Autonomic Nervous Pattern Recognition of Students’ Learning States in Real Classroom Situation

Shi, Chen; Hu, Zhaonian; Li, Shanshan; Hu, Xiaorou; Liu, Guangyuan; Wang, Wen

doi:10.1109/tcss.2021.3132957

Cited by 2 publications

(2 citation statements)

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“…We extracted 39 initial RR interval features commonly used to quantify autonomic nervous activities in literature [4,5,6,20,36], as shown in Table 2. In order to nd out the RR interval features that were most relevant to learning state identi cation, we applied sequential backward selection (SBS) for feature selection [36].…”

Section: Methodsmentioning

confidence: 99%

“…The above-mentioned studies have focused on the statistical analysis of the relationship between physical exercise at a speci c heart rate and cognition [3,11,31], and have rarely analyzed the impact of physical exercise on neurophysiological activities corresponding to a speci c learning state [13,22]. Previous study has shown that knowledge processing, cognitive load and mental fatigue of students in the learning process have speci c autonomic physiological patterns [5]. However, the impact of active break on the autonomic nervous activity of these classroom learning states remains to be explored.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Active break on Autonomic Nervous Pattern Recognition of Learning States

Sun

zhang

Wang

et al. 2022

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Purpose: This paper explores the effect of active break on the distinguishability of autonomic nervous patterns of learning states through machine learning. Method: First, we collected electrocardiogram (ECG) data of 77 subjects before and after active break, and accurately located the R-wave peaks from the ECG signal to calculate the RR interval series. Second, the RR interval samples were segmented according to the inclusion criteria of certain learning states. The initial 39 ECG features were empirically calculated, and the optimal feature combination for learning states recognition was selected through sequential backward selection and leave-one-subject-out cross test. Finally, we established binary-classification models of pairs of learning states and compared their performance of learning states recognition before and after active break. Discussion: Active break can promote or inhibit the students’ academic performance. Besides statistical analysis shows the stability of the cognitive ability. What’s more active break increases the physiological response to fatigue Conclusion: (1) The autonomic nervous patterns of knowledge input-processing and retrieval-processing and those of cognitive load matching and mismatching in knowledge retrieval processing became less distinguishable with the effect of active break. (2) The autonomic nervous patterns of mental fatigue and no fatigue states became more distinguishable with the effect of active break. (3) Stronger parasympathetic nervous activities make students achieve better academic performance during using new knowledge to solve problem stage.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Active break on Autonomic Nervous Pattern Recognition of Learning States

Sun

zhang

Wang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Empowering Student Learning Through Artificial Intelligence: A Bibliometric Analysis

Yun,

Lee,

Choi

2024

Journal of Educational Computing Research

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Scholarly interest in artificial intelligence (AI) has surged as researchers delve into its transformative impact on various aspects of our lives. AI poses both benefits and challenges, particularly in the context of educators' endeavors to comprehend the intricacies of students' learning processes. Although the use of AI to enhance and assist student learning is relatively new, the exponential growth of scholarly attention and publications in AI and student learning in recent years underscores the compelling necessity for further inquiry. Investigating this area is crucial for understanding the emerging trends in this research domain. This study aims to provide insights into the burgeoning research trajectories on AI from a student learning perspective. Using a bibliometric approach, this study examined 663 scholarly articles pertaining to the interface between AI and student learning published between 1961 and 2024. Our findings reveal four major thematic areas including AI in education and educational technology, AI-driven learning environments, essential AI enablers, and human learning and highlight promising avenues at this intersection.

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Autonomic Nervous Pattern Recognition of Students’ Learning States in Real Classroom Situation

Cited by 2 publications

References 53 publications

The Effect of Active break on Autonomic Nervous Pattern Recognition of Learning States

The Effect of Active break on Autonomic Nervous Pattern Recognition of Learning States

Empowering Student Learning Through Artificial Intelligence: A Bibliometric Analysis

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