AIGC Scenario Analysis and Research on Technology Roadmap of Internet Industry Application

The explosion of ChatGPT is considered to be a milestone in the normalization of artificial intelligence education applications.On the technical line,the cross-modal AI generation application based on human feedback system is accelerated.In the business model,the scenes to realize interactive functions are constantly enriched.This paper reviews the evolution process of AIGC,closely follows the current situation of the coexistence of business acceleration and technical worries in the application of artificial intelligence education,analyzes the application of AIGC education in 7 subdivided fields,and analyzes the optimization direction of application cases from the perspective of perception-cognition-creation technology maturity matrix.The 3 recommendations and 2 follow-up research directions will promote the scientific application of artificial intelligence education in the AIGC period.

Assessing ChatGPT's Educational Capabilities and Application Potential

Purpose:ChatGPT is a generative artificial intelligence(Al)technology that can solve multiple complex tasks.ChatGPT-4 can facilitate educational empowerment in China through technology to understand and generate Chinese text.Although ChatGPT's benefits have been widely discussed,its educational capabilities have not been systematically assessed.This study provides evidence of and insights into the educational applications of Al tools in China.Design/Approach/Methods:This study uses various tests to systematically assess the latest iteration of the Al chatbot ChatGPT-4,including the Watson-Glaser Critical Thinking Appraisal(WGCTA),Five Core Competencies Questionnaire,and written test of China's 2022 National Teacher Certificate Examination(NTCE).Findings:The WGCTA results suggest that ChatGPT requires strong critical thinking.Compared with the other four competencies,the tool showed a lower aptitude for creativity.Regarding its educational applications,ChatGPT performed well on the 2022 NTCE written test.As technology enhances,ChatGPT and similar Al tools have potential applications in China for lesson planning,student self-learning,classroom interaction,and checking assignments.OriginalityNalue:This study systematically tested ChatGPT at a logical level and assessed its core competencies and educational applications.The study innovatively used 2022 NTCE data to test ChatGPT,with results providing support for the application of generative Al in future curricula and instruction in China.

Implementation of STEM Tinkering Approaches in Primary School Education in Greece

In this paper,they are presented the experiences that emerged from the implementation of STEM approaches in primary education in Greece,given the context in which schools operate.The aim is to stimulate discussion on the possibilities and feasibility of introducing STEM activities in Greek schools.Two public schools in Attica were selected as pilot schools to test the proposal in which eight different actions were implemented in different grades.Indicatively,one of the actions implemented for nine classroom hours in elementary school(K5)is presented.From the literature review and the implementation of these actions,it has been demonstrated that while existing curricula have not incorporated the STEM education methodology into their goals,there are links that allow teachers to apply it.In addition,it appears that the scenarios will have to be carefully designed,be clearly depicted and receptive to accepting solutions proposed.In parallel,the positive effect of these actions has multiple levels of impact on students(cognitive domain,affective domain,and psychomotor domain).

Contrastive Study of Learning by APPs and Traditional Classroom Learning Among College Students

In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with the background of Constructivism. Learning by APPs and traditional classroom learning have their prospective advantages and disadvantages on learning time,space, contents, methods, efficiency and supervision. Learners can make full use of educational APPs, combining APPs with traditional classroom learning to realize blending learning and achieve high-efficiency.

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.

Modelling the Dropout Patterns of MOOC Learners

We conduct a survival analysis for the viewing durations of massive open online courses.The hazard function of the empirical duration data presents as a bathtub curve with the Lindy effect in its tail.To understand the evolutionary mechanisms underlying these features,we categorize learners into two classes based on their different distributions of viewing durations,namely lognormal distribution and power law with exponential cutoff.Two random differential equations are provided to describe the growth patterns of viewing durations for the two classes respectively.The expected duration change rate of the learners featured by lognormal distribution is supposed to be dependent on their past duration,and that of the remainder of learners is supposed to be inversely proportional to time.Solutions to the equations predict the features of viewing duration distributions,and those of the hazard function.The equations also reveal the features of memory and memorylessness for the respective viewing behaviors of the two classes.