How to promote interaction in cooperative learning tasks is discussed from a theoretical perspective in order to maximize the benefits of cooperative learning. A classroom instructional model is presented and examined...How to promote interaction in cooperative learning tasks is discussed from a theoretical perspective in order to maximize the benefits of cooperative learning. A classroom instructional model is presented and examined to illustrate how successful and effective interaction is carried out to create the optimal conditions for second language acquisition.展开更多
At present,classroom instruction should be a self-regulated process and the learner who is self-motivated to explore problems and situations.For learning,the students are learning through the web as a source of knowle...At present,classroom instruction should be a self-regulated process and the learner who is self-motivated to explore problems and situations.For learning,the students are learning through the web as a source of knowledge,the learning environment should be shifted to a learner-centered rather than teacher-centered environment.Commerce education is to be directed towards mastery in its conventions and principles,towards thinking and solving problems in scientific ways,towards developing a positive outlook to the discipline at the higher secondary level.Attitude towards learning is associated with the academic performance of commerce-related tasks and improving achievement.It should be one of the basic features in designing effective commerce classroom instruction.In the present study,students’attitudes can be enhanced by using a blended learning instructional strategy targeting the variables of learner attitude towards learning of instructional transaction,learning task,classroom interaction,and assessment.The study employs pretest-posttest non-equivalence control group design under the quasi-experimental method.The sample consists of 80 students of standard XII,40 students each in the experimental group and control group.Statistical techniques of descriptive statistics,t-test,and Cohen’s d were used for comparing the pretest and posttest scores of attitude towards learning and measuring the effect size between experimental and control groups.The findings of the study showed that there is a significant difference in the mean posttest scores of attitude towards learning between the experimental group and control group and the blended learning instructional strategy is more beneficial in developing the attitude of higher secondary school students when compared to constructivist teaching strategy.展开更多
In recent years, China's education reform has been continuously promoted, especially the implementation of the new curriculum reform, which has made great progress in China's education modernization. Based on ...In recent years, China's education reform has been continuously promoted, especially the implementation of the new curriculum reform, which has made great progress in China's education modernization. Based on the requirements of the new curriculum reform, the concept of learning task group arises at the historic moment and is organically integrated with high school Chinese teaching, which plays a very important role in promoting the optimization of high school Chinese teaching contents and methods. In this paper, based on the "learning task group" of high school Chinese multi-text reading teaching to conduct an in-depth exploration, combined with the current development of high school Chinese multi-text reading teaching, put forward scientific and reasonable suggestions, in order to improve our country's high school Chinese education level, promote the modernization of our country's education to further develop, provide more reliable reference.展开更多
To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previ...To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch.This study demonstrates the scaling law of simulationto-real(Sim2Real)transfer learning for several machine learning tasks in materials science.Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases.Observing the scaling behavior offers various insights for database development,such as determining the sample size necessary to achieve a desired performance,identifying equivalent sample sizes for physical and computational experiments,and guiding the design of data production protocols for downstream real-world tasks.展开更多
Second language learning is a multifaceted and dynamic process involving numerous individual difference factors.These cognitive,conative,affective,and social factors influence,predict,or even sometimes determine the o...Second language learning is a multifaceted and dynamic process involving numerous individual difference factors.These cognitive,conative,affective,and social factors influence,predict,or even sometimes determine the outcome of second language learning.This paper attempts to explore the role of emotion,motivation,self-efficacy,and flow in second language learning by reviewing Albert’s book,including the research context,affective factors,an overview of second language learning tasks,three empirical studies,and pedagogical implications.The summary and review of the findings provide insights and suggestions for second language teaching.展开更多
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License,which permits any non-commercial use,sharing,distribution and reproduction in any medium ...Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License,which permits any non-commercial use,sharing,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the CreativeCommonslicence,and indicate if you modified the licensed material.You do not have permission under this licence to share adapted material derived from this article or parts of it.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.To view a copy of this licence,visit http://creativecommons.org/licenses/by-nc-nd/4.0/.展开更多
The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence perfo...The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence performance in electronic and catalytic applications,yet existing machine learning(ML)approaches typically focus on single-property prediction and rely on hand-crafted features,limiting their generality.Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning(AEGCNN-MTL)for simultaneous prediction of multiple material properties.On a DFT-computed BDG dataset of 2613 structures,AEGCNN-MTL achieved high accuracy(R2=0.9905 for band gap and 0.9778 for work function),and under identical training budgets,outperformed representative single-task GNN baselines.When transferred to the QM9 benchmark,the framework delivered competitive performance across 12 diverse quantum chemical properties,demonstrating strong generalization capability.These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput,multi-property screening and the data-driven discovery of multifunctional materials.展开更多
The historical interaction sequences of users play a crucial role in training recommender systems that can accurately predict user preferences.However,due to the arbitrariness of user behaviors,the presence of noise i...The historical interaction sequences of users play a crucial role in training recommender systems that can accurately predict user preferences.However,due to the arbitrariness of user behaviors,the presence of noise in these sequences poses a challenge to predicting their next actions in recommender systems.To address this issue,our motivation is based on the observation that training noisy sequences and clean sequences(sequences without noise)with equal weights can impact the performance of the model.We propose the novel self-supervised Auxiliary Task Joint Training(ATJT)method aimed at more accurately reweighting noisy sequences in recommender systems.Specifically,we strategically select subsets from users’original sequences and perform random replacements to generate artificially replaced noisy sequences.Subsequently,we perform joint training on these artificially replaced noisy sequences and the original sequences.Through effective reweighting,we incorporate the training results of the noise recognition model into the recommender model.We evaluate our method on three datasets using a consistent base model.Experimental results demonstrate the effectiveness of introducing the self-supervised auxiliary task to enhance the base model’s performance.展开更多
文摘How to promote interaction in cooperative learning tasks is discussed from a theoretical perspective in order to maximize the benefits of cooperative learning. A classroom instructional model is presented and examined to illustrate how successful and effective interaction is carried out to create the optimal conditions for second language acquisition.
文摘At present,classroom instruction should be a self-regulated process and the learner who is self-motivated to explore problems and situations.For learning,the students are learning through the web as a source of knowledge,the learning environment should be shifted to a learner-centered rather than teacher-centered environment.Commerce education is to be directed towards mastery in its conventions and principles,towards thinking and solving problems in scientific ways,towards developing a positive outlook to the discipline at the higher secondary level.Attitude towards learning is associated with the academic performance of commerce-related tasks and improving achievement.It should be one of the basic features in designing effective commerce classroom instruction.In the present study,students’attitudes can be enhanced by using a blended learning instructional strategy targeting the variables of learner attitude towards learning of instructional transaction,learning task,classroom interaction,and assessment.The study employs pretest-posttest non-equivalence control group design under the quasi-experimental method.The sample consists of 80 students of standard XII,40 students each in the experimental group and control group.Statistical techniques of descriptive statistics,t-test,and Cohen’s d were used for comparing the pretest and posttest scores of attitude towards learning and measuring the effect size between experimental and control groups.The findings of the study showed that there is a significant difference in the mean posttest scores of attitude towards learning between the experimental group and control group and the blended learning instructional strategy is more beneficial in developing the attitude of higher secondary school students when compared to constructivist teaching strategy.
文摘In recent years, China's education reform has been continuously promoted, especially the implementation of the new curriculum reform, which has made great progress in China's education modernization. Based on the requirements of the new curriculum reform, the concept of learning task group arises at the historic moment and is organically integrated with high school Chinese teaching, which plays a very important role in promoting the optimization of high school Chinese teaching contents and methods. In this paper, based on the "learning task group" of high school Chinese multi-text reading teaching to conduct an in-depth exploration, combined with the current development of high school Chinese multi-text reading teaching, put forward scientific and reasonable suggestions, in order to improve our country's high school Chinese education level, promote the modernization of our country's education to further develop, provide more reliable reference.
基金support from MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(project ID:hp210264)JST CREST(Grant Numbers JPMJCR19I3,JPMJCR22O3,JPMJCR2332)+5 种基金MEXT/JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas(19H05820)Grant-in-Aid for Scientific Research(A)(19H01132)Grant-in-Aid for Research Activity Start-up(23K19980)Grant-in-Aid for Scientific Research(C)(22K11949)Computational resources were provided by Fugaku at the RIKEN Center for Computational Science,Kobe,Japan(hp210264)the supercomputer at the Research Center for Computational Science,Okazaki,Japan(project:23-IMS-C113,24-IMS-C107).
文摘To address the challenge of limited experimental materials data,extensive physical property databases are being developed based on high-throughput computational experiments,such as molecular dynamics simulations.Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch.This study demonstrates the scaling law of simulationto-real(Sim2Real)transfer learning for several machine learning tasks in materials science.Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases.Observing the scaling behavior offers various insights for database development,such as determining the sample size necessary to achieve a desired performance,identifying equivalent sample sizes for physical and computational experiments,and guiding the design of data production protocols for downstream real-world tasks.
基金supported by the Higher Education Research and Reform Project Fund(granted by Guangzhou University of Chinese Medicine 2023)the 2024 GZUCM Social Science Supporting Fund(Grant No.:2024ZXPY18).
文摘Second language learning is a multifaceted and dynamic process involving numerous individual difference factors.These cognitive,conative,affective,and social factors influence,predict,or even sometimes determine the outcome of second language learning.This paper attempts to explore the role of emotion,motivation,self-efficacy,and flow in second language learning by reviewing Albert’s book,including the research context,affective factors,an overview of second language learning tasks,three empirical studies,and pedagogical implications.The summary and review of the findings provide insights and suggestions for second language teaching.
文摘Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License,which permits any non-commercial use,sharing,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the CreativeCommonslicence,and indicate if you modified the licensed material.You do not have permission under this licence to share adapted material derived from this article or parts of it.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.To view a copy of this licence,visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
基金supported by the key project of science and technology research program of Chongqing Education Commission of China(KJZD-K202501109)the National Natural Science Foundation of China(U22A20434)Scientific research foundation of Ministry of Industry and Information Technology of the People's Republic of China(TC220A04A-43).
文摘The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties.For boron-doped graphene(BDG),both the band gap and work function critically influence performance in electronic and catalytic applications,yet existing machine learning(ML)approaches typically focus on single-property prediction and rely on hand-crafted features,limiting their generality.Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning(AEGCNN-MTL)for simultaneous prediction of multiple material properties.On a DFT-computed BDG dataset of 2613 structures,AEGCNN-MTL achieved high accuracy(R2=0.9905 for band gap and 0.9778 for work function),and under identical training budgets,outperformed representative single-task GNN baselines.When transferred to the QM9 benchmark,the framework delivered competitive performance across 12 diverse quantum chemical properties,demonstrating strong generalization capability.These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput,multi-property screening and the data-driven discovery of multifunctional materials.
基金supported by the Program for Student Innovation Through Research and Training of Guizhou University under Grant No.2023SRT071.
文摘The historical interaction sequences of users play a crucial role in training recommender systems that can accurately predict user preferences.However,due to the arbitrariness of user behaviors,the presence of noise in these sequences poses a challenge to predicting their next actions in recommender systems.To address this issue,our motivation is based on the observation that training noisy sequences and clean sequences(sequences without noise)with equal weights can impact the performance of the model.We propose the novel self-supervised Auxiliary Task Joint Training(ATJT)method aimed at more accurately reweighting noisy sequences in recommender systems.Specifically,we strategically select subsets from users’original sequences and perform random replacements to generate artificially replaced noisy sequences.Subsequently,we perform joint training on these artificially replaced noisy sequences and the original sequences.Through effective reweighting,we incorporate the training results of the noise recognition model into the recommender model.We evaluate our method on three datasets using a consistent base model.Experimental results demonstrate the effectiveness of introducing the self-supervised auxiliary task to enhance the base model’s performance.
