Training and Implementation of Subjective Questions Scoring System Based on the Baidu Qianfan Model Platform

Leveraging the Baidu Qianfan model platform,this paper designs and implements a highly efficient and accurate scoring system for subjective questions,focusing primarily on questions in the field of computer network technology.The system enhances the foundational model by utilizing Qianfan’s training tools and integrating advanced techniques,such as supervised fine-tuning.In the data preparation phase,a comprehensive collection of subjective data related to computer network technology is gathered,cleaned,and labeled.During model training and evaluation,optimal hyperparameters and tuning strategies are applied,resulting in a model capable of scoring with high accuracy.Evaluation results demonstrate that the proposed model performs well across multiple dimensions-content,expression,and development scores-yielding results comparable to those of manual scoring.

An Overview of Formative Assessment

Formative assessment is integral to classroom instruction and learning.This paper gives an overview of the major issues in formative assessment,including its definition,scope,and the assessment process.Formative assessment can be performed by a teacher,peer,and even AI,includes self-assessment,and involves the process of elicitating and interpreting evidence of learning and using the information to make decisions on further learning.

Hybrid Micro-forming Processes and Quality Evaluation of Micro-double Gear

Micro-gear is an important actuating component used widely in the micro electro mechanical systems(MEMS) devices.The technologies of micro-forming and precision assembly are urgently developed to manufacture the micro-double gear with central shaft.In the paper,a novel hy-brid-forming process with two kinds of piercing method have been proposed to manufacture the micro-double gear using micro forming technology.The tests of hybrid forming process were carried out with two steps and the micro-double gear was successfully manufactured with good surface quality.The results also show that the hybrid micro-forming process with central piercing method can improve the defects of inclining shaft generated by double-ended piercing method.The quality evaluation of micro-double gear was conducted with surface roughness,micro-hardness and impact tests.The results show that the micro-double gear with good mechanical properties can meet the requirements of application for milli-machines.

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.

Screening of COVID-19 Patients Using Deep Learning and IoT Framework

In March 2020,the World Health Organization declared the coronavirus disease(COVID-19)outbreak as a pandemic due to its uncontrolled global spread.Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease.However,the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing.To handle COVID19 testing problems,we apply the Internet of Things and artificial intelligence to achieve self-adaptive,secure,and fast resource allocation,real-time tracking,remote screening,and patient monitoring.In addition,we implement a cloud platform for efficient spectrum utilization.Thus,we propose a cloudbased intelligent system for remote COVID-19 screening using cognitiveradio-based Internet of Things and deep learning.Specifically,a deep learning technique recognizes radiographic patterns in chest computed tomography(CT)scans.To this end,contrast-limited adaptive histogram equalization is applied to an input CT scan followed by bilateral filtering to enhance the spatial quality.The image quality assessment of the CT scan is performed using the blind/referenceless image spatial quality evaluator.Then,a deep transfer learning model,VGG-16,is trained to diagnose a suspected CT scan as either COVID-19 positive or negative.Experimental results demonstrate that the proposed VGG-16 model outperforms existing COVID-19 screening models regarding accuracy,sensitivity,and specificity.The results obtained from the proposed system can be verified by doctors and sent to remote places through the Internet.

Evaluation of Petroleum System in Xihu Depression, East China Sea Shelf Basin

According to the theory of petroleum system and the characteristics of petroleum geology, the Xihu depression in the East China Sea shelf basin is divided into four petroleum systems: the Pinghu Formation as source rock and the Huagang Formation as reservoir rock, the Huagang Formation as source rock and reservoir rock, the Paleocene as source rock and the burial-hill as reservoir rock, and the Miocene as source rock and reservoir rock. The system with the Pinghu Formation as source rock and the Huagang Formation as reservoir rock is the most important one in the depression, which has high hydrocarbon generation and accumulation efficiency and is the most important object to hydrocarbon exploration at present.

On Improvement of Teaching Quality for a Selected Mathematical Topic Using Artificial Neural Networks Modeling(With a Case Study)

This paper motivated and inspired by an interdisciplinary critical educational issue adopted for a research work approach. It concerned with application of realistic Artificial Neural Networks (ANNs) models integrating reading brain function with multi-sensory cognitive learning theory. Specifically, these models adopted to improve tutoring quality (academic achievement) while teaching children “how to read?” considering the analysis and evaluation of phonics methodology. Herein, quantitative analysis and evaluation of this issue performed by considering two computer aided learning (CAL) packages concerned with a specific selected mathematical topic namely: long division process. Via realistic modeling of packages using (ANNs) based upon associative memory learning paradigm. In more details, at educational field practice; both CAL packages have been applied for teaching children algorithmic steps performing long division processes. Moreover, learning performance evaluation of presented packages considers children outcomes’ achievement after tutoring for suggested Mathematical Topic either with or without associated tutor’s voice. Interestingly, statistical analysis of obtained educational case study results at children classrooms (for both applied packages) versus classical tutoring proved to be in well agreement with obtained after ANNs computer simulation results.

Autonomous Mechanism of College English Teaching and Learning

The aim of college English teaching in China is to build the teaching mode with the Internet and to foster talents with comprehensive quality. This paper proposes to build mechanisms of dual autonomy, dual classroom, and dual evaluation in universities, especially those in undeveloped areas, to facilitate both teaching and learning.

Critical Review of Literature on Radiologic Technology Education Program Evaluation

Curriculum has achieved a varied record of success in influencing health based practices and developing professional skills. Designing and implementing an effective radiologic technology educational program curriculum requires a disciplined pedagogical approach where the instructor performs a thorough situational analysis, develops a theory based and pragmatic learning plan, and implements a course of study in accordance with the established educational guidelines and requirements. Diligent efforts are needed to enhance the relationship amongst curriculum developers and evaluators. The collection of information at the formative stage: followed by process evaluation to assess implementation as the curriculum progresses, and summative evaluation to assess impact is required for accreditation of program in the United States by the Joint Review Committee for Education in Radiologic Technology. Formative evaluation research is used to enhance effectiveness of the curriculum, guide development of teaching and learning strategies, and reveal promising and ineffective components of curriculum. This review of literature provides evidence as to what is considered to be the best practice in the program evaluation/accreditation process.

Process-based deep learning model:3D prediction method for shot peen forming of an aircraft panel

Shot peen-forming is a more precise method of forming aircraft panels than conventional methods.The traditional method of acquiring the process parameters relies mainly on prior theoretical knowledge and trial-and-error.Despite the finite element method’s ability to replace some experimentation,it still cannot realize the design of shot peen forming processes parameters of an aircraft panel based on a known contour.This study uses an innovative model-based deep learning approach to predict aircraft panel deformation and active design the shot peening parameters.The prediction time is less than 1 second,resulting in a significant reduction in computational time.The shot peen forming process parameters and the geometric structure characteristics of the aircraft panel are divided into independent channels to establish a high-dimensional feature map,which are used to train the deep learning model.The forming contours of the 2024-T351 high-strength aluminum alloy panel are predicted under different shot peening processes.In addition,the process parameters are designed according to the known contour of the forming process.To verify the precision of the proposed method,the designed shot peen forming process is used to manufacture a single curvature aircraft panel with a curvature radius of 3500 mm.There is good agreement between the forming contour and the theoretical design contour.The maximum deformation error is less than 1 mm and its mean error is 7.8%.The mean curvature radius error is 5.668%.The proposed method provides a new and practical reference to the precise design of the shot peen-forming process.

基于理想过程模型的探究学习评价——探究学习评价的新思路

在教育情境下,探究学习被定义为旨在达到特定高阶能力目标的学习过程。因此,其评价应侧重于过程取向,而非结果取向。为刻画理想的探究学习过程并保留情境信息,特别是学习目标信息,需对特定的探究学习设计进行理想过程建模,所生成的理想过程模型可作为评价实际发生的探究学习的标尺。探究学习的理想过程建模包括以下五个步骤:(1)完成探究学习的详案设计;(2)绘制探究学习活动所蕴含的知识建模图;(3)具体描述探究学习的整体过程;(4)将探究学习整个过程划分为多个环节,并绘制探究环节图;(5)将所有环节图按照探究学习的一般过程连接成一个整体,形成理想过程模型。对实际发生的探究学习也应进行上述操作,并在完成预处理后,生成探究学习的真实过程模型。通过比较真实过程模型与理想过程模型,找出差异点,依此可以评估实际发生的探究学习。这种评价方法不仅有助于了解探究学习的质量,还能为探究学习设计的改进提供反馈,更为重要的是,它可以使探究学习成为日常教学的一部分。

异形孔筒形锻件成形表面不平整度分析及验证

针对异形孔筒形锻件成形过程中金属在反向流动中流速不均匀导致的锻件上表面产生不平整缺陷的问题,以椭圆孔筒形锻件为例,采用数值模拟与试验相结合的手段,探究了锻件上表面不平整现象的成因。分别从一维、二维、三维建立了异形孔筒形锻件上表面不平整度的评判准则,进一步揭示了成形工艺参数对异形孔筒形锻件上表面不平整度的影响规律。结果表明:异形孔筒形锻件上表面不平整度随模具预热温度的升高而降低,随坯料始锻温度的升高而增大,始锻温度比模具预热温度对上表面不平整度的影响更大;提高成形速度可以降低金属的流动阻力,减小坯料的温降,改善坯料变形和温度分布的均匀性,从而降低异形孔筒形锻件上表面的不平整度;异形孔筒形锻件上表面不平整度还受椭圆孔短长轴比值的影响,凸模横截面积恒定时,随短长轴比值增大,不平整度评价指标均呈下降趋势;凸模短轴值恒定,长轴值超过锻件外径的2/3时,随短长轴比值增大,不平整度评价指标呈现先增大后减小的趋势,长轴值小于锻件外径的2/3时,不平整度评价指标随短长轴比值增大呈现下降趋势。

过程性评价对工科物理教学质量的影响研究

本研究以课堂测验的形式探索过程性评价对学习效果的影响。学生层面上,通过使用学习投入问卷对工科学生的大学物理学习投入进行测量,发现过程性评价除了检查课堂学习效果,还是提升学生情感投入的一个有效途径。教师层面,通过使用Rasch模型对大学物理课堂测试数据进行分析,发现过程性评价有助于出题教师优化测试题库,任课教师根据测试反馈也可以不断建构和修正教学内容,提高教学质量。

基于核心素养的中学语文鉴赏评价学习进阶框架研究

明确学生认知发展阶段与思维提升路径,对于落实语文课程标准、培养学生核心素养具有重要意义。本研究借鉴国际学习进阶理论与实践经验,构建鉴赏评价能力学习进阶框架,并描述其进阶水平,再结合测评数据和言语报告进行校验,从认知动作、认知对象、认知条件三个方面修正学习进阶水平。研究为系统构建语文关键能力进阶框架提供了研究样例,为开展指向鉴赏评价能力进阶的阅读教学提供了理论依据,为设计鉴赏评价能力的过程性评价提供了参考思路。

大学生全过程合作学习的组织策略和量化评价——以沈阳化工大学中外教师联合授课的材料力学课程为例

“通过合作而非竞争实现卓越”是高等教育发展的基本原则。课程是培养学生合作意识和合作能力的重要载体,文章以中外教师联合授课的材料力学课程为例,提出基于课程积分制的大学生全过程合作学习组织策略,包括简化的异质分组和团队合作成绩计算两个部分。在教学过程中,教师以先修课成绩为基础完成异质分组;学生通过完成合作学习任务获得合作学习成绩。全过程的合作学习方式注重全过程和量化评价,有助于提高学生的学习积极性和学习效果。

卓越教师经历的学习过程特征及启示——10位个案教师学习经历的跨案例研究

卓越教师培养一直在教育政策和区域教师教育工作中倍受重视,但缺乏微观层面对这一群体教师所经历的学习过程特征的探讨。基于对10位卓越教师所经历的学习过程的个案研究成果,应用跨案例研究法,采用“环境—过程—结果”(EPO)框架得到了卓越教师经历的学习过程中所呈现的特征:卓越教师在学习环境中体验到高挑战性的任务和积极的组织氛围,在学习过程中表现出高水平的内部学习动机和情感意志,学习互动以个体主动投入式为主,经历的学习内容具有多元性和差异性,学习结果更多指向来自内部的专业成就感或价值感。对卓越教师学习设计者和教师继续教育决策者提出如下建议:卓越教师的学习环境需要突出工作场域和高挑战任务特征,教师的学习过程中需要加强学习互动的设计,教师的学习结果预期亟须超越以往狭隘的知识或能力观。

智能技术赋能虚拟科学探究学习过程评价与适应性反馈研究

虚拟科学探究学习过程的自动评价和适应性反馈是提升探究学习效果的重要支撑手段。研究首先从虚拟科学探究学习过程要素表征和分析模型、自动分析与实时评价、适应性反馈三个方面概述了智能技术在虚拟科学探究学习中的应用现状,总结了当前技术应用面临的深层次探究要素表征难、不确定探究过程刻画难、适应性反馈生成难等现实挑战。其次,在此基础上,提出了基于活动流的底层计算模型构建、复杂探究过程动态监测与自动评价、可解释性归因的自适应反馈内容生成三个关键技术。再次,基于关键技术设计了虚拟科学实验自主探究学习平台的技术架构。最后,研究总结了当前研究的创新之处并提出未来建议,为智能技术赋能虚拟科学探究学习领域开展更深入的技术探索提供有益参考。

基于AHP法的混合式教学质量评价指标及权重研究

为了评估混合式教学的质量,文中建立了一个涵盖教学组织、教学能力、教学态度和教学效果等内容的混合式教学质量评价指标体系。通过层次分析法(AHP)构建判断矩阵,计算一致性指标和权重向量,得到了混合式教学评估中各项评价指标的权重,从而为混合式教学质量评价提供科学依据。

研究生“自主学习与科研”能力评价体系构建及应用

目的:科学合理地构建研究生“自主学习与科研”能力评价体系,对高校培养研究生此方面能力具有重要参考价值。方法:文章以自主学习能力与科研能力的内涵与关系为基础,构建了研究生“自主学习与科研”能力评价指标体系,并以某高校为例运用网络层次分析法进行应用。结果:该校研究生自主学习能力低于科研能力;自主学习能力中,过程控制与结果反馈影响最大,自我认知影响最低;科研能力中,逻辑推理能力、写作能力和创新能力的影响均较高,实践能力影响最低。结论:该校可通过加大课程平时考核占比、利用线上资源优化学生自主学习过程、注重科研团队成员间的互助作用、优化课程模式保障教学质量等措施提升该能力的培养水平。