensors. For example (cameras, microphones, and gyroscopes) that record large amounts of input data in a stream mode. This study presents the design of a DDNN with end devices, edges, and clouds that spans computer hierarchies. The idea presented is considered one of the new ideas because it depends on two layers to distinguish, namely the convolutional layer and the pooling layer. The main objective behind using these two layers in one proposal is to provide and obtain the best results. Finally, we discovered that the proposed technique produced the best results in terms of accuracy and cost, with the precision of the definition reaching 99 % and the cost being quite affordable at 25. As a result, we conclude that these results are far superior to those achieved by the researchers in their ideas provided in previous recent literature
Development and Creation of Ancient Sandstone Carvings Using 3D Software Tools and Mobile/Tablet Devices
Get PDFIn the area of Phayao Province that used to be part of the ancient Lanna Kingdom, there are unique works of art that can be found until today, which are sandstone carvings caused by religious beliefs resulting in works such as Buddha statues. These are art related to religion. Nowadays, these works of art have started to fade away and receive less and less attention from people. The creation of art objects in this research therefore uses the interpretation of the creative objects in line with people’s lifestyles by selecting the lotus, which is a plant related to rivers, and is important in religion. In the lotus, which is interconnected and aligned with Buddhism, and in harmony with a way of life that aligns with the river, creative works are found in various forms, connecting people in Buddhism, including sandstone carving. In this creation, digital technology tools and methods are used to collect data to create a 3D work piece. Photogrammetry is used to record detailed proportions and information and customization of 3D work pieces. In these steps, mobile phone-type tools are used to collect image data to create the 3D work or an application is used to customize 3D work pieces from a tablet device to create prototypes of sandstone sculptures from the artisans that remain today. This can be seen in creating, maintaining, and recording digital data, creation, and the integration of knowledge. This demonstrates that today’s tools and portable devices can help create more creative pieces of work and preserve art and culture
Prediction of Students Performance Level Using Integrated Approach of ML Algorithms
Get PDFIn this paper, the efficacy of machine learning (ML) techniques for predicting the academic success of students is investigated. In issues pertaining to higher education, as well as machine learning, deep learning, and its linkages to educational data, predicting student achievement is essential. The choice of courses and the development of effective future study plans for students can be easier with the help of the capacity to forecast a student's success. In addition to predicting student achievement, it makes it easier for instructors and administrators to keep an eye on children so that they can offer support and integrate trainings for the greatest outcomes. In this study, we define the idea of predicting the student performance in education and its several iterations. We discuss a number of ML approaches, such as the Fuzzy C-Means, the Multi-Layer Perceptron (MPL), the Logistic Regression (LR), and the Random Forest (RF) algorithms, for predicting student achievement in the classroom. The models for forecasting student performance that are now in use and those that have been proposed in this paper are carefully investigated. The paper examines different combinations of the algorithms including FCM – MLP, FCM – LR, and FCM – RF, and provides the detailed results of each combination. These strategies are assessed using quantitative standards including accuracy, detection rate, and false alarm rate
Immersive English Online Teaching Model Using Original Film as Teaching Resources
Get PDFThe traditional English teaching mode generally consists of reciting words, phrases and texts with high intensity, mechanically memorizing grammar and doing a lot of exercises. This way not only causes the majority of students lack of practical ability to use English, but also difficult to stimulate students’ interest in learning. In this study, an immersive on-line teaching mode of audio-visual film English is constructed. Compared with the general on-line teaching mode, the model has a learning state feedback module based on convolutional neural network and support vector machine. Teachers can pay attention to students’ learning state in time and adjust teaching strategies according to their learning state. Three classes of the same school and grade were selected for comparative experiment. Offline teaching, traditional online teaching and improved online teaching were adopted in one semester respectively. The effectiveness of the improved method was proved by analyzing the class status and final grades of students in the whole semester. Through network training and practical tests, compared with the students who adopt traditional online teaching, the students who adopt this mode have greatly improved their learning state and final exam scores. At a time when the epidemic is still lingering, this mode can provide a positive reference for the development of online teaching
A MOOC-Based Hybrid Teaching Model of College English
Get PDFIn the era of intelligence, Internet + technology is widely used in various fields, and English Teaching in the education industry of colleges and universities gradually tends to be an online and offline mixed teaching mode. However, under the MOOC model, the feedback of College Students’ English learning and the recognition of their knowledge level has become new difficulties. Aiming at the feedback of students’ learning situation under the mixed mode of College English teaching, this paper uses the optimized Bayesian knowledge tracking model (BKTM) to predict students’ English learning situation and introduces students’ learning behavior and forgetting behavior to optimize parameters. Finally, a performance verification experiment is carried out by analyzing the students’ answer performance in College English mixed teaching. The results show that the prediction errors of the four knowledge points of 60 students in the two classes are all about 7%, and the maximum error is 11%. Experiments show that the model has high accuracy and stable performance in predicting the probability of mastering knowledge points