至今在量測認知負荷仍然是使用問卷的方式進行蒐集，而填寫問卷是屬於一種主觀的方式，它可能會因受測者自身的觀點或經驗等因素而產生影響。因此若能利用資料探勘的方式找尋到客觀生理訊號與認知負荷之間的關聯性，未來便可間接使得認知負荷能得到客觀的量化，而不再只能依靠認知負荷問卷的主觀量測方式。因此，本研究主要目的為透過關聯法則分析出專注力與認知負荷之間的關聯，以及應用演算法推估認知負荷的可能性探討。此外，本研究在推估認知負荷的過程中發現使用觀察決策樹節點特徵的方式，進行資料結合能夠減少對模型精準度所造成的影響。
本研究使用穿戴式腦電圖設備與認知負荷問卷，蒐集兩門課程修課學生的課堂與課後專注值及認知負荷，作為探討專注力與認知負荷之間的關聯以及推估可能性的資料，並將所有使用資料進行特徵的提取與量化後，搭配關聯法則找尋各負荷與專注力之間的相關性；搭配決策樹的分類規則找尋關聯法則未發現到特徵重要性及規則；搭配決策樹以及類神經網路分析應用演算法推估認知負荷的可能性以及觀察決策樹節點後合併資料對於預測分類模型精準度的影響。
根據各種實驗的結果，發現使用關聯法則能夠得知超出負荷的受試者在整個活動過程中表現出專注力不集中的狀態；高負荷的受試者無法在活動過程中保持一定時間的注意力；中負荷的受試者可能會依科目的不同表現出較長時間的注意力且低負荷的受試者幾乎沒有專注於事件。但使用決策樹進行演算法課程認知負荷分類時，發現除了中負荷的學生們，能夠保持較長時間的注意力於課堂上外，低負荷與高負荷的學生們也能夠保持較長時間的注意力於課堂，這表示了負荷類型可能會因學習任務的不同而有不一樣的專注力特徵，並非所有相同負荷都一定會有相同的專注力情形。
此外，用決策樹與類神經網路發現有能夠進行認知負荷分類的可能性，且透過決策樹節點的特徵觀察能夠將不同課程的資料進行合併，能夠減少模型預測精準度下降的問題。因此，本研究最終實現了使用關聯規則分析注意力與認知負荷之間的相關性，並應用決策樹與類神經網路發現到了認知負荷推估的可能性，以及透過觀察決策樹節點特徵的方法減少模型預測精準度下降。

To date, data for the measurement of cognitive load are still collected via questionnaires. Filling out questionnaires is a subjective method that can be affected by factors such as the subject's personal opinions or experiences. Thus, it is desirable for the correlation between objective physiological signals and cognitive load to be found by data exploration. In this manner, one can objectively quantify cognitive load rather than rely on the subjective measurement method of cognitive load questionnaires. The goals of this study are to analyze the relationship between concentration and cognitive load through association rules and explore algorithms to classify cognitive load. The results of this study use association rules to discover the correlation between cognitive load and attention. In addition, the present study highlights the feature of attention, which can potentially be used to classify cognitive load types. Finally, the problem of improper data merging and decreased precision of the supervised model is reduced by observing the features of decision tree nodes and combining different data characteristics.

[1]王逸翔, "基於腦波注意力之影音教材學習診斷系統," 碩士, 電機工程學系, 國立臺灣師範大學, 台北市, 2015.
[2]林軒鈺, "透過腦波分析探討背景干擾及專注力對小學生學習效果之影響," 碩士, 數位學習科技學系碩士班, 國立臺南大學, 台南市, 2013.
[3]梁景棠, "以腦波儀器量測不同難易度之數位與傳統教材於學習專注力並探討其學習成效與認知負荷," 碩士, 資訊工程系, 南臺科技大學, 台南市, 2016.
[4]陳毅霖, "以腦波儀器進行微型實驗與足尺實驗並探討專注力與認知負荷之關聯," 碩士, 資訊工程系, 南臺科技大學, 台南市, 2017.
[5]游智名, "基於腦波訊號發展注意力辨識系統," 碩士, 工業教育學系, 國立臺灣師範大學, 台北市, 2013.
[6]黃建豪, "以腦波訊號各種足尺實驗探討專注力程度與持續時間," 碩士, 資訊工程系, 南臺科技大學, 台南市, 2019.
[7]M. Agaoglu, "Predicting instructor performance using data mining techniques in higher education," IEEE Access, vol. 4, pp. 2379-2387, 2016.
[8]G. Agapito, M. Milano, P. H. Guzzi, and M. Cannataro, "Extracting cross-ontology weighted association rules from gene ontology annotations," IEEE/ACM transactions on computational biology and bioinformatics, vol. 13, no. 2, pp. 197-208, 2015.
[9]R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th int. conf. very large data bases, VLDB, 1994, vol. 1215, pp. 487-499.
[10]Ø. Anmarkrud, A. Andresen, and I. Bråten, "Cognitive load and working memory in multimedia learning: Conceptual and measurement issues," Educational Psychologist, vol. 54, no. 2, pp. 61-83, 2019.
[11]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, 2010.
[12]E. N. Azizah, U. Pujianto, and E. Nugraha, "Comparative performance between C4. 5 and Naive Bayes classifiers in predicting student academic performance in a Virtual Learning Environment," in 2018 4th International Conference on Education and Technology (ICET), 2018: IEEE, pp. 18-22.
[13]R. Brünken, T. Seufert, and F. Paas, "Measuring cognitive load," 2010.
[14]P. Campisi and D. La Rocca, "Brain waves for automatic biometric-based user recognition," IEEE transactions on information forensics and security, vol. 9, no. 5, pp. 782-800, 2014.
[15]A. J. Casson, D. C. Yates, S. J. Smith, J. S. Duncan, and E. Rodriguez-Villegas, "Wearable electroencephalography," IEEE engineering in medicine and biology magazine, vol. 29, no. 3, pp. 44-56, 2010.
[16]S.-C. Cheng and Y.-P. Cheng, "An Adaptive Approach to Quantify Plant Features by Using Association Rule-Based Similarity," IEEE Access, vol. 7, pp. 32197-32205, 2019.
[17]J. Engström, E. Johansson, and J. Östlund, "Effects of visual and cognitive load in real and simulated motorway driving," Transportation research part F: traffic psychology and behaviour, vol. 8, no. 2, pp. 97-120, 2005.
[18]N. Friedman, T. Fekete, Y. a. K. Gal, and O. Shriki, "EEG-based Prediction of Cognitive Load in Intelligence Tests," Frontiers in human neuroscience, vol. 13, p. 191, 2019.
[19]P. Gerjets, K. Scheiter, and G. Cierniak, "The scientific value of cognitive load theory: A research agenda based on the structuralist view of theories," Educational Psychology Review, vol. 21, no. 1, pp. 43-54, 2009.
[20]L. Haiyang, Z. Wang, P. Benachour, and P. Tubman, "A time series classification method for behaviour-based dropout prediction," in 2018 IEEE 18th international conference on advanced learning technologies (ICALT), 2018: IEEE, pp. 191-195.
[21]H. Heuer and A. Breiter, "Student success prediction and the trade-off between big data and data minimization," DeLFI 2018-Die 16. E-Learning Fachtagung Informatik, 2018.
[22]M. Hlosta, Z. Zdrahal, and J. Zendulka, "Ouroboros: early identification of at-risk students without models based on legacy data," in Proceedings of the seventh international learning analytics & knowledge conference, 2017, pp. 6-15.
[23]Y.-M. Huang, Y.-P. Cheng, S.-C. Cheng, and Y.-Y. Chen, "Exploring the Correlation Between Attention and Cognitive Load Through Association Rule Mining by Using a Brainwave Sensing Headband," IEEE Access, vol. 8, pp. 38880-38891, 2020.
[24]J.-L. Hung, B. E. Shelton, J. Yang, and X. Du, "Improving predictive modeling for at-Risk student identification: a multistage approach," IEEE Transactions on Learning Technologies, vol. 12, no. 2, pp. 148-157, 2019.
[25]E. B. Hunt, "Concept learning: An information processing problem," 1962.
[26]G.-J. Hwang, L.-H. Yang, and S.-Y. Wang, "A concept map-embedded educational computer game for improving students' learning performance in natural science courses," Computers & Education, vol. 69, pp. 121-130, 2013.
[27]H. Ilgaz, A. Altun, and P. Aşkar, "The effect of sustained attention level and contextual cueing on implicit memory performance for e-learning environments," Computers in Human Behavior, vol. 39, pp. 1-7, 2014.
[28]A. R. Iyanda, O. D. Ninan, A. O. Ajayi, and O. G. Anyabolu, "Predicting student academic performance in computer science courses: a comparison of neural network models," Int. J. Mod. Educ. Comput. Sci.(IJMECS), vol. 10, no. 6, pp. 1-9, 2018.
[29]A. Jindal, A. Dua, K. Kaur, M. Singh, N. Kumar, and S. Mishra, "Decision tree and SVM-based data analytics for theft detection in smart grid," IEEE Transactions on Industrial Informatics, vol. 12, no. 3, pp. 1005-1016, 2016.
[30]Y.-C. Kuo, H.-C. Chu, and M.-C. Tsai, "Effects of an integrated physiological signal-based attention-promoting and English listening system on students' learning performance and behavioral patterns," Computers in Human Behavior, vol. 75, pp. 218-227, 2017.
[31]J. Kuzilek, M. Hlosta, and Z. Zdrahal, "Open university learning analytics dataset," Scientific data, vol. 4, p. 170171, 2017.
[32]R. L. Lamb, D. B. Vallett, T. Akmal, and K. Baldwin, "A computational modeling of student cognitive processes in science education," Computers & Education, vol. 79, pp. 116-125, 2014.
[33]E. Lau, L. Sun, and Q. Yang, "Modelling, prediction and classification of student academic performance using artificial neural networks," SN Applied Sciences, vol. 1, no. 9, p. 982, 2019.
[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, 2014.
[35]E. L. Leyman, G. A. Mirka, D. B. Kaber, and C. M. Sommerich, "Cervicobrachial muscle response to cognitive load in a dual-task scenario," Ergonomics, vol. 47, no. 6, pp. 625-645, 2004.
[36]M. Li et al., "Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor," IEEE Sensors Journal, vol. 19, no. 15, pp. 6497-6507, 2019.
[37]F.-R. Lin and C.-M. Kao, "Mental effort detection using EEG data in E-learning contexts," Computers & Education, vol. 122, pp. 63-79, 2018.
[38]J. M. Luna, M. Pechenizkiy, M. J. del Jesus, and S. Ventura, "Mining context-aware association rules using grammar-based genetic programming," IEEE transactions on cybernetics, vol. 48, no. 11, pp. 3030-3044, 2017.
[39]M.-Y. Ma and C.-C. Wei, "A comparative study of children's concentration performance on picture books: age, gender, and media forms," Interactive Learning Environments, vol. 24, no. 8, pp. 1922-1937, 2016.
[40]G. Makransky, T. S. Terkildsen, and R. E. Mayer, "Adding immersive virtual reality to a science lab simulation causes more presence but less learning," Learning and Instruction, vol. 60, pp. 225-236, 2019.
[41]V. A. Maksimenko et al., "Artificial neural network classification of motor-related eeg: An increase in classification accuracy by reducing signal complexity," Complexity, vol. 2018, 2018.
[42]J. McGonagle, G. Shaikouski, and C. Williams, "Backpropagation. Brilliant. org," ed, 2018.
[43]W. Muhammad et al., "Pancreatic cancer prediction through an artificial neural network," Frontiers in Artificial Intelligence, vol. 2, p. 2, 2019.
[44]N. Nourbakhsh, Y. Wang, F. Chen, and R. A. Calvo, "Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks," in Proceedings of the 24th Australian Computer-Human Interaction Conference, 2012, pp. 420-423.
[45]F. G. Paas, "Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach," Journal of educational psychology, vol. 84, no. 4, p. 429, 1992.
[46]F. G. Paas and J. J. Van Merriënboer, "Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach," Journal of educational psychology, vol. 86, no. 1, p. 122, 1994.
[47]I. P. Panapakidis and A. S. Dagoumas, "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, vol. 172, pp. 132-151, 2016.
[48]A. Pinkus, "Approximation theory of the MLP model in neural networks," Acta numerica, vol. 8, pp. 143-195, 1999.
[49]J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014.
[50]J. R. Quinlan, "Induction of decision trees," Machine learning, vol. 1, no. 1, pp. 81-106, 1986.
[51]F. Ren, Z. Pei, and K. Wu, "Selection of Satisfied Association Rules via Aggregation of Linguistic Satisfied Degrees," IEEE Access, vol. 7, pp. 91518-91534, 2019.
[52]S. Rizvi, B. Rienties, and S. A. Khoja, "The role of demographics in online learning; A decision tree based approach," Computers & Education, vol. 137, pp. 32-47, 2019.
[53]G. J. Simon, P. J. Caraballo, T. M. Therneau, S. S. Cha, M. R. Castro, and P. W. Li, "Extending association rule summarization techniques to assess risk of diabetes mellitus," IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 1, pp. 130-141, 2013.
[54]J. Sweller, J. J. Van Merrienboer, and F. G. Paas, "Cognitive architecture and instructional design," Educational psychology review, vol. 10, no. 3, pp. 251-296, 1998.
[55]N. Tomasevic, N. Gvozdenovic, and S. Vranes, "An overview and comparison of supervised data mining techniques for student exam performance prediction," Computers & Education, vol. 143, p. 103676, 2020.
[56]M. Wasif, H. Waheed, N. R. Aljohani, and S.-U. Hassan, "Understanding Student Learning Behavior and Predicting Their Performance," in Cognitive Computing in Technology-Enhanced Learning: IGI Global, 2019, pp. 1-28.
[57]M.-H. Wong, H.-Y. A. Sze-To, L.-Y. P. Lo, T.-M. C. Chan, and K.-S. Leung, "Discovering binding cores in protein-DNA binding using association rule mining with statistical measures," IEEE/ACM transactions on computational biology and bioinformatics, vol. 12, no. 1, pp. 142-154, 2014.
[58]B. Xie and G. Salvendy, "Review and reappraisal of modelling and predicting mental workload in single-and multi-task environments," Work & stress, vol. 14, no. 1, pp. 74-99, 2000.
[59]J. Xu et al., "Pupillary response based cognitive workload index under luminance and emotional changes," in CHI'11 Extended Abstracts on Human Factors in Computing Systems, 2011, pp. 1627-1632.
[60]W. Yu, "Discovering frequent movement paths from taxi trajectory data using spatially embedded networks and association rules," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 855-866, 2018.
[61]L. Zhang, W. Wang, and Y. Zhang, "Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics," IEEE Access, vol. 7, pp. 45032-45047, 2019.