基于计算机和网络的《综合英语》课程自主学习模式研究

由四年基于计算机和网络的《综合英语》课程自主学习模式的教学实验,通过问卷调查和对实验班、普通班的期末考试成绩的统计分析,证明该教学模式能有效提高学生自主学习英语的能力和英语学习成绩,从而更好地实现《综合英语》课程提高学生综合英语技能的教学目标。     

高职课程成绩增值评价方法应用研究

在高职课程成绩评价方面,为坚持以人为本的理念,挖掘和发挥学生潜在价值和能力,在课程成绩评价过程中采用了增值评价法、传统考试试卷评价法,并基于该课程学习成绩测试数据,运用SPSS19．0进行了分析和讨论。同时,就这两种评价方法对两个班100名学生进行了问卷调查,得出如下结论：增值评价法与传统考试试卷评价法所产生的评价结果存在明显差异。前者在高职课程成绩评价方面更具效能,更适合以能力为本位的高职课程的教学和学生学习成绩的评价。     

初一学生数学学业自尊、成就目标和成绩的相关性研究

研究初一学生数学学业自尊、成就目标和数学学习成绩关系。采用数学学业自尊量表和成就目标量表,调查成都和平学校268名初一学生数学学业自尊、成就目标。使男女生数学成绩无显著性差异;男生数学学业自尊显著高于女生;男生掌握趋近目标高于女生;女生成绩回避目标高于男生。数学掌握趋近目标、成绩趋近目标以及数学自尊和数学成绩呈正相关;掌握回避目标与数学自尊、掌握趋近以及数学成绩呈负相关。成绩回避和成绩趋近是影响数数学学业自尊和数学学习成绩主要因素。本研究对于促进初一学生数学学业自尊和学习成绩的教育实践具有理论指导意义。     

成就目标对高中生数学成绩的影响——一个有调节的中介模型

采用“成就目标问卷”和“学习投入量表”对904名高中生进行调查,并收集他们在高一下学期阶段测试的数学成绩,来探讨学习投入在高中生成就目标与数学成绩之间的中介作用,以及教学模式的调节作用．结果表明：掌握趋近正向预测数学成绩,成绩回避负向预测数学成绩;学习投入在掌握趋近、成绩回避与数学成绩之间起部分中介作用;教学模式对掌握趋近与数学成绩之间中介作用的前半条路径有调节作用,与网络教学相比,在课堂教学中掌握趋近的高中生对学习投入的预测作用更显著．掌握趋近与数学成绩之间是有调节的中介效应,成绩回避与数学成绩之间只有简单中介效应．     

影响文科大学生学习成绩的若干因素分析——以华南农业大学社会统计学课程为例

笔者调查发现学生的数学效能感、对课程难度效应评价和学习投入度,并不显著影响学习成绩;学生的性别因素对学习成绩具有显著作用,男生成绩显著低于女生;学生对教师教学进度体系感知对学习成绩具有显著作用,那些对老师教学体系评价高的学生成绩显著低于评价低的学生。相对于性别,教学进度体系中的课程进度作用更大、更重要,那些认为老师课程进度越快的学生,成绩越低。以此为据提出了大学教学中课程进度＂适度化＂的具体建议。     

大学生学习成绩排序的统计方法评析

在成绩分数单位不等值的情况下,采用求百分制分教总分或平均分、平均学分成绩、课程学分绩点和平均学分绩点的方法,对数据直接进行加、减、乘,除运算来统计大学生的学习成绩都是不科学的.而采用既能反映并解决学生学习质量不同问题,又能体现和解决学生学习数量差异问题的术标准分数平均学分的方法,对大学生的学习成绩进行统计则是科学的.文章对现有大学生学习成绩排序的统计方法进行了评析,并阐述了求标准分教平均学分的统计方法和其在具体运用中需要注意的问题.     

本科生英语成绩和专业课成绩相关性研究

本研究从本科生大学英语成绩、四级考试成绩和专业课成绩入手,运用社会科学统计软件包对其交叉进行相关性研究,探索英语学习是否冲击其他课程学习。结果发现大学英语学习并没有冲击其他专业课程学习,学习成绩与课程类别关系不大。     

网络学习空间在初三英语词汇复习中的应用

随着新课程教育的不断改革,对初中英语教学提 出了相应要求标准,其中明确提出在进行英语词汇教学过程 中,应该要注重网络学习空间的有效应用。英语词汇作为学生 英语学习成绩得以提高的重要基础和前提’通过网络学习空间 的利用,从而实现教与学的理想目标效果。     

贝叶斯网络在学生成绩管理中的应用

在教务管理系统中大量学生成绩数据的基础上,使用信息论中的信息增益来确定课程之间的相互关系,自动构建了贝叶斯网络模型进行成绩的预测,及时发现成绩有可能不及格的同学,尽早对这些同学进行帮扶和管理,有效提高了学校的学生管理水平,为提升教学质量打下坚实的基础。     

初中生成就目标取向与学业效能感、学业成绩的关系研究

为了解初中生成就目标取向与学业效能感、学业成绩的关系,采用问卷对204名初中生进行了调查,所有数据采用SPSS16.0进行统计分析。结果发现：（1）初中生在成绩接近目标和成绩回避目标上存在显著的性别差异,学业效能感在年级上差异显著。（2）掌握目标取向和成绩接近目标取向与学业效能感和学业成绩之间有显著正相关,对学业效能感和学业成绩有正向预测作用;成绩回避目标取向与学业效能感之间相关不显著,对学业成绩有负向预测作用。（3）学业效能感在成就目标取向与学业成绩之间起着部分中介作用。     

基于粒子群算法优化的城市供水量预测模型研究

鉴于BP神经网络、RBF神经网络在城市供水量预测精度上的不足,利用粒子群算法优化两者相关参数,实现更高预测精度,并通过建立BP神经网络、RBF神经网络、PSO-BP神经网络、PSO-RBF神经网络分别对城市供水量数据进行仿真预测。最终测试样本统计结果显示：RBF神经网络比BP神经网络平均相对误差（MRE）低约1%,在拟合度（R2）上高约0.014;PSO-BP神经网络比BP神经网络在MRE上降低约1.25%,在R2上提高约0.05;PSO-RBF神经网络比RBF神经网络在MRE上降低约0.3%,在R2上提高约0.072。由此说明RBF神经网络比BP神经网络在城市供水量预测方面更有优势,并且利用粒子群算法优化神经网络模型参数可有效提升神经网络预测精度。     

Why extraversion is not enough: the mediating role of initial peer network centrality linking personality to long-term academic performance

Academic performance (i.e., grade point average) determines career entry factors as well as career success and is thus crucial for students’ future careers. Besides individual factors such as personality traits, individuals’ social embeddedness has been shown to enhance performance. Regarding academic performance, relationships to fellow students (peers), which bundle into one’s developmental network, are a valuable source of psychosocial and career support because occupying central positions within a social network provides the benefit of being able to access career-enhancing resources. Integrating individual and social-contextual factors for the purpose of examining academic performance is therefore plausible. Research results indicate that personality, especially extraversion, might predict performance through network centrality. In this study, we examined this assumption by focusing on extraversion and centrality in the peer developmental network of recently acquainted German psychology bachelor students (N?=?47, 15% male). In a longitudinal design, we analyzed the impact of extraversion and centrality on students’ academic performance at the end of their studies. Results revealed that centrality (i.e., popularity) mediates the relationship of extraversion with academic performance, indicating that extraverted students (regardless of their agreeableness) are more popular among their peers, which, in turn, enhances their academic performance. That is, the likelihood of getting superior final grades depends on whether students manage to attract peers at the very beginning of their university life, which is easier for extraverts. These findings emphasize the importance of the social embeddedness of people, highlight its long-term effects on performance, and yield several implications for research and practice.     

父母陪伴方式对子女学业成绩的影响 ——基于省域内大规模调查数据的实证分析

利用江苏省2020年度基础教育学业质量监测数据,采用多元线性回归及夏普利值分解技术,分析了父亲、母亲不同陪伴方式对学生学业成绩的影响.结果表明:父亲陪伴和母亲陪伴一样,对子女的学业成绩有着重要的影响;不同的父母陪伴方式对子女的成绩影响不同,父母的情感陪伴对子女学业成绩具有正向影响,娱乐陪伴和学习陪伴并不能有效促进甚至负...     

Inner speech in the learning context and the prediction of students’ learning strategy and academic performance

Abstract

The present study investigated whether and how learning-specific inner speech predicted students’ learning strategy and academic performance. Frequencies of inner speech use in specific learning settings were assessed. Four dimensions of inner speech including self-criticism, self-reinforcement, self-management, and social assessment were investigated and linked to the learning strategy and academic performance. Data were collected from both secondary school students and university students. The results indicated that both the cognitive regulative function (self-management) and the affective regulatory function (self-criticism or self-reinforcement) of inner speech contributed to students’ learning strategy, while only the cognitive regulative function of inner speech significantly predicted students’ academic performance. Furthermore, the prediction of inner speech to academic performance was partly mediated by the learning strategy.     

基于组合神经网络模型的新冠疫情传播预测分析

在全球抗击新型冠状病毒肺炎（COVID-19）疫情的过程中,合理的疫情传播预测对于疫情防控有重要参考意义。为了对病毒传播进行合理预测,针对传统疫情传播预测模型存在的不足,提出一种组合式神经网络的疫情传播预测模型,并将其应用于湖北省1月29日-3月15日每日新增确诊人数预测及湖北省每日累计确诊人数预测。预测结果分析显示,该神经网络预测模型预测结果可靠有效。模型性能分析结果表明,组合式神经网络预测模型平均相对误差（MRE）不超过0.16,均方误差（MSE）不超过0.1,均方根误差（RMSE）为0.262 9,性能明显优于其它几种神经网络预测模型。基于武汉市与广东省疫情传播预测的实证结果显示模型具有较好的适用性及准确性。     

Working memory updating as a predictor of academic attainment

There is growing evidence supporting the importance of executive functions, and specifically working memory updating (WMU), for children’s academic achievement. This study aimed to assess the specific contribution of updating to the prediction of academic performance. Two updating tasks, which included different updating components, were administered to 97 fourth-grade children. The keeping track task involves retrieval and substitution of information, while the numerical updating task also includes a transformation component. Academic attainment was assessed through standardised tests of verbal comprehension, arithmetic operations, mathematical problems and an assessment made by the teacher. The relative contribution to academic attainment, of the updating measures and measures related to intelligence, was compared. Results showed that both updating tasks are predictive measures of academic attainment, although the numerical updating task appeared to be a more consistent predictor of children’s performance. The relationship between updating and academic attainment is discussed, and possible educational implications are considered. The role of the transformation component of WMU is highlighted. This component could make a distinct and independent contribution to performance and, by extension, could be particularly relevant to the prediction of academic achievement.     

Prediction model for the inclusion of key competences in the primary school curriculum through the teachers’ perceptions in the Region de Murcia / Modelo de predicción para la inclusión de las competencias en el currículo de educación primaria según la percepción del profesorado de la Región de Murcia

The purposes of this paper were twofold: (a) to analyse primary school teachers’ perceptions of the inclusion of the key competences in the curriculum; and (b) to test a hypothetical prediction model about the inclusion of the key competences in the curriculum according to their perceptions. The participants were 1,010 teachers from 110 schools. The results showed that the teachers need training on this new competency-based approach to teaching, learning and evaluation. The hypothetical prediction model confirmed the high predictiveness of pre-service teacher education and continuous professional development regarding the proper development of the main elements of competency-based learning which contribute to achieving the purposes of today’s education. One of the possible recommendations that could facilitate the inclusion of the key competences in the curriculum according to their perception is teacher training geared towards pedagogical models adapted to the reality of teaching today which optimize teachers’ professional performance and students’ academic achievement.     

Social capital in the classroom: a study of in-class social capital and school adjustment

Social capital is generally considered beneficial for students’ school adjustment. This paper argues that social relationships among pupils generate social capital at both the individual and the class levels, and that each has its unique effect on pupils’ performance and well-being. The sample in this study consists of 1036 children in 60 first-grade classes in 46 Dutch elementary schools. Multilevel regression results show that a substantial proportion of the variance in school adjustment can be attributed to the class level and that both individual-level and classroom-level social capital have substantial effects on school adjustment. At the individual level, the size of one’s network is more important than its structure. At the collective level, social capital also has a ‘dark side’ because it can have negative effects on adjustment, lowering the academic performance in a class.     

一种改进的BP神经网络在教学过程中的应用

利用一种改进的BP神经网络（PSO-BP）建立的教学成绩给定模型,在教学过程中所涉及的平时成绩给定的指标体系基础上,利用给定指标值作为输入,成绩估算值作为输出,通过PSO对BP神经网络的参数进行训练和学习,并利用Matlab软件建立实验平台。实验结果表明：新算法充分利用了神经计算的快速性以及粒子群算法的全局寻优能力,使得模型具有良好的辨识精度,可以较好地解决教学过程中平时成绩给定的动态问题。     

基于改进 SSD 网络的目标检测方法

传统目标检测方法存在准确率低、可靠性差、效率低等问题,无法满足对大量图片准确、高效处理的需求。对 SSD 网络结构进行改进,删除原网络最后两个预测层,对保留各预测层的默认框个数和宽高比进行优化,同时对保留的最后一个预测层的网络参数进行改进。改进后的 SSD 网络减少了网络参数和计算量,对存在遮挡、目标较小等情况的图片数据具有更好的检测精度和检测效果,同时模型检测的 mAP 提高了约 5.1%。改进后的网络模型解决了传统方法的不足,可以实时、准确、高效地对大量图片数据进行目标检测处理。