臺灣博碩士論文加值系統

情感式家教系統係將情感辨識之因素加入智慧型家教系統。情感辨識則指辨識學生在學習過程時所產生的情感狀態，給予適時的回饋，以提升學生的學習興趣。本研究分為三階段發展時期：(1)進行情緒辨識系統與教學策略之規劃與設計；(2)設計數位藝術課程內容模組、情緒回饋之互動代理人玩偶模組以及系統之人機介面；(3)對於系統進行整合與評估，並進行兩階段式評估分析。藉由人性化的互動設計，希望能增進學習者的學習動機，進而提高其學習績效。
本研究的系統評估流程採用：(1)原型評估：系統使用性量表(SUS)問卷與專家的啟發式評估之評估方法；(2)三角評估：觀察、問卷和訪談之質量兼施的評估方法。藉由以上評估方法探討：(1)對於家教系統加入了情感運算功能的使用性是否良好；(2)使用者對於情感式家教系統的滿意度如何；(3)情感式家教系統的互動能否吸引使用者；(4)情感式家教系統能否提高使用者對於數位藝術課程的學習動機；(5)情感式家教系統對於不同領域的使用者，是否有不一樣的自我知覺學習績效提升。經評估結果發現，使用者對於系統之使用性相當良好，滿意度相當高；並且系統能吸引使用者，能提高使用者的學習動機，甚是能提高自我知覺的學習績效。

Affective tutoring system (ATS) is uses the factor of affective recognition with intelligent tutoring system (ITS). The aim of this study is to improve learning interests by recognizing the emotion states of students during learning and giving adequate feedback. This study consists of three research stages: (1) Design both the emotion recognition system and the tutoring strategy module. (2) Design the digital arts learning content module, the emotion feedback mechanism via the HCI design of interactive agent dolls.(3) Integrate and evaluate the whole system by two-stage evaluation. We hope that the learners’ motivations and interests could be enhanced via affective interaction design, and hence their learning performance could be improved.
The system evaluation processes of this study adopts: (1) Prototype evaluation: The method of evaluation combines a system usability scale (SUS) questionnaire and heuristic evaluation by experts. (2) Triangulation evaluation: This method of evaluation uses both qualitative and quantitative research that includes observation, questionnaires and interview. By the above method, the following points are investigated: (1) Is the usability of ATS good or bad. (2) How about the satisfaction of ATS users. (3) Whether the interactivity of ATS is attractive to users. (4) Whether ATS increased the motivation of learning in digital art or not. (5) Whether ATS has different impact on self-consciousness learning achievement for users from different domains. According the result of evaluation, we can find the usability of system was rated high by users with high satisfaction ratings. Furthermore, ATS is not only attractive to users, but also increases learning motivation and self-consciousness learning achievement.

摘要
ABSTRACT
目錄
圖目錄
表目錄
第一章 緒論
1.1研究背景
1.2研究動機
1.3研究目的與研究問題
1.4論文架構
第二章 文獻探討
2.1情緒運算相關研究
2.1.1 情緒分類
2.1.2 情緒辨識方法
2.2家教系統
2.2.1智慧型家教系統(ITS)
2.2.2情感式家教系統(ATS)
2.2.3 代理人(Agent)
2.2.4 代理理論 (Agency theory)
2.3 教學策略
2.3.1 圖優效果(Picture Superiority Effect)
2.3.2 提示回饋
2.4 情緒與學習
2.5 數位藝術
第三章 研究設計
3.1 研究流程
3.2系統架構
3.3情緒辨識模組
3.3.1 資料前處理
3.3.2 交互訊息處理建立情緒字典
3.3.3 語言結構訊息
3.3.4語意線索情緒投票演算法 (Semantic Clues Emotion Voting Algorithm; SeCeVa)
3.3.5支持向量機 (Support Vector Machines; SVM)
3.3.6 合併辨識情緒結果
3.4 家教模組
3.4.1教學策略
3.4.2互動代理人玩偶之情緒回饋
3.5 數位藝術課程模組
3.6 介面模組
3.7 情境模擬：使用者腳本
3.8設計與評估流程
3.8.1原型設計及原型評估
3.8.2 專家評估
3.8.3 三角測量評估
3.9 研究工具
3.9.1 系統使用性量表(SUS)
3.9.2使用者互動滿意度量表(QUIS)
3.9.3 鄭氏學習績效量表
第四章 實驗結果
4.1系統開發環境
4.2 情緒辨識結果
4.3 使用性評估實驗設計
4.4 評估結果
4.4.1原型評估之使用性量表(SUS)
4.4.2專家評估分析
4.4.3 修正後系統之三角測量評估分析
4.3.4 小結
第五章 結論與未來展望
5.1 結論
5.2 未來展望
中文參考文獻
英文參考文獻

中文參考文獻
Jorgensen, D. L.著，王昭正、朱瑞淵譯 (1999)。參與觀察法。臺北：弘智文化。
中央研究院詞庫小組 (1993)。中文詞類分析技術報告，第95-5號。
王文科、王智弘 (2009)。教育研究法(第十三版)。台北：五南圖書出版公司。
王偉哲 (2010)。結合交互訊息與語意線索之情緒辨識機制。 國立台南大學數位學習科技學系碩士論文。未出版：台南。
呂佳華 (2009)。藝術與人文學習領域教育政策與其落實情形之檢證：以雲林縣清新國小的施行現況為例。南華大學美學與視覺藝術學報，1，13-14。
林宇中 (2003)。基於語意內容分析之情緒分類系統。國立成功大學資訊工程學系碩士論文，未出版，台南。
林珮淳、范銀霞 (2004)。從數位藝術探討互動觀念、媒介與美學。台灣藝術大學藝術學報，2004 年第74 期，台北，頁100。
陳建雄 (2006)。互動設計－跨越人電腦互動。台北：全華科技。
曾勤閔、許有真 (2010)。導入情緒因素之提示系統對使用者績效的影響。資訊管理學報，17 (2)，1-27。
楊世瑩 (2006)。SPSS 統計分析實務。台北：旗標出版股份有限公司。
葉謹睿 (2005)。數位藝術概論。台北：藝術家出版社。
廖翎吟 (2003)。數位藝術應用於藝術與人文領域教學網頁課程設計與評估。數位藝術教育網路期刊，第三卷。
潘淑滿 (2003)。質性研究：理論與運用。台北：心理出版社。
鄭傑仁 (2008)。高等教育中學生持續使用教育模擬遊戲之探討。 高雄第一科大資訊管理系碩士論文。未出版：高雄。
英文參考文獻
Ammar M. B., Neji M., Alimi A. M.,& Gouardères G. (2010). The Affective Tutoring System. Expert Systems with Applications 37, 3013–3023.
Anderson, J. R., Corbett, A. T., Koedinger, K. R. & Pelletier, R., (1995). Cognitive tutors: lessons learned. Journal of the Learning Sciences 4, 167–207.
Atchariyachanvanich, K., Okada, H. & Sonehara, N. (2006). What keeps online customers repurchasing through the internet? ACM SIGecom Exchanges, 6(2): 47-57.
Bhattacherjee, A. (1998). Managerial influences on intraorganizational information technology use: A principal-agent model, Decision Sciences, 29(1):139-162.
Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance, Decision Support Systems, 32(2): 201-214.
Brooke, J. (1996). SUS: A quick and dirty usability scale. In Jordan, P., Thomas, B., Weerdmeester, B., & McClelland, I. (Eds.), Usability evaluation in industry. (189-194). London: Taylor & Francis.
Burleson, W., Picard, R. (2004). Affective agents: Sustaining motivation to learn through failure and a state of stuck. Citeseer
Carletta, J. (1996). Assessing Agreement on Classification Tasks: The Kappa Statistic. Computational Linguistics 22 (2), 249–254.
Carr, T.H., McCauley, C., Sperber, R. D. & Parmelee, C. M. (1982). Words, pictures and priming: On semantic activation, conscious identification and the automaticity of information processing. Journal of Experimental Psychology: Human Perception and Performance, 8, 757-777
Chakraborty S., Roy D. & Basu A. (2010). Development of Knowledge Based Intelligent Tutoring System Advanced Knowledge Based Systems: Model, Applications & Research (Eds. Sajja & Akerkar), Vol. 1, pp 74 – 100.
Cheong, J. H. & Park, M. C. (2005). Mobile internet acceptance in Korea, Internet Research, 15(2): 125-140.
Chi M. T. H., Siler S., Jeong H., Yamauchi T., Hausmann R. G. (2001). Learning from human tutoring, Cognitive Science 25 (4) 471–533.
Chin, J. P., Diehl, V. A. & Norman, K. L. (1988). Development of an instrument measuring user satisfaction of the human-computer interface. Proceedings of SIGCHI ''88, (pp. 213-218), New York: ACM/SIGCHI.
Chuang H.-C., Wang C.-Y., Chen G.-D., Liu C.-C. & Liu B.-J. (2010). Design and Evaluate the Affective Interface of the E-learning System. 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010.
Craig, S., Graesser, A., Sullins, J., Gholson, B. (2004). Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media 29.
Creswell, J. W. (2008). Education research: planning conducting, and evaluating quantitative and qualitative research(3rd ed.). Upper Saddle River, N. J.: Pearson Education, Inc.
Creswell, J. W., & Clark, V. L. P. (2007). Designing and conducting mixed methods research. Thousand Oaks: Sage Publications.
D’Mello, S. K., Craig, S. D., Gholson, B., Franklin, S., Picard, R. W., & Graesser, A. C. (2005). Integrating affect sensors in an intelligent tutoring system. In Affective Interactions: The Computer in the Affective Loop Workshop at International conference on Intelligent User Interfaces (pp. 7–13). New York: AMC Press.
D’Mello, S. K., Picard, R. W., & Graesser, A. C. (2007). Toward an affect sensitive autotutor. IEEE Intelligent Systems, in press.
D’Mello, S., Craig, S., Witherspoon, A., Mcdaniel, B., Graesser, A. (2008). Automatic detection of learner''s affect from conversational cues. User Modeling and User-Adapted Interaction 18, 45-80
D’Mello, S.K., Chipman, P., Graesser, A.C., (2007). Posture as a predictor of learner’s affective engagement, Proceedings of the 29th Annual Cognitive Science Society, pp. 905–910.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3): 319-339.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theorical models. Management Science, 35(8): 982-1003.
Dumas, J. S., & Redish, J. C. (1999). A Pratical Guide to Usability Testing (Revised Edition). Exeter, UK: Intellect.
Duncan, T. G. & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire, Educational Psychologist, 40(2): 117–128.
Eisenhardt, K. M. (1989). Agency theory: An assessment and review, Academy of Management Review, 14(1): 57-74.
Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2), 124-129.
Ekman, P., Levenson, R., Friesen, W. (1983). Autonomic nervous system activity distinguishes among emotions. Science 221, 1208-1210
Fan, R. E., Chen, P. H., & Lin, C. J. (2005). Working Set Selection Using Second Order Information for Training Support Vector Machines. Journal of Machine Learning Research, Vol. 6, pp. 1889–1918.
Ferreira A., & Atkinson J. (2009). Designing a feedback component of an intelligent tutoring system for foreign language. Knowledge-Based Systems 22 , 496–501.
Gay, L. R., Mills, G. E., & Airasian, P. (2009). Education research: Competencies for analysis and applications (9th ed.). Upper Saddle River, N. J.: Prentice Hall.
Gee, J. P., (2004). Situated Language and Learning: A Critique of Traditional Schooling. Routledge, Taylor & Francis, London, UK.
Graesser, A. C., D’Mello, S. K., Chipman, P., King, B., McDaniel, B., (2007). Exploring relationships between affect and learning with AutoTutor. In: Luckin, R., Koedinger, K., Greer, J. (Eds.), Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work. IOS Press, Amsterdam, pp. 16–23.
Graesser, A. C., D’Mello, S., Person, N. K., (2009). Metaknowledge in tutoring. In: Hacker, D., Donlosky, J., Graesser, A. C. (Eds.), Handbook of Metacognition in Education. Taylor & Francis, Mahwah, NJ.
Graesser, A., McDaniel, B., Chipman, P., Witherspoon, A., D''Mello, S., Gholson, B. (2006). Detection of emotions during learning with AutoTutor. Proceedings of the 28 th Annual Meetings of the Cognitive Science Society 285-290
Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H., Ventura, M., Olney, A., Louwerse, M.M., (2004). AutoTutor: a tutor with dialogue in natural language. Behavioral Research Methods, Instrumentation, and Computers 36, 180–193.
Heijden, H. V. D. (2003). Factors influencing the usage of websites : the case of a generic portal in the Netherlands, Information and Management, 40: 541-549.
Huana L. & Ren F., (2009). "The study on text emotional orientation based on a three-dimensional emotion space model," in Natural Language Processing and Knowledge Engineering, 2009. NLP-KE 2009. International Conference on, 2009, pp. 1-6.
Jensen, M. C. and Meckling, W. H. (1976). The theory of the firm: Managerial behavior, agency costs and ownership structure, Journal of Financial Economics, 3(1): 305-360.
Koedinger, K.R., Corbett, A., (2006). Cognitive tutors: technology bringing learning science to the classroom. In: Sawyer, R.K. (Ed.), The Cambridge Handbook of the Learning Sciences. Cambridge University Press, New York, pp. 61–77.
Kort, B., Reilly, R., & Picard, R. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy building a learning companion. In Proceedings IEEE international conference on advanced learning technology: Issues, achievements and challenges (pp. 43–48).
Lin K. H.-C., Tsai I-H., Sun R.-T., (2011). “Ontology-based Affective Tutoring System on Digital Arts”, WACI 2011, SSCI 2011 Workshop on Affective Computational Intelligence (IEEE Symposium Series on Computational Intelligence), Paris, France.
Litman, D. J. & Forbes-Riley, K. (2006). Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogues with both Human and Computer Tutors. Speech Communication, in press.
Manning, C.D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press, London England.
Mao X., & Li Z. (2009). Implementing Emotion-Based User-Aware E-Learning. Spotlight on Works in Progress, CHI, Boston, MA, USA.
Mao X., & Li Z. (2010). Agent based affective tutoring systems: A pilot study. Computers & Education 55, 202–208.
Marshall, C. & G. B. Rossman (1999). Designing qualitative research. Thousand Oaks, Calif., Sage Publications.
Mitrovic, A., McGuigan, N., Martin, B. Suraweera, P., Milik, N., Holland, J., (2008). Authoring constraint-based tutors in ASPIRE: a case study of a capital investment tutor. In: Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecom-munications, AACE, Chesapeake, VA, pp. 4607–4616.
Moon, J.-W. & Kim, Y-G. (2001). Extending the TAM for a World-Wide-Web context. Information and Management, 38(4): 217-230.
Nielsen, J. (1994). Heuristic Evaluation. In Nielsen, J. & Mark, R. L. (1994). Usability Inspection Methods, John Wiley & Sons, New York.
Norman, D. A. (2007). The Design of Future Things. New York: Basic Books.
Paivio, A., & Csapo, K (1973). Picture superiority in free recall: Imagery or dual coding? Cognitive Psychology, 5, 176-206.
Pérez Y. H., Gamboa R. M., & Ibarra O. M. (2004). Modeling Affective Responses in Intelligent Tutoring Systems. Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT’04).
Picard, R. (1997). Affective computing. The MIT Press, Cambridge, MA
Plutchik R., (1991). The emotions: Univ Pr of Amer.
Pour P. A., Hussain M. S., AlZoubi O., D''Mello S. K., Calvo R. A.(2010). The Impact of System Feedback on Learners'' Affective and Physiological States. Intelligent Tutoring Systems (1) : 264-273
Preece, Sharp & Rogers (2001). Interaction design: Beyond human–computer interaction by, ISBN 0471492787
Premkumar, G & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models, Omega, 36: 64-75.
Quan, C. and Ren, F. (2010). A blog emotion corpus for emotional expression analysis in Chinese, Computer Speech and Language,24(4), pp. 726–749.
QUIS (2006). http://lap.umd.edu/quis/.
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178.
Russell, J., (2003). Core affect and the psychological construction of emotion. Psychological Review 110, 145–172.
Sarrafzadeh, A. (2002). Representing domain knowledge structure in Intelligent Tutoring Systems. Proceedings of the International Conference on Information and Communication Technologies in Education. Spain, November 02, 665-9.
Sarrafzadeh, A., Alexander, A., Dadgostar, F., Fan, C., Bigdeli, A. (2008). How do you know that I don''t understand? A look at the future of intelligent tutoring systems. Elsevier Journal- Computers in Human Behavior, 24 (4), 2008, pp. 1342-1363.
Sharp, H., Rogers, Y. & Preece, J. (2007). Interaction Design: Beyond Human-Computer Interaction. John Wiley and Sons.
Shepard, R. N. (1967). Recognition memory for words, sentences, and pictures. Jounal of Verbal Learning and Verbal Behavior, 6, 156-163.
Shepard, R. N. (1967). Recognition memory for words, sentences, and pictures. Jounal of Verbal Learning and Verbal Behavior, 6, 156-163.
Slavomir S., Branko Ž. & Ani G. (2003). Ontology as a Foundation for Knowledge Evaluation in Intelligent E-learning Systems. Faculty of Natural Sciences and Mathematics and Education, University of Split, Teslina 12, 21000 Split, Croatia.
Stankov, S., Glavinic, V., Rosic, M. (2000). On knowledge representation in an intelligent tutoring system. In: The Fourth International Conference on Intelligent Engineering Systems (INES-2000), 17-19.
Stern, & Haugsjaa E. (2004). Applications of AI in Education. [Article posted on the World Wide Web] from the World Wide Web: http://www.acm.org/crossroads/xrds3-1/aied.html
Strauss, A., & Corbin, J. (1990). Basic of qualitative research: Grounded theory procedures & techniques. Thousand Oaks, CA: Sage.
Thao N., Bass I., Mingkun L., & Sethi I. K., (2005). "Investigation of combining SVM and decision tree for emotion classification," in Multimedia, Seventh IEEE International Symposium on, p. 5 pp.
Thayer. R. E. (1989). The Biopsychology of Mood and Arousal. Oxford University Press.
Thong, Y. L., Hong, S. J. & Tam, Y. K. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64: 799-810.
VanLehn, K., (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education 16 (3), 227–265.
Vapnik, V. (1979). Estimation of Dependences Based on Empirical Data [in Russian]. Nauka, Moscow. (Englishtranslation: Springer Verlag, New York, 1982).
Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies, Management Science, 46(2): 184-204.
Vesterinen, E. (2001). Affective Computing. Tik-111.590 Digital media research seminar, Helsinki, Finland.
Wang, C.-Y., Chang, C.-W. & Chen, G.-D. (2009). Design an empathic virtual human to encourage and persuade learners in e-learning systems. ACM Multimedia MDTL.
Yang C., Lin K. H.-Y., & Chen H.-H., (2007). "Emotion Classification Using Web Blog Corpora," in Web Intelligence. IEEE/WIC/ACM International Conference, pp. 275-278.
Yang, C., Lin, K. H. Y., & Chen, H.-H. (2007). Building Emotion Lexicon from Weblog Corpora. In Proceedings of 45th Annual Meeting of Association for Computational Linguistics (acl 2007), poster, June 23rd-30th, 2007, Prague, Czech Republic, 133-136.
Yu, F.-Y., Chang, L.-J., Liu, Y.-H. & Chan, T.-W. (2002). Learning preferences towards computerised competitive modes, Journal of Computer Assisted Learning, 18: 341- 350.