The differentiated instructional strategy has been widely applied in countries around the world. However, the differentiated instruction of the design courses for engineering majors still has some barriers in term of learners, teachers, and attributes of the design courses in the smart education environment. To discuss the influences of the differentiated instructional strategy of the design courses for engineering majors on course achievement of learners in the smart education environment, a case study based on the course of “Architectural Interior Hand-painted Renderings” was conducted using two-group pre-test and post-test methods. The differentiated instructional strategy for this course in the smart education environment was designed to investigate its actual effect toward improving the learners’ course achievement, as well as its staged efficiency in preview, classroom learning, and creating learning through the student’s t-test from perspectives of its influence significance and staged efficiency. Results show that compared with traditional instructional strategy, differentiated instructional strategy can improve course achievement of learners significantly. During classroom learning, learners from the differentiated instruction class achieve significant improvements in term of “picture composition”, “line drawing”, “perspective drawing” and “color construction” compared with those during the preview. Learners further achieve significant improvements in terms of “picture composition,” “line drawing,” “perspective drawing,” and “creativity” during creating learning compared with those during classroom learning. Differentiated instruction has significant influences on improvement of the learners’ course achievement at different levels. This study provides method references to differentiated instruction design for engineering majors in smart education environment and offers a way to fulfill personalized needs of differential learners and improve teaching efficiency.
Rural tourism is the purpose of vacation tourism, which is an important support for rural economic development. Neural network algorithms are the second way to simulate human thinking. The purpose of this paper is to obtain benefits and diversify risks through dynamic simulation analysis and comprehensive quantitative analysis based on the neural network algorithm, so as to improve the core competitiveness of rural tourism. This paper first designs the neural network algorithm model, then analyzes the dynamic simulation model and comprehensive quantitative analysis, and then uses the artificial neural network algorithm to analyze and predict the core competitiveness of rural tourism. The results confirm the effectiveness of the algorithm. Under the demand forecast of the core competitiveness of rural tourism, using the artificial neural network algorithm, taking city A as an example, the number of tourists in rural tourism in city A from 2017 to 2021 was analyzed. The year with the largest number of people is 2021, with 2,586,000 people, and the year with the largest growth rate is 2019, with 2,576,900 people, a growth rate of 24.16%. Comparing the experimental data of each group, the results show that the algorithm has high efficiency in solving problems and also confirms the scientific validity of the algorithm.
“Carbon neutrality” refers to the total amount of carbon dioxide or greenhouse gas emissions directly or indirectly produced by a country, business, product, activity or individual over a period of time. Through afforestation, energy saving and emission reduction, etc., to offset the carbon dioxide or greenhouse gas emissions generated by itself, to achieve positive and negative offset, to achieve relative “zero emissions”, which belongs to the same term as “carbon peaking” in energy saving and emission reduction. “Carbon” means carbon dioxide, and “neutralization” means a positive and negative balance. The carbon dioxide or greenhouse gas emitted is offset by afforestation, energy saving and emission reduction, which is called “carbon neutrality”. Goal decomposition is the process of gradually completing the overall goal according to multiple fields to form a goal system. Target decomposition should be carried out according to the principle of integration and integration. That is, the overall goal is decomposed into sub-goals of different levels and departments, and the synthesis of each sub-goal reflects the overall goal and ensures the realization of the overall goal. This paper aims to study the decomposition of building carbon neutrality goals to sustainable energy development strategies, combining energy conservation and emission reduction with the building industry to reduce environmental pollution and energy waste. Based on the theory of sustainable development, combined with the development characteristics of China’s new energy industry, this paper makes a theoretical analysis of the sustainable development of the new energy industry from the three systems of economy, society and ecology. Using the new growth theory and the dissipative structure theory, the restricting effect of the depletion of basic energy on economic development and the influence on the alternation of industrial structure are discussed. The experimental results of this paper show that the building energy consumption intensity will reach 1.43tC/10,000 yuan, 1.25tC/10,000 yuan in the ideal scenario, and 1.1tC/10,000 yuan in the low-carbon scenario.
Vocational and technical higher education in China has made great progress. However, classroom learning inefficiency is still observed. Based on the composition theory of classroom instructional time and the characteristics of classroom teaching in vocational and technical colleges, evaluation indexes of the effectiveness of classroom learning time were proposed in this study, and the effects of teaching behaviors on the effectiveness of the classroom learning time were analyzed through the observation on teaching behaviors and learning efficiency. Using the classroom teaching of "equivalence calculation in engineering economy" in a vocational and technical college in Chongqing, China as the subject, the classroom observation results were given, while the effects of frequency, cumulative time and the essence of teaching behaviors on the average value of learning time effectiveness (AEVp) and learning effectiveness per unit time (EV) were analyzed. Results show that the longer the time spent on a teaching behavior, the greater the AEVp would be, while EV differs, and "questioning"-typed teaching behaviors usually had positive effects on EV. Results indicate that the conversion of reasonable intervals between teaching behaviors helps to improve EV, and arrangement of personalized learning tasks and the strategies of answering questions need to be taken seriously. This study revealed the relationship between teaching behaviors and the effectiveness of classroom learning time, which may provide the evidence for optimizing instructional design and provide a path to fulfill the personalized learning needs.
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