Empowered learning through microworlds and teaching methods: a text mining and meta-analysis-based systematic review

  • Joana Martinho Costa Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, Lisboa, Portugal
  • Sérgio Moro Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, Lisboa, Portugal https://orcid.org/0000-0002-4861-6686
  • Guilhermina Miranda Instituto de Educação, Universidade de Lisboa, Lisboa, Portugal https://orcid.org/0000-0003-4423-4522
  • Taylor Arnold Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, USA
Keywords: meta-analysis, microworlds, simulations, teaching methods, technology-enhanced learning


Microworlds are simulations in computational environments where the student can manipulate objects and learn from those manipulations. Since their creation, they have been used in a wide range of academic areas to improve students learning from elementary school to college. However, their effectiveness is unclear since many studies do not measure the acquired knowledge after the use of microworlds but instead they focus on self-evaluation. Furthermore, it has not been clear whether its effect on learning is related to the teaching method. In this study, we perform a meta-analysis to ascertain the impact of microworlds combined with different teaching methods on students’ knowledge acquisition. We applied a selection criterion to a collection of 668 studies and were left with 10 microworld applications relevant to our learning context. These studies were then assessed through a meta-analysis using effect size with Cohen’s d and p-value. Our analysis shows that the cognitive methods combined with microworlds have a great impact on the knowledge acquisition (d = 1.03; p < 0.001) but failed to show a significant effect (d = 0.21) for expository methods.


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How to Cite
Costa J. M., Moro S., Miranda G., & Arnold T. (2020). Empowered learning through microworlds and teaching methods: a text mining and meta-analysis-based systematic review. Research in Learning Technology, 28. https://doi.org/10.25304/rlt.v28.2396
Original Research Articles