Intelligent Tutoring Systems for Psychomotor Training – A Systematic Literature Review

Neagu, Laurentiu-Marian; Rigaud, Éric; Travadel, Sébastien; Dascălu, Mihai; Rughiniș, Răzvan

doi:10.1007/978-3-030-49663-0_40

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…However, recent advances in portable technologies, large data processing, and sensor development lead to the emergence of new ITSs in such diverse fields as medicine (Almiyad et al, 2017;Alvarez et al, 2015;Skinner et al, 2018), sport (Lee & Kim, 2010), driving (Ropelato et al, 2018), and industry (Hodaie et al, 2018;Marinescu-Muster et al, 2021;Westerfield et al, 2015). Despite the wide proliferation of these applications, limited amount of them was tested and reported in the research field of AI in education (Santos, 2016), the observation shared by another recent review of psychomotor intelligent tutoring systems for training motorcognitive skills (Neagu et al, 2020).…”

Section: Intelligent Tutoring Systems For Motor-cognitive Skillsmentioning

confidence: 99%

Moving-Target Intelligent Tutoring System for Marksmanship Training

Zotov

Kramkowski²

2022

Int J Artif Intell Educ

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Intelligent tutoring systems (ITSs) may augment military training systems and mitigate existing limitations in training personnel and resources. A study was conducted to investigate the effectiveness of an embedded rifle marksmanship ITS for Moving Targets (MT-ITS). MT-ITS has two main components: (1) a Smart Sight System that provides a perceptual cue to help trainees adjust their point of aim to account for a target’s speed, direction of movement, and distance, and (2) a performance-based algorithm that delivers shooting performance feedback to trainees.The MT-ITS was tested in an experiment where participants engaged moving targets in a virtual shooting range. Moving targets were presented at different speeds, direction of movement, and distances. Two types of marksmanship training were compared: with ITS and without ITS (a standard training). The ITS training group produced better hit rate and aiming accuracy scores than the standard training group, requiring less practice to achieve asymptotic results. Implications for the design of embedded trainers with ITS for marksmanship specifically and for training motor skills in general are discussed in the context of future research directions.

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Section: Intelligent Tutoring Systems For Motor-cognitive Skillsmentioning

confidence: 99%

Moving-Target Intelligent Tutoring System for Marksmanship Training

Zotov

Kramkowski²

2022

Int J Artif Intell Educ

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“…1. Feedback is an essential dimension of psychomotor development and consequently Selfit efficiency [22]. Before starting a session, Selfit queries trainees to self-evaluate their fatigue level, motivation to train, sleep quality, and stress level.…”

Section: The Selfit Intelligent Tutoring Systemmentioning

confidence: 99%

OntoStrength: An Ontology for Psychomotor Strength Development

Neagu¹,

Rigaud²,

Guarnieri³

et al. 2022

IxD&A

Self Cite

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An ontology is a formal, explicit description of concepts and relations from a domain while considering underlying properties, restrictions, and instances.With the advancement of the Semantic Web, the rise of the Educational Semantic Web, and the lack of uniformity between approaches for knowledge representation, ontologies are becoming more and more popular, including adaptive learning environments such as the Intelligent Tutoring Systems (ITSs). OntoStrength is an ontology developed to support the Selfit ITS, a platform that aims to improve the fundamental human psychomotor skills and, more specifically, bio-motor strength abilities. The goal of Selfit is to prevent the negative consequences of a sedentary lifestyle and accidents involving inadequate strength skills. Most ontologies in the sports domain support the development of digital solutions for sports performance and data collected during competitions. In contrast, OntoStrength’s goal is to contribute to the development of digital solutions dedicated to bio-motor strength ability analysis. OntoStrength considers other bio-motor skills like speed, endurance, or flexibility, as well as other activities like muscle analysis, movement patterns, or training load management, to support sport, professional and daily-life activities. OntoStrength enables the personalization of strength development programs in the Selfit ITS by providing a comprehensive data layer for its student, domain, and tutoring models.

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“…Conceptually we want to determine the reliability of subscales after removing the influence of the general factor. Omega hierarchical subscale (OmegaHS) provides this estimate; see Equation ( 6) for computing omegaHS for the affective subscale and Equation (7) for the cognitive subscale: ECV: explained common variance; PUC: percent uncontaminated correlations.Coefficient Omega is a measure of internal consistency, which overcomes the limitations of earlier measures. Omega hierarchical provides the proportion of variance in total scores attributed solely to the general factor.…”

Section: Omegah Subscalementioning

confidence: 99%

“…4 Application of artificial intelligence (AI) technology in education typically involves a model of the teacher or master teacher 5,6 and model of the student. [7][8][9] Models of both teacher and student range from simple to very complex, incorporating notions from intelligence, [10][11][12] personality, 13,14 and even attitudes. 15,16 The guiding principle in the construction of these models is as follows: the more veridical they are, the more successful they will have.…”

Section: Introductionmentioning

confidence: 99%

The importance of identifying the dimensionality of constructs employed in simulation and training for AI

Coovert

Bennett

2021

Journal of Defense Modeling & Simulation

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Advances at the intersection of artificial intelligence (AI) and education and training are occurring at an ever-increasing pace. On the education and training side, psychological and performance constructs play a central role in both theory and application. It is essential, therefore, to accurately determine the dimensionality of a construct, as it is often employed during both the assessment and development of theory, and its practical application. Traditionally, both exploratory and confirmatory factor analyses have been employed to establish the dimensionality of data. Due in part to inconsistent findings, methodologists recently resurrected the bifactor approach for establishing the dimensionality of data. The bifactor model is pitted against traditional data structures, and the one with the best overall fit (according to chi-square, root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker–Lewis index (TLI), and standardized root mean square residual (SRMR)) is preferred. If the bifactor structure is preferred by that test, it can be further examined via a suite of emerging coefficients (e.g., omega, omega hierarchical, omega subscale, H, explained common variance, and percent uncontaminated correlations), each of which is computed from standardized factor loadings. To examine the utility of these new statistical tools in an education and training context, we analyze data where the construct of interest is trust. We chose trust as it is central, among other things, to understanding human reliance upon and utilization of AI systems. We utilized the above statistical approach and determined the two-factor structure of widely employed trust scale is better represented by one general factor. Findings like this hold substantial implications for theory development and testing, prediction as in structural equation modeling (SEM) models, as well as the utilization of scales and their role in education, training, and AI systems. We encourage other researchers to employ the statistical measures described here to critically examine the construct measures used in their work if those measures are thought to be multidimensional. Only through the appropriate utilization of constructs, defined in part by their dimensionality, are we to advance the intersection of AI and simulation and training.

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Intelligent Tutoring Systems for Psychomotor Training – A Systematic Literature Review

Cited by 12 publications

References 4 publications

Moving-Target Intelligent Tutoring System for Marksmanship Training

Moving-Target Intelligent Tutoring System for Marksmanship Training

OntoStrength: An Ontology for Psychomotor Strength Development

The importance of identifying the dimensionality of constructs employed in simulation and training for AI

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