Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

An Empirical Study of the Influence of Online Learning Behaviors on Learning Outcomes in Blended Teaching Mode—Taking College English Listening as an Example

Blended teaching, which integrates the advantages of online and offline teaching, has become the main direction of higher education teaching reform. In the era of education big data, research on the online learners’ behavior based on data mining has attracted more and more attention from higher education researchers. However, in the field of foreign language teaching, research on the relationship between online learning behaviors and learning outcomes in the blended teaching mode is still at an early stage. Taking the course College English Listening in Zhejiang Yuexiu University (ZYU) as an example, this study conducted a comprehensive data analysis of online learning behaviors of 152 students of ZYU to explore the influence of online learning behaviors on learning outcomes in the blended teaching mode by utilizing Microsoft Excel and SPSS.20 statistic software. The result shows that the number of course login, the quantity and the quality of forum replies, the number of note submission, the quality of the notes, the average score of vocabulary tests, the number of the times of taking listening tests and the average score of listening tests are all significantly and positively correlated with students’ learning outcomes, while the study does not find a correlation between students’ learning outcomes and the number of the times of taking vocabulary tests, the total length of online learning and the length of video viewing. Based on the study results, implications are put forward to give reference for the teaching design and the management of the foreign language blended courses.

TELF-PSPOC: Three-layer Ensemble Learning Framework for Predicting Student Performance of Online Courses

Virtual learning environment(VLE)MOOC provides large-scale data of resources,activities,and interactions within a course structure for predicting student performance.But it is challenging to extract and learn efficient features from student behaviors.In this paper,a three-layer ensemble learning framework for predicting student performance of online courses(TELF-PSPOC)at an early phase is proposed to analyze data collected from Open University Learning Analytics Dataset(OULAD).First,feature augmentation of student behavior is proposed to enrich current features of student performance,including pass rate and grades of all staged tests,daily clicks of online resources.Second,three-layer ensemble feature learning with heterogeneous classifiers(TEFL-HC)is proposed to benefit the integration of tree model and neural network.Compared with current two-layer ensemble learning,pretraining of features prevents overfitting while using nonlinear regression.The experiment shows that our TELF-PSPOC performs better than several baseline models.Besides,the relationship of the learning results and student behavior via VLE is further discovered.

Analyzing Student Behavior in Online Programming Courses

Rather than maintaining the classic teaching approach, a growing number of schools use the blended learning system in higher education. The traditional method of teaching focuses on the result of students' progress. However, many student activities are recorded by an online programming learning platform at present. In this paper, we focus on student behavior when completing an online open-ended programming task. First, we conduct statistical analysis to examine student behavior on the basis of test times and completed time. By combining these two factors, we then classify student behavior into four types by using k-means algorithm. The results are useful for teachers to enhance their understanding of student learning and for students to know their learning style in depth. The findings are also valuable to re-design the learning platform.

Brain Storm Optimization Based Clustering for Learning Behavior Analysis

Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.

Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering

Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic. In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use questionnaires designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learning outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have better online learning behavior and learning result than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.

基于改进C4.5的E-learning教学辅助系统的研究与实现

为提高E-learning(数字化学习)中学生自主学习以及教师管理学习的效率,提出将决策树C4.5算法应用于数字化学习平台中的决策分析,设计了基于决策树的E-learning教学辅助系统,根据学生的在线学习行为预测其学习效果,以尽早发现问题。基于Fayyad边界点判定原理和数学的等价无穷小理论,对传统C4.5算法作出两点改进,在E-learning系统中,运用改进的算法先训练出在线学习行为与学习效果间的决策树模型,导出分类规则,而后进行学习效果预测。实验结果表明,改进后的算法具有比较高的预测准确率,能够为学习者和教学者提供决策支持。

E-learning环境下的网络学习行为研究

网络学习是一种新兴的学习模式。E-learning环境下学习者的学习行为呈现出系列新特征。明确网络学习行为的概念、内涵及研究的意义,从行为科学和行为理论的角度研究网络学习行为的新视角,对网络学习的深入具有重要的意义。

“潜水”还是“学习”:在线协作学习中“在线倾听”行为再审视

学习者“在线倾听”行为是一种复杂的社会化活动,是在线协作学习对话的前提和基础,但并未引起正视和积极理解。基于对话教学理论视角,研究采用滞后序列分析、内容分析、社会网络分析等研究方法,分析学习者“在线倾听”行为模式特征,及其对学习者认知过程、批判性思维和学习绩效的潜在影响。研究发现,学习者“在线倾听”行为是在线协作学习的常态行为,存在显著性差异,在时间结构上呈现周期性和阵发性;专注的“在线倾听”,高频次的回顾/反思倾听,有益于学习者高水平知识建构和批判性思维发展;高、低绩效学习者群体的“在线倾听”行为模式存在显著差异,高绩效组“在线倾听”行为更专注、行为路径更完整。这些发现为理解和解释数字教育场景下在线学习发生的内隐规律和内在机制提供理论支撑和实践参考。

信息技术环境下大学生高效学习行为分析与对策

信息技术环境下的学习模式具有开放性、交互性、虚拟性、智能性和便捷性等优势,在“互联网+教育”和疫情常态化的大背景下,在线教育成为高校学生学习的重要方式。同时,由于在线学习的时空阻隔问题,学生缺少直接有效的监督、课堂互动交流不足、且容易受到外界环境的干扰,上述因素不同程度地影响教学效果。该文在“高效学习”的定义框架上,深入探讨学生在线学习行为的内外在特征和运行机制,梳理高效学习行为影响因素,并从“学校-教师-学生”和“课前-课中-课后”二个维度六个方面探讨高效提升学生学习效果的应对措施。

英语学习者网络学习行为与教学对策实证研究

网络学习行为分析着眼于探究在线学习者内隐学习的偏好,发现零散学习行为中的底层规则,洞察各类行为的内部联系。在分析研究背景和进行文献综述的基础上,对英语学习者网络学习行为与教学对策展开实证研究。最后,得出结论,即应从学习者内驱力、教师信息素养、课程大纲和平台设计等方面进行提高和创新,为搭建自适应教学平台和促进学生个性化学习提供便利。

在线学习者学习行为研究——基于扎根理论的探索分析

近年来,在线学习凭借较强的包容性、便利性、课程丰富多样性等优点发展迅速,但是其在带来许多便利的同时也面临着辍学率高的问题,如何提高在线课程的完成率是一个亟待解决的问题。为更深层次地探寻在线学习者学习行为的影响因素和内在机理,采用扎根理论方法,对学习者的学习过程进行探索,并以此构建出在线学习者学习行为的理论模型,给出促进在线课程完成率提高的建议。

基于画像技术的在线学习行为分析研究

由于在线学习的时空分离特性,如何监督和掌握学生的学习状态并提供及时的学习干预,成为在线教学首要解决的难题。文章从学生学习画像的角度出发,将画像技术和学习行为分析技术相结合,构建时效性较强的学生个体和群体画像,可视化地呈现了学生行为数据并进行干预,以提高学习质量。

基于超星平台的飞机性能工程课程群线上教学质量监控指标研究

针对线上教学质量不均衡和难以监控的问题,有必要对混合式教学模式下的线上教学质量进行监控和评价。以飞机性能工程课程群为对象,开展学习效果和线上学习行为的关联性研究。基于超星平台4个学年的统计数据,初步形成6个一级指标、24个二级指标。首先通过数据预处理删除无效和共线性指标;然后通过描述性统计分析,得出样本数据的分布规律,删除异常指标;接着对15个指标进行相关性分析,得出影响期末考试成绩的相关性指标;最后对剩余指标进行主成分分析并进行排序,形成飞机性能工程课程群线上教学质量监控指标的依次为签到完成率(x_(8))、章节测验总均分(x_(24))、视频观看时长(x_(3))、章节测验参与均分(x_(23))、章节学习次数(x_(1))、任务完成率(x_(5))、章节测验参与率(x_(22))7个学生线上学习行为指标。

在线教育学习者学习有效性评价及差异性研究

在线教育具有开放性和共享性特征,深入挖掘在线学习者学习行为,能够有助于学生及时了解学习效果。选取在线教育平台思政类、外语类和管理类课程为对象,研究在线学习者的学习有效性及群体差异性特征。研究结果表明,不同类型课程的学习者行为和成绩分布具有相似性,大部分学习者并未实现高效的学习效率,学习行为多且成绩较好的“勤奋收获者”与“学习体验者”学习效率之间存在明显的差异性。基于此,提出在线教育的过程中,应当加强阶段性监督,对缺乏学习积极性的学生进行及时干预,完善线上教育的筛选制度等措施,以期提高学习者的效率水平和发挥在线课程的最大利用程度。

SPOC混合式教学中学习行为与效果关系研究--以大学英语课程为例

基于SPOC的混合式教学正在成为教育创新的基本形态,其教与学的过程产生了海量的在线学习行为数据。随着现代信息技术的迅速发展,网络技术在教育中的应用日益广泛和深入,学生在线学习行为和学习效果的关系研究正在成为新的研究热点。研究尝试利用学习分析技术,通过描述性分析、相关性分析以及回归分析,考察大学英语SPOC混合式教学中学生在线学习行为和学习效果的关系,探索利用在线学习行为预测学生成绩并提供个性化教学的可能性,以期为提高大学英语SPOC混合教学效果提供进路。研究发现,学生的在线视频观看完成度、在线作业得分、测试得分、以及参与在线讨论次数与期末考试成绩呈显著正相关趋势,与测试相关的在线学习数据可以用来预测学生成绩。基于此,文章提出了提高SPOC混合教学效果的策略:加强在线学习行为管理、强化在线学习反馈机制。

大学生在线编程学习行为投入与学习效果分析研究

基于EduCoder平台的C语言程序设计实践项目学习者行为投入和测试成绩数据,构建在线编程学习行为投入测量框架。采用系统聚类和K-Means聚类方法将学习者划分为出色突破型、努力完成型、边缘落后型三个类别,并依据不同类别学习者特征提出指导策略。使用逐步回归方法测量在线编程学习行为投入对学习效果的影响程度,结果表明:“参与”维度的出勤次数、“专注”维度的关卡完成数和“学术挑战”维度的附加训练对学习效果具有显著性正向影响,“专注”维度的实训总耗时对学习效果具有负向影响,“坚持”维度对学习效果未表现出显著影响。根据研究结果,提出开展小组协作学习、加强学习互动、合理划分学习难度、提升编程学习效能感等相关建议。

混合教学模式下学生学习行为特征分析及提升策略研究

目的 通过对混合教学模式下学生学习行为进行分析,从而有针对性地实施教学干预,提升学生学习效果。方法 对同一课程2019级和2020级高职生在线平台课程中的学习行为数据进行分析。通过采用因子分析、聚类分析等数据挖掘方法,对学生的观看视频时长、回答问题次数等学习行为数据进行聚类分析、相关性分析,寻找影响学生成绩的重要影响因素,有针对性地对2021级学生实施教学干预。结果 高职生具有被动型学生占比大、消极型和积极型学生占比小的特点,学习质量主要受教师的关注度、章节测验、作业、课程积分等因素的影响;教学过程中,过程评价中的学习行为与学业成绩显著相关,对学业成绩有促进作用。在教学设计过程中通过关注成分矩阵中数值较高的学习行为,提高线下课堂互动参与度和线上资源辅助效率,2021级学生的整体学习成绩有所提升。结论 混合式教学模式下,线上教学是线下教学的有力补充,应充分发挥线上线下教学的优势,实施教学干预,改进教学策略,提升学生学习质量。

应用型本科高校在线学习行为分析与学习绩效评估

在线学习行为是指学习者通过在线学习平台的各种交互工具、在线课程资源和其他支持资源,以学习者为中心,采用各种交互策略,通过在线学习活动的参与和互动,实现知识建构、技能形成和自我反思的过程。2020年的全球公共卫生突发事件让在线教育成为主要的教学方式,也使得应用型本科高校的在线教学问题受到广泛关注。利用线上问卷调查和线下访谈等方式对学生学习行为进行分析,构建了在线学习绩效评估模型。通过对应用型本科高校的627名学生进行问卷调查,发现学生的学习绩效总体情况较好,但学生在课程、课程难度和课程结构等方面存在差异,建议高校应根据学生个体差异性制订个性化学习计划。

面向边缘讨论参与行为的人机协同学习干预模型构建及其应用

边缘讨论参与行为制约了在线协作会话的效能发挥,如何识别并矫正边缘讨论参与行为是当前亟需解决的问题。为此,文章依托教学系统设计理论、学习干预模型等,构建了面向边缘讨论参与行为的人机协同学习干预模型,包含识别、归因、干预、评估四个模块。随后,文章进行了此模型的教学应用,并通过干预前后对比实验验证了模型的应用效果,结果表明:人机协同学习干预模型能有效矫正边缘讨论参与行为,模型中的归因和干预模块具有较高的接受度。面向边缘讨论参与行为的人机协同学习干预模型的设计与实践,可以为在线协作会话中的干预研究、人机协同学习干预的理论探索、个性化学习服务等提供参考。