Human-Computer Interaction Corporate Law Education for Directors: A Machine Learning Approach

被引:1
作者
Du, Qiao [1 ]
Subramanian, Murali [2 ]
Pan, Daohua [3 ]
机构
[1] China Univ Polit Sci & Law, Civil Commercial & Econ Law Sch, Beijing, Peoples R China
[2] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, India
[3] Heilongjiang Vocat Coll Nationalities, Dept Elect Informat Engn, Harbin, Peoples R China
关键词
Human-computer interaction; corporate law education; eye tracking; emotion recognition; cNN; EMOTION RECOGNITION; EYE-TRACKING; PERFORMANCE; ATTENTION;
D O I
10.1080/10447318.2023.2301251
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With the formation and growth of the company, corporate law is continuously established and enhanced. It significantly contributes to the company's healthy growth and brings business operations inside the legal framework. As a result, corporate law education is crucial for employees, particularly directors. Fundamentally, corporate law education is a learning process. The metaverse period has increased learners' needs for learning environments, and human-computer interaction technology will offer all-encompassing support for the smart learning environment. The board of directors needs to adequately monitor each director's learning level as they study corporate law, which will inevitably be detrimental to the company's long-term growth. The most efficient method to address this issue is to use eye movement data to mine different eye movement patterns, followed by an analysis of the learning state of corporate law of directors. The scanning path analysis is used to examine the similarities and differences of the directors' eye movement behaviors during the study of corporate law to enhance the state of corporate law learning. However, the learning status of corporate law cannot be determined only by the eye tracking of directors. We employ the convolutional neural networks (CNN) -based emotion recognition model to provide the directors constructive criticism about their learning state and offer suggestions for the learning mode. The experimental results demonstrate that time series-based eye movement pattern mining can identify directors' viewing habits, and clustering can reveal different learning strategies that can be used to evaluate directors' corporate law learning status. Additionally, the CNN-based emotion recognition model experiment also shows that the established model has an accuracy of 97.0035% and an F1 of 0.9412 in the CASIA-FaceV5 dataset, which helps evaluate the emotions of directors when learning company law.
引用
收藏
页码:1705 / 1717
页数:13
相关论文
共 54 条
  • [1] Questions clustering using canopy-K-means and hierarchical-K-means clustering
    Alian M.
    Al-Naymat G.
    [J]. International Journal of Information Technology, 2022, 14 (7) : 3793 - 3802
  • [2] Behavioral Governance and Self-Conscious Emotions: Unveiling Governance Implications of Authentic and Hubristic Pride
    Bodolica, Virginia
    Spraggon, Martin
    [J]. JOURNAL OF BUSINESS ETHICS, 2011, 100 (03) : 535 - 550
  • [3] Does Board Co-Working Experience Influence Directors' Decisions Toward Internationalization?
    Chen, Hsiang-Lan
    Chang, Chiao-Yi
    Hsu, Wen-Tsung
    [J]. MANAGEMENT INTERNATIONAL REVIEW, 2017, 57 (01) : 65 - 92
  • [4] Reading comprehension based on visualization of eye tracking and EEG data
    Cheng, Shiwei
    Hu, Yilin
    Fan, Jing
    Wei, Qianjing
    [J]. SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (11)
  • [5] New approaches to the analysis of eye movement behaviour across expertise while viewing brain MRIs
    Crowe, Emily M.
    Gilchrist, Iain D.
    Kent, Christopher
    [J]. COGNITIVE RESEARCH-PRINCIPLES AND IMPLICATIONS, 2018, 3
  • [6] Eraslan S, 2015, J WEB ENG, V14, P363
  • [7] Eye tracking in surgical education: gaze-based dynamic area of interest can discriminate adverse events and expertise
    Fichtel, Eric
    Lau, Nathan
    Park, Juyeon
    Parker, Sarah Henrickson
    Ponnala, Siddarth
    Fitzgibbons, Shimae
    Safford, Shawn D.
    [J]. SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, 2019, 33 (07): : 2249 - 2256
  • [8] Unaware yet reliant on attention: Experience sampling reveals that mind-wandering impedes implicit learning
    Franklin, Michael S.
    Smallwood, Jonathan
    Zedelius, Claire M.
    Broadway, James M.
    Schooler, Jonathan W.
    [J]. PSYCHONOMIC BULLETIN & REVIEW, 2016, 23 (01) : 223 - 229
  • [9] Exploring Gender Differences in Computational Thinking Learning in a VR Classroom: Developing Machine Learning Models Using Eye-Tracking Data and Explaining the Models
    Gao, Hong
    Hasenbein, Lisa
    Bozkir, Efe
    Goellner, Richard
    Kasneci, Enkelejda
    [J]. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 2023, 33 (04) : 929 - 954
  • [10] Dynamic Eye Tracking as a Predictor and Outcome Measure of Social Skills Intervention in Adolescents and Adults with Autism Spectrum Disorder
    Greene, Rachel K.
    Parish-Morris, Julia
    Sullivan, Miranda
    Kinard, Jessica L.
    Mosner, Maya G.
    Turner-Brown, Lauren M.
    Penn, David L.
    Wiesen, Christopher A.
    Pallathra, Ashley A.
    Brodkin, Edward S.
    Schultz, Robert T.
    Dichter, Gabriel S.
    [J]. JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS, 2021, 51 (04) : 1173 - 1187