利用眼動追蹤與腦電圖探究學生解題策略之差異

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：29

、訪客IP：3.141.30.96

姓名

洪詩亭(Shih-Ting Hung) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

利用眼動追蹤與腦電圖探究學生解題策略之差異
(Exploring Differences in Problem-Solving Strategies among Students using Eye-Tracking and Electroencephalography)

相關論文

★ 使用梳狀濾波器於相位編碼之穩態視覺誘發電位腦波人機介面	★ 應用電激發光元件於穩態視覺誘發電位之腦波人機介面判斷
★ 智慧型手機之即時生理顯示裝置研製	★ 多頻相位編碼之閃光視覺誘發電位驅動大腦人機介面
★ 以經驗模態分解法分析穩態視覺誘發電位之大腦人機界面	★ 利用經驗模態分解法萃取聽覺誘發腦磁波訊號
★ 明暗閃爍視覺誘發電位於遙控器之應用	★ 使用整體經驗模態分解法進行穩態視覺誘發電位腦波遙控車即時控制
★ 使用模糊理論於穩態視覺誘發之腦波人機介面判斷	★ 利用正向模型設計空間濾波器應用於視覺誘發電位之大腦人機介面之雜訊消除
★ 智慧型心電圖遠端監控系統	★ 使用隱馬可夫模型於穩態視覺誘發之腦波人機介面判斷與其腦波控制遙控車應用
★ 使用類神經網路於肢體肌電訊號進行人體關節角度預測	★ 使用等階集合法與影像不均勻度修正於手指靜脈血管影像切割
★ 應用小波編碼於多通道生理訊號傳輸	★ 結合高斯混合模型與最大期望值方法於相位編碼視覺腦波人機介面之目標偵測

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2025-6-30以後開放)

摘要(中)

本研究旨在利用眼動追蹤和腦電圖技術探究資優學生和一般學生在解題策略
上的差異，比較資優學生與一般學生在面對不同題目型態時的行為和心理反應。儘管資優學生在認知能力和學習表現上具有獨特之處，然而對於他們解題策略的了解仍然相對有限。因此，透過詳細分析眼動追蹤資料和腦電波活動，本研究揭示了資優生與一般學生在認知過程和解題策略上的顯著差異。
本研究採用了眼動追蹤技術和腦電圖同步量測技術，以獲取解題過程中的眼睛注視行為和大腦神經活動的記錄。參與者包括一組資優學生和一組與之相對應的一般學生，兩組學生接受了相同的問題解題任務，並在解題過程中進行眼動追蹤和腦電波的同步量測。研究結果顯示資優生在解題策略上更傾向於建立並運用心智模型，並有效整合文字與圖片資訊；相較之下，一般學生則更多地專注於題目的表面特徵，他們的解題策略多依賴於題目與答案之間的直接對應。此外，在答題正確率方面，資優生普遍高於一般學生，尤其是在含圖片題型中更為明顯。
腦電圖分析進一步顯示，資優生在解題過程中表現出更高的θ、α 和β 頻段的腦波能量變化，尤其在處理圖片資訊時。這些腦波特徵表明資優生在建構心智模型和訊息整合過程中的認知負荷和專注力較高。
本研究的發現對於理解學生的認知歷程和提升教學方法具有重要意義，特別是在設計適合不同學習能力學生的教學策略方面。這些結果對於教育實踐和個別化教學具有重要意義，教育工作者可以更有效地識別並培養學生的解題能力，促進學生們的最佳發展。

摘要(英)

This study aims to explore the differences in problem-solving strategies between gifted and average students using eye-tracking and electroencephalography (EEG) techniques. The research focuses on comparing the behavioral and psychological responses of gifted and average students when facing different types of problems. Despite the unique cognitive abilities and learning performances of gifted students, understanding of their problem-solving strategies is still relatively limited. Therefore, through analysis of eye-tracking data and brainwave activity, this study reveals significant differences in cognitive processes and problem-solving strategies between the two groups of students.
The study employed eye-tracking technology and simultaneous EEG measurement techniques to record eye-gazing behavior and brain neural activity during the problem-solving process. Participants included a group of gifted students and a corresponding group of average students, both of whom undertook the same tasks, with simultaneous measurement of eye movement and brainwaves. The results show that gifted students are more inclined to build and use mental models and effectively integrate textual and pictorial information. In contrast, average students focused more on the surface features of the problems, relying heavily on direct correspondences between the questions and answers. Moreover, in terms of accuracy, gifted students generally outperformed average students, especially in problems that included pictorial information.
EEG analysis further revealed that gifted students exhibited higher brainwave energy changes in the θ, α, and β frequency bands during problem-solving, particularly when processing pictorial information. These brainwave characteristics suggest a higher cognitive load and attention level in gifted students during the construction of mental models and information integration processes.
The findings of this study are significant for understanding students′ cognitive processes and improving teaching methods, especially in designing instructional strategies suitable for students with different learning abilities. These results have important implications for educational practice and individualized teaching, enabling educators to more effectively identify and cultivate students′ problem-solving abilities, fostering optimal development in students.

關鍵字(中)

★ 眼動追蹤
★ 腦電圖
★ 解題策略
★ 認知負荷

關鍵字(英)

★ Eye-tracking
★ EEG
★ Problem-solving strategy
★ Cognitive load

論文目次

中文摘要 i
Abstract ii
目錄 iv
圖目錄 v
表目錄 vii
第一章緒論 1
1-1 研究動機與目的 1
1-2 文獻探討 3
1-2-1 解題策略 3
1-2-2 眼動追蹤認知處理指標 5
1-2-3 大腦解題認知歷程 6
第二章研究設計與方法 7
2-1 系統架構 7
2-1-1 腦電波與眼動追蹤同步量測系統 7
2-1-2 受試者與實驗設置 9
2-2 系統資料處理 11
2-2-1 眼動追蹤資料處理 11
2-2-2 基於FTR或SCR特徵之隨機森林分類 14
2-2-3 EEG資料分析 15
第三章結果與討論 17
3-1 眼動追蹤結果 17
3-2 EEG結果 24
3-3 討論 36
第四章結論與未來展望 45
第五章參考文獻 47

參考文獻

[1] D. Kuhn, "Science as argument: Implications for teaching and learning scientific thinking," Science education, vol. 77, no. 3, pp. 319-337 %@ 0036-8326, 1993.
[2] L. Bao, K. Koenig, Y. Xiao, J. Fritchman, S. Zhou, and C. Chen, "Theoretical model and quantitative assessment of scientific thinking and reasoning," Physical Review Physics Education Research, vol. 18, no. 1, p. 010115, 2022.
[3] A. W. Inhoff and R. Radach, "Definition and computation of oculomotor measures in the study of cognitive processes," Eye guidance in reading and scene perception, pp. 29-53, 1998.
[4] M. Keskin, K. Ooms, A. O. Dogru, and P. De Maeyer, "Exploring the cognitive load of expert and novice map users using EEG and eye tracking," ISPRS International Journal of Geo-Information, vol. 9, no. 7, pp. 429 %@ 2220-9964, 2020.
[5] K. Ooms, P. De Maeyer, and V. Fack, "Study of the attentive behavior of novice and expert map users using eye tracking," Cartography and Geographic Information Science, vol. 41, no. 1, pp. 37-54 %@ 1523-0406, 2014.
[6] M. Borys et al., "An analysis of eye-tracking and electroencephalography data for cognitive load measurement during arithmetic tasks," 2017, pp. 287-292 %@ 1509051600: IEEE.
[7] N. Peitek et al., "Correlates of programmer efficacy and their link to experience: A combined EEG and eye-tracking study," 2022, pp. 120-131.
[8] A. B. Lewis and R. E. Mayer, "Students′ miscomprehension of relational statements in arithmetic word problems," Journal of Educational psychology, vol. 79, no. 4, pp. 363 %@ 1939-2176, 1987.
[9] R. E. Mayer, Thinking, problem solving, cognition. WH Freeman/Times Books/Henry Holt & Co, 1992.
[10] M. Hegarty, R. E. Mayer, and C. E. Green, "Comprehension of arithmetic word problems: Evidence from students′ eye fixations," Journal of educational psychology, vol. 84, no. 1, pp. 76 %@ 1939-2176, 1992.
[11] M. Hegarty, R. E. Mayer, and C. A. Monk, "Comprehension of arithmetic word problems: A comparison of successful and unsuccessful problem solvers," Journal of educational psychology, vol. 87, no. 1, pp. 18 %@ 1939-2176, 1995.
[12] A. B. Khedher, I. Jraidi, and C. Frasson, "Tracking students’ analytical reasoning using visual scan paths," 2017, pp. 53-54 %@ 1538638703: IEEE.
[13] A. Ben Khedher, I. Jraidi, and C. Frasson, "Static and dynamic eye movement metrics for students’ performance assessment," Smart Learning Environments, vol. 5, pp. 1-12, 2018.
[14] K. Scheiter, C. Schubert, and A. Schüler, "Self‐regulated learning from illustrated text: Eye movement modelling to support use and regulation of cognitive processes during learning from multimedia," British Journal of Educational Psychology, vol. 88, no. 1, pp. 80-94 %@ 0007-0998, 2018.
[15] S. Lallé, C. Conati, and R. Azevedo, "Prediction of student achievement goals and emotion valence during interaction with pedagogical agents," 2018, pp. 1222-1231.
[16] D. Toker, S. Lallé, and C. Conati, "Pupillometry and head distance to the screen to predict skill acquisition during information visualization tasks," 2017, pp. 221-231.
[17] R. Gaschler, J. N. Marewski, and P. A. Frensch, "Once and for all—How people change strategy to ignore irrelevant information in visual tasks," The Quarterly Journal of Experimental Psychology, vol. 68, no. 3, pp. 543-567 %@ 1747-0218, 2015.
[18] C. Godau, "From marbles to numbers—Estimation influences looking patterns on arithmetic problems," Psychology, vol. 5, no. 02, p. 127, 2014.
[19] M. A. Just and P. A. Carpenter, "A theory of reading: from eye fixations to comprehension," Psychological review, vol. 87, no. 4, pp. 329 %@ 1939-1471, 1980.
[20] M. Hegarty, "Mental animation: Inferring motion from static displays of mechanical systems," Journal of experimental psychology: learning, memory, and cognition, vol. 18, no. 5, pp. 1084 %@ 1939-1285, 1992.
[21] K. Rayner, "Eye movements in reading and information processing: 20 years of research," Psychological bulletin, vol. 124, no. 3, pp. 372 %@ 1939-1455, 1998.
[22] R. Radach, J. Hyona, and H. Deubel, The mind′s eye: Cognitive and applied aspects of eye movement research. Elsevier, 2003.
[23] A. Friedman and L. S. Liebelt, "On the time course of viewing pictures with a view towards remembering," in Eye Movements: Routledge, 2017, pp. 137-155.
[24] J. H. Goldberg and X. P. Kotval, "Computer interface evaluation using eye movements: methods and constructs," International journal of industrial ergonomics, vol. 24, no. 6, pp. 631-645 %@ 0169-8141, 1999.
[25] C. I. Johnson and R. E. Mayer, "An eye movement analysis of the spatial contiguity effect in multimedia learning," Journal of Experimental Psychology: Applied, vol. 18, no. 2, pp. 178 %@ 1939-2192, 2012.
[26] F. Paas, J. E. Tuovinen, H. Tabbers, and P. W. M. Van Gerven, "Cognitive load measurement as a means to advance cognitive load theory," in Cognitive Load Theory: Routledge, 2016, pp. 63-71.
[27] R. Brunken, J. L. Plass, and D. Leutner, "Direct measurement of cognitive load in multimedia learning," Educational psychologist, vol. 38, no. 1, pp. 53-61 %@ 0046-1520, 2003.
[28] Z. Chen, Y. He, and Y. Yu, "Enhanced functional connectivity properties of human brains during in-situ nature experience," PeerJ, vol. 4, pp. e2210 %@ 2167-8359, 2016.
[29] R. Shriram, M. Sundhararajan, and N. Daimiwal, "EEG based cognitive workload assessment for maximum efficiency," Int. Organ. Sci. Res. IOSR, vol. 7, pp. 34-38, 2013.
[30] K. Gupta, R. Hajika, Y. S. Pai, A. Duenser, M. Lochner, and M. Billinghurst, "In ai we trust: Investigating the relationship between biosignals, trust and cognitive load in vr," 2019, pp. 1-10.
[31] G. Buzsaki and A. Draguhn, "Neuronal oscillations in cortical networks," science, vol. 304, no. 5679, pp. 1926-1929 %@ 0036-8075, 2004.
[32] G. Buzsáki, C. Geisler, D. A. Henze, and X.-J. Wang, "Interneuron diversity series: circuit complexity and axon wiring economy of cortical interneurons," Trends in neurosciences, vol. 27, no. 4, pp. 186-193 %@ 0166-2236, 2004.
[33] P. D. Antonenko and D. S. Niederhauser, "The influence of leads on cognitive load and learning in a hypertext environment," Computers in Human Behavior, vol. 26, no. 2, pp. 140-150 %@ 0747-5632, 2010.
[34] H. Lee, "Measuring cognitive load with electroencephalography and self-report: focus on the effect of English-medium learning for Korean students," Educational Psychology, vol. 34, no. 7, pp. 838-848 %@ 0144-3410, 2014.
[35] W. Klimesch, "EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis," Brain research reviews, vol. 29, no. 2-3, pp. 169-195 %@ 0165-0173, 1999.
[36] A. Gevins and M. E. Smith, "Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style," Cerebral cortex, vol. 10, no. 9, pp. 829-839 %@ 1460-2199, 2000.
[37] S. Lei and M. Roetting, "Influence of task combination on EEG spectrum modulation for driver workload estimation," Human factors, vol. 53, no. 2, pp. 168-179 %@ 0018-7208, 2011.
[38] A. Keil, T. Mussweiler, and K. Epstude, "Alpha-band activity reflects reduction of mental effort in a comparison task: a source space analysis," Brain Research, vol. 1121, no. 1, pp. 117-127 %@ 0006-8993, 2006.
[39] A. Gevins et al., "Monitoring working memory load during computer-based tasks with EEG pattern recognition methods," Human factors, vol. 40, no. 1, pp. 79-91 %@ 0018-7208, 1998.
[40] J. B. Brookings, G. F. Wilson, and C. R. Swain, "Psychophysiological responses to changes in workload during simulated air traffic control," Biological psychology, vol. 42, no. 3, pp. 361-377 %@ 0301-0511, 1996.
[41] S. H. Fairclough and L. Venables, "Effects of task demand and time-on-task on psychophysiological candidates for biocybernetic control," 2004, vol. 48, pp. 85-89 %@ 1541-9312: SAGE Publications Sage CA: Los Angeles, CA.
[42] A. Gevins and M. E. Smith, "Neurophysiological measures of cognitive workload during human-computer interaction," Theoretical issues in ergonomics science, vol. 4, no. 1-2, pp. 113-131 %@ 1463-922X, 2003.
[43] P. Sauseng, W. Klimesch, M. Schabus, and M. Doppelmayr, "Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory," International journal of Psychophysiology, vol. 57, no. 2, pp. 97-103 %@ 0167-8760, 2005.
[44] M. E. Smith, L. K. McEvoy, and A. Gevins, "The impact of moderate sleep loss on neurophysiologic signals during working-memory task performance," Sleep, vol. 25, no. 7, pp. 56-66 %@ 1550-9109, 2002.
[45] V. Krassanakis, V. Filippakopoulou, and B. Nakos, "EyeMMV toolbox: An eye movement post-analysis tool based on a two-step spatial dispersion threshold for fixation identification," Journal of Eye Movement Research, vol. 7, no. 1 %@ 1995-8692, 2014.
[46] L. Breiman, "Random forests," Machine learning, vol. 45, pp. 5-32 %@ 0885-6125, 2001.
[47] S. Akshay, Y. J. Megha, and C. B. Shetty, "Machine learning algorithm to identify eye movement metrics using raw eye tracking data," 2020, pp. 949-955 %@ 1728158214: IEEE.
[48] R. Zemblys, "Eye-movement event detection meets machine learning," BIOMEDICAL ENGINEERING 2016, vol. 20, no. 1 %@ 2029-3380, 2017.
[49] R. Zemblys, D. C. Niehorster, O. Komogortsev, and K. Holmqvist, "Using machine learning to detect events in eye-tracking data," Behavior research methods, vol. 50, pp. 160-181, 2018.
[50] M. Shojaeizadeh, S. Djamasbi, R. C. Paffenroth, and A. C. Trapp, "Detecting task demand via an eye tracking machine learning system," Decision Support Systems, vol. 116, pp. 91-101 %@ 0167-9236, 2019.
[51] W. Schnotz and M. Bannert, "Construction and interference in learning from multiple representation," Learning and instruction, vol. 13, no. 2, pp. 141-156 %@ 0959-4752, 2003.
[52] A. Marini, S. Carlomagno, C. Caltagirone, and U. Nocentini, "The role played by the right hemisphere in the organization of complex textual structures," Brain and language, vol. 93, no. 1, pp. 46-54 %@ 0093-934X, 2005.
[53] N. Jaušovec, "Differences in EEG alpha activity related to giftedness," Intelligence, vol. 23, no. 3, pp. 159-173 %@ 0160-2896, 1996.
[54] F. Zhao, W. Schnotz, I. Wagner, and R. Gaschler, "Eye Tracking Indicators of Reading Approaches in Text-Picture Comprehension," Frontline Learning Research, vol. 2, no. 5, pp. 46-66, 2014.
[55] A. L. Benton, "Disorders of spatial orientation," 1969.
[56] M. Hunter, "Right-Brained Kids in Left-Brained Schools," Today′s Education, vol. 65, no. 4, pp. 45-8, 1976.
[57] M. Kinsbourne and W. L. Smith, "Hemispheric disconnection and cerebral function," 1974.
[58] C. Knox and D. Kimura, "Cerebral processing of nonverbal sounds in boys and girls," Neuropsychologia, vol. 8, no. 2, pp. 227-237 %@ 0028-3932, 1970.
[59] E. K. Warrington and P. Rabin, "Perceptual matching in patients with cerebral lesions," Neuropsychologia, vol. 8, no. 4, pp. 475-487 %@ 0028-3932, 1970.
[60] R. Rubenzer, "The role of the right hemisphere in learning & creativity implications for enhancing problem solving ability," Gifted Child Quarterly, vol. 23, no. 1, pp. 78-100 %@ 0016-9862, 1979.
[61] R. Taylor, "Computers and hemispheric functioning," 1978.
[62] Ö. Örün and Y. Akbulut, "Effect of multitasking, physical environment and electroencephalography use on cognitive load and retention," Computers in Human Behavior, vol. 92, pp. 216-229 %@ 0747-5632, 2019.
[63] E. M. Bowden and M. J. Beeman, "Getting the right idea: Semantic activation in the right hemisphere may help solve insight problems," Psychological science, vol. 9, no. 6, pp. 435-440 %@ 0956-7976, 1998.
[64] B. Samples, "Educating for Both Sides of the Human Mind," Science Teacher, vol. 42, no. 1, pp. 21-23, 1975.

指導教授

李柏磊(Po-Lei Lee)

審核日期

2024-1-5

推文