簡易檢索 / 詳目顯示

研究生: 葉國良
Yeh, Kuo-Liang
論文名稱: 運用Petri-Net建構基本電學知識構圖及初學者認知途徑以強化適性學習之研究
Study of Applying Petri-Net to Constructing Fundamental-Electricity Knowledge Graph and Novices' Cognition Pathway for Intensifying Adaptive Learning
指導教授: 戴建耘
學位類別: 博士
Doctor
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 130
中文關鍵詞: 初學者知識構圖派翠西網路認知途徑適性學習
英文關鍵詞: Adaptive Learning, Cognition Pathway, Novices, Knowledge Graph, Petri-Net
DOI URL: http://doi.org/10.6345/NTNU202100258
論文種類: 學術論文
相關次數: 點閱:160下載:13
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 電機與電子領域專業知識概念間存在錯綜複雜的關係,具有相同學習成效的學生可能存在不同的迷思概念與結構,而多數既有學習導引機制缺乏推薦學習者適性學習內容與途徑的規劃,因此,技職專業人才的培育面臨極大的障礙與困境。由於科技的進步,知識構圖被廣為應用於推薦系統,找出符合使用者需求的新訊息。本研究由5 位技術型高中電機與電子群專業科目知識概念專家、8 位試題發展專家與5 位電機與電子群業界專家及資深教師,運用三回合修正式德菲法剖析電機電子領域核心基礎:「基本電學」12個主題的58個專業概念(其中,篩選出4個奠基概念、4個核心概念及11個綜整性概念)與95個對應相關性。利用具有圖形特性的派翠西網路 (Petri-Net) 技術,進而建構出「基本電學」Petri-Net知識構圖,並發現對後續學習影響最鉅的概念依序為電路型態及特性、電的單位、向量運算及電壓。此外,本研究以初探性導入建立不同學習類型個案學生的Petri-Net知識構圖,並剖析他們個別的學習歷程與狀態,結果顯示Petri-Net知識構圖的應用得以:1.提供視覺化學習鷹架增強初學者的認知結構;2.適性診斷不同類型學習者的迷思概念;3. 推估後續概念的學習效果;4. 推薦個人化學習內容與路徑助益自主學習與補救教學。此外,運用Petri-Net知識構圖所視覺化呈現的學習認知途徑,能有效分析並導引初學者適性化的學習;763份評量紀錄的回歸分析結果顯示,除「4-2迴路電流法」、「5-1電容器」及「6-2電感器」三個綱要概念外,初學者在基本電學各概念的知識概念構圖模式均能顯著預測其效標概念的學習成效。據此,本研究提出相關的討論與建議,作為發展測評系統及擬定教學策略參考。

    There exists an intricate relationship between professional knowledge concepts in the electrical and electronic engineering fields. Students with the same learning performance of professions might have an extremely different understanding from each other, and so do individual's concept structure. However, most of existing learning guidance mechanisms could not recommend adaptive and personalized learning contents and pathways to the learner. Thus, it was faced with serious barriers and difficulties to cultivate professional talents. With the advantages of information technologies, the Knowledge Graph (KG) has been widely applied in the recommender systems to facilitate the representation of knowledge structure and mining new messages or knowledge that meets user's needs. This study applied a three-round modified Delphi approach conducted by 18 domain experts to identifying 58 concepts (four cornerstone conceptions, four keystone conceptions, and 11 capstone conceptions were highlighted), and the corresponding interdependence relationship of the core course: "fundamental-electricity" in the electrical and electronic engineering domain, and then the Petri-Net technology with graphic features was used to construct its KG, so-called Expert Petri-Net KG. The preliminary exploration case studies were conducted to create personalized Petri-Net KG for three different learning types of students and to analyze their learning progress and status. Finally, 736 assessment records were used for regression analysis. The major results and findings of this study would be depicted below:
    1. The "Circuit pattern and characteristics" is the most important concept, which affects the learning of the subsequent 12 concepts, and the total impact reaches to 6. Followed by concepts of "units", "vector operations" and "voltage" in order.
    2. The proposed Petri-Net KG provides students with a visualized learning scaffolding for discovering experts' cognitive structure. It also clarifies those prior concepts for each conception.
    3. By utilization of weights of inter-relationships between concepts and their prior concepts, the reasoning engine would adaptively diagnose their misconceptions, and further predict student's learning effectiveness of subsequent concepts.
    4. Different learning types of students have different types of cognitive structures. By integrating student's learning portfolio data into the proposed Petri-Net KG, the reasoning engine would recommend an adaptive and personalized learning pathway.
    5. On the other side, novices' knowledge concept map models of fundamental-electricity can be used to predict learning performance of "criterion concept" significantly, exception of criterion concept "4-2 cyclic current method", "5-1capacitor" and "6-2 inductance".
    Reasons for the findings and implications for future research are discussed.

    第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 5 第三節 待答問題 6 第四節 重要名詞定義 7 第五節 研究範圍與限制 8 第二章 文獻探討 11 第一節 學習理論 11 第二節 概念認知結構與途徑 15 第三節 派翠西網路 20 第四節 學生問題分析表 27 第五節 小結 33 第三章 研究設計與實施 35 第一節 研究架構 35 第二節 研究流程 37 第三節 研究假設 40 第四節 研究資料來源 40 第五節 研究對象 41 第六節 研究方法 43 第七節 研究工具 51 第八節 資料處理方法 60 第九節 學術倫理 60 第四章 研究發現與討論 63 第一節 「基本電學」課程概念分析 63 第二節 建構「基本電學」課程Petri-Net知識構圖 68 第三節 不同學習類型學生學習認知途徑案例 74 第四節 不同學習類型學生迷思概念 86 第五節 學習者知識概念間預測力及與專家差異 92 第五章 結論與建議 101 第一節 研究結論 101 第二節 建議 104 參考文獻 109 附錄 修正式德懷術專家問卷 123

    文崇一、楊國樞(2000)。訪問調查法。社會及行為科學研究法下冊。台北:東華。
    王文科(2001)。教育研究法(六版)。臺北市:五南。
    王進成(2001)。應用鷹架學習理論及派翠西網路技術在網路化輔助學習模式之研究—以[電腦硬體裝修丙級學科技能檢定] 為例(未出版之碩士論文)。國立臺灣師範大學,臺北市。
    朱則剛(1994)。教育工學的發展與派典演化。台北市:師大書苑。
    朱家儀、黃秀霜、陳惠萍(2013)。攜手計畫課後扶助方案補救教學方法之探究。課程與教學,16(1),93-114。
    余民寧(2002)。教育測驗與評量-成就測驗與教學評量。臺北:心理。
    余民寧(2006)。影響學習成就因素的探討。教育資料與研究,73,11-23。
    余民寧(2011)。教育測驗與評量-成就測驗與教學評量(第三版)。臺北:心理。
    余民寧、李昭鋆(2018)。補救教學中個別化教學對學生學習成效之影響分析。教育科學研究期刊,63(1),247-271。
    呂良正(2014)。臺大土木系Capstone課程經驗分享。評鑑雙月刊,49,21-24。
    周金城、陳昭雄(2012)。探究課程前補救教學方案推動之成效-以中部某科技大學化學加強基礎課程為例。科學教育研究與發展季刊,64,1-26。
    周雅釧、黃志勝、施淑娟、郭伯臣(2009)。結合線上診斷評量系統之適性補救教學研究。網際網路技術學刊,10(4),419-425。
    林生傳(1998)。建構主義的教學評析。課程與教學季刊,1(3),1-14。
    林義男(1985),大學生對大學教學的滿意程度與學習成就的關係。國立臺灣教育學院輔導學系輔導學報,8,1-18。
    林蓓伶、潘昌志、蘇少祖、陳柏熹(2018)。十二年國教國中階段自然科學領域素養導向評量試題之開發與初探。教育科學研究期刊,63(4),295-337。doi:10.6209/JORIES.201812_63(4).0010
    邱富源(2016)。從英國 2016 BETT 教育與科技訓練博覽會看行動學習發展趨勢。臺灣教育評論月刊,5(12),35-36。
    洪素敏、楊德清(2002)。創意教學─分數的補救教學。科學教育研究與發展季刊,29(12),37-5。
    洪榮昭(2001)。PBL教學策略。技術及職業教育雙月刊,61,10-12。
    徐琍沂、徐遠雄(2020)。整合鷹架理論和翻轉教學模式融入專題式學習課程。教學實踐與創新,3(1),129-163。Doi: 10.3966/261654492020030301004
    高毓秀、王曉萍、王淑慧(2011)。技職體系五年制護專生臨床實習之學習態度調查研究。醫護科技期刊,13,(2),101-113。
    張新仁(2001)。實施補救教學之課程與教學設計。教育學刊,17,85-106。
    張新仁等合著(2003)。學習與教學新趨勢。台北:心理。
    教育部(2000)。科學過程取向(美國)Science - A Process Approach, SAPA (USA)。雙語詞彙、學術名詞暨辭書資訊網。取自http://terms.naer.edu.tw/detail/1307708/。
    教育部(2011)。十二年國民基本教育實施計畫。108課綱資訊網。取自http://12basic.edu.tw/File/LevelFile_38/十二年國民基本教育實施計畫.doc
    教育部(2013)。電機與電子群-職校課程綱要。教育部全球資訊網。取自http://ws.moe.edu.tw/001/Upload/userfiles/_3_電機與電子群-職校課程綱要.pdf。
    教育部(2019)。十二年國民基本教育技術型高級中等學校群科課程綱要—電機與電子群。教育部國民及學前教育屬全球資訊網。取自https://www.k12ea.gov.tw/files/class_schema/課綱/22-電機與電子群/十二年國民基本教育技術型高級中等學校群科課程綱要—電機與電子群.pdf。
    符碧真(2017)。大學學習成果總檢驗:合頂石—總結性課程。教育研究集刊,63(1),31-67。
    莊婷伃(2016)。總結性整合式課程基本介紹。國立台灣大學教學資源中心電子報。取自https://ctld.ntu.edu.tw/_epaper/news_detail.php?f_s_num=549
    許天維、蔡清斌、鄭百成、曾建維、俞克斌、永井正武(2013)。部分給分S-P表分析法擴張提案及其在數學測驗上的應用。測驗統計年刊,21(上),13-40。
    陳志文、朱蕙君(2015)。以流程概念構圖為鷹架之學習策略建立技職院校旅運管理資訊系統之專業技能學習模式。數位學習科技期刊,7(1),59-72。
    陳李綢(1983)。大專男女生自我統整程度與職業選擇、學習漏意度及交母養育方式之比較研究。教育心理學報,16,89-98。
    陳啓明、陳瓊森(1992)。發展紙筆測驗以探究高一學生對直流電路的迷思概念。科學教育,3,21-72。
    陳清檳,黃文喜,張文宗(2010)。技術型高中電機與電子群學生父母管教方式、學習態度與學習成效知覺之研究。教育與多元文化研究期刊,2,223-260。
    曾柏瑜、陳淑麗(2010)。初任補救教學大專生的專業成長研究。特殊教育研究學刊,35(1),39-61。
    游森期、余民寧(2006)。知識結構診斷評量與S-P表之關連性研究。政大教育與心理研究,29(1),183-208。
    甯自強(1993)。「建構式教學法」的教學觀:由根本建構主義的觀點來看。國教學報,5,33-39。
    黃瑞琴(1991)。質性教育研究。臺北市:心理。
    經濟部國際貿易局(2020)。進出口值表。中華民國進出口貿易統計全球資訊網。取自:https://cuswebo.trade.gov.tw/FSC3210F/FSC3210S。
    葉倩亨(2001)。路徑搜尋網路分析應用於大一心理學學習效果評量之研究。教育與心理研究,24(下),421-449。
    葉國良,李懿芳,潘瑛如(2013)。高中職與五專一年級學生社經背景、心理狀態與學習滿意度之研究。教育資料庫建置與應用國際學術研討會,國立臺灣師範大學。
    賈志琳、李政軒(2020)。人工智慧的實踐:對話式日語學習智慧家教系統進行日語補救教學之研究。教育實踐與研究,33(2),1-41。
    廖梨伶、劉潔心、施淑芳、鄭其嘉、張子超(2019)。青少年健康素養:由健康促進觀點建構定義與能力指標。教育科學研究期刊,64(1),25-51。doi: 10.6209/JORIES.201903_64(1).0002
    臺灣區電機電子工業同業公會(2020)。進出口統計。臺灣區電機電子工業同業公會全球資訊網。取自:http://www.teema.org.tw/industrial-performance.aspx。
    劉春榮(2011)。我國國民教育議題與發展。我國百年教育回顧與展望,67-80。國家教育研究院,新北市。
    潘世尊(1997)。Rogers人本教育理論與建構主義教學模式二之發展:國小數學教學的行動研究。應用心理研究,8,209-238。
    潘道仁(2012)。十二年國民基本教育完全中學優質化課程發展之困境與因應建議。臺灣教育評論月刊,1(10),47-54。
    蔡秉燁、鍾靜蓉(2003)。詮釋結構模式運用於結構化教學設計之研究。教育研究資訊,11(2),1-40。
    蔡清斌、許天維、曾建維、永井正武(2014)。結合學習迷思學生區與迷思次序演算法的提案。測驗學刊,61(2),183-211。
    鄭敬譯(2004)。私立技術學院數位學習導入之研究。生活科技教育,37(5),7-13。
    鄭麗玉(2000)。認知與教學,臺北市:五南。
    戴建耘(2015)。因應十二年國教運用S-P表與派翠西網路技術強化技術型高中電機與電子群專業科目教學診斷分析與適性補救教學策略之研究。行政院國家科學委員會專案研究報告(MOST 103-2511-S-003 -054 -)。
    戴建耘、葉國良、高曼婷、袁宇熙、張明文(印製中)。應用Petri-Net建構知識構圖強化適性學習診斷與學習推薦之研究。教育科學研究期刊。
    謝政達(2008)。國小藝術教科書與九年一貫課程能力指標的校準研究。課程與教學,11(4),109-136。
    羅希哲、溫漢儒、曾國鴻(2007)。概念圖融入電腦輔助教學法應用於高中生化學科之學習成效及態度之研究。科學教育學刊,15(2),169-194。
    羅綸新、張正杰、童元品、楊文正(2013)。高中生海洋科學素養及迷思概念評量分析。教育科學研究期刊,58(3),51-83。

    Amatriain, X., & Basilico, J. (2013). Nichols, P. D., Chipman, S., & Brennan, R. (Eds.) (1995). Cognitively Diagnostic Assessment. Hillsdale, NJ: Erlbaum. Retrieved from https://netflixtechblog.com/system-architectures-for-personalization-and-recommendation-e081aa94b5d8
    Ausubel, D. P. (1968). Educational Psychology: A Cognitive View. New York, N. Y.: Holt, Rinehart & Winston.
    Balado, J., Díaz-Vilariño, L., Arias, P., & Novo, A. (2019). A safety analysis of roundabouts and turbo roundabouts based on Petri nets. Traffic injury prevention, 20(4), 400-405.
    Basu, S., Biswas, G., Kinnebrew, J. (2017). Learner modeling for adaptive scaffolding in a computational thinking-based science learning environment. User Modeling & User-Adapted Interaction, 27(1), 5-53. doi: 10.1007/s11257-017-9187-0
    Berg, B. L. (1998). Handbook of Methods in Cultural Anthropology. Boston: Allyn & Bacon.
    Bishop, M., Burley, D., Buck, S., Ekstrom, J. J., Futcher, L., Gibson, D., & Parrish, A. (2017, May). Cybersecurity curricular guidelines. In IFIP World Conference on Information Security Education (pp. 3-13). Springer, Cham. doi: 10.4018/978-1-5225-7847-5.ch009
    Bloom, B. S. (1968). Learning for mastery. Evaluation Comment, 1(2), 1-5.
    Bloom, B., Englehart, M. Furst, E., Hill, W., & Krathwohl, D. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive Domain. NY: Longmans, Green.
    Bloom, B.S. (1981). All Our Children Learning. U.S., New York, N. Y.: McGraw-Hill.
    Brand, G. S., Wopereis, I., & Vermetten, Y. (2005). Information problem solving by experts and novices - analysis of a complex cognitive skill. Computers in Human Behavior, 21, 487-508. doi: 10.1016/j.chb.2004.10.005
    Brown, A. L. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, Motivation, and Understanding (pp. 65-116). Hillsdale, New Jersey: Lawrence Erlbaum Associates.
    Brusilovsky, P., Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. The Adaptive Web (pp. 3-53). Springer, Berlin. doi: 10.1007/978-3-540-72079-9_1
    Brusilovsky, P., Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13, 159–172.
    Chang, C. Y., Yeh, T. K., & Barufaldi, J. P. (2010). The positive and negative effects of science concept tests on student conceptual understanding. International Journal of Science Education, 32(2), 265-282. doi: 10.1080/09500690802650055
    Chang, J. C., Li, S. C., Chang, M., & Heh, J. S. (2006). Monitoring the experiment process and diagnosing the experiment mistakes made by students with Petri-Net modeling. Edutainment 2006, LNCS 3942, 108–115.
    Chang, Y. C., & Chu, C. P. (2010). Applying learning behavioral Petri-Nets to the analysis of learning behavior in web-based learning environments. Information Sciences, 180(6), 995-1009.
    Chen, S., Ke, J., & Chang, J. (1990). Knowledge representation using fuzzy Petri-Nets. IEEE Trans. Knowl. Data Eng., 2(3), 311–319.
    Chou, H. W. (2001). Influences of cognitive style and training method on training effectiveness. Computer & Education, 37, 11-25.
    Chu, H. C., Hwang, G. J., & Liang, Y. R. (2014). A cooperative computerized concept mapping approach to improving students' learning performance in web-based information-seeking activities. Journal of Computers in Education. 1(1), 19-33. doi: 10.1007/s40692-014-0001-2
    Cohen, Jacob (1988). Statistical Power Analysis for the Behavioral Sciences (second ed.). Lawrence Erlbaum Associates.
    Dalkey, N. C. (1969). An experimental study of group opinion. Futures, 1(5), 408-426. doi: 10.1016/S0016-3287(69)80025-X
    Delbecq, A. L., Van de ven, A. H., & Gustafson, D. H. (1975). Group Techniques for Program Planning. Glenview, IL: Scott, Foresman Co.
    Deng, Y., Lu, D., Huang, D., Chung, C. J., & Lin, F. (2019, May). Knowledge graph based learning guidance for cybersecurity hands-on labs. Proceedings of the ACM Conference on Global Computing Education (194-200). NY: ACM. doi: 10.1145/3300115.3309531
    Dinero, T. E., & Blixt, S. L. (1988). Information about tests from Sato's S-P chart. College Teaching, 36(3), 123-128. doi: 10.1080/87567555.1988.10532421
    Du, Z., & He, Y. (2006). Plan specification of multi-agent based on coloured Petri nets. Proceedings of the International Conference on Advanced Information Networking and Applications, AINA 2006 (912–916).
    Duque, R., Bollen, L., Anjewierden, A. & Bravo, C. (2012). Automating the analysis of problem-solving activities in learning environments: the co-lab case study. J. UCS, 18(10), 1279–1307.
    Ehrlinger, L., & Wöß, W. (2016). Towards a definition of knowledge graphs. Joint Proceedings of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS16), 1695, Leipzig, Germany, Sep. 13-14, 2016.
    Farmer, E. I. (1998). A Delphi study of research priorities in tech prep. Journal of Vocational and Technical Education, 15(1), 42-49. doi: 10.21061/jcte.v15i1.695
    Fensel, D., Şimşek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., & Wahler, A. (2020). Introduction: What Is a Knowledge Graph? In: Knowledge Graphs. NY: Springer. doi: 10.1007/978-3-030-37439-6_1
    Ferry, B., Hedberg, J. and Harper, B. (1998). How do preservice teachers use concept maps to organize their curriculum content knowledge? Journal of Interactive Learning Research, 9(1), 83-104.
    Frohberg, D., Goth, C., & Schwabe, G. (2009). Mobile learning projects - a critical analysis of the state of the art. Journal of Computer Assisted Learning, 25, 307- 331.
    Gagné, R. M. (1984). Learning outcomes and their effects: useful categories of human performance. American Psychologist, 39(4), 377–385. DOI 10.1037/0003-066X.39.4.377
    Gagné, R. M. (1985). The Conditions of Learning (4th ed.). NY: Holt, Rinehart & Winston.
    Gay, L. R., Mills, G. E., & Airasian, P. W. (2012). Educational Research: Competencies for Analysis and Applications. Englewood Cliffs, NJ: Merrill, Prentice-Hall.
    George, J. (1990). Personality, Affect, and Behavior in Groups. Journal of Applied Psychology, 75, 107-116.
    Glaser, R., & Chi, M. T. H. (1988). Overview. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.), The Nature of Expertise (pp. xv-xxviii). Mahwah, NJ: Lawrence Erlbaum Associates.
    Glasersfeld, V. E.(1995). Radical Constructivism: A Way of Knowing and Learning. Washington, D. C.: The Falmer Press.
    Glick, W., (1985). Conceptualizing and measuring organizational and psychological climate: Pitfalls in multilevel research. Academy of Management Review, 10(3), 126-137. doi: 10.2307/258140
    Goldsmith, T. E., & Davenport, D. M. (1995). Similarity, Structure, & Knowledge: Arepresentational approach to assessment. In P. D. Nichols, S. F. Chipman, & R. L. Brennan (Eds.), Cognitive Diagnostic Assessment. Hillsdale, NJ: Lawrence Erlbrum Associates.
    Greenfield, P. M. (1994). Mind and Media: The Effects of Television, Computers and Video Games. Cambridge, Mass.: Harvard University Press.
    Gregory, R. L. (1987). The Oxford Companion to the mind. Oxford: Oxford University Press.
    Guan, N., Song, D., & Liao, L. (2019). Knowledge graph embedding with concepts. Knowledge-Based Systems, 164(15), 38-44. doi: 10.1016/j.knosys.2018.10.008
    Hannafin, M. J., Land, S. M. and Oliver, K. M. (1999). Open learning environments: Foundations, methods, and models. In Reigeluth (Ed.), Instructional - Design Theory and Models, 2, 131-134. Mahwah, New Jersey: London.
    Hauhart, R. C., & Grahe, J. E. (2010). The undergraduate capstone course in the social sciences: Results from a reginal survey. Teaching Sociology, 38(1), 4-17.
    Hogan, K., & Pressley, M. (1997). Scaffolding Student Learning: Instructional Approaches and Issues. Cambridge, MA: Brookline Books.
    Holdsworth, A., Watty, K., & Davies, M. (2009). Developing Capstone Experiences. Retrieved from http://melbourne-cshe.unimelb.edu.au/__data/assets/pdf_file/0020/1761203/Capstone_Guide_09.pdf
    Hruz, B. and Zhou, M. C. (2007). Modeling and Control of Discrete Event Dynamic Systems. London, UK: Springer.
    Hsu, Y. S., Lin, L. F., Wu, H. K., Lee, D. Y., & Hwang, F. K. (2012). A novice-expert study of modeling skills and knowledge structures about air quality. Journal of Science Education & Technology, 21(5), 588-606. doi: 10.1007/s10956-011-9349-5
    Hu, T. C., & Hsu, Y. J. (2020). Effects of a remedial program on beginner-level, low-achieving EFL learners. Bulletin of Educational Psychology, 51(4), 687-711.
    Hwang, G. J., Kuo, F. R., Chen, N. S., & Ho, H. J. (2014). Effects of an integrated concept mapping and web-based problem-solving approach on students' learning achievements, perceptions and cognitive loads. Computers & Education, 71, 77-86. doi: 10.1016/j.compedu.2013.09.013
    Hwang, G. J., Wu, P. H., & Ke, H. R. (2011). An interactive concept map approach to supporting mobile learning activities for natural science courses. Computers & Education, 57(4), 2272-2280. doi: 10.1016/j.compedu.2011.06.011
    Ifenthaler, Dirk.(2010). Bridging the gap between expert-novice differences: The model-based feedback approach. Journal of Research on Technology in Education (International Society for Technology in Education), 43(2), 103-117.
    James, L. R. (1982). Aggregation bias in estimates of perceptual agreement. Journal of Applied Psychology, 67, 219-229. doi: 10.1037/0021-9010.67.2.219
    James, L. R., Demaree, R. G., and Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85-98.
    James, L. R., Demaree, R. G., and Wolf, G. (1993). Rwg: An assessment of with - group interrater agreement. Journal of Applied Psychology, 78(2), 306-309.
    Jensen, K. (1992). Coloured Petri Nets: Basic Concepts, Analysis Methods, and Practical Use. New York, N. Y.: Springer-Verlag.
    Jia, B., Huang, X., & Jiao, S. (2018). Application of semantic similarity calculation based on knowledge graph for personalized study recommendation service. Educational Sciences Theory & Practice. 18(6), 2958-2966. DOI:10.12738-estp. doi: 10.12738/estp.2018.6.195
    Knight, V. F., Wood, L., McKissick, B. R., & Kuntz, E. M. (2020). Teaching science content and practices to students with intellectual disability and autism. Remedial & Special Education, 41(6), 327-340. DOI: 10.1177/0741932519843998.
    Ku, T. D., Shih, J. L., & Hung, S. H. (2014). The integration of concept mapping in a dynamic assessment model for teaching and learning accounting. Educational Technology & Society, 16 (1), 141–153.
    Kuo, R., Chang, M., Dong, D. X. and Heh, J. S. (2002). Applying knowledge map to intelligent agents in problem solving systems. World Conference on Educational Multimedia, Hypermedia & Telecommunications, Denver, Colorado, USA, Jun. 24-29.
    Latif, R. A., Mohamed, R., Dahlan, A. H., Nor, H., & Mohd, Z. M. (2016). Using Delphi technique making sense of consensus in concept mapping structure and multiple choice questions (MCQ). Education in Medicine Journal, 8(3), 89-98. doi: 10.5959/eimj.v8i3.421
    Lim, K. Y., Lee, H. W., & Grabowski, B. (2009). Does concept mapping strategy work for everyone? The levels of generativity and learners' self-regulated learning skills. British Journal of Educational Technology, 40(4), 606–618. doi: 10.1111/j.1467-8535.2008.00872.x
    Ludwig, B. (1997). Predicting the future: Have you considered using the Delphi methodology? Journal of Extension, 35(5), 1-4.
    Malikowski, S. R., Thompson, M. E., & Theis, J. G. (2007). A model for research into course management systems-bridging technology and learning theory. Journal of Educational Computing Research, 36(2), 149-173. doi: 10.2190/1002-1T50-27G2-H3V7
    Mitrovic, A. (2012). Fifteen years of constraint-based tutors: What we have achieved and where we are going. User Modeling & User-Adapted Interaction, 22(1-2), 39-72. doi: 10.1007/s11257-011-9105-9
    Motiwalla, L., & Tello, S. (2000). Distance learning on the internet: An exploratory study. The Internet and Higher Education, 2(4), 253-264.
    Murry, J. W., & Hammoms, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.
    Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1), 11-33. doi: 10.1109/JPROC.2015.2483592
    Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in Science Teaching, 27(10), 937-949.
    Novak, J. D. and Gowin, D. B. (1984). Learning How to Learn. London: Cambridge University Press.
    Noy, N., Gao,Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019) . Industry-scale knowledge graphs lessons and challenges. Communications of the ACM, 62(8), 36-43. DOI 10.11453331166
    Nyffenegger, F. (2019). Wiki Mind Map. Retrieved from https://www.uni-potsdam.de/eteachingwiki/mindmap/
    Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1, 115-117. doi: 10.1207/s1532690xci0102_1
    Peterson, J. L. (1981). Petri-Net Theory and the Modeling of Systems. Englewood Cliffs, New Jersey: Prentice-Hall.
    Ricci F. L., Consorti, F., Pecoraro, F., Luzi, D., Mingarelli, V., Miotti, S., & Tamburis, O. (2020). Understanding Petri Nets in health sciences Education: The health issue network perspective. Studies in health technology and informatics, 270, 484-488.
    Rosenfield, S. (2002). Developing instructional consultants: From novice to competent to expert. Journal of Educational & Psychological Consultation, 13(12), 97-111.
    Sato, T. (1969). A method of analyzing data gathered by the response analyzer for diagnosis of student performance and the quality of instructional sequence, IECE of Japan Annual Conference.
    Sato, T. and Kurata, M. (1997). Basic S-P score table characteristics. NEC Research and Development, 47, 64-71.
    Schunk, D. H. (2012). Learning Theories, An Educational Perspective (6th Ed.). Boston, MA: Pearson Education Inc. 219-223.
    Schunk, D. H. (1981). Modeling and attributional effect on children´s achievement: A self-efficacy analysis. Journal of Educational Psychology, 73, 93-105.
    Schvaneveldt, R. W. (1990). Proximities, networks, & schemata. In R. W. Schvaneveldt (Ed.), Pathfinder Associative Networks: Studies in Knowledge Organization. Norwood, NJ: Ablex.
    Shen, V. R. L., Wang, Y. Y., Yang C. Y., & Yeh, S. T. (2012). Verification of problem-based learning systems using modified Petri nets. Experts System with Applications, 39, 12636-12649. doi: 10.1016/j.eswa.2012.05.019
    Shen, Victor R. L. (2003). Reinforcement learning for high-level fuzzy Petri nets. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, 33(2), 351- 362.
    Shen, Victor R. L. (2006). Knowledge representation using high-level fuzzy Petri nets. IEEE Transactions on Systems, Man, and Cybernetics- Part A: Systems and Humans, 36(6), 2120- 2127.
    Sheu, T. W., Chen, T. L., Tsai, C. P., Tzeng, J. W., Deng, C. P., & Nagai, M. (2013). Analysis of students' misconception based on rough set theory. Journal of Intelligent Learning Systems and Applications, 5, 67-83. doi: 10.4236/jilsa.2013.52008
    Singhal, A. (2012, May 16). Introducing The Knowledge Graph: Things, Not Strings. Retrieved from https://googleblog.blogspot.co.at/2012/05/introducing-knowledge-graph-things-not.html.
    Sowa, John F. (1995). Top-level ontological categories. International Journal of Human-Computer Studies, 43, 669-85.
    Tan, W., & Zhou, M. C. (2013). Business and Scientific Workflows: A Service-Oriented Approach. Hoboken, NJ: IEEE Press/Wiley.
    Taylor, J., Sharples, M., Malley, C. O., Vavoula, G., & Waycott, J. (2006). Towards a task model for mobile learning: A dialectical approach. International Journal of Learning Technology, 2, 138–158.
    Taylor, R. E. (1992). Pros and cons of the Delphi technique and suggested relationship to management science. Decision Science in The Public Sector. North Whitefield, ME: Felicity Press, Publishers.
    Ting, M. Y., & Kuo, B. C. (2016). A knowledge-structure-based adaptive dynamic assessment system for calculus learning. Journal of Computer Assisted Learning, 32(2), 105-119. doi: 10.1111/jcal.12119
    Tough, A. (1982). Intentional Changes. Chicago: Follett.
    Turns, J., Atman, C.J. & Adams, R. (2000). Concept maps for engineering education: a cognitively motivated tool supporting varied assessment functions. IEEE Transactions on Education, 43(2), 164-173.
    Vygotsky, L. S. (1962). Thought and Language. (E. Hanfmann & G. Vaker, Trans.). Cambridge MA: MIT Press. (Original work published 1934)
    Vygotsky, L. S.(1981). The Genesis of Higher Mental Functions. In J.V.Wertsch(Ed.),The concept of activity in soviet psychology (pp.144-188). NY:M.E.Sharpe.
    Waltz, C. F., Strickland, O. L., & Lenz, E. R. (1991). Measurement in Nursing Research (2nd Ed.). PA: A. Davis.
    Wang, B. T., Sheu, T. W., & Masatake, N. (2011). Evaluating the English-learning of engineering students using the Grey S-P chart: A Facebook case study in Taiwan. Global Journal of Engineering Education, 13(2), 51-56.
    Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M. (2019). Exploring high-order user preference on the knowledge graph for recommender systems. ACM Transactions on Information Systems, 37(3), 1-26. DOI:10.1145-3312738. doi: 10.1145/3312738
    Wang, S., Li, Z., & Zhang, Z. (2006). Application of advanced self-adaptation learning and inference techniques to fuzzy Petri net expert system. Proceedings of the International Conference on Machine Learning and Cybernetics (2227–2232).
    Westbroek, H. B., van Rens, L., van den Berg, E., & Janssen, F. (2020). A practical approach to assessment for learning and differentiated instruction. International Journal of Science Education, 42(6), 955-976. DOI: 10.1080/09500693.2020.1744044.
    Wu, H. K. (2010). Modelling a complex system: Using novice-expert analysis for developing an effective technology-enhanced learning environment. International Journal of Science Education, 32(2), 195-219.
    Wu, N. Q. and Zhou, M. C. (2010). System Modeling and Control with Resource-Oriented Petri Nets. New York, N. Y.: CRC Press.
    Xie, Y. & Lin, S. Y. (2019). Using word clouds to support students' knowledge integration from online inquiry: An Investigation of the Process and Outcome. Interactive Learning Environments, 27(4), 478-496. doi: 10.1080/10494820.2018.1484774
    Yu, H., Li, H., Mao, D., & Cai, Q. (2020). A relationship extraction method for domain knowledge graph construction. World Wide Web, 23(2), 735-753. DOI:10.1007/s11280-019-00765-y.
    Yu, S. K. (1995). Knowledge representation and reasoning using fuzzy Pr/T net-systems. Fuzzy Sets and Systems, 75(1), 33-45. doi: 10.1016/0165-0114(94)00326-3.
    Zhan, P. (2020). A Markov estimation strategy for longitudinal learning diagnosis: Providing timely diagnostic feedback. Educational & Psychological Measurement, 80(6), 1145-1167.
    Zhou, M. C., & Venkatesh, K. (1998). Modeling, Simulation and Control of Flexible Manufacturing Systems: A Petri Net Approach. Singapore: World Scientific.
    Žitko, Z., Končar, M., & Cifrek, M. (2018). Process analysis and decision support tools in parenteral nutrition in children. Studies in health technology and informatics, 255, 87-91.

    下載圖示
    QR CODE