Cromley, Jennifer G.; Du, Yang; Dane, Aygul Parpucu
In: Journal of Science Education and Technology, Bd. 29, S. 216–229, 2019.
Abstract | Links | BibTeX | Schlagwörter: A, drawing to learn, mental models, STEM, strategies, visual representation
@article{Cromley2019,
title = {Drawing-to-Learn: Does Meta-Analysis Show Differences Between Technology-Based Drawing and Paper-and-Pencil Drawing?},
author = {Jennifer G. Cromley and Yang Du and Aygul Parpucu Dane},
editor = {Drawing-to-Learn: Does Meta-Analysis Show Differences Between Technology-Based Drawing and Paper-and-Pencil Drawing?},
url = {https://doi.org/10.1007/s10956-019-09807-6},
doi = {10.1007/s10956-019-09807-6},
year = {2019},
date = {2019-12-21},
journal = {Journal of Science Education and Technology},
volume = {29},
pages = {216–229},
abstract = {Drawing-to-learn is a specific learning/reading strategy studied across many domains. In response to gaps in our knowledge about drawing-to-learn, we conducted a systematic meta-analysis of the literature published since the influential 2005 Van Meter and Garner literature review. We analyzed the benefits of directed learner-generated visual representations such as sketching, drawing, or computer-assisted creation of a full or partial static image. Forty-one peer-reviewed articles were screened in, together with 9 dissertations and 2 other documents; published from 2005 to 2018, these included 53 studies and 166 effects based on 8111 participants. The overall effect of drawing-to-learn across all dependent variable types (factual, inferential, and transfer) and both types of effects—comparing different types of drawing and comparing drawing to non-drawing conditions—was a significant g = 0.69. The overall effect was significant but differed across outcomes (g = 0.85 for factual, g = 0.44 for inferential, and g = 0.22 for transfer). Analyses across 6 moderators are presented. Not only does the literature continue to show that drawing-to-learn is better than the status quo, but directed drawing improves factual as well as inferential and transfer learning. Finally, researchers have found ways to improve drawing-to-learn instruction so that it can be even more effective than the simple directive to make a drawing.},
keywords = {A, drawing to learn, mental models, STEM, strategies, visual representation},
pubstate = {published},
tppubtype = {article}
}
Drawing-to-learn is a specific learning/reading strategy studied across many domains. In response to gaps in our knowledge about drawing-to-learn, we conducted a systematic meta-analysis of the literature published since the influential 2005 Van Meter and Garner literature review. We analyzed the benefits of directed learner-generated visual representations such as sketching, drawing, or computer-assisted creation of a full or partial static image. Forty-one peer-reviewed articles were screened in, together with 9 dissertations and 2 other documents; published from 2005 to 2018, these included 53 studies and 166 effects based on 8111 participants. The overall effect of drawing-to-learn across all dependent variable types (factual, inferential, and transfer) and both types of effects—comparing different types of drawing and comparing drawing to non-drawing conditions—was a significant g = 0.69. The overall effect was significant but differed across outcomes (g = 0.85 for factual, g = 0.44 for inferential, and g = 0.22 for transfer). Analyses across 6 moderators are presented. Not only does the literature continue to show that drawing-to-learn is better than the status quo, but directed drawing improves factual as well as inferential and transfer learning. Finally, researchers have found ways to improve drawing-to-learn instruction so that it can be even more effective than the simple directive to make a drawing.