Meta-SEE:Intelligent and Interactive Learning Framework for Software Engineering Education Based on Metaverse and Metacognition

With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.

Research and Practice of Hybrid Teaching for Software Testing based on Interactive Learning

To address the problems of insufficient number of personalized exercises and cases and teachers’lack of grasp of students’weak knowledge points in the current software testing online courses,we study the strategy of establishing and updating intelligent exercise sets and case libraries and analyze the answers and dig out the weak points of knowledge through group intelligence reasoning and interactive machine learning methods.This will help teachers to make uniform and targeted explanations,reduce manual judgment,and achieve intelligent teaching quality reform,and implement the educational concepts of“keeping up with the times”and“teaching according to students’abilities”.

Design Principles-Based Interactive Learning Tool for Solving Nonlinear Equations

Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer interaction.Thus,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and constraints.It also compares these methods by the number of iterations and time required to display the result.This study helps students learn these methods using interactive learning tools instead of relying on traditional teaching methods.The tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with root-finding.Users with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed tool.The positive results of the user evaluation showed that the tool can be recommended to other users.

URI Online Judge： A New Interactive Learning Approach

The URI online judge is a new online tool created with the main purpose of making programming practice more dynamic, interesting and stimulating for those who have just entered into the art of programming. The URI online judge allows problem corrections in real time, interactivity between users, besides it allows flexibility in the choice of the programming language and it makes some supporting materials available. During the short time in which the tool has being used we have observed that it is a very good tool for self-study. As users of programming portals, the authors noticed some details that would be important to be implemented in a new tool, such as the separation of problems by categories. Another fundamental detail is the fact that this tool is available in two languages （Portuguese and English）. This might facilitate the learning process for beginners, both locally and globally.

Practice and Research on the Novel Interactive Teaching and Learning Methodology for Clothing Design Major Education

In this paper, we research on the novel interactive teaching and learning methodology for clothing design major education. Clothing in the cultivation of innovative talents should be on the premise of enhancing knowledge base and at the same time pay attention to the cross of disciplines and combination, make subject mutual infiltration and influence each other. Teachers should combine the product experience and innovative thinking method and design performance to help the students complete the whole process of fashion design. In the future research, we will combine the advances of multimedia teaching with our clothing design major education to make it more effective.

An Interactive Online Platform for Algorithms Teaching

Learning programming has become an important part of education.However,most students have extreme difficulty learning programming and complex algorithms.This is because programming has a hierarchical logic.Solving complex problems requires students to develop skills in decomposing problems.To this end,this paper describes an effective method to develop an online platform for teaching complex algorithms.

Analysis of the effect of professional teaching staff construction in the training of low-level nurses in operating room

Objective:To explore the application effect of constructing professional teaching staff in low-level training in operating room,so as to further optimize the teaching strength of operating room in our hospital and improve the training effect of junior nurses.Methods:Thirty-eight low-level nurses in the operating room of a third-class hospital in Yantai were selected for half a year's nurse training.With theoretical scores,overall nursing performance,nurses'self-awareness evaluation system and nurses'satisfaction with tutors as evaluation criteria,and based on the selection of high-quality teachers,various evaluation indexes before and after the training of low-level nurses in the operating room were compared and evaluated through the cultivation of practical teaching teachers'ability and the application of a series of teaching methods based on the change of c ompetence-based education(CBE)teaching mode,the application of guided learning interactive canadian education(BOPPPS)teaching model and p roblem-b ased l earning(PBL)teaching method.Results:After the training,the examination scores of low-level nurses were significantly improved(P<0.05),the teaching quality was highly recognized by low-level nurses,the quality of low-level nurses was improved,and patients'satisfaction with nurses was improved.Conclusion:It is of great significance to assist the construction of professional teachers in evidence-based medicine.Through the training of practical teaching teachers'ability and the application of a series of teaching methods based on the change of CBE teaching model,the application of BOPPPS teaching model and PBL teaching method,the training results of nurses have been significantly improved and improved,which is worthy of clinical reference and promotion.

Behaviour of Humans and Behaviour of Models in Dynamic Space

This paper addresses new trends in quantitative geography research. Modern social science research--including economic and social geography--has in the past decades shown an increasing interest in micro-oriented behaviour of actors. This is inter alia clearly reflected in SIMs （spatial interaction models）, where discrete choice approaches have assumed a powerful position. This paper aims to provide in particular a concise review of micro-based research, with the aim to review the potential--but also the caveats---of micro models to map out human behaviour. In particular, attention will be devoted to interactive learning principles that shape individual decisions. Lessons from cognitive sciences will be put forward and illustrated, amongst others on the basis of computational neural networks or spatial econometric approaches. Particular attention will be paid to non-linear dynamic spatial models, amongst others, in the context of chaos theory and complexity science. The methodology of deductive reasoning under conditions of large data bases in studying human mobility will be questioned as well. In this context more extensive attention is given to ceteris paribus conditions and evolutionary thinking. The relevance of the paper will be illustrated by referring to various spatial applications in different disciplines and different application areas, e.g. in geography, regional science or urban economics.

CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network

Recently, facial-expression recognition (FER)has primarily focused on images in the wild, includingfactors such as face occlusion and image blurring, ratherthan laboratory images. Complex field environmentshave introduced new challenges to FER. To addressthese challenges, this study proposes a cross-fusion dualattention network. The network comprises three parts:(1) a cross-fusion grouped dual-attention mechanism torefine local features and obtain global information;(2) aproposed C2 activation function construction method,which is a piecewise cubic polynomial with threedegrees of freedom, requiring less computation withimproved flexibility and recognition abilities, whichcan better address slow running speeds and neuroninactivation problems;and (3) a closed-loop operationbetween the self-attention distillation process andresidual connections to suppress redundant informationand improve the generalization ability of the model.The recognition accuracies on the RAF-DB, FERPlus,and AffectNet datasets were 92.78%, 92.02%, and63.58%, respectively. Experiments show that this modelcan provide more effective solutions for FER tasks.

INTEGRATING COLLEGE ENGLISH COURSE AND COMPUTER MULTIMEDIA:CONSTRUCTING THE NEW AND IDEAL INTERACTIVE LEARNING ENVIRONMENT

With the social and economic development of our country, education at college level is changing rapidly.The popularity of modern information technology education is a hot subject. In recent years, the infusion ofmultimedia into teaching and learning has altered considerably the instructional strategy in our educationalinstitutions and changed the way teachers teach and students learn. The traditional teacher-centric method ofteaching used for decades in our educational system has been modified and enhanced. Currently, moderneducation theory is moving from the traditional recall of facts, principles, or correct procedures into the areasof creative thinking, problem solving, analysis and evaluation. These are skills which are very much needed intoday's knowledge based economy. This shift in focus on learning has presented our educators with seriouschallenges as well as opportunities in restructuring their curriculum to meet the rising demands of theknowledge based society, which is currently being initiated by the Ministry of Education of China.In this paper, we focus on designing a course which is oriented towards a constructivist based paradigm byusing multimedia as an instructional tool, and where students are active learners, involved in constructing theirown knowledge in the learning process and determining how to reach their own learning outcomes. A surveywas carried out to ascertain the reactions of students enrolled in an interactive multimedia course in WenzhouUniversity, Zhejiang province towards this constructivist based learning mode. The results indicated that thesestudents reacted positively towards this study mode and improved their interpersonal and collaborative learningskills.

Real-time control for fuel-optimal Moon landing based on an interactive deep reinforcement learning algorithm

In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.

An Interactive Learning Algorithm for Acquisition of Concepts Represented as CFl

In this paper, an interactive learning algorithm of context-free language is presented. This algorithm is designed especially for system SAQ, which is a system for formal specification acquisition and verification. As the kernel of concept acquisition subsystem (SAQ/CL) of SAQ, the algorithm has been implemented on SUN SPARC workstation. The grammar to be obtained can represent sentence structure naturally.

Game Interactive Learning:A New Paradigm towards Intelligent Decision-Making

Decision-making plays an essential role in various real-world systems like automatic driving,traffic dispatching,information system management,and emergency command and control.Recent breakthroughs in computer game scenarios using deep reinforcement learning for intelligent decision-making have paved decision-making intelligence as a burgeoning research direction.In complex practical systems,however,factors like coupled distracting features,long-term interact links,and adversarial environments and opponents,make decision-making in practical applications challenging in modeling,computing,and explaining.This work proposes game interactive learning,a novel paradigm as a new approach towards intelligent decision-making in complex and adversarial environments.This novel paradigm highlights the function and role of a human in the process of intelligent decision-making in complex systems.It formalizes a new learning paradigm for exchanging information and knowledge between humans and the machine system.The proposed paradigm first inherits methods in game theory to model the agents and their preferences in the complex decision-making process.It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice.Human interactions are involved when the learning process needs guidance from additional knowledge and instructions,or the human wants to understand the learning machine better.We perform preliminary experimental verification of the proposed paradigm on two challenging decision-making tasks in tactical-level War-game scenarios.Experimental results demonstrate the effectiveness of the proposed learning paradigm.

A Feature Selection Method for Prediction Essential Protein

Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.

A SOCIAL PERSPECTIVE ON SECOND LANGUAGE DEVELOPMENT AND INSTRUCTION

Currently research on developing socio-cultural and linguistic competence simultaneously in the language classroom is gaining increasing attention from EFL practitioners and curriculum designers. This paper contends that albeit second language learning is a complex phenomenon with different variables concerning the psychological factors of the learners and the socio-cultural elements of the contexts, an interactional approach to second language learning can ensure that a social perspective of second language development and instruction contributes to having a positive effect on the nature and quality of language learning, which activates the autonomous learning motivation and creates diversity in the learning atmosphere.

Bridging cognitive gaps between user and model in interactive dimension reduction

Interactive machine learning(ML)systems are difficult to design because of the‘‘Two Black Boxes’’problem that exists at the interface between human and machine.Many algorithms that are used in interactive ML systems are black boxes that are presented to users,while the human cognition represents a second black box that can be difficult for the algorithm to interpret.These black boxes create cognitive gaps between the user and the interactive ML model.In this paper,we identify several cognitive gaps that exist in a previously-developed interactive visual analytics(VA)system,Andromeda,but are also representative of common problems in other VA systems.Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system.These include designing new visual features to help people better understand how Andromeda processes and interacts with data,as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process.We evaluate our designs through both qualitative and quantitative analysis,and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks.