Online-Journals.org (International Association of Online Engineering)
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Prolonged Online Learning: An Exploratory Mixed-Methods Study on EFL Learners’ Needs and Need Satisfaction: Online learning: needs and need satisfaction
No full textIn the face of the COVID-19 pandemic, English programs were switched to online regardless of learners’ wishes in several regions. In such a challenging circumstance, learners’ needs should be specially attended to. Situated within the framework of Self-Determination Theory, the current study explores the fundamental needs of relatedness, competence, and autonomy of EFL (English as a foreign language) learners and the satisfaction of those needs in fully online learning. The study draws upon qualitative data collected from focus groups (seven students), and quantitative data collected from a survey (183 students). Findings indicate strong teacher support in fields other than autonomy and relatedness. Also, students were highly satisfied with both their technological and academic competence but were neither happy with the in-classroom communication nor provided space for autonomy. Based on the findings, implications to enhance learners’ need satisfaction in prolonged post-pandemic online learning are discussed
Collaborative Grouping and Interactive Relationship Construction of College Students Based on Group Preference
Get PDFCollaborative learning allows learners to gain more learning resources, learning experiences, and even learning habits from other group learners, thus improving themselves and achieving better learning outcomes. The existing studies only fully consider the differences in learners' learning preferences, while ignoring the preference of each group and the balance between groups, which is not conducive to the overall improvement of learners' achievements in each group. To this end, this article focuses on studying collaborative grouping and interactive relationship construction of college students based on group preference. A collaborative learning grouping algorithm based on group preference is proposed, considering the differences in learners' learning preferences and the preference of learning groups in a balanced way, and the problems in collaborative grouping of college students are described. A group preference-based collaborative learning grouping algorithm is designed, the main ideas and implementation process of the algorithm are expounded, experiments are designed to compare the intra-group difference degree of different grouping algorithms, and the algorithm’s evaluation method is introduced. The experimental results verify the effectiveness of the proposed algorithm
Influence Mechanism of Structural Characteristics of Interdisciplinary Knowledge Network on College Students’ Innovation Ability
Get PDFInterdisciplinary scientific research method plays a significant role in promoting technological innovation. As the principal subject in scientific research activities, college students need to improve their innovation ability using interdisciplinary knowledge resources and knowledge network. Existing researches are mostly carried out from a single perspective of knowledge network, and ignore systematical analysis for interdisciplinary multilayer network. For that reason, the influence mechanism of structural characteristics of interdisciplinary knowledge network on college students’ innovation ability is established in this article. An interdisciplinary-dependent multilayer network is built and network nodes are divided into interdisciplinary knowledge elements, scientific research theme, and discipline and specialty. Structure attribute of interdisciplinary knowledge network and relationship attribute of interdisciplinary knowledge elements are also used to calculate indicators at different knowledge dimensions, and specific indicator measurement modes are proposed. The regression model is built to explore direct influence mechanism of dynamic and static characteristics of interdisciplinary knowledge network on college students’ innovation ability. A regression model is also built to further verify the regulating effect of disciplinary knowledge’s network location attribute of college students’ specialty in influence relationship between interdisciplinary knowledge network and college students’ innovation ability. Last, corresponding analysis result is given based on experiment result
Reform and Practice of Project-Based Teaching Mode Based on Online Open Course Platform
Get PDFProject-based practical teaching is an important part of the teaching reform of fashion design and intelligent manufacturing industry course in vocational colleges, and also an important measure to enhance the effectiveness of teaching. A good practical teaching base is a strong guarantee for the smooth development of project-based teaching. This paper explores the reform and innovation of project-based teaching of fashion design and intelligent manufacturing specialty, and further studies the construction of project-based base focusing on base practice teaching and online courses, to further integrate project-based teaching resources, give play to the role of network-based virtual platform, build perfect project processes, improve project-based teaching course resources of base, and realize co-building and sharing of teaching resources between colleges and enterprises, so as to further improve the teaching quality of project-based teaching of fashion design and intelligent manufacturing specialty, and develop more qualified technical talents
Investigating the Validity and Reliability of a Comprehensive Essay Evaluation Model of Integrating Manual Feedback and Intelligent Assistance
Get PDFHow to respond effectively and efficiently to students’ writing and to maximize the potential of feedback to promote student writing skills deserves careful consideration. The use of intelligent algorithms to assist teachers in the manual evaluation of students’ essays is of practical value and importance in the context of "artificial intelligence + education". The existing intelligent evaluation techniques are subject to the interference of many factors such as openness of questions and students' language expression abilities. For this reason, this study conducts a study on comprehensive essay evaluation method with intelligent assistance and manual feedback and on reliability and validity tests. Before the intelligent evaluation, the semantic integrity of students' essays is analyzed, and a semantic integrity analysis model of students' essays based on BERT model is constructed. A fusion similarity algorithm for essay answer key points is proposed by extracting these characteristics that have an impact on the evaluation results, such as technique preferences, paragraph content and paragraph topic of the essays. The Siamese and ESIM networks are combined to propose an intelligent evaluation model for students’ essays, and the model framework and working principle are described in detail. The experimental results verify the effectiveness of the constructed model
Big Data Analysis and Forecast of Employment Position Requirements for College Students
Get PDFWith the help of natural language processing and machine learning, we can analyze the information of online recruitment text posted by employer companies and dig the requirement features of these employment positions, and this is a meaningful work with practical value. However, existing methods for analyzing online recruitment information are too simple to withstand the mass data on the Internet, so this paper aims to study the analysis and forecast of employment position requirements for college students based on big data analysis. At first, the recruitment information of companies is preprocessed, keywords in the recruitment text are extracted by the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm in the Python Chinese word segmentation toolkit, words in the recruitment text are segmented using the open source tool Word2Vec, and the uncounted words in the recruitment text are identified based on the Conditional Random Field (CRF) model. Then, this paper compares the occurrence probability of skills learnt by college students in a certain company employment position with the occurrence probability of the skills in all employment positions on the website, so as to find out the core skills learnt by college students that can match with the job positions required by companies. At last, this paper builds a XGBoost model to forecast the employment position requirements for college students, and verifies the validity of the model using experimental results
Intelligent Blockchain-Based Secure Framework for Transaction in Mobile Electronic Payment System
Get PDFThe rate of smartphone purchases is rising daily, and mobile payments are now frequently accepted in various areas. It is essential to transfer money completely safe as well as quick. The encrypted distributed ledgers function provides verified real-time transaction confirmation without the necessity for intermediaries like banks and clearinghouses; blockchain provide quick, secure, decentralized, and inexpensive transaction services. Blockchain technology makes money transfer simpler with transparency and financial data security. Through these capabilities, blockchain has attracted interest from around the world. However, some challenges arise while completing some financial security needs. This work proposes a framework for secure mobile payments based on blockchain technology. The advantages of blockchain are discussed and how blockchain technology provides multi-level authentication to secure mobile based financial transactions. Due to the increased safety and confidentiality of users on mobile payment apps, the suggested system takes into account the need of developing a safe application for the mobile transactions. We have also addressed the security related challenges of blockchain based payment applications and provided potential solutions
Interaction Multi-Agent Models' Automatic Alignment with MDA Higher Abstraction Level
Get PDFWith the massive growth of the software sector as well as the erratic needs of end users, agent-based information systems and Model Driven Architecture (MDA) approach are among the liveliest and significant fields of experimentation and improvement to emerge in the recent decade. In this vein, we suggest in this research an innovative method that automates the construction and the generation processes of the interaction multi-agent models from the business requirements engineering models at the MDA highest abstraction levels. So, our defiance is to align the Agent Modeling Language (AML) Communicative Interaction diagram with the E3value model dealing with the MDA approach. The ATLAS-Transformation Language (ATL) is applied to automate the model alignment process. The goal is to reduce project effort, time, and development costs as all alignment process is automatically done, boosting the chances of being more competitive in the software business
The Performance Analysis of Machine Learning Algorithms for Credit Card Fraud Detection
Get PDFThis paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As the usage of digital money or plastic money grows in developing nations, so does the risk of fraud. To counter these scams, we need a sophisticated fraud detection method that not only identifies the fraud but also detects it before it occurs efficiently. We have introduced the notion of credit card fraud and its many variants in this research. Numerous ML fraud detection approaches are studied in this paper including Principal Component Analysis (PCA) data mining and the Fuzzy C-Means methodologies, as well as the Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The existing and proposed models for credit card fraud detection have been thoroughly reviewed, and these strategies have been compared using quantitative metrics including accuracy rate and characteristics curves. This paper discusses the shortcomings of existing models and proposes an efficient technique to analyze the fraud detection
The Effect of Changing Targeted Layers of the Deep Dream Technique Using VGG-16 Model
Get PDFThe deep dream is one of the most recent techniques in deep learning. It is used in many applications, such as decorating and modifying images with motifs and simulating the patients' hallucinations. This study presents a deep dream model that generates deep dream images using a convolutional neural network (CNN). Firstly, we survey the layers of each block in the network, then choose the required layers, and extract their features to maximize it. This process repeats several iterations as needed, computes the total loss, and extracts the final deep dream images. We apply this operation on different layers two times; the former is on the low-level layers, and the latter is on the high-level layers. The results of applying this operation are different, where the resulting image from applying deep dream on the high-level layers are clearer than those resulting from low-level layers. Also, the loss of the images of low-level layers ranges between 31.1435 and 31.1435, while the loss of the images of upper layers ranges between 20.0704 and 32.1625