共查询到20条相似文献,搜索用时 500 毫秒
1.
2.
3.
采用半结构化访谈、问卷调查等研究方法设计职前化学教师教学信念的调查问卷,了解职前化学教师教学信念的现况,并且从性别和是否为班干部等方面分析影响职前化学教师教学信念的因素。在问卷调查和个案访谈的基础上分析职前化学教师教学信念的类型及取向。大多数职前化学教师教学信念趋向于中间类型(建构与传统的过渡),职前化学教师教学信念的建构落后于化学课程改革设计的需求。 相似文献
4.
5.
大学生的学业成就是衡量高等教育质量的重要因素之一。由社会认知理论、建构主义学习和人本主义学习理论可知,高校大学生的化学自我效能感应能促进自我调节学习策略的运用,对学业成就与综合素质提升具有积极效应,但仍需要进行验证。故本研究目的:探讨高校大学生的化学自我效能感对学业成就的影响性,并考察自我调节学习策略的中介作用。研究方法:采用大学生的化学自我效能感量表、自我调节学习策略量表、化学专业学业成就量表等工具,通过整群抽样的方式,对国内某高校的536名化学化工专业大学生进行问卷调查。研究结果显示:大学生的化学自我效能感与化学专业学业成就显著正相关;大学生的化学自我效能感与自我调节学习策略显著正相关;自我调节学习策略与化学专业学业成就显著正相关;自我调节学习策略在化学自我效能感与化学专业学业成就之间起部分中介效应。 相似文献
6.
运用自编“高中生化学学科核心素养现状调查问卷”对湖北省范围内16个市(自治州)4类学校的36519名高中生首次进行大规模化学学科核心素养调查。结果表明:高中生化学学科核心素养总体表现良好,其中宏观辨识与微观探析素养水平最高,证据推理与模型认知素养水平最低;男生的化学学科核心素养及其5个维度上的表现均显著高于女生;高中生的个人维度为首要影响因素,其次为学校维度,家庭维度总体虽然影响程度不大,但是家庭经济状况对其化学学科核心素养具有较大影响;化学学习策略、化学学习动机、化学学习适应性对高中生化学学科核心素养及其5个维度具有显著正向预测作用,为强影响因素;湖北省骨干教师及市县高中化学学科教研员预测的影响化学学科核心素养因素的排序结果与实际学生调查结果存在差异。 相似文献
7.
8.
采用“中学化学课堂环境问卷”作为研究工具,对深圳市、长春市、佳木斯市等3个地区的4所高中861名学生的化学课堂环境进行调查,并进行差异分析。研究结果表明,高中生对化学课堂环境的总体感知较好,7个维度中“学生凝聚力”“平等”“任务取向”“合作”等4个维度的平均分大于3.4,“教师支持”和“参与”维度的平均分在3.0~3.4,只有“探究”维度的平均分小于3.0。城市高中生对课堂环境的感知明显好于乡镇高中生,学生心目中所期待的化学课堂比实际的化学课堂更积极。 相似文献
9.
高中生化学探究学习现状的调查分析 总被引:2,自引:1,他引:1
为了解高中生化学探究学习的现状,设计了问卷,调查了江苏省重点中学的高中生。就高中生对探究学习的认识、兴趣、探究学习能力以及化学探究中学习策略水平和运用情况等进行调查分析。提出了开展化学探究学习,要重视学生在探究学习中的主体地位;要提供探究学习的时间、空间、策略;要提高学生探究意识和探究能力等对策。 相似文献
10.
学习策略在高中化学学习中使用情况的调查 总被引:1,自引:1,他引:0
以认知主义学习理论为指导构建了学习策略的结构模型,并结合高中化学学科特点编制了一套问卷,对高中生化学学习中运用学习策略的情况进行调查。结果显示:(1)化学学习中优等生与困难生在学习策略使用水平上存在显著差异。(2)男生在复述与记忆、注意力集中与保持、目标与计划策略的使用水平上不如女生;(3)高年级学生在寻求他人帮助策略的使用水平上不如低年级同学。 相似文献
11.
介绍一个结合4种教学策略(情境教学、探究性学习、合作学习、混合式教学)面向非化学专业类大一学生开展元素化学教学的案例。以垃圾分类为主题,学生分组协作完成探究性学习任务,调查不同种类垃圾中存在的化学元素及其用途,通过线下课堂展示、线上成果共享以及校外推广等3项活动传播探究结论。对比活动前后收集的数据,活动前有75%的学生只认识原子序数前20的化学元素,活动后学生认识的元素数量明显增多,平均值是原来的1.7倍。元素中文名称与元素符号记忆混乱的情况得到改善。最后问卷调查表明活动提高了学生上课的积极性并且对了解生活中的化学元素有帮助。 相似文献
12.
13.
14.
15.
RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. 相似文献
16.
化学系统性思维强调化学子系统之间,以及化学系统与其他学科系统之间的关系,有助于学习者整合、应用化学知识解释化学现象、解决化学问题。内外交织的多个不同系统很容易让学生迷失在纷繁复杂的概念体系中,需要借助SOCME,OPM,BOTG,CLD,SFD等可视化图形工具厘清各个系统之间的关系,以表征化学系统性思维。在明确化学系统性思维内涵的基础上,开展“化学平衡”教学改革,探索绿色化学课程建设,开展游戏化学习、服务性学习、深度学习、项目学习、工作坊或研讨会,有助于化学系统性思维培养实践的改革与落地。横向关联化学系统与其他学科系统的关系,纵向深入分析化学子系统之间的关系,是进一步开展化学系统性思维教学的关键。这就需要多学科的协同攻关,既要关注化学知识的社会应用,也要抓住化学学科本质和特征,才可以围绕化学概念和社会问题,建构纵横交织的多系统影响关系,促进学生化学系统性思维的发展。 相似文献
17.
18.
Organic light-emitting diode (OLED) materials have exhibited a wide range of applications. However, the further development and commercialization of OLEDs requires higher quality OLED materials, including materials with a high thermal stability. Thermal stability is associated with the glass transition temperature (Tg) and decomposition temperature (Td), but experimental determinations of these two important properties generally involve a time-consuming and laborious process. Thus, the development of a quick and accurate prediction tool is highly desirable. Motivated by the challenge, we explored machine learning (ML) by constructing a new dataset with more than 1,000 samples collected from a wide range of literature, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibited the best prediction performance, where the values of mean absolute error, root mean squared error, and R2 were 17.15 K, 24.63 K, and 0.77 for Tg prediction and 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the ML models were further tested by two applications, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based models. In order to extend the practical application of the ML-based models, an online prediction platform was constructed. This platform includes the optimal prediction models and all the thermal stability data under study, and it is freely available at http://www.oledtppxmpugroup.com. We expect that this platform will become a useful tool for experimental investigation of Tg and Td, accelerating the design of OLED materials with desired properties. 相似文献
19.
设计了《影响高中生化学有效学习因素调查问卷》,在具有代表性的普通中学进行大样本的问卷调查,调查数据经计算机统计处理,结果发现:学习习惯、学习方法、学习内容难度等3个维度对高中生有效学习具有显著影响,学习环境维度对高中生有效学习影响不明显;这4个维度对男女生的影响差别不是很大,但对不同年级学生的影响度发生了变化。由此可以为中学一线化学教师的有效教学策略提供有益的启示。 相似文献
20.
Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms. 相似文献