深度预习,筑起有效课堂的第一块砖

深度预习是以课文本身为基点．在初读课文、解决生字难词的基础上．指导学生最大程度地开发和利用一切课程资源，尽其所能地理解课文的深度、拓展课文内容的广度的一种有效学习手段。预习是真正的自主学习，是学生构建新知的过程。深度预习可以充分发挥学生的智慧，让学生通过各种途径，对一个新的知识对象预先进行了解，主动求疑和思考．培养和提高学生求知的欲望和自学的能力，为学生的终生学习奠基。

促进数学深度预习的探索

针对当下数学教学中存在的浅层预习问题,探索如何促进数学深度预习.数学深度预习属于“有意义发现学习”的范畴,包括“看目标,心有数”“研课本,画批注”“做题目,炼方法”“找缺陷,提问题”等步骤.引领数学深度预习的内容有学习目标、学习情境、学习问题等.评价数学深度预习的时机与手段包括课前检查、课中展示、阶段性问卷调查等.

深度预习,促进数学课堂教学方式的变革

预习是一种学习方法,指学生在学习某个知识点之前的自主学习,以期达到更好的教学效果。传统学习方法中的预习,多是学生凭借已有认知经验,对新知的尝试性学习,存在一定的表层性、零碎性及偏移性。随着课堂教学改革,培养学生深度预习,进一步推进深度教学,有效提升学生的学习动力、学习能力和学习毅力,这关乎学生核心素养与关键能力的培育,关乎学生认知深度、系统性与综合性,关乎学生学习能力、信息能力、表达能力与创新能力。

培养中学生语文课前深度预习能力的思考

在语言学习中,预学习是一个非常重要的学习步骤。学生必须在老师的指导下完成自己的练习,思考,阅读等活动。改进教学方法是教学改革的核心。因此,为了提高中学语文教学的效率,有必要改进中学生学前教育的教学方法,培养学生的课前深度阅读能力,引导学生在语言学习过程中完成自己。

培养深度预习,促进数学课堂教学变革

预习一种学习方法,是指学生在学习某个知识点之前的自主学习,以期达到更好的教学效果。传统学习方法中的预习,多是学生凭借已有认知经验,对新知的尝试性学习,存在一定的表层性、零碎性及偏移性;当下培养学生深度预习,进一步推进深度教学,促进教学方式的变革,有效提升学生的学习动力、学习能力和学习毅力,这关乎学生核心素养与关键能力培育,关乎学生认知深度、系统性与综合性,关乎学生学习能力、信息能力、表达能力与创新能力。

让语文课堂无边界——以小说《孤独之旅》浅谈语文课前“深度预习”之效用

文章先分析了学生是小说教材的新历者、学习者、生成者,是生成文体、节选的小说教材所具有的授课价值,从而引出如何将有限的课堂,通过“深度预习”,促进课堂的高效与优化。并以《孤独之旅》为例,有效分析了如何真正做到精典长篇小说节选教材的有效授课方式,引领学生真正领会精典的魅力.

抖音、土豆视频辅助高一新生深度预习物理知识的探索

结合抖音、土豆视频这两款软件的特点,辅助高一新生预习高中物理概念和规律。根据抖音、土豆视频特点,教师需要筛选抖音、土豆视频的资源,选择合适的视频提供给学生预习,录制丰富多彩的物理实验帮助学生预习,并适当的开展物理习题课,深度学习物理概念和规律。利用抖音、土豆视频,不仅提高了学生自主学习兴趣,培养学生的实验能力,促进学生自主学习的能力养成。

培养中学生语文课前深度预习能力的思考

预习是学习阶段中最为重要的一种学习习惯,也是语文学习中不可或缺的部分,是顺利进行课堂教学的前提和实施素质教育的关键。在语文学习中,课前预习要求教师在指导学生的过程中,引导下学生学会自行完成联系、思考、阅读等学习活动。若想要提升教学效率,就必须改善中学生课前预习的教学方法,对学生课前深度预习能力进行培养,引导学生在语文学习过程中自主完成深入探究学习。

道德与法治课堂中的深度预习研究

国家续推教学改革,启动"深度学习"教改项目。深度预习中出现的认识浅薄、敷衍应付等诸多问题,成为初中道德与法治学科学习的虚设环节。本文以此为"抓手",界定了深度预习的内涵与价值,分析了深度预习存在问题及原因,着重探讨了初中道德与法治课深度预习的建构措施。

让语文课堂无边界——以小说《孤独之旅》教学为例

学生是小说课文的学习者、生成者.教师在对经典长篇小说节选教材进行教学时,应做到课前引领、课上指导、课后深入三方位全面安排,以实现语文课堂无边界,可以促进课堂优化并引领学生真正领会经典的魅力.

Anomaly detection of earthquake precursor data using long short-term memory networks

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

Probabilistic interval prediction of metro-to-bus transfer passenger flow in the trip chain

To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows,the application of a probabilistic interval prediction model is proposed to predict transfer passenger flows.First,bus and metro data are processed and matched by association to construct the basis for public transport trip chain extraction.Second,a reasonable matching threshold method to discriminate the transfer relationship is used to extract the public transport trip chain,and the basic characteristics of the trip based on the trip chain are analyzed to obtain the metro-to-bus transfer passenger flow.Third,to address the problem of low accuracy of point prediction,the DeepAR model is proposed to conduct interval prediction,where the input is the interchange passenger flow,the output is the predicted median and interval of passenger flow,and the prediction scenarios are weekday,non-workday,and weekday morning and evening peaks.Fourth,to reduce the prediction error,a combined particle swarm optimization(PSO)-DeepAR model is constructed using the PSO to optimize the DeepAR model.Finally,data from the Beijing Xizhimen subway station are used for validation,and results show that the PSO-DeepAR model has high prediction accuracy,with a 90%confidence interval coverage of up to 93.6%.

Short-time prediction for traffic flow based on wavelet de-noising and LSTM model

Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.

Terminal Traffic Flow Prediction Method Under Convective Weather Using Deep Learning Approaches

In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is proposed.On the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)data.The terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of weather.MICL model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area.Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather.The results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and LSTM.In the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.

中学生学习微习惯调查分析研究

为了研究优秀中学生身上存在的一些微小习惯与其优秀本身的某种联系,该研究通过观察,问卷以及座谈,问询等方式对中学生的学习微习惯进行了调查,分析了中学生微习惯对成绩的重要性,以及初探了微习惯养成策略。

Construction and optimization of traditional Chinese medicine constitution prediction models based on deep learning

Objective To cater to the demands for personalized health services from a deep learning per-spective by investigating the characteristics of traditional Chinese medicine(TCM)constitu-tion data and constructing models to explore new prediction methods.Methods Data from students at Chengdu University of Traditional Chinese Medicine were collected and organized according to the 24 solar terms from January 21,2020,to April 6,2022.The data were used to identify nine TCM constitutions,including balanced constitution,Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,phlegm dampness constitution,damp heat constitution,stagnant blood constitution,Qi stagnation constitution,and specific-inherited predisposition constitution.Deep learning algorithms were employed to construct multi-layer perceptron(MLP),long short-term memory(LSTM),and deep belief network(DBN)models for the prediction of TCM constitutions based on the nine constitution types.To optimize these TCM constitution prediction models,this study in-troduced the attention mechanism(AM),grey wolf optimizer(GWO),and particle swarm op-timization(PSO).The models’performance was evaluated before and after optimization us-ing the F1-score,accuracy,precision,and recall.Results The research analyzed a total of 31655 pieces of data.(i)Before optimization,the MLP model achieved more than 90%prediction accuracy for all constitution types except the balanced and Qi deficiency constitutions.The LSTM model's prediction accuracies exceeded 60%,indicating that their potential in TCM constitutional prediction may not have been fully realized due to the absence of pronounced temporal features in the data.Regarding the DBN model,the binary classification analysis showed that,apart from slightly underperforming in predicting the Qi deficiency constitution and damp heat constitution,with accuracies of 65%and 60%,respectively.The DBN model demonstrated considerable discriminative power for other constitution types,achieving prediction accuracy rates and area under the receiver op-erating characteristic(ROC)curve(AUC)values exceeding 70%and 0.78,respectively.This indicates that while the model possesses a certain level of constitutional differentiation abili-ty,it encounters limitations in processing specific constitutional features,leaving room for further improvement in its performance.For multi-class classification problem,the DBN model’s prediction accuracy rate fell short of 50%.(ii)After optimization,the LSTM model,enhanced with the AM,typically achieved a prediction accuracy rate above 75%,with lower performance for the Qi deficiency constitution,stagnant blood constitution,and Qi stagna-tion constitution.The GWO-optimized DBN model for multi-class classification showed an increased prediction accuracy rate of 56%,while the PSO-optimized model had a decreased accuracy rate to 37%.The GWO-PSO-DBN model,optimized with both algorithms,demon-strated an improved prediction accuracy rate of 54%.Conclusion This study constructed MLP,LSTM,and DBN models for predicting TCM consti-tution and improved them based on different optimisation algorithms.The results showed that the MLP model performs well,the LSTM and DBN models were effective in prediction but with certain limitations.This study also provided a new technology reference for the es-tablishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.

A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves

Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.

对一种新教学模式的思考——读《杜郎口“旋风”》有感

在有效教学和自主学习理论的指导下,文章分析了"杜郎口"教学模式中的核心思想,并认为此教学模式是课堂教学改革和自主学习模式的践行者,提供了一种有效的教学模式。本文提出课堂教学中的"拓宽补窄"思路以及如何促进学生进步、争当"小小老师"的措施,从而为"杜郎口"新教学模式的借鉴和推广提供有益参考。