小学数学预习型微课设计探析

预习型微课是教师在课前引导小学低年级学生完成预习的一种微课,集容量小、时间短、内容精、形式多于一体。根据小学低年级学生(六七岁)的年龄特点和认知基础,此类微课以教材例题为预习内容,重点引导学生按照"读一读、做一做、想一想、问一问"四个步骤进行预习,逐步掌握数学文本阅读、数学操作实践、数学思考与表达等学习方法,培养学生自觉预习的意识和习惯,逐步提高学生自主学习的能力,涵养核心素养。

预习型微课在小学数学教学中的应用研究

在课程改革不断深入的大背景下,通过微课引导学生进行课前预习逐渐成为众多小学老师重点讨论的热点话题,预习型微课的教育方式能够有效激发学生的学习兴趣,是教育进步与社会发展必然趋势。作为小学数学教师,应该在教学中适当引入这种教学模式,帮助学生更好地融入课堂教学中,使课堂教学的质量与效率得到显著提升。基于此,文章从多个角度分析了预习型微课在小学数学教学中的有效应用。

“预习探究型”课堂教学模式的实践

本文是通过实施小学数学“预习探究型”课堂教学二年多来的总结，提出建构“预习探究型”课堂教学模式的结构，和实施“预习探究型”课堂教学模式的策略。通过课前引导学生预习，课中主动探究，合作交流，解决问题。改变学生单纯地接受教师传授的教学方式，以培养学生的探究精神和创新能力。

高中物理教学中实施“任务型自主学习模式”探微

新课改要求老师突破传统的教学方法，更新教学手段，以提升教学质量。主要围绕高中物理教学，就任务型自主学习模式在其中的运用进行简要的分析。

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.

培养学生记日记习惯 提高其数学表达能力

数学日记是以日记的形式记录学习数学的情况 ,可以分为预习型、随堂型、巩固型、反思型、生活型、探究型。通过记数学日记的过程 ,既能提高学生的表达及文字组织能力 ,还能培养学生的自主学习。

对高等数学非数学专业教学的几点思考

高等数学教学特别强调师生双边活动,更应以学生为主体,教师为主导。对于高校非数学专业的学生来说,一定要以提高学生的学习兴趣和教师的教学效果为目的。在方法上要提倡"问题研究型"教学,宜采用"预习指导型"教学,要适当安排"实践尝试型"教学,常穿插"启发设疑和趣味性"教学,尤其要有效运用"多媒体"教学多媒体教学。

巧用精彩微课 激活数学课堂

微课教学模式非常符合当前倡导的自主性学习和探究性学习,本文结合笔者的教学实践阐述了在初中数学课堂的教学中,合理应用不同类型的精彩"微课",突出学生的学习主体性,提高学生的自主学习能力和创新实践能力,从而激活数学课堂,提高课堂教学实效.

例说课堂教学中的当场检测

学生语文知识的构建、学习技能的形成,与及时有效的练习息息相关。当场检测是学生语文学习的重要方式。学生在测查中能够开启智慧阅读之门,引领思维的深度发展,形成持久的学习力。语文教学中的当场检测有预习型检测、过程型检测、后续型检测等。

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.

Prediction model of in-hospital mortality in elderly patients with acute heart failure based on retrospective study

Objectives The aim of this study was to develop a clinical risk model that is predictive of in-hospital mortality in elderly patients hos- pitalized with acute heart failure （AHF）. Methods 2486 patients who were 60 years and older from intensive care units of Cardiology De- partment in the hospital were analyzed. Independent risk factors for in-hospital mortality were obtained by binary logistic regression and then used to establish the risk prediction score system （RPSS）. The area under the curve （AUC） of receiver operator characteristic and C-statistic test were adopted to assess the performance of RPSS and to compare with previous get with the guidelines-heart failure （GWTG-HF）. Re- sults By binary logistic regression analysis, heart rate （OR： 1.043, 95% CI： 1.030-1.057, P 〈 0.001）, left ventricular ejection fraction （OR： 0.918, 95% CI： 0.833~）.966, P 〈 0.001）, pH value （OR： 0.001, 95% CI： 0.000-0.002, P 〈 0.001）, renal dysfunction （OR： 0.120, 95% CI： 0.066M）.220, P 〈 0.001） and NT-pro BNP （OR： 3.463, 95% CI： 1.870-6.413, P 〈 0.001） were independent risk factors of in-hospital mortal- ity for elderly AHF patients. Additionally, RPSS, which was composed of all the above-mentioned parameters, provided a better risk predic- tion than GWTG-THF （AUC： 0.873 vs. 0.818, P = 0.016）. Conclusions Our risk prediction model, RPSS, provided a good prediction for in-hospital mortality in elderly patients with A/IF.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency for the network to transform to an issue of local solution, a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP, that is, the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights, trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

在英语教学中如何让学生进行有效的词汇厚积

影响理解和应用英语的最重要因素是词汇，如何让职校生在英语学习上找回自信。文章采用了预习任务型策略、小组合作学习的策略、游戏策略对学生进行有效地词汇积累。

Error assessment of laser cutting predictions by semi-supervised learning

Experimentation data of perspex glass sheet cutting, using CO2 laser, with missing values were modelled with semi-supervised artificial neural networks. Factorial design of experiment was selected for the verification of orthogonal array based model prediction. It shows improvement in modelling of edge quality and kerf width by applying semi-supervised learning algorithm, based on novel error assessment on simulations. The results are expected to depict better prediction on average by utilizing the systematic randomized techniques to initialize the neural network weights and increase the number of initialization. Missing values handling is difficult with statistical tools and supervised learning techniques; on the other hand, semi-supervised learning generates better results with the smallest datasets even with missing values.

Mine water discharge prediction based on least squares support vector machines

In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.

Can the Dominant Trait Indicator Predict Success in a Financial Accounting Principles Course？ A Preliminary Look

The Myers Briggs Type Indicator （MBTI） test has been widely used in schools and career placement organizations to counsel individuals into compatible career choices. The test has also been utilized in academia to enhance instructor＇s knowledge of the different learning styles and thus allows them to develop strategies to increase students＇ learning. The test is a forced-choice self-reporting exam comprised of 126 questions. Based on Jung＇s theory of personality type, the test seeks to categorize personality types into 16 discrete groups based on the four preference poles （Myers, 1962）. The poles are based on the preference for： （1） introversion （I） or extroversion （E）; （2） sensing （S） or intuition （N）; （3） thinking （T） or feeling （F）; and （4）judging （J） or perception （P）. Laribee （1994） studied American accounting students and found that certain personality traits were over represented in upper-level accounting courses, while Macdaid, McCaulley, and Kainz （1986） found that the same personality trait groups were over-represented in the profession. Oswick and Barber （1998）, however, found no significant relationship between the grade earned in an introductory accounting course and the personality traits as identified by the MBTI with 344 UK-based accounting students. This study investigates the relationship between a student＇s academic success in a financial accounting principles course and the MBTI personality type indicators. The type distribution of 59 historically black colleges and universities＇ （HBCU） business administration majors was analyzed and separated into two groups. The groups were then tested to determine if there was a significant difference in the mean grade of the groups in accounting principles.

Solar flare forecasting using learning vector quantity and unsupervised clustering techniques

In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are extracted from SOHO/MDI longitudinal magnetograms as measures. Based on these pa- rameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Con- sidering the imbalanced problem in dataset, the K-means clustering, as an unsupervised clustering algorithm, is used to convert imbalanced data to balanced ones. Finally, the learning vector quantity is employed to predict the flares level within 48 hours. Experimental results indicate that the performance of the proposed flare forecasting model with sequential data is improved.

A time-series modeling method based on the boosting gradient-descent theory

The forecasting of time-series data plays an important role in various domains. It is of significance in theory and application to improve prediction accuracy of the time-series data. With the progress in the study of time-series, time-series forecasting model becomes more complicated, and consequently great concern has been drawn to the techniques in designing the forecasting model. A modeling method which is easy to use by engineers and may generate good results is in urgent need. In this paper, a gradient-boost AR ensemble learning algorithm (AREL) is put forward. The effectiveness of AREL is assessed by theoretical analyses, and it is demonstrated that this method can build a strong predictive model by assembling a set of AR models. In order to avoid fitting exactly any single training example, an insensitive loss function is introduced in the AREL algorithm, and accordingly the influence of random noise is reduced. To further enhance the capability of AREL algorithm for non-stationary time-series, improve the robustness of algorithm, discourage overfitting, and reduce sensitivity of algorithm to parameter settings, a weighted kNN prediction method based on AREL algorithm is presented. The results of numerical testing on real data demonstrate that the proposed modeling method and prediction method are effective.

An effective fault prediction model developed using an extreme learning machine with various kernel methods

System analysts often use software fault prediction models to identify fault-prone modules during the design phase of the software development life cycle. The models help predict faulty modules based on the software metrics that are input to the models. In this study, we consider 20 types of metrics to develop a model using an extreme learning machine associated with various kernel methods. We evaluate the effectiveness of the mode using a proposed framework based on the cost and efficiency in the testing phases. The evaluation process is carried out by considering case studies for 30 object-oriented software systems. Experimental results demonstrate that the application of a fault prediction model is suitable for projects with the percentage of faulty classes below a certain threshold, which depends on the efficiency of fault identification(low: 47.28%; median: 39.24%; high: 25.72%). We consider nine feature selection techniques to remove the irrelevant metrics and to select the best set of source code metrics for fault prediction.