学生数据分析下的教学改革思考

在教育教学中,教育人员无法全面掌握学生数据,导致对学生的教学和管理效率低、难度高。本文思考基于学生数据分析的教学改革,其以学生的多项主观和客观信息作为输入。综合应用深度学习、大数据处理等计算机技术,对学生进行多方面的智能分析,并将分析结果分享给教育教学人员,以期提高学生学习效果,降低问题学生概率,从总体上提高教育教学效率和质量。

智慧校园环境下的学生轨迹数据分析技术

轨迹数据是学生行为数据的重要组成部分,包含学生的日常行为、学习习惯、生活规律等丰富的信息,为校园学生管理、教学管理、安防预警等提供决策支持。智慧校园是学生轨迹数据采集和分析的基础。文章在实践经验基础上,对智慧校园环境下学生轨迹数据分析的概念、步骤和技术手段进行分析和归纳。

用数据分析促进学生成功的研究——《机构用数据分析促进学生成功》的报告解读及启示

促进学生成功是美国高校的核心工作目标,《机构用数据分析促进学生成功》调查了美国高等教育研究机构相互合作对大数据进行分析与整合,为教育决策和学生提供服务的情况,阐述了其研究发现以及使用数据分析核心的领域,并给出了四条建议:从制度上确定和扩展IR,IT和学生事务部门的职责;去除组织孤岛,改善沟通水平;优先测量学生成果和增加定性数据的使用.该报告对我们启示是:学生成功服务和研究可作为一套服务系统和研究项目纳入高校的日常工作,借鉴报告中数据收集渠道、类型、利用、对学生干预的途径等,建立学生数据集成平台,改进各部门合作方式,成立数据分析团队,利用数据分析促进学生成功.

数据分析系统的设计和功能

数据分析系统就是指面对大量数据,用恰当的统计方法对其进行分析,从中把有用的信息提取出来,通过细致的研究和概括总结形成客观结论。以多传感器的PM2.5数据分析系统为例,从基础概述、软硬件组成、功能实现和应用等方面进行系统论述。并分析其特点突出的结构,对其功能进行一定程度的说明,旨在从细节方面展现数据分析系统的设计与功能。

学生体质健康状况形成性评价机制的研究——以厦门医学高等专科学校为个案研究

为了更客观评价学生体质健康状况,有效提高学生体质。本课题采用文献资料法、实验测试法和数理统计分析法,对我校2010级大专护理180名学生4项体质健康指标分两组别三个阶段的测试评价方式进行测评。运用SPSS16.0中一般线性模型(general linear model,GLM)的repeated measures过程对4项体质健康指标重复测量数据的均值进行重复测量方差分析。结果显示:实验组学生的4项体质健康指标重复测量数据的均值高于对照组,说明了阶段性地评价,学生体质明显提高,效果优于终结性评价。构建与实施学生体质健康状况形成性评价机制方案,有效提高学生自主学习,积极主动参与锻炼能力,促进学生全面发展。

Gaining Satisfaction and Trust in a Brand Website

This study attempts to examine whether ＂trust＂ and ＂satisfaction＂ in a brand website are affected by the level of website interactivity as well as the four facets （aesthetics, playfulness, service excellence, and customer return on investment） of the Experiential Value. Three interactive versions （low, medium, and high） of a website for a fictitious laptop brand were designed. An online experiment was conducted 180 students participated in the study. Data analysis indicated that higher levels of trust and satisfaction were generated when participants were exposed to the low interactive version of the website. Regarding the four facets of Experiential Value, aesthetics and customer return on investment （croi） have positive relationship with both trust and satisfaction in the brand website. Playfulness appears to have negative whereas excellence does not have significant relationship with trust and satisfaction. Limitations and guidelines for future research are provided.

English Class Design to Improve Students＇ Oral Fluency

The paper discusses the concept of oral fluency and some factors which influence students＇ oral fluency, analyses the data from first-year English majors in USST and also helps design some effective ways to provide every chance to ＂force＂ students to speak English. Finally, the paper points out that oral class is absolutely not the only class to practice speaking English, other classes such as Comprehensive English, Extensive English, etc., can also be used to improve the students＇ oral fluency effectively if designed properly

Study on Motivational Changes of Non-English Majors

This paper is on the study of motivational changes of Chinese non-English major students in their first two years of college English learning. The current study was carried out through the quantitative research method. The research question to be addressed is：During the first two years of college English learning, does non-English majors＇ learning motivation （i.e. types of motivation and motivational intensity） change？ The analyses of the data reveal that motivational changes do exist in the first two years of college English learning. Generally, most types of motivation （i.e. intrinsic interest motivation, information medium motivation, personal development motivation, and social responsibility motivation） decrease significantly except for the slight increase of immediate achievement motivation, and the decline of motivational intensity is also obvious.

Characterizing and annotating the genome using RNA-seq data

Bioinformatics methods for various RNA-seq data analyses are in fast evolution with the improvement of sequencing technologies. However, many challenges still exist in how to efficiently process the RNA-seq data to obtain accurate and comprehensive results. Here we reviewed the strategies for improving diverse transcriptomic studies and the annotation of genetic variants based on RNA-seq data. Mapping RNA-seq reads to the genome and transcriptome represent two distinct methods for quantifying the expression of genes/transcripts. Besides the known genes annotated in current databases, many novel genes/transcripts(especially those long noncoding RNAs) still can be identified on the reference genome using RNA-seq. Moreover, owing to the incompleteness of current reference genomes, some novel genes are missing from them. Genome-guided and de novo transcriptome reconstruction are two effective and complementary strategies for identifying those novel genes/transcripts on or beyond the reference genome. In addition, integrating the genes of distinct databases to conduct transcriptomics and genetics studies can improve the results of corresponding analyses.

A selective overview of feature screening for ultrahigh-dimensional data

High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.

An overview of human protein databases and their application to functional proteomics in health and disease

Functional proteomics can be defined as a strategy to couple proteomic information with biochemical and physiological analyses with the aim of understanding better the functions of proteins in normal and diseased organs.In recent years,a variety of publicly available bioinformatics databases have been developed to support protein-related information management and biological knowledge discovery.In addition to being used to annotate the proteome,these resources also offer the opportunity to develop global approaches to the study of the functional role of proteins both in health and disease.Here,we present a comprehensive review of the major human protein bioinformatics databases.We conclude this review by discussing a few examples that illustrate the importance of these databases in functional proteomics research.