Drawing, as a skill, is closely tied to many creative fields and it is a unique practice for every individual. Drawing has been shown to improve cognitive and communicative abilities, such as visual communication, problem-solving skills, students’ academic achievement, awareness of and attention to surrounding details, and sharpened analytical skills. Drawing also stimulates both sides of the brain and improves peripheral skills of writing, 3-D spatial recognition, critical thinking, and brainstorming. People are often exposed to drawing as children, drawing their families, their houses, animals, and, most notably, their imaginative ideas. These skills develop over time naturally to some extent, however, while the base concept of drawing is a basic skill, the mastery of this skill requires extensive practice and it can often be significantly impacted by the self-efficacy of an individual. Sketchtivity is an AI tool developed by Texas A&M University to facilitate the growth of drawing skills and track their performance. Sketching skill development depends in part on students’ self-efficacy associated with their drawing abilities.
Gauging the drawing self-efficacy of individuals is critical in understanding the impact that this drawing practice has had with this new novel instrument, especially in contrast to traditional practicing methods. It may also be very useful for other researchers, educators, and technologists.
This study reports the development and initial validation of a new 13-item measure that assesses perceived drawing self efficacy. The13 items to measure drawing self efficacy were developed based on Bandura’s guide for constructing Self-Efficacy Scales. The participants in the study consisted of 222 high school students from engineering, art, and pre-calculus classes. Internal consistency of the 13 observed items were found to be very high (Cronbach alpha: 0.943), indicating a high reliability of the scale. Exploratory Factor Analysis was performed to further investigate the variance among the 13 observed items, to find the underlying latent factors that influenced the observed items, and to see if the items needed revision.
We found that a three model was the best fit for our data, given fit statistics and model interpretability. The factors are: Factor 1: Self-efficacy with respect to drawing specific objects; Factor 2: Self-efficacy with respect to drawing practically to solve problems, communicating with others, and brainstorming ideas; Factor 3: Self-efficacy with respect to drawing to create, express ideas, and use one’s imagination. An alternative four-factor model is also discussed.
The purpose of our study is to inform interventions that increase self-efficacy. We believe that this assessment will be valuable especially for education researchers who implement AI-based tools to measure drawing skills.This initial validity study shows promising results for a new measure of drawing self-efficacy. Further validation with new populations and drawing classes is needed to support its use, and further psychometric testing of item-level performance. In the future, this self-efficacy assessment could be used by teachers and researchers to guide instructional interventions meant to increase drawing self-efficacy.
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Age-Related Differences in the Influence of Category Expectations on Episodic Memory in Early Childhood
Previous research evaluating the influence of category knowledge
on memory found that children, like adults, rely on category
information to facilitate recall (Duffy, Huttenlocher, &
Crawford, 2006). A model that combines category and target
information (Integrative) provides a superior fit to preschoolers
recall data compared to a category only (Prototype) and target
only (Target) model (Macias, Persaud, Hemmer, & Bonawitz,
in revision). Utilizing data and computational approaches from
Macias et al., (in revision), we explore whether individual and
age-related differences persist in the model fits. Results revealed
that a greater proportion of preschoolers recall was best
fit by the Prototype model and trials where children displayed
individuating behaviors, such as spontaneously labeling, were
also best fit by the Prototype model. Furthermore, the best fitting
model varied by age. This work demonstrates a rich complexity
and variation in recall between developmental groups
that can be illuminated by computationally evaluating individual
differences.
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- Award ID(s):
- 1911656
- NSF-PAR ID:
- 10142867
- Date Published:
- Journal Name:
- 41st Annual Meeting of the Cognitive Science Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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