马红亮的帖子 | 若恩科创教育

Font size

A-
A
A+

Site color

R
A
A
A

首页课程资源素养测评教师研修科学研究新闻公告

马红亮的帖子

一、问卷调查实例分析

Rosenzweig, E.Q., & Chen, XY. (2023). Which STEM careers are most appealing? Examining high school students’ preferences and motivational beliefs for different STEM career choices. IJ STEM Ed 10, 40. https://doi.org/10.1186/s40594-023-00427-6 (使用在线问卷的方式调查了美国东南地区一个公立学校的778名高中学生，剔除无法使用和无效问卷后，对剩余526名高中生的STEM职业倾向和隐含动机进行了分析)
Rosenzweig, E. Q., Chen, X. Y., Song, Y., Baldwin, A., Barger, M. M., Cotterell, M. E., ... & Lemons, P. P. (2024). Beyond STEM attrition: changing career plans within STEM fields in college is associated with lower motivation, certainty, and satisfaction about one’s career. International Journal of STEM Education, 11(1), 1-18. https://doi.org/10.1186/s40594-024-00475-6（使用在线问卷的方式调查了美国一个大型研究型公立大学的1727（剔除后的样本量）名本科生，涵盖了42门课程和所有主要的STEM学科，对学生们的职业计划，计划的改变，计划的动机，以及对这些计划的满意度和坚持计划的确定性进行了分析）
Castle, S. D., Byrd, W. C., Koester, B. P., Pearson, M. I., Bonem, E., Caporale, N., ... & Matz, R. L. (2024). Systemic advantage has a meaningful relationship with grade outcomes in students' early STEM courses at six research universities. International Journal of STEM Education, 11(1), 1-20.
https://doi.org/10.1186/s40594-024-00474-7 (研究了美国六所大型公立研究型大学的早期STEM课程的成绩。样本选择了所有在至少一门STEM课程中获得数字分数的本科生，本研究发现了他们在性别、种族/民族、收入和第一代地位方面的系统性不平等，并调查了它们在同行机构之间的相似程度)

二、访谈研究实例分析

Dancy, M., Hodari, A.K. (2023). How well-intentioned white male physicists maintain ignorance of inequity and justify inaction. IJ STEM Ed 10, 45. https://doi.org/10.1186/s40594-023-00433-8（对27名物理学专家进行半结构化访谈，之后以扎根理论为主进行归纳编码，并釆用批评性话语分析进行意义建构）
Zhang, E. Y., Hundley, C., Watson, Z., Farah, F., Bunnell, S., & Kristensen, T. (2023). Learning by doing: A multi‐level analysis of the impact of citizen science education. Science Education. 1-28. https://doi.org/10.1002/sce.21810 （在乡村学校开展持续三年的科学教育，对本科生教师、乡村教师以及学生进行半结构访谈、反思性问卷调查等）

三、媒体话语实例分析

Nam, B. H., & Bai, Q. (2023). ChatGPT and its ethical implications for STEM research and higher education: a media discourse analysis. International Journal of STEM Education, 10(1), 1-24. https://doi.org/10.1186/s40594-023-00452-5 （本研究采用比较媒体话语分析方法，分析了来自四个媒体的 72 篇文章：(a) Springer Nature; (b) The Chronicle of Higher Education; (c) Inside Higher Ed; and (d) Times Higher Education. 本研究通过阐明与STEM研究和高等教育中伦理写作相关的最主要政策问题，以及不同利益相关者对作者身份和学术诚信的盲目性的限制，总结了学术界对ChatGPT和人工智能伦理未来研究的一些政策启示和建议。）

(由李晓斐编辑 - 原提交时间 2023年06月13日星期二 08:48)

在场景中看此帖

二、数据分析实例

1. 一元线性回归

Chang, D., Hwang, GJ., Chang, SC. et al. (2021). Promoting students’ cross-disciplinary performance and higher order thinking: a peer assessment-facilitated STEM approach in a mathematics course. Education Tech Research Dev 69, 3281–3306.
Rosenzweig, E.Q., Chen, XY., Song, Y. et al. (2024).Beyond STEM attrition: changing career plans within STEM fields in college is associated with lower motivation, certainty, and satisfaction about one’s career. IJ STEM Ed 11, 15 . https://doi.org/10.1186/s40594-024-00475-6(使用线性回归对大学期间改变STEM领域的职业计划与学生对当前职业计划的动机（即感知的能力相关的信念和对该职业计划的任务价值观的看法）、对当前职业计划的满意度以及大学后追求这些职业计划的确定性有什么关系进行分析)

2. 多元线性回归

Tsarava, K. (2022). A cognitive definition of computational thinking in primary education. Computers & Education, 179, 104425. （调查研究，分析了多个认知技能对CT能力的预测）
Liu,Y., & Benoliel, P. (2022). “National context, school factors, and individual teacher characteristics: Which matters most for teacher collaboration?”. Teaching and Teacher Education,120, 103885. (使用2018 TALIS、2018 PISA以及2004 GLOBE的数据，通过多层线性模型，分析了教师特征、学校因素以及国家政策和文化变迁对教师合作的影响。)

Chen, Y., Cao, L., Guo, L.， & Cheng，J.M. (2022). Driving is believing: Using telepresence robots to access makerspace for teachers in rural areas. British Journal of Educational Technology. (在实验干预之后，用问卷调查了乡村教师对远程机器人操作的感受，然后用多元线性回归分析考察了具身、社会存在、行为参与、情感参与对认知参与的预测关系。)
Sun L, Hu L, Yang W, Zhou D, Wang X. (2021). STEM learning attitude predicts computational thinking skills among primary school students. Journal of Computer Assistted Learning. 37:346–358 （用阶层回归分析判断STEM的学习态度能否预测CT技能：第一阶段，性别和年级、数学和科学成绩作为预测变量；第二阶段，STEM的学习态度和三个因素作为预测变量，最后所有因素都作为预测变量进入。）
Rozgonjuk, D., Kraav, T., Mikkor, K. et al. (2020). Mathematics anxiety among STEM and social sciences students: the roles of mathematics self-efficacy, and deep and surface approach to learning. International Journal of STEM Education 7, 46 .
Förster, M. et al. (2022). Pre-class video watching fosters achievement and knowledge retention in a flipped classroom. Computers & Education, 179, 104399.
Reeves,T.D., Hamilton, V., & Onder, Y.(2022). Which teacher induction practices work? Linking forms of induction to teacher practices, self-efficacy, and job satisfaction. Teaching and Teacher Education,109. (对美国参与2018TALIS调查的736名教师的数据进行量化分析，因变量有三个：教师实践、自我效能感以及工作满意度，自变量为入职培训，控制变量包括性别、年龄、学历、教学经验等，研究者运用R语言对这些变量进行三次多元线性回归分析——one for each dependent variable。）

Lachner, A. et al. (2021). Fostering pre-service teachers' technological pedagogical content knowledge (TPACK): A quasi-experimental field study , Computers & Education,174. 浏览课程单元讨论

在场景中看此帖

二、数据分析实例

（一）效度检验

1. 量化检验：探索性因素分析-Exploratory Factor Analysis (EFA)

Guo, X., Hao, X., Deng, W. et al. (2022). The relationship between epistemological beliefs, reflective thinking, and science identity: a structural equation modeling analysis. International Journal of STEM Education, 9. （以翻译后的多个量表为工具，以两所公立学校的544名高中生为样本，将样本随机分为两半，首先对每一个量表进行了EFA检验（n=269））

2. 量化检验：验证性因素分析-Confirmatory Factor Analysis (CFA)

Zhang, YM. (2022). Individual differences matter in the effect of teaching presence on perceived learning: From the social cognitive perspective of self-regulated learning. Computers & Education, 179, 104427. （对翻译和改编多个量表，包括教学存在、学习感知等量表，在15名大学生中做了试测，之后依据被试的反馈意见在正式调查之前进行了修订，接着对每个量表计算了Cronbach's α和验证性因子分析CFA。同时对国内的大学生抑制控制量表也进行了CFA检验）
Srakaya, M. , Srakaya, D. A. , & Korkmaz, Z. . (2020). The impact of STEM attitude and thinking style on computational thinking determined via structural equation modeling. Journal of Science Education and Technology, 29(1), 1-12. （对于改编或翻译自成熟的量表，通常只做信度分析和验证性因子分析。）
Guo, X., Hao, X., Deng, W. et al. (2022). The relationship between epistemological beliefs, reflective thinking, and science identity: a structural equation modeling analysis. International Journal of STEM Education, 9. （以翻译后的多个量表为工具，以两所公立学校的544名高中生为样本，将样本随机分为两半，对每一个量表均进行了EFA检验（n=269）和CFA检验（n=268））
Qin, C., et al. (2023) . Scratch versus LEGO robots: Which engages undergraduates more in programming education? Journal of Computer Assisted Learning, 39(3), 935-953. https://doi.org/10.1111/jcal.12778 (借鉴以往研究文献中的量表和题项，整理了学习投入量表和心流体验量表，其中学习投入两边有三个维度、22项题目，心流体验量表有四个维度、9个题目。对两个量表应用PLS-SEM进行验证性因子分析，检验了因子载荷、内部信度a、组合信度CR、聚敛效度、和区分效度。)

3. 量表开发：EFA & CFA

Korkmaz,Ö., Çakir,R. & Özden,M.Y. (2017). A validity and reliability study of the computational thinking scales (CTS).Computers in Human Behavior,72:558-569.（因子分析分两个阶段，其中探索性因素分析的样本为726名大学生，验证性因子分析的样本为589名学生。对自编的CTS量表先进行探索性性因子分析，KMO和Bartlett值显示符合对74个原始题项进行因素分析；经过主成分分析和最大方差旋转法，发现21个题项的因子载荷在各因子维度均小于0.4，24个题项的因子载荷（大于0.4）分布在不同的因子，因此这45个题项从量表中删除，然后对剩余题项重新进行因素分析。）
Sun L, Hu L, Yang W, Zhou D, Wang X. (2021). STEM learning attitude predicts computational thinking skills among primary school students. Journal of Computer Assistted Learning. 37:346–358
Sun L.H., Hu, L.L.,& Zhou, D.H. (2022).Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education,181. （依据DeVellis 的6步骤方法开发了编程态度的量表，即决定测量内容、产生测量题目、决定量表形式和效度检验过程、征求专家意见、进行试测、进行量表效度和信度检验。在试测阶段，共有1180名7年级学生参与，然后将样本随机分成两组，一组用来进行EFA，一组进行CFA。）
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu,S. K. W. (2023). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 00, 1–23. https://doi.org/10.1111/bjet.13411
Playton, S. C., Childers, G. M., & Hite, R. L. (2023). Measuring STEM Career Awareness and Interest in Middle Childhood STEM Learners: Validation of the STEM Future-Career Interest Survey (STEM Future-CIS). Research in Science Education, 1-18.https://doi.org/10.1007/s11165-023-10131-8

4. 主观检验：文献、专家意见、试测（内容效度和表面效度）

《STEM教育国际前沿研究专题》：Dasgupta, C. , Magana, A. J. , & Vieira, C. . (2019). Investigating the affordances of a CAD enabled learning environment for promoting integrated stem learning. Computers & Education, 129, 122-142. 浏览课程单元讨论
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu,S. K. W. (2023). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 00, 1–23. https://doi.org/10.1111/bjet.13411

（二）信度检验

1. 测验量表信度

Kwon, K., Ottenbreit-Leftwich, A.T., Brush, T.A. et al. (2021). Integration of problem-based learning in elementary computer science education: effects on computational thinking and attitudes. Educational Technology Research and Development, 69, 2761–2787. （三套自编CT知识测试题，分别计算了三套题的Cronbach’s alpha检验，并计算了Spearman–Brown coefficients。此外，对于改编的态度量表也分别计算了Cronbach’s alpha）
Žanko, Ž., Mladenovic, M., & Krpan, D. (2022). Analysis of school students' misconceptions about basic programming concepts. Journal of Computer Assisted Learning, 38(3), 719–730. （使用同一套Python编程试卷，对4所小学的5年级学生进行了干预后和5个月后的延迟测试，计算了测试试卷的 Cronbach's alpha系数，以及题项的难度、区分度以及点二列相关。）
Tsarava, K. et al. (2022). A cognitive definition of computational thinking in primary education. Computers & Education，2022. 104425.

2.测验试题-项目分析

Kong, S. & Lai, M. (2022).Effects of a teacher development program on teachers' knowledge and collaborative engagement, and students' achievement in computational thinking concepts. British Journal of Educational Technology, （对教师CT内容知识测验进行了项目分析，包括分析测验前测中每一道题目的难度和区分度，删除了前后测中区分度不好的题目。对于保留的项目，不是分数直接简单相加，而是每个题目得分乘以其难度系数之后再相加。最后分别计算了该测验前测和后测的Cronbach alpha值。）
Jiang, S., & Wong, G.K.W. (2022). Exploring age and gender differences of computational thinkers in primary school: A developmental perspective. Journal of Computer Assisted Learning, 1,(38),60-75. （对4-6年级学生计算思维技能，采用自编测试题的方式进行，分析了测试题的难度、区分度，同时对测试题在485名学生中应用项目反映理论进行了的验证，计算了信度。）
Anwar, S. et al. (2022). The effectiveness of an integrated STEM curriculum unit on middle school students' life science learning. Journal of Research in Science Teaching. （对中学生生物课程的跨学科学习效果进行了评价，从PISA、TIMSS等抽出相关题项，组成了生物学习知识测试题，包括7道选择题和6个开放问题。使用项目反映理论分析题项、验证信度，进行了题目的难度分析）

在场景中看此帖

（一）中介效应分析

Sun L.H., Hu, L.L.,& Zhou, D.H. (2022).Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education,181.（应用SPSS Process插件，对初中生编程经验、编程态度以及计算思维技能三者的关系，做了中介效应分析，其中自变量为编程经验、编程态度是中介变量，CT技能是因变量。）

Wang, N., Tan, AL., Zhou, X. et al. (2023). Gender differences in high school students’ interest in STEM careers: a multi-group comparison based on structural equation model. International Journal of STEM Education, 10, 59.

Chun, Y., Jabbari, J., Huang, W., & Graham, C. (2024).Can training and apprentice programs in STEM increase worker life satisfaction and optimism?. International Journal of STEM Education, 11(1), 6.https://doi.org/10.1186/s40594-023-00461-4

（二）调节效应分析

Sing, C.C., Teo, T., Huang, F. et al. (2022). Secondary school students’ intentions to learn AI: testing moderation effects of readiness, social good and optimism. Education Tech Research Dev 70, 765–782 (2022). (在检验结构方程模型的基础上，应用path-by-path方面进行了调节分析，揭示了意愿、社会效应以及乐观对研究模型的调节作用。）

（三）调节中介效应分析

Kong,S., & Lin, T. (2022) High achievers’ attitudes, flow experience, programming intentions and perceived teacher support in primary school: A moderated mediation analysis. Computers & Education, 190. 104598. （调研了54所小学的432名小学生，应用Process宏分析了高成就者所感知的态度、心流体验、编程期待以及所感知到的教师支持的中介调节关系。）

在场景中看此帖

一、重复测量设计

Kong, S. C. , Lai, M. , & Sun, D. . (2020). Teacher development in computational thinking: design and learning outcomes of programming concepts, practices and pedagogy. Computers & Education, 151, 103872. https://doi.org/10.1016/j.compedu.2020.103872 【以76名小学教师为对象，设计开发了2门课程，1门侧重于CT知识和教学法，1门侧重于教学实践和反思，结果提升了教师计算思维的概念和教学实践】

二、2*2因素设计

Tsai, C-W. , et al. (2023). The effects of online peer-facilitated learning and distributed pair programming on students’learning. Computers & Education https://doi.org/10.1016/j.compedu.2023.104849(采用2*2因素设计，以四个班的学生为对象，研究了在线同伴促进学习和分布式编程对学生在线学习效果的影响，包括编程技能、学习喜悦度和学习意愿)

在场景中看此帖