Application of Wearable Technology for the Acquisition of Learning Motivation in an Adaptive E-Learning Platform

Bauer, Mathias; Bräuer, Cassandra; Schuldt, Jacqueline; Niemann, Moritz; Krömker, Heidi

doi:10.1007/978-3-319-94619-1_4

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…A brief description of each of the ML/AI technologies considered is provided in Table 11. ( Abdurrahman et al, 2021), (Bhalla et al, 2021), (De Bruyne et al, 2021), (Delvigne et al, 2022), (Delvigne et al, 2020), (Georgiou & Demiris, 2017), (Jones et al, 2020), (Liebers et al, 2021), (Leiker et al, 2016), (Migovich et al, 2021), (Noghabaei et al, 2021), (Porssut et al, 2022), (Shi et al, 2020), (Tsai et al, 2021), (Wilson et al, 2021), (Zhang et al, 2017) From (Porssut et (Granato et al, 2020), (Ihmig et al, 2020), (Islam et al, 2019), (Kim et al, 2019), (Luong et al, 2020), (Martin et al, 2020), (Migovich et al, 2021), (Sakib et al, 2020), (Sayis et al, 2022), (Tabbaa et al, 2022), (Tsai et al, 2021), (Wilson et al, 2021), (Zhang et al, 2017) From (Martin et al, 2020) (continued) (Bauer et al, 2019), (Bin Suhaimi et al, 2020), (Delvigne et al, 2022), (Delvigne et al, 2020), (Jones et al, 2020), (Kaur et al, 2019), (Kim et al, 2020), (McDermott et al, 2022), …”

Section: Ai/ml Uses and Definitions Of Approachesmentioning

confidence: 99%

Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

Blackmore,

Smith,

Bailey

et al. 2024

Simulation & Gaming

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Background The addition of biofeedback and artificial intelligence (AI) in simulation training and serious games has shown promising results in improving the effectiveness of training and can lead to increased engagement, motivation, and retention of information. This systematic literature review explores the integration of biofeedback and artificial intelligence into eXtended reality (XR) training scenarios and is the first review to provide a consolidated overview of applied biofeedback and AI technologies in this area. Method This review was conducted using keywords related to biofeedback, AI, XR, and training and included papers that: contained the use of biofeedback and AI in XR training scenarios; reported on at least one outcome related to training effectiveness; were published in English; were peer-reviewed; date from 1 January 2016 – 7 February 2022. Results The results indicate that many studies collect two or more biosignals using a single biosensing device. This is particularly relevant in applied settings, where ease of use and minimal interference in training/education activities is desired. Also, that light, portable devices such as wrist bands, wireless straps, or headbands are preferred. Additionally, eye tracking, electrodermal activity (EDA), and photoplethysmograms (PPG) present as particularly useful biomarkers of stress and/or cognitive load in XR training contexts. A wide variety of machine learning (ML) approaches were used to support biofeedback systems in XR environments. However, a limited number of studies employed real-time analysis of biosignals (just 1% of studies) which indicates current challenges in implementing such systems. Conclusion The majority of papers meeting the selection criteria were from the fields of education and healthcare. Further research in other domains, such as defense and general industry, is needed to gain a comprehensive understanding of the potential for biofeedback and AI integration in XR training scenarios used in these domains.

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Section: Ai/ml Uses and Definitions Of Approachesmentioning

confidence: 99%

Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

Blackmore,

Smith,

Bailey

et al. 2024

Simulation & Gaming

View full text Add to dashboard Cite

show abstract

“…The models can broadly be classified into two: models for modeling student characteristics based on physiological data and those based on interaction data. The physiological approach is targeted at behaviors undertaken by students through facial expression, eye-gaze, and neural specificity (Taylor et al, 2004 ), posture and body language (Boulay, 2011 ), and wearable sensory devices such as electroencephalography (EEG) and smartwatches (Bauer et al, 2019 ) are usually employed to capture some of the behaviors. The approach determines behaviors based on complex interactions among physiological, biological, and cognitive systems (Beauchaine, 2001 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Orji

Vassileva²

2022

Front. Artif. Intell.

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Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.

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“…A new trend is to use brain-imaging techniques such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) to provide relevant students' information (e.g., level of attention, emotion, learning state, motivation, and so on) for adaptive learning systems in order to enable an enhanced personalized experience (Y. Li et al, 2011;C.-C. Wang & Hsu, 2014;Bauer et al, 2019;Ghiani et al, 2015;Mailhot et al, 2018). Therefore, adaptive learning environments could rely on direct pieces of evidence of students' current cognitive phenomena (Spüler et al, 2016), instead of depending only on learners' explicit interaction behavior for adaptation such as correctness of responses and reaction times (Käser et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

On the joint use of Artificial Intelligence and Brain-Imaging Techniques in Technology-enhanced Learning Environments: A Systematic Literature Review

Tenório

Santos

Accete

et al. 2021

RBIE

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Recent meta-analysis and literature reviews support that adaptive learning systems are components of effective instruction. These exciting results are motivating researchers to explore new technologies that provide relevant students' information to promote a better-personalized experience for students to achieve better learning outcomes in technology-enhanced learning environments. A new trend is related to the studies that use brain-imaging techniques to provide relevant students' information for educational systems, aiming to enable an enhanced personalized experience. Some of these studies are making use of artificial intelligence to provide real-time monitoring of students' cognitive phenomena supplied by brain-imaging techniques such as electroencephalography and functional magnetic resonance imaging. Therefore, considering the relevance of the application of artificial intelligence in studies that use brain-imaging techniques combined with technology-enhanced learning environments and the lack of a current understanding of how these techniques have been used in this context, we present a systematic literature review (SLR) that aims to explore which artificial intelligence algorithms have been adopted, what are their purposes in studies that apply brain-imaging techniques in educational technologies and which were the results reported in these studies related to the use of artificial intelligence algorithms. The systematic literature review was conducted according to the recommendation of a well-accepted guideline to perform a rigorous review of the current literature. The search was conducted in seven academic databases in January 2020 and resulted in a total of 6089 studies that was reduced to 20 studies for the final analysis.

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Application of Wearable Technology for the Acquisition of Learning Motivation in an Adaptive E-Learning Platform

Cited by 13 publications

References 12 publications

Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

On the joint use of Artificial Intelligence and Brain-Imaging Techniques in Technology-enhanced Learning Environments: A Systematic Literature Review

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