Teaching and learning floating and sinking: A meta-analysis

被引:1
|
作者
Schwichow, Martin [1 ]
Zoupidis, Anastasios [2 ]
机构
[1] Univ Educ Freiburg, Dept Phys & Phys Educ, Kunzenweg 21, D-79117 Freiburg, Germany
[2] Democritus Univ Thrace, Dept Primary Level Educ, Alexandroupolis, Greece
关键词
buoyancy; conceptual understanding; embodied cognition; floating and sinking; hands-on science; primary education; secondary education; ELEMENTARY-SCHOOL; CONCEPTUAL CHANGE; SCIENCE-EDUCATION; DENSITY; DISCOVERY; KNOWLEDGE; BELIEFS; MODELS; CONSTRUCTIVIST; EXPLANATIONS;
D O I
10.1002/tea.21909
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Floating and sinking (FS) is a key topic in science education, both at primary and secondary levels. The interpretation of FS phenomena, however, is challenging due to the difficulty of the scientific concepts and explanatory models involved (e.g., density, buoyancy), along with students' everyday experiences, which conflict with scientific explanations. Consequently, many studies over the last few decades have investigated how FS could be taught effectively to students of different ages while utilizing multiple teaching approaches. This meta-analysis summarizes findings from 69 intervention studies on teaching FS conducted between 1977 and 2021. Over all studies, we estimated a mean effect size of g = 0.85 (95% CI = 0.71, 0.99). This large effect size demonstrates that, although FS is a challenging concept, teaching FS is effective even in elementary school. Moreover, in a moderator analysis, we investigate the effect of intervention characteristics, students' age, as well as study design, and assessment features on the mean study effect size. To analyze the effect of these moderator variables, we use a three-level hierarchical meta-regression model for dealing with multiple effect sizes from single studies. We found two intervention characteristics that explain variance in study effect sizes: longer lasting interventions result in larger effect sizes and interventions where hands-on experiments are applied are more effective than those utilizing virtual experiments. Furthermore, studies with a treatment-control group comparison have significantly smaller effect sizes than studies with a pre-post design. We discuss the implications of our findings regarding the moderator variables for effective teaching of FS and further research on FS.
引用
收藏
页码:487 / 516
页数:30
相关论文
共 50 条
  • [31] Teaching Parents to Be Responsive: A Network Meta-analysis
    Sokolovic, Nina
    Rodrigues, Michelle
    Tricco, Andrea C.
    Dobrina, Roksana
    Jenkins, Jennifer M.
    [J]. PEDIATRICS, 2021, 148 (02)
  • [32] The relationship between research and teaching: A meta-analysis
    Hattie, J
    Marsh, HW
    [J]. REVIEW OF EDUCATIONAL RESEARCH, 1996, 66 (04) : 507 - 542
  • [33] Learning Disabilities and Anxiety: A Meta-Analysis
    Nelson, Jason M.
    Harwood, Hannah
    [J]. JOURNAL OF LEARNING DISABILITIES, 2011, 44 (01) : 3 - 17
  • [34] The Effects of Dogs on Learning: A Meta-Analysis
    Reilly, Katie M.
    Adesope, Olusola O.
    Erdman, Phyllis
    [J]. ANTHROZOOS, 2020, 33 (03): : 339 - 360
  • [35] A Meta-Analysis of Organizational Learning and IT Assimilation
    Roberts, Nicholas
    Gerow, Jennifer E.
    Jeyaraj, Anand
    Roberts, Sara
    [J]. DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS, 2017, 48 (04): : 51 - 68
  • [36] A Meta-Analysis of Ten Learning Techniques
    Donoghue, Gregory M.
    Hattie, John A. C.
    [J]. FRONTIERS IN EDUCATION, 2021, 6
  • [37] A Meta-Analysis of Blended Learning Trends
    Mahmud, Malissa Maria
    Ubrani, Marisha Barth
    Foong, Wong Shiau
    [J]. 2020 11TH INTERNATIONAL CONFERENCE ON E-EDUCATION, E-BUSINESS, E-MANAGEMENT, AND E-LEARNING (IC4E 2020), 2020, : 30 - 36
  • [38] A meta-analysis of the effect of IT on learning outcomes
    Lim, J
    Chang, TT
    [J]. INFORMATION TECHNOLOGY AND ORGANIZATIONS: TRENDS, ISSUES, CHALLENGES AND SOLUTIONS, VOLS 1 AND 2, 2003, : 375 - 378
  • [39] A meta-analysis of the effectiveness of ALEKS on learning
    Fang, Ying
    Ren, Zhihong
    Hu, Xiangen
    Graesser, Arthur C.
    [J]. EDUCATIONAL PSYCHOLOGY, 2019, 39 (10) : 1278 - 1292
  • [40] A Meta-Analysis of Overfitting in Machine Learning
    Roelofs, Rebecca
    Fridovich-Keil, Sara
    Miller, John
    Shankar, Vaishaal
    Hardt, Moritz
    Recht, Benjamin
    Schmidt, Ludwig
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32