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Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data (2312.05368v1)

Published 8 Dec 2023 in cs.AI, cs.CY, cs.HC, and cs.LG

Abstract: This study employed multimodal learning analytics (MMLA) to analyze behavioral dynamics during the ABCDE procedure in nursing education, focusing on gaze entropy, hand movement velocities, and proximity measures. Utilizing accelerometers and eye-tracking techniques, behaviorgrams were generated to depict various procedural phases. Results identified four primary phases characterized by distinct patterns of visual attention, hand movements, and proximity to the patient or instruments. The findings suggest that MMLA can offer valuable insights into procedural competence in medical education. This research underscores the potential of MMLA to provide detailed, objective evaluations of clinical procedures and their inherent complexities.

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References (53)
  1. Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking. Human factors 0, 0 (May 2022), 187208221085335. https://doi.org/10.1177/00187208221085335
  2. The Evidence of Impact and Ethical Considerations of Multimodal Learning Analytics: A Systematic Literature Review. In The Multimodal Learning Analytics Handbook, Michail Giannakos, Daniel Spikol, Daniele Di Mitri, Kshitij Sharma, Xavier Ochoa, and Rawad Hammad (Eds.). Springer International Publishing, Cham, 289–325. https://doi.org/10.1007/978-3-031-08076-0_12
  3. Roger Azevedo and Dragan Gašević. 2019. Analyzing Multimodal Multichannel Data about Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges. Computers in human behavior 96 (July 2019), 207–210. https://doi.org/10.1016/j.chb.2019.03.025
  4. Paulo Blikstein. 2013. Multimodal learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (Leuven, Belgium) (LAK ’13). Association for Computing Machinery, New York, NY, USA, 102–106. https://doi.org/10.1145/2460296.2460316
  5. Synchronizing eye tracking and optical motion capture: How to bring them together. Journal of eye movement research 11, 2 (May 2018), 1–16. https://doi.org/10.16910/jemr.11.2.5
  6. R M Burian. 1997. Exploratory experimentation and the role of histochemical techniques in the work of Jean Brachet, 1938-1952. History and philosophy of the life sciences 19, 1 (1997), 27–45.
  7. Jackie S Cha and Denny Yu. 2022. Objective Measures of Surgeon Non-Technical Skills in Surgery: A Scoping Review. Human factors 64, 1 (Feb. 2022), 42–73. https://doi.org/10.1177/0018720821995319
  8. Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19). Association for Computing Machinery, New York, NY, USA, 2145–2155. https://doi.org/10.1145/3292500.3330690
  9. The Role of Metacognition and Self-regulation on Clinical Reasoning: Leveraging Multimodal Learning Analytics to Transform Medical Education. In The Multimodal Learning Analytics Handbook, Michail Giannakos, Daniel Spikol, Daniele Di Mitri, Kshitij Sharma, Xavier Ochoa, and Rawad Hammad (Eds.). Springer International Publishing, Cham, 105–129. https://doi.org/10.1007/978-3-031-08076-0_5
  10. Message from the LAK 2011 General & Program Chairs. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM, New York, NY, 1–2.
  11. Wenqiang Cui. 2019. Visual Analytics: A Comprehensive Overview. IEEE Access 7 (2019), 81555–81573. https://doi.org/10.1109/ACCESS.2019.2923736
  12. Investigating Interaction Dynamics: A Temporal Approach to Team Learning. In Methods for Researching Professional Learning and Development: Challenges, Applications and Empirical Illustrations, Michael Goller, Eva Kyndt, Susanna Paloniemi, and Crina Damşa (Eds.). Springer International Publishing, Cham, 187–209. https://doi.org/10.1007/978-3-031-08518-5_9
  13. Berna Devezer and Erkan Buzbas. 2021. Minimum Viable Experiment to Replicate. http://philsci-archive.pitt.edu/21475/
  14. From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning 34, 4 (2018), 338–349.
  15. Multilabel Classification of Nursing Activities in a Realistic Scenario. In Activity and Behavior Computing, Md Atiqur Rahman Ahad, Sozo Inoue, Daniel Roggen, and Kaori Fujinami (Eds.). Springer Singapore, Singapore, 269–288. https://doi.org/10.1007/978-981-15-8944-7_17
  16. ABCDE approach to victims by lifeguards: How do they manage a critical patient? A cross sectional simulation study. PloS one 14, 4 (April 2019), e0212080. https://doi.org/10.1371/journal.pone.0212080
  17. What Can Analytics for Teamwork Proxemics Reveal About Positioning Dynamics In Clinical Simulations? Proc. ACM Hum.-Comput. Interact. 5, CSCW1 (April 2021), 1–24. https://doi.org/10.1145/3449284
  18. Development of infants’ attention to faces during the first year. Cognition 110, 2 (Feb. 2009), 160–170. https://doi.org/10.1016/j.cognition.2008.11.010
  19. Gerd Gigerenzer. 1991. From tools to theories: A heuristic of discovery in cognitive psychology. Psychological review 98, 2 (April 1991), 254–267. https://doi.org/10.1037/0033-295X.98.2.254
  20. Predicting webpage aesthetics with heatmap entropy. Behaviour & information technology 40, 7 (May 2021), 676–690. https://doi.org/10.1080/0144929X.2020.1717626
  21. “Sitting at the Stern and Holding the Rudder”: Teachers’ Reflections on Action in Higher Education Based on Student Agency Analytics. In Digital Teaching and Learning in Higher Education: Developing and Disseminating Skills for Blended Learning, Leonid Chechurin (Ed.). Palgrave Macmillan, Cham, 71–91. https://doi.org/10.1007/978-3-031-00801-6_4
  22. Eye Tracking: A comprehensive guide to methods and measures. OUP Oxford, Oxford.
  23. Edwin Hutchins. 1995. Cognition in the Wild. MIT Press, Cambridge, MA.
  24. Low-cost IMU Data Denoising using Savitzky-Golay Filters. In 2019 International Conference on Communications, Signal Processing, and their Applications (ICCSPA). IEEE, Piscataway, 1–5. https://doi.org/10.1109/ICCSPA.2019.8713728
  25. Daniel A Keim. 2001. Visual exploration of large data sets. Commun. ACM 44, 8 (Aug. 2001), 38–44. https://doi.org/10.1145/381641.381656
  26. Daniel A Keim. 2002. Information visualization and visual data mining. IEEE transactions on visualization and computer graphics 8, 1 (Jan. 2002), 1–8. https://doi.org/10.1109/2945.981847
  27. Accurate Indoor Proximity Zone Detection Based on Time Window and Frequency with Bluetooth Low Energy. Procedia computer science 56 (Jan. 2015), 88–95. https://doi.org/10.1016/j.procs.2015.07.199
  28. Michaela Kolbe and Margarete Boos. 2019. Laborious but Elaborate: The Benefits of Really Studying Team Dynamics. Frontiers in psychology 10 (June 2019), 1478. https://doi.org/10.3389/fpsyg.2019.01478
  29. Capturing cognitive load management during authentic virtual reality flight training with behavioural and physiological indicators. Journal of computer assisted learning 39, 5 (April 2023), 1553–1563. https://doi.org/10.1111/jcal.12817
  30. Assessing how visual search entropy and engagement predict performance in a multiple-objects tracking air traffic control task. Computers in Human Behavior Reports 4 (Aug. 2021), 100127. https://doi.org/10.1016/j.chbr.2021.100127
  31. How prior knowledge affects problem-solving performance in a medical simulation game: Using game-logs and eye-tracking. Computers in human behavior 99 (Oct. 2019), 268–277. https://doi.org/10.1016/j.chb.2019.05.035
  32. Towards a physiological computing infrastructure for researching students’ flow in remote learning. Technology Knowledge and Learning 27, 1 (Sept. 2021), 365–384. https://doi.org/10.1007/s10758-021-09569-4
  33. Feature selection for distance-based regression: An umbrella review and a one-shot wrapper. Neurocomputing 518 (2023), 344–359.
  34. David J C MacKay. 2003. Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge.
  35. Lessons Learnt from a Multimodal Learning Analytics Deployment In-the-wild. ACM Trans. Comput.-Hum. Interact. 31, 1 (Sept. 2023), 1–41. https://doi.org/10.1145/3622784
  36. Where is the teacher? Digital analytics for classroom proxemics. Journal of Computer Assisted Learning 16 (May 2020), 1. https://doi.org/10.1111/jcal.12444
  37. Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in human behavior 139 (Feb. 2023), 107540. https://doi.org/10.1016/j.chb.2022.107540
  38. Kaveh Momen and Geoff R Fernie. 2010. Nursing activity recognition using an inexpensive game controller: An application to infection control. Technology and health care: official journal of the European Society for Engineering and Medicine 18, 6 (2010), 393–408. https://doi.org/10.3233/THC-2010-0600
  39. Current and Future Microsurgical Skills Assessment. In Learning and Career Development in Neurosurgery: Values-Based Medical Education, Ahmed Ammar (Ed.). Springer International Publishing, Cham, 349–356. https://doi.org/10.1007/978-3-031-02078-0_30
  40. Research trends in multimodal learning analytics: A systematic mapping study. Computers and Education: Artificial Intelligence 4 (Jan. 2023), 100136. https://doi.org/10.1016/j.caeai.2023.100136
  41. Ronald W Schafer. 2011. What Is a Savitzky-Golay Filter? [Lecture Notes]. IEEE Signal Processing Magazine 28, 4 (July 2011), 111–117. https://doi.org/10.1109/MSP.2011.941097
  42. Healthcare professionals’ knowledge of the systematic ABCDE approach: a cross-sectional study. BMC emergency medicine 22, 1 (Dec. 2022), 202. https://doi.org/10.1186/s12873-022-00753-y
  43. C E Shannon. 1948. A mathematical theory of communication. The Bell system technical journal 27, 3 (1948), 379–423. https://doi.org/10.1145/584091.584093
  44. A review of gaze entropy as a measure of visual scanning efficiency. Neuroscience and biobehavioral reviews 96 (Jan. 2019), 353–366. https://doi.org/10.1016/j.neubiorev.2018.12.007
  45. Friedrich Steinle. 1997. Entering New Fields: Exploratory Uses of Experimentation. Philosophy of science 64, S4 (1997), S65–S74. https://doi.org/10.1086/392587
  46. Initial assessment and treatment with the Airway, Breathing, Circulation, Disability, Exposure (ABCDE) approach. International journal of general medicine 5 (Jan. 2012), 117–121. https://doi.org/10.2147/IJGM.S28478
  47. Measurement and Management of Cognitive Load in Surgical Education: A Narrative Review. Journal of surgical education 80, 2 (Feb. 2023), 208–215. https://doi.org/10.1016/j.jsurg.2022.10.001
  48. Philip H Winne. 2019. Paradigmatic Dimensions of Instrumentation and Analytic Methods in Research on Self-Regulated Learning. Computers in human behavior 96 (July 2019), 285–289. https://doi.org/10.1016/j.chb.2019.03.026
  49. A New Era in Multimodal Learning Analytics: Twelve Core Commitments to Ground and Grow MMLA. Journal of Learning Analytics 8, 3 (2021), 10–27.
  50. Eye-Tracking Technology to Determine Procedural Proficiency in Ultrasound-Guided Regional Anesthesia. The journal of education in perioperative medicine : JEPM 24, 1 (Jan. 2022), E684. https://doi.org/10.46374/volxxiv_issue1_zurca
  51. Affective computing in education: A systematic review and future research. Computers & education 142 (Dec. 2019), 103649. https://doi.org/10.1016/j.compedu.2019.103649
  52. Scalability, Sustainability, and Ethicality of Multimodal Learning Analytics. In LAK22: 12th International Learning Analytics and Knowledge Conference (Online, USA) (LAK22). Association for Computing Machinery, New York, NY, USA, 13–23. https://doi.org/10.1145/3506860.3506862
  53. METS: Multimodal Learning Analytics of Embodied Teamwork Learning. In LAK23: 13th International Learning Analytics and Knowledge Conference (Arlington, TX, USA) (LAK2023). Association for Computing Machinery, New York, NY, USA, 186–196. https://doi.org/10.1145/3576050.3576076

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