學術產出-學位論文
文章檢視/開啟
書目匯出
-
題名 應用量質性整合框架於學習模式與表現之分析
Analysis of Learning Patterns and Performance – An Integrated Quantitative and Qualitative Approach作者 吳怡潔
Wu, Yi-Chieh貢獻者 廖文宏
Liao, Wen-Hung
吳怡潔
Wu, Yi-Chieh關鍵詞 學習模式
學習成效評估
3D建模軟體
STEAM教案
K-12國民教育
英語輔助學習
智慧語音助理
Learning patterns
Performance evaluation
3D modeling software
Qmodel Creator
STEAM lesson plan
K-12 education
Computer Assisted Language Learning (CALL)
Intelligent Voice Assistant日期 2021 上傳時間 1-十一月-2021 11:58:20 (UTC+8) 摘要 數位科技應用於教育的趨勢，不僅體現於學校軟硬體基礎建設的建置、強化，也有愈來愈多的教師樂於嘗試新科技，例如導入3D列印工具，設計整合式的創新教案。有別於傳統的數理教程，此種教案以「做中學」為核心精神，首要目標為激發學生的創造力、想像力、以及面對挑戰的應變能力。本論文主要關注學習過程中的兩大面向：1) 學習模式（與任務無關）、以及學習成效評估（與任務有關）。我們以3D建模軟體的學習過程，作為第一個研究案例。參與學生在課堂上學習多種建模軟體，過程中的操作記錄檔、螢幕錄影、以及建模成品，皆用以本研究案例之分析。對應於所關注的兩大面向，我們提出以下類型的指標：（一）學習行為特徵量化，其中涵蓋：有效操作期間 (Effective Operating Period, EOP)、試誤期間 (Trial-and-Error Period, TEP)、實作期間 (Implementation Period, IP) 等等；（二）學習成效評量，由於與任務相關，在此定義為3D模型之複>雜度評估，其中包括：細節程度 (Degree of Detail, DoD)、輪廓 (shape, Cf)、分割 (partition, Cp)、以及區塊比例 (block-ratio, Cr) 複雜度，等模型評估指標。基於上述提出的指標，我們可以分析參與者的學習體驗及模型完成度，進而錨定影響學習模式及學習成效之關鍵因素。教師亦可取得學生們更切實的學習狀態，作為回饋參考。在第二個研究案例中，我們著重於探討小學英語學習歷程，採用目前最熱門的智慧語音助理Amazon Echo Dot，根據當學期英語課本內容，實作專用之技能（skill）與參與者互動，並從使用紀錄中分析參與者之使用習慣。我們與政大實小英語教師合作、徵求學生及家長同意在家安裝Echo Dot後，進行為期一學期的實驗。我們主要採用語音和字詞的特徵，用以評估學生們的英文學習成效。為了瞭解持續與語音助理對話的關鍵因素，我們從分析活躍使用者的對話模式開始，並發掘那些在學習過程中可能的影響因素。我們期待從學習過程中的第一手資料及其分析結果，獲取有助於瞭解評估學生們的英語學習成效的線索。
As more schools incorporate technologies into their curriculum to stimulate the creativity of K-12 students with a learning-by-doing approach, it becomes crucial to understand how users work with the novel tools and to evaluate integrated lesson plans in the STEAM (Science, Technology, Engineering, Arts and Math) educational framework.Our work focuses on two perspectives during the learning process: learning patterns (task-independent), and the evaluation of the outcome (task-dependent). In the first case study of the thesis, we took the learning process of 3D modeling software as the case study. Participants operation logs, screen recordings, and finished work for respective 3D modeling software were recorded and analyzed. Two types of indicators have been developed. One is concerned with the quantification of learning behavior, including Effective Operating Period (EOP), Trial-and-Error Period (TEP), and Implementation Period (IP). The other has to do with the evaluation of learning outcome, i.e., the complexity of 3D models, including the Degree of Detail (DoD), shape (Cf), partition (Cp), and block-ratio (Cr) complexity. Based on the proposed features, we are able to identify the key factors that affect students` learning experiences and performance in terms of learning patterns and model completeness. Through these indicators, instructors can gain better insights into student`s learning status of 3D modeling software.In the second case study, we focus on exploring the English learning process for elementary school students. We employ Amazon Echo Dot, one of the most popular intelligent voice assistant nowadays, as a tool to facilitate language learning. We developed an Amazon Skill that incorporates the content from English textbooks for the participants to interact with using voice input. The operation logs from Echo Dot faithfully reveal student`s usage patterns and preferences. A semester-long experiment has been conducted with the assistance of the Affiliated Experimental Elementary School (AEES) of National Chengchi University. After data collection has been completed, we utilize acoustic and transcript evaluation metrics to examine the voice recordings and user logs. Our initial analysis focuses the active users, i.e., participants who have continued to engage in conversations with the voice assistant. Several questions regarding user behavior are prompted and responded to based on the collected and processed data. Analyzing the content of the conversation will help disclose more detailed information regarding the learning process. The progress of individual students can also be monitored to determine if further assistance is needed.參考文獻 [1] Sharan B Merriam and Lisa M Baumgartner. Learning in adulthood: A comprehensive guide. John Wiley & Sons, 2020.[2] Vanessa Marcy. Adult learning styles: How the vark© learning style inventory can be used to improve student learning. Perspectives on Physician Assistant Education, 12(2):117–120, 2001.[3] Ana Luísa de Oliveira Pires. Higher education and adult motivations towards lifelong learning. European journal of vocational training, 46(1):129–150, 2009.[4] Ming-Hung Lin, Huang-g Chen, et al. A study of the effects of digital learning on learning motivation and learning outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7):3553–3564, 2017.[5] Richard M Ryan and Edward L Deci. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1):54– 67, 2000.[6] National Research Council et al. How people learn: Brain, mind, experience, and school: Expanded edition. National Academies Press, 2000.[7] Margaret Honey, Greg Pearson, and Heidi Schweingruber. STEM integration in K-12 education: Status, prospects, and an agenda for research. National Academies Press Washington, DC, 2014.[8] Giovanni Cesaretti, Enrico Dini, Xavier De Kestelier, Valentina Colla, and Laurent Pambaguian. Building components for an outpost on the lunar soil by means of a novel 3d printing technology. Acta Astronautica, 93:430–450, 2014.[9] Amr Mahmoud and Michael Bennett. Introducing 3-dimensional printing of a human anatomic pathology specimen: potential benefits for undergraduate and postgraduate education and anatomic pathology practice. Archives of pathology & laboratory medicine, 139(8):1048–1051, 2015.[10] David Thornburg, Norma Thornburg, and Sara Armstrong. The invent to learn guide to 3D printing in the classroom: Recipes for success. Constructing Modern Knowledge Press, Torrance, CA, 2014.[11] John L Irwin, Joshua M Pearce, and Gerald Anzalone. The reprap 3-d printer revolution in stem education. In 2014 ASEE annual conference & exposition, pages 24–1242, 2014.[12] Yuichiro Anzai and Herbert A Simon. The theory of learning by doing. Psychological review, 86(2):124, 1979.[13] Robert Maloy, Suzan Kommers, Allison Malinowski, Irene LaRoche, et al. 3d modeling and printing in history/social studies classrooms: Initial lessons and insights. Contemporary Issues in Technology and Teacher Education, 17(2):229–249, 2017.[14] Yu-Hung Chien and Po-Ying Chu. The different learning outcomes of high school and college students on a 3d-printing steam engineering design curriculum. International Journal of Science and Mathematics Education, 16(6):1047–1064, 2018.[15] Chen-Chi Hu, Tze-Hsiang Wei, Yu-Sheng Chen, Yi-Chieh Wu, and Ming-Te Chi. Intuitive 3d cubic style modeling system. In SIGGRAPH Asia 2015 Posters, page 27. ACM, 2015.[16] I-Sheng Lin, Tsai-Yen Li, Fang-Chi Liang, and Yong-Teng Lin. A collaborative learning system for sharing 3d models: 3d model co-learning space. In Advanced Learning Technologies (ICALT), 2017 IEEE 17th International Conference on, pages 502–506. IEEE, 2017.[17] Autodesk Inc. Tinkercad, accessed April 29, 2018. https://www.tinkercad.com/.[18] David Crystal. English as a global language. Ernst Klett Sprachen, 2003.[19] WCV Wu and PHN Wu. Creating and authentic efl learning environment to enhance student motivation to study English. Asian EFL Journal, 10(4):211–226, 2008.[20] Ching-ning Chien, Wei Lee, and Li-hua Kao. Collaborative teaching in an esp program. Asian EFL Journal, 10(4):114–133, 2008.[21] Department of Information Services. Premier lays out blueprint to make Taiwan bilingual by 2030, accessed Dec. 6, 2018. https://english.ey.gov.tw/Page/61BF20C3E89B856/c12b6ae2-41c8-4eb5-aef1-4ef3cb3aa9ce.[22] Alan Davies. 17 the native speaker in applied linguistics. The handbook of applied linguistics, page 431, 2004.[23] Panchanan Mohanty. Reflections on teaching and learning of English as a second language in India. In Functional Variations in English, pages 311–320. Springer, 2020.[24] Joy Education Group. 3–6 year–old: Beginner level, accessed Oct. 31, 2020. http: //je.joy.com.tw/02_Level_Intro.php.[25] Hess International Educational Group. 4–6 year–old, accessed Oct. 31, 2020. http: //www.hess.com.tw/tw/education/elementary/courses/.[26] GIRAFFE. Kindergarten, accessed Oct. 31, 2020. http://www.giraffe.com.tw/GiraffeTeaching.html?goal.[27] Carol Benson. Summary overview. mother tongue-based education in multi-lingual contexts. Improving the quality of mother tongue-based literacy and learning. Case studies from Asia, Africa and South America, pages 2–11, 2008.[28] Andy Kirkpatrick. English as a lingua franca in ASEAN: A multilingual model, volume 1. Hong Kong University Press, 2010.[29] Stephen Walter and Diane Dekker. The lubuagan mother tongue education experiment (flc). a report of comparative test results. Report presented to the Committee on Basic Education and Culture, Committee on Higher and Technical Education, House of Representatives. Quezon City, Philippines, 2008.[30] Yolanda S Quijano and Ofelia H Eustaquio. Language-in-education policies and their implementation in Philippine public schools. Mother tongue as bridge language of instruction: Policies and experiences in Southeast Asia, pages 84–92, 2009.[31] Scientific United Nations Educational and Paris (France) Cultural Organization. Education for All by 2015: Will We Make It?. ERIC Clearinghouse, 2007.[32] Andy Kirkpatrick. English as an international language in Asia: Implications for language education. In English as an international language in Asia: Implications for language education, pages 29–44. Springer, 2012.[33] Jennifer Jenkins. The phonology of English as an international language. Oxford university press, 2000.[34] Barbara Seidlhofer. English as a lingua franca. ELT journal, 59(4):339–341, 2005.[35] Amazon. Echo & alexa, accessed Oct. 31, 2020. https://www.amazon.com/smart-home-devices/b?ie=UTF8&node=9818047011.[36] Google. Google assistant is ready and built-in to specific speakers, accessed Oct.31, 2020. https://assistant.google.com/platforms/speakers/.[37] Andreas Holzinger. Usability engineering methods for software developers. Communications of the ACM, 48(1):71–74, 2005.[38] Jan D Vermunt and Vincent Donche. A learning patterns perspective on student learning in higher education: state of the art and moving forward. Educational psychology review, 29(2):269–299, 2017.[39] Frank Coffield, David Moseley, Elaine Hall, Kathryn Ecclestone, Frank Coffield, David Moseley, Elaine Hall, Kathryn Ecclestone, et al. Learning styles and pedagogy in post-16 learning: A systematic and critical review. Technical Report TD/TNC 79.71, Learning & Skills Research Centre, London, England, 2004.[40] Carol Evans and Jan D Vermunt. Styles, approaches, and patterns in student learning. British Journal of Educational Psychology, (2):185–195, 2013.[41] Jan D Vermunt and Yvonne J Vermetten. Patterns in student learning: Relationships between learning strategies, conceptions of learning, and learning orientations. Educational psychology review, 16(4):359–384, 2004.[42] Jill Nemiro, Cesar Larriva, and Mariappan Jawaharlal. Developing creative behavior in elementary school students with robotics. The Journal of Creative Behavior, 51(1):70–90, 2017.[43] Edwin A Kirkpatrick. Fundamentals of child study. Macmillan, 1908.[44] ClarkLeonardHull.Simpletrialanderrorlearning:Astudyinpsychologicaltheory. Psychological Review, 37(3):241, 1930.[45] Abram Amsel. Frustrative nonreward in partial reinforcement and discrimination learning: Some recent history and a theoretical extension. Psychological review, 69(4):306, 1962.[46] Ronald R Schmeck. Error-produced frustration as a factor influencing the probability of occurrence of further errors. Journal of experimental psychology, 86(2):153, 1970.[47] Hubert L Dreyfus, Stuart E Dreyfus, and Lotfi A Zadeh. Mind over machine: The power of human intuition and expertise in the era of the computer. IEEE Expert, 2(2):110–111, 1987.[48] Stuart E Dreyfus and Hubert L Dreyfus. A five-stage model of the mental activities involved in directed skill acquisition. Technical Report ORC-80-2, California Univ Berkeley Operations Research Center, 1980.[49] Hubert L Dreyfus and Stuart E Dreyfus. The ethical implications of the five-stage skill-acquisition model. Bulletin of Science, Technology & Society, 24(3):251–264, 2004.[50] Hubert L Dreyfus and Stuart E Dreyfus. From socrates to expert systems: The limits of calculative rationality. In Philosophy and technology II, pages 111–130. Springer, Dordrecht, 1986.[51] Bogdan Valentan, Tomaž Brajlih, I Drstvensek, and J Balic. Basic solutions on shape complexity evaluation of stl data. Journal of Achievements in Materials and Manufacturing Engineering, 26(1):73–80, 2008.[52] Jarek Rossignac. Shape complexity. The visual computer, 21(12):985–996, 2005.[53] James D Gardiner, Julia Behnsen, and Charlotte A Brassey. Alpha shapes: Deter- mining 3d shape complexity across morphologically diverse structures. BMC evolutionary biology, 18(1):184, 2018.[54] Torsten Fiolka, Jörg Stückler, Dominik A Klein, Dirk Schulz, and Sven Behnke. Sure: Surface entropy for distinctive 3d features. In International Conference on Spatial Cognition, pages 74–93, Berlin, Heidelberg, 2012. Springer.[55] Wen-Hung Liao and Po-Ming Chen. Analysis of visual elements in logo design. In International Symposium on Smart Graphics, pages 73–85. Springer, 2014.[56] Stephen D Krashen. Bilingual education and second language acquisition theory. Schooling and language minority students: A theoretical framework, pages 51–79, 1981.[57] Nobuyuki Hino and Setsuko Oda. Clil pedagogy for eil in higher education. In Functional Variations in English, pages 295–309. Springer, 2020.[58] Gary Ross, Stephen Henneberry, and Glen Norris. Speaking with your computer: A new way to practice and analyze conversation. AI and Machine Learning in Language Education, page 152, 2019.[59] Natasha Randall. A survey of robot-assisted language learning (rall). ACM Transactions on Human-Robot Interaction (THRI), 9(1):1–36, 2019.[60] Gilbert Dizon and Daniel Tang. A pilot study of alexa for autonomous second language learning. CALL and complexity, page 107, 2019.[61] Patrick D Hales, Melissa Anderson, Tonya Christianson, Amber Gaspar, Billi Jo Meyer, Beth Nelson, Krista Shilvock, Mary Steinmetz, Makenzi Timmons, and Michelle Vande Weerd. Alexa?: Possibilities of voice assistant technology and artificial intelligence in the classroom. Empowering Research for Educators, 3(1):4, 2019.[62] Jinjin Zhao, Shreyansh Bhatt, Candace Thille, Dawn Zimmaro, Neelesh Gattani, and Josh Walker. Introducing alexa for e-learning. In Proceedings of the Seventh ACM Conference on Learning@ Scale, pages 427–428, 2020.[63] Alex Housen and Folkert Kuiken. Complexity, accuracy, and fluency in second language acquisition. Applied linguistics, 30(4):461–473, 2009.[64] Garima Tyagi and Rohit Singh. Implementing call system using natural language processing tools. In Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), 2019.[65] G. Bradski. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 25(11), 2000.[66] Oxford University Press. Everybody up teacher’s site, accessed Aug. 31, 2021. https://elt.oup.com/teachers/everybodyup/.[67] Nivja H De Jong and Ton Wempe. Praat script to detect syllable nuclei and measure speech rate automatically. Behavior research methods, 41(2):385–390, 2009.[68] David Snyder, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. X-vectors: Robust dnn embeddings for speaker recognition. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5329–5333. IEEE, 2018.[69] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20(3):273–297, 1995. 描述 博士
國立政治大學
資訊科學系
104753502資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104753502 資料類型 thesis dc.contributor.advisor 廖文宏 zh_TW dc.contributor.advisor Liao, Wen-Hung en_US dc.contributor.author (作者) 吳怡潔 zh_TW dc.contributor.author (作者) Wu, Yi-Chieh en_US dc.creator (作者) 吳怡潔 zh_TW dc.creator (作者) Wu, Yi-Chieh en_US dc.date (日期) 2021 en_US dc.date.accessioned 1-十一月-2021 11:58:20 (UTC+8) - dc.date.available 1-十一月-2021 11:58:20 (UTC+8) - dc.date.issued (上傳時間) 1-十一月-2021 11:58:20 (UTC+8) - dc.identifier (其他 識別碼) G0104753502 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137669 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 104753502 zh_TW dc.description.abstract (摘要) 數位科技應用於教育的趨勢，不僅體現於學校軟硬體基礎建設的建置、強化，也有愈來愈多的教師樂於嘗試新科技，例如導入3D列印工具，設計整合式的創新教案。有別於傳統的數理教程，此種教案以「做中學」為核心精神，首要目標為激發學生的創造力、想像力、以及面對挑戰的應變能力。本論文主要關注學習過程中的兩大面向：1) 學習模式（與任務無關）、以及學習成效評估（與任務有關）。我們以3D建模軟體的學習過程，作為第一個研究案例。參與學生在課堂上學習多種建模軟體，過程中的操作記錄檔、螢幕錄影、以及建模成品，皆用以本研究案例之分析。對應於所關注的兩大面向，我們提出以下類型的指標：（一）學習行為特徵量化，其中涵蓋：有效操作期間 (Effective Operating Period, EOP)、試誤期間 (Trial-and-Error Period, TEP)、實作期間 (Implementation Period, IP) 等等；（二）學習成效評量，由於與任務相關，在此定義為3D模型之複>雜度評估，其中包括：細節程度 (Degree of Detail, DoD)、輪廓 (shape, Cf)、分割 (partition, Cp)、以及區塊比例 (block-ratio, Cr) 複雜度，等模型評估指標。基於上述提出的指標，我們可以分析參與者的學習體驗及模型完成度，進而錨定影響學習模式及學習成效之關鍵因素。教師亦可取得學生們更切實的學習狀態，作為回饋參考。在第二個研究案例中，我們著重於探討小學英語學習歷程，採用目前最熱門的智慧語音助理Amazon Echo Dot，根據當學期英語課本內容，實作專用之技能（skill）與參與者互動，並從使用紀錄中分析參與者之使用習慣。我們與政大實小英語教師合作、徵求學生及家長同意在家安裝Echo Dot後，進行為期一學期的實驗。我們主要採用語音和字詞的特徵，用以評估學生們的英文學習成效。為了瞭解持續與語音助理對話的關鍵因素，我們從分析活躍使用者的對話模式開始，並發掘那些在學習過程中可能的影響因素。我們期待從學習過程中的第一手資料及其分析結果，獲取有助於瞭解評估學生們的英語學習成效的線索。 zh_TW dc.description.abstract (摘要) As more schools incorporate technologies into their curriculum to stimulate the creativity of K-12 students with a learning-by-doing approach, it becomes crucial to understand how users work with the novel tools and to evaluate integrated lesson plans in the STEAM (Science, Technology, Engineering, Arts and Math) educational framework.Our work focuses on two perspectives during the learning process: learning patterns (task-independent), and the evaluation of the outcome (task-dependent). In the first case study of the thesis, we took the learning process of 3D modeling software as the case study. Participants operation logs, screen recordings, and finished work for respective 3D modeling software were recorded and analyzed. Two types of indicators have been developed. One is concerned with the quantification of learning behavior, including Effective Operating Period (EOP), Trial-and-Error Period (TEP), and Implementation Period (IP). The other has to do with the evaluation of learning outcome, i.e., the complexity of 3D models, including the Degree of Detail (DoD), shape (Cf), partition (Cp), and block-ratio (Cr) complexity. Based on the proposed features, we are able to identify the key factors that affect students` learning experiences and performance in terms of learning patterns and model completeness. Through these indicators, instructors can gain better insights into student`s learning status of 3D modeling software.In the second case study, we focus on exploring the English learning process for elementary school students. We employ Amazon Echo Dot, one of the most popular intelligent voice assistant nowadays, as a tool to facilitate language learning. We developed an Amazon Skill that incorporates the content from English textbooks for the participants to interact with using voice input. The operation logs from Echo Dot faithfully reveal student`s usage patterns and preferences. A semester-long experiment has been conducted with the assistance of the Affiliated Experimental Elementary School (AEES) of National Chengchi University. After data collection has been completed, we utilize acoustic and transcript evaluation metrics to examine the voice recordings and user logs. Our initial analysis focuses the active users, i.e., participants who have continued to engage in conversations with the voice assistant. Several questions regarding user behavior are prompted and responded to based on the collected and processed data. Analyzing the content of the conversation will help disclose more detailed information regarding the learning process. The progress of individual students can also be monitored to determine if further assistance is needed. en_US dc.description.tableofcontents Contents摘要 iAbstract iiiContents vList of Figures ixList of Tables xiiChapter 1 Introduction 11.1 3D Printing in the K-12Classrooms 21.2 Second Foreign Language Learning of the K-12 Students using Intelligent Voice Assistant 41.3 Main Contributions 61.3.1 Identifying Features for Learning Patterns 71.3.2 Defining the Degree of Completion for 3D Models 71.3.3 Devising an Integrated Quantitative and Qualitative Approach 81.3.4 Developing the Alexa Skill/Lesson Plans & Proposed Indicators for Second Foreign Language(SFL) Learning 9Chapter 2 Literature Review 102.1 Studies of Learning Behavior: Learning Pattern 102.2 Studies for Measuring Outcome #1: 3-D Model Complexity132.3 Studies for Measuring Outcome #2: Second Language Acquisition & Computer Assisted Language Learning(CALL) 15Chapter 3 Case Study of 3D Modeling: Methodology 173.1 Usage Pattern Related Features 173.1.1 From the Log of Qmodel Creator 173.1.2 From the Operation Recording 193.1.3 Indicators of User Behavior 213.2 3D Model Related Features 223.2.1 Degree of Detail(DoD) 223.2.2 Mixed Model Features 24Chapter 4 Case Study of 3D Modeling: Experimental Design . . . . . . . . . . 294.1 The First Stage – Qmodel Creator Workshops 294.1.1 Dataset Collection 294.1.2 Expert Evaluation 304.2 The Second Stage – Integrated 3D Printing Courses 314.2.1 Dataset Collection 314.2.2 Expert Evaluation 324.2.3 Interview with Students 32Chapter 5 Case Study of 3D Modeling: Experimental Results 345.1 The First Stage – Qmodel Creator Workshops 345.1.1 Overall Distribution 355.1.2 How long does the user take to finish a modeling task? 375.1.3 Which is used more frequently: Intuitive modeling or Traditional 3D editing? 385.1.4 When users create models, is the process smooth? Is Trial-and-Error required? 395.1.5 What is the degree of completion? 415.1.6 Summary of the stage 415.2 The Second Stage – Integrated 3D Printing Courses 425.2.1 Overall Distribution 435.2.2 Correlation with Score 455.2.3 Differences between Groups of Scores 465.2.4 User Categories Comparison 475.2.5 Summary of the Stage 48Chapter 6 Case Study of CALL: Methodology 496.1 The Developed Amazon Skill 496.2 The Assessment Methods 516.2.1 Skill Related Indicators 516.2.2 Indicators for Out of Skill Usage 516.3 Dataset Collection 536.3.1 Participants 536.3.2 Notifications and Events 54Chapter 7 Case Study of CALL: Experimental Results 557.1 Overall Observation 557.2 The Assessment Results 557.2.1 Question 1: Did the active users open NCCU (ES) English Textbook Practice frequently? How about their performance in the skill? 567.2.2 Question 2: What functions did active users trigger most often? 587.2.3 Question 3: How about the language measures of active users? Does it cause frustration in the learning process of active users, or promote trial-and-error behavior? 607.3 Further Analysis 607.3.1 Sibling Analysis – User#27 617.3.2 Sibling Analysis – User#15 627.4 Summary 62Chapter 8 Conclusions 64References 66Appendix 74A.1 The First Stage of the Case Study #1 75A.2 The Second Stage of the Case Study #1 76A.3 Detailed Results of Case Study #2 79 zh_TW dc.format.extent 8820717 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104753502 en_US dc.subject (關鍵詞) 學習模式 zh_TW dc.subject (關鍵詞) 學習成效評估 zh_TW dc.subject (關鍵詞) 3D建模軟體 zh_TW dc.subject (關鍵詞) STEAM教案 zh_TW dc.subject (關鍵詞) K-12國民教育 zh_TW dc.subject (關鍵詞) 英語輔助學習 zh_TW dc.subject (關鍵詞) 智慧語音助理 zh_TW dc.subject (關鍵詞) Learning patterns en_US dc.subject (關鍵詞) Performance evaluation en_US dc.subject (關鍵詞) 3D modeling software en_US dc.subject (關鍵詞) Qmodel Creator en_US dc.subject (關鍵詞) STEAM lesson plan en_US dc.subject (關鍵詞) K-12 education en_US dc.subject (關鍵詞) Computer Assisted Language Learning (CALL) en_US dc.subject (關鍵詞) Intelligent Voice Assistant en_US dc.title (題名) 應用量質性整合框架於學習模式與表現之分析 zh_TW dc.title (題名) Analysis of Learning Patterns and Performance – An Integrated Quantitative and Qualitative Approach en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Sharan B Merriam and Lisa M Baumgartner. Learning in adulthood: A comprehensive guide. John Wiley & Sons, 2020.[2] Vanessa Marcy. Adult learning styles: How the vark© learning style inventory can be used to improve student learning. Perspectives on Physician Assistant Education, 12(2):117–120, 2001.[3] Ana Luísa de Oliveira Pires. Higher education and adult motivations towards lifelong learning. European journal of vocational training, 46(1):129–150, 2009.[4] Ming-Hung Lin, Huang-g Chen, et al. A study of the effects of digital learning on learning motivation and learning outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7):3553–3564, 2017.[5] Richard M Ryan and Edward L Deci. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1):54– 67, 2000.[6] National Research Council et al. How people learn: Brain, mind, experience, and school: Expanded edition. National Academies Press, 2000.[7] Margaret Honey, Greg Pearson, and Heidi Schweingruber. STEM integration in K-12 education: Status, prospects, and an agenda for research. National Academies Press Washington, DC, 2014.[8] Giovanni Cesaretti, Enrico Dini, Xavier De Kestelier, Valentina Colla, and Laurent Pambaguian. Building components for an outpost on the lunar soil by means of a novel 3d printing technology. Acta Astronautica, 93:430–450, 2014.[9] Amr Mahmoud and Michael Bennett. Introducing 3-dimensional printing of a human anatomic pathology specimen: potential benefits for undergraduate and postgraduate education and anatomic pathology practice. Archives of pathology & laboratory medicine, 139(8):1048–1051, 2015.[10] David Thornburg, Norma Thornburg, and Sara Armstrong. The invent to learn guide to 3D printing in the classroom: Recipes for success. Constructing Modern Knowledge Press, Torrance, CA, 2014.[11] John L Irwin, Joshua M Pearce, and Gerald Anzalone. The reprap 3-d printer revolution in stem education. In 2014 ASEE annual conference & exposition, pages 24–1242, 2014.[12] Yuichiro Anzai and Herbert A Simon. The theory of learning by doing. Psychological review, 86(2):124, 1979.[13] Robert Maloy, Suzan Kommers, Allison Malinowski, Irene LaRoche, et al. 3d modeling and printing in history/social studies classrooms: Initial lessons and insights. Contemporary Issues in Technology and Teacher Education, 17(2):229–249, 2017.[14] Yu-Hung Chien and Po-Ying Chu. The different learning outcomes of high school and college students on a 3d-printing steam engineering design curriculum. International Journal of Science and Mathematics Education, 16(6):1047–1064, 2018.[15] Chen-Chi Hu, Tze-Hsiang Wei, Yu-Sheng Chen, Yi-Chieh Wu, and Ming-Te Chi. Intuitive 3d cubic style modeling system. In SIGGRAPH Asia 2015 Posters, page 27. ACM, 2015.[16] I-Sheng Lin, Tsai-Yen Li, Fang-Chi Liang, and Yong-Teng Lin. A collaborative learning system for sharing 3d models: 3d model co-learning space. In Advanced Learning Technologies (ICALT), 2017 IEEE 17th International Conference on, pages 502–506. IEEE, 2017.[17] Autodesk Inc. Tinkercad, accessed April 29, 2018. https://www.tinkercad.com/.[18] David Crystal. English as a global language. Ernst Klett Sprachen, 2003.[19] WCV Wu and PHN Wu. Creating and authentic efl learning environment to enhance student motivation to study English. Asian EFL Journal, 10(4):211–226, 2008.[20] Ching-ning Chien, Wei Lee, and Li-hua Kao. Collaborative teaching in an esp program. Asian EFL Journal, 10(4):114–133, 2008.[21] Department of Information Services. Premier lays out blueprint to make Taiwan bilingual by 2030, accessed Dec. 6, 2018. https://english.ey.gov.tw/Page/61BF20C3E89B856/c12b6ae2-41c8-4eb5-aef1-4ef3cb3aa9ce.[22] Alan Davies. 17 the native speaker in applied linguistics. The handbook of applied linguistics, page 431, 2004.[23] Panchanan Mohanty. Reflections on teaching and learning of English as a second language in India. In Functional Variations in English, pages 311–320. Springer, 2020.[24] Joy Education Group. 3–6 year–old: Beginner level, accessed Oct. 31, 2020. http: //je.joy.com.tw/02_Level_Intro.php.[25] Hess International Educational Group. 4–6 year–old, accessed Oct. 31, 2020. http: //www.hess.com.tw/tw/education/elementary/courses/.[26] GIRAFFE. Kindergarten, accessed Oct. 31, 2020. http://www.giraffe.com.tw/GiraffeTeaching.html?goal.[27] Carol Benson. Summary overview. mother tongue-based education in multi-lingual contexts. Improving the quality of mother tongue-based literacy and learning. Case studies from Asia, Africa and South America, pages 2–11, 2008.[28] Andy Kirkpatrick. English as a lingua franca in ASEAN: A multilingual model, volume 1. Hong Kong University Press, 2010.[29] Stephen Walter and Diane Dekker. The lubuagan mother tongue education experiment (flc). a report of comparative test results. Report presented to the Committee on Basic Education and Culture, Committee on Higher and Technical Education, House of Representatives. Quezon City, Philippines, 2008.[30] Yolanda S Quijano and Ofelia H Eustaquio. Language-in-education policies and their implementation in Philippine public schools. Mother tongue as bridge language of instruction: Policies and experiences in Southeast Asia, pages 84–92, 2009.[31] Scientific United Nations Educational and Paris (France) Cultural Organization. Education for All by 2015: Will We Make It?. ERIC Clearinghouse, 2007.[32] Andy Kirkpatrick. English as an international language in Asia: Implications for language education. In English as an international language in Asia: Implications for language education, pages 29–44. Springer, 2012.[33] Jennifer Jenkins. The phonology of English as an international language. Oxford university press, 2000.[34] Barbara Seidlhofer. English as a lingua franca. ELT journal, 59(4):339–341, 2005.[35] Amazon. Echo & alexa, accessed Oct. 31, 2020. https://www.amazon.com/smart-home-devices/b?ie=UTF8&node=9818047011.[36] Google. Google assistant is ready and built-in to specific speakers, accessed Oct.31, 2020. https://assistant.google.com/platforms/speakers/.[37] Andreas Holzinger. Usability engineering methods for software developers. Communications of the ACM, 48(1):71–74, 2005.[38] Jan D Vermunt and Vincent Donche. A learning patterns perspective on student learning in higher education: state of the art and moving forward. Educational psychology review, 29(2):269–299, 2017.[39] Frank Coffield, David Moseley, Elaine Hall, Kathryn Ecclestone, Frank Coffield, David Moseley, Elaine Hall, Kathryn Ecclestone, et al. Learning styles and pedagogy in post-16 learning: A systematic and critical review. Technical Report TD/TNC 79.71, Learning & Skills Research Centre, London, England, 2004.[40] Carol Evans and Jan D Vermunt. Styles, approaches, and patterns in student learning. British Journal of Educational Psychology, (2):185–195, 2013.[41] Jan D Vermunt and Yvonne J Vermetten. Patterns in student learning: Relationships between learning strategies, conceptions of learning, and learning orientations. Educational psychology review, 16(4):359–384, 2004.[42] Jill Nemiro, Cesar Larriva, and Mariappan Jawaharlal. Developing creative behavior in elementary school students with robotics. The Journal of Creative Behavior, 51(1):70–90, 2017.[43] Edwin A Kirkpatrick. Fundamentals of child study. Macmillan, 1908.[44] ClarkLeonardHull.Simpletrialanderrorlearning:Astudyinpsychologicaltheory. Psychological Review, 37(3):241, 1930.[45] Abram Amsel. Frustrative nonreward in partial reinforcement and discrimination learning: Some recent history and a theoretical extension. Psychological review, 69(4):306, 1962.[46] Ronald R Schmeck. Error-produced frustration as a factor influencing the probability of occurrence of further errors. Journal of experimental psychology, 86(2):153, 1970.[47] Hubert L Dreyfus, Stuart E Dreyfus, and Lotfi A Zadeh. Mind over machine: The power of human intuition and expertise in the era of the computer. IEEE Expert, 2(2):110–111, 1987.[48] Stuart E Dreyfus and Hubert L Dreyfus. A five-stage model of the mental activities involved in directed skill acquisition. Technical Report ORC-80-2, California Univ Berkeley Operations Research Center, 1980.[49] Hubert L Dreyfus and Stuart E Dreyfus. The ethical implications of the five-stage skill-acquisition model. Bulletin of Science, Technology & Society, 24(3):251–264, 2004.[50] Hubert L Dreyfus and Stuart E Dreyfus. From socrates to expert systems: The limits of calculative rationality. In Philosophy and technology II, pages 111–130. Springer, Dordrecht, 1986.[51] Bogdan Valentan, Tomaž Brajlih, I Drstvensek, and J Balic. Basic solutions on shape complexity evaluation of stl data. Journal of Achievements in Materials and Manufacturing Engineering, 26(1):73–80, 2008.[52] Jarek Rossignac. Shape complexity. The visual computer, 21(12):985–996, 2005.[53] James D Gardiner, Julia Behnsen, and Charlotte A Brassey. Alpha shapes: Deter- mining 3d shape complexity across morphologically diverse structures. BMC evolutionary biology, 18(1):184, 2018.[54] Torsten Fiolka, Jörg Stückler, Dominik A Klein, Dirk Schulz, and Sven Behnke. Sure: Surface entropy for distinctive 3d features. In International Conference on Spatial Cognition, pages 74–93, Berlin, Heidelberg, 2012. Springer.[55] Wen-Hung Liao and Po-Ming Chen. Analysis of visual elements in logo design. In International Symposium on Smart Graphics, pages 73–85. Springer, 2014.[56] Stephen D Krashen. Bilingual education and second language acquisition theory. Schooling and language minority students: A theoretical framework, pages 51–79, 1981.[57] Nobuyuki Hino and Setsuko Oda. Clil pedagogy for eil in higher education. In Functional Variations in English, pages 295–309. Springer, 2020.[58] Gary Ross, Stephen Henneberry, and Glen Norris. Speaking with your computer: A new way to practice and analyze conversation. AI and Machine Learning in Language Education, page 152, 2019.[59] Natasha Randall. A survey of robot-assisted language learning (rall). ACM Transactions on Human-Robot Interaction (THRI), 9(1):1–36, 2019.[60] Gilbert Dizon and Daniel Tang. A pilot study of alexa for autonomous second language learning. CALL and complexity, page 107, 2019.[61] Patrick D Hales, Melissa Anderson, Tonya Christianson, Amber Gaspar, Billi Jo Meyer, Beth Nelson, Krista Shilvock, Mary Steinmetz, Makenzi Timmons, and Michelle Vande Weerd. Alexa?: Possibilities of voice assistant technology and artificial intelligence in the classroom. Empowering Research for Educators, 3(1):4, 2019.[62] Jinjin Zhao, Shreyansh Bhatt, Candace Thille, Dawn Zimmaro, Neelesh Gattani, and Josh Walker. Introducing alexa for e-learning. In Proceedings of the Seventh ACM Conference on Learning@ Scale, pages 427–428, 2020.[63] Alex Housen and Folkert Kuiken. Complexity, accuracy, and fluency in second language acquisition. Applied linguistics, 30(4):461–473, 2009.[64] Garima Tyagi and Rohit Singh. Implementing call system using natural language processing tools. In Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), 2019.[65] G. Bradski. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 25(11), 2000.[66] Oxford University Press. Everybody up teacher’s site, accessed Aug. 31, 2021. https://elt.oup.com/teachers/everybodyup/.[67] Nivja H De Jong and Ton Wempe. Praat script to detect syllable nuclei and measure speech rate automatically. Behavior research methods, 41(2):385–390, 2009.[68] David Snyder, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. X-vectors: Robust dnn embeddings for speaker recognition. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5329–5333. IEEE, 2018.[69] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20(3):273–297, 1995. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101644 en_US
