Using Animation to Enrich Learning Experience in Sketch-Based Physics Tutoring Systems

Cheema, Salman; LaViola, Joseph J.

doi:10.1007/978-3-319-94794-5_10

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…We have found computer-based learning environments that use the handwriting or sketch-based methods as the data input to improve the learning of the students for a variety of domains, such as music theory (Barreto et al, 2016), physics (Cheema and LaViola, 2012;Lee et al, 2012;Cheema and LaViola Jr., 2018), Japanese kanji writing , drawing techniques (Dixon et al, 2010;Cummmings et al, 2012), mathematics (Laviola, 2007;Anthony et al, 2007a;Vuong et al, 2010;Anthony et al, 2012;Pacheco-Venegas et al, 2015;Phon-Amnuaisuk et al, 2015;Wang et al, 2016), biology , essentials of writing (Thompson et al, 2016), engineering statics concepts (Valentine et al, 2015), freehand engineering sketching (Hilton et al, 2019), digital circuit design (Alvarado et al, 2015), and geometry (Kang et al, 2016(Kang et al, , 2017. From this set of works, we have selected only the papers that are more similar to the research goal of our work and the domain of equations solving.…”

Section: Literature Reviewmentioning

confidence: 99%

Does handwriting impact learning on math tutoring systems?

Morais¹,

Jaques²

2021

Informatics in Education

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Intelligent Tutoring Systems (ITSs) for Math still use traditional data input methods: computers' keyboard and mouse. However, students usually solve math tasks using paper and pen. Therefore, the gap between the manner the students work and the requirements imposed by these typing-based systems expose students to an extraneous cognitive load, impairing their learning. Our study investigates the impact of the data input method on students' learning and fluency in solving equations using step-based math ITSs. More specifically, we have considered the standard typing and handwriting input methods. We hypothesized that the students would be more fluent using their handwriting with online recognition to solve math equations than using the typing input method. This fluency indicates a reduction in cognitive load, freeing working memory for logical reasoning instead of interface preconditions, leading to improved learning. We have conducted an experiment with 55 seventh-grade students from a private school to validate the hypothesis, randomly assigned to control and experimental groups. Each group used one of the input methods on two different devices (desktop computers and tablets). Although students using handwriting solved more equations and were faster than students who typed their equations, we could not find statistically significant differences in the learning between students that used typing or handwriting. Additionally, we have found that the input method used in a not ideal device (e.g., handwriting with a computer's mouse instead of using a touch screen device) can negatively affect the students' performance.

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