Modelling children's inhibitory skills using learning data from an educational app.
Background: Early literacy and numeracy skills are developed during early childhood. Among the many factors that influence the development of such skills, the literature shows that the executive functions, especially the response inhibition (RI)—that is the capability to block out or to tune out wha...
| Published in: | Journal of Computer Assisted Learning Vol. 39; no. 3; pp. 856 - 869 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Jun2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163886540&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163886540 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Jun2023 vid: 39 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 163886540 161888102 163886540 163886540 10.1111/jcal.12773 163886540 ppf: 856 ppct: 13 formats: tig: atl: Modelling children's inhibitory skills using learning data from an educational app. aug: au: Medeiros Machado, Guilherme Bonnin, Geoffray Castagnos, Sylvain Hoareau, Lara Thomas, Aude Tazouti, Youssef affil: Bird Team, LORIA, University of Lorraine, Nancy, France sug: subj: Mobile Applications Utilization Executive Function In Infancy and Childhood Child Development Learning Methods Models, Educational Human Mathematics Child Behavior Socioeconomic Factors Task Performance and Analysis Head Physiology Toes Physiology Shoulder Physiology Multiple Regression Data Science Funding Source Child Child: 6-12 years ab: Background: Early literacy and numeracy skills are developed during early childhood. Among the many factors that influence the development of such skills, the literature shows that the executive functions, especially the response inhibition (RI)—that is the capability to block out or to tune out what can be considered irrelevant information or action to the learning task—is one of the most essential functions. There are specific tests used to appraise these children's inhibition skills, but these tests are generally time‐consuming, and demand specialized human resources. Objectives: We present a computational approach to model children's RI behaviour through the analysis of educational traces left in an educational app. This modelling allows the automatic and instant identification of the RI level of children without the need of a human‐conducted test. Methods: Our modelling is based on two definitions of RI found in the literature, from which we derived a mathematical formalism of three variables we used to query the traces dataset and isolate the RI behaviour of each student from the learning traces generated in the app. The sample population is composed of children from diverse socioeconomic backgrounds. The model is then assessed by comparing it to a traditional human‐conducted RI test suitable for kindergarten children, the Head‐Toes‐Knees‐Shoulder (HTKS) task. Results and conclusions: The results show that our RI model can explain an important part of the HTKS variance (up to 0.45 according to the adjusted R2) when taking the HTKS results as a dependent variable for a multiple regression model. In practice, our model can be integrated in a learning app and become a powerful tool for instant preliminary identification of dysfunctional RI behaviour, especially in the early stages of children's education. Once students are identified by our model as having a dysfunctional RI behaviour, teachers can rapidly act to help them. Besides, the proposed model requires only very simple data to work, which means it can be easily integrated into different learning apps. Lay Description: Executive functions are important predictors of school success, especially inhibitionInhibition tests are costly, time‐consuming, and have a short lifespanLearning analytics (LA) can help to automatically model students' behaviourThis article will focus on the students' dysfunctional inhibition (DI) behaviourWe successfully modelled DI behaviour using simple traces found in a learning gameThe repetition of the same wrong answer is an important indicator for DIThis result can help researchers to integrate a DI indicator into a LA dashboardThis result can also help teachers in supporting struggling students pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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