博碩士論文 103187005 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:58 、訪客IP:3.145.89.48
姓名 施彥安(Yen-An Shih)  查詢紙本館藏   畢業系所 學習與教學研究所
論文名稱
(A synthesis of data-driven pattern analysis on co-construction concept map activity and social learning network)
相關論文
★ 社群媒體中結構化知識活動對英文為外語學生預寫成效之研究★ 認知風格與先備知識於預測、模擬、觀察、解釋科學探究活動之影響
★ 雲端概念構圖結合小組互動於國小六年級自然科學習成效之研究★ TIMSS 2015臺灣資料中學生變項與數學成就之關聯:學習分析取向
★ 以行動者網絡理論探究座落於國小的列聯表★ 多媒體階層圖於商用英文寫作之成效研究
★ 實施課前結構階層圖對於國中生寫作活動 成效之研究★ 學生對於線上概念構圖融入國文科教學的觀點
★ 同儕互教對國中生數學學習成效之影響:準備、講解、互動三階段分析★ 幼兒數概念體驗的遊戲化學習環境設計
★ 一行禪師正念教育思想之研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 社群平台如Facebook已經成為學習者不可或缺的一部份,社會學習網路可以在相關的社群平台中發現,它呈現學習者、教師、和學習模組之間的互動網絡。然而,對於一個社會學習網絡平台,其在教育應用需考慮諸多面向,例如:學習者多元化、學習者同質性等。也因為其複雜的社會學習網絡結構,一個評量學生在社群網絡學習特性的方法是必需的。因此,本論文旨在探討上述的研究方向,並提出一個以教育為出發點所設計的社會學習網絡平台 CoCoing.info。本論文主要分為二個實證研究,(1)基於CoCoing.info平台的社會學習網絡形態分析,以及(2)學生同質性在巢狀學習網絡之影響。二個實證研究皆包含其核心研究目的、研究設計、及研究貢獻之探討。本論文研究結果發現:(1)學生持續使用CoCoing.info進行學習活動進而發展出高密度互動的學習網絡、(2)教師在社會學習網絡中扮演關鍵的學習互動角色、(3)學生在平台上的互動呈現巢狀的社會網絡結構,其指出學生在社會學習環境是以群組模式進行學習、(4)巢狀學習網路比社會學習網路有更高的學生同質性的表現、以及(5)巢狀學習網路的巢狀大小與學生同質性呈現正向關係。
摘要(英) Social network sites (SNSs) such as Facebook have been an essential aspect of learners’ daily life all over the world. Social learning network (SLN), a network of interaction between learners, teachers, and educational practices, could be found in SNSs that are specifically designed for educational environments. However, to understand the effects of SLN on a designed social learning platform, researchers must consider how varying individuals and social context influence social learning. Because complexity and interconnectivity of connections in SLNs, an approach for scrutinizing student patterns is needed and timely. Therefore, this dissertation aims to fill those research gaps with a new developed social network learning platform called CoCoing.info. The investigation is focusing on students’ SLNs on CoCoing.info based on two empirical studies, including (1) the SLN pattern analysis on CoCoing.info and (2) the effects of student homophily on cellular learning network. Each study has core research questions, designed analysis approaches, and insightful discussion of the research findings and indications. Overall, the findings of this dissertation revealed that (1) students continued to use CoCoing.info to interact with their peers and developed high density interaction patterns, (2) teachers on the CoCoing.info had key player roles in the SLNs, (3) a cellular learning network revealed students learned in a group format in social learning environment, (4) cellular learning network has higher student homophily than SLN, and (5) the cell size in cellular learning network has positive correlation toward student homophily development.
關鍵字(中) ★ 社會學習網路
★ 巢狀學習網路
★ 社會網絡分析
★ 概念圖
★ 學生同質性
★ 學生分群
關鍵字(英) ★ social learning network
★ cellular learning network
★ social network analysis
★ concept map
★ student homophily
★ student grouping
論文目次 Table of Contents
Abstract i
摘要 ii
Table of Contents iii
Table of Figures v
Table of Tables vi
Chapter 1: Introduction 1
1.1 Research background 1
1.1.1 The impacts of social network sites on educational settings 1
1.1.2 The era of student generated content 2
1.1.3 The mining on invaluable educational big data 3
1.2 Research motivation 5
1.2.1 The booming of social learning environment 5
1.2.2 The diversity and complexity of social interaction 6
1.3 Research scenario and questions 8
1.4 Definition of terms 10
Chapter 2: Literature review 12
2.1 Social network 12
2.1.1 SNSs 12
2.1.2 SLN, networked learning, and network-based knowledge 14
2.1.3 Student grouping 16
2.1.4 Key players in SLN 17
2.1.5 Student homophily 19
2.2 Technology-supported concept mapping 20
2.3 Educational data mining 22
2.3.1 Social network analysis 24
2.3.2 Text mining for student modeling 26
Chapter 3: Social learning network platform-CoCoing.info 28
3.1 Social network construction 29
3.2 Instant message 31
3.3 Concept map construction 33
3.4 Database design and data collection 35
Chapter 4 (Study I): The SLN pattern analysis on CoCoing.info 37
4.1 Research purposes 37
4.2 Methods 37
4.3 Results 38
4.4 Discussion 42
4.4.1 High density interaction 42
4.4.2 Key players 42
4.4.3 Cellular learning network 43
Chapter 5 (Study II): The effects of student homophily on cellular learning network 45
5.1 Research purposes 45
5.2 Methods 46
5.2.1 Analysis of the KS and IF in each social tie 46
5.2.2 Data analysis 47
5.3 Results 48
5.4 Discussion 50
5.4.1 The student homophily effects on cellular learning network 50
5.4.2 The relationship between student homophily and cell size on cellular learning network 51
Chapter 6: Conclusions 53
References 54

參考文獻 References
Adamopoulos, P., Ghose, A., & Todri, V. (2018). The impact of user personality traits on word of mouth: Text-mining social media platforms. Information Systems Research, 29(3), 612-640.
Al-Rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2015). The role of social media for collaborative learning to improve academic performance of students and researchers in Malaysian higher education. International Review of Research in Open and Distributed Learning, 16(4), 177-204.
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A Review and synthesis. Telematics and Informatics, 37, 13-49.
Ananiadou, S., Thompson, P., Thomas, J., Mu, T., Oliver, S., Rickinson, M., Sasaki, Y., Weissenbacher, D., & McNaught, J. (2010). Supporting the education evidence portal via text mining. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1925), 3829-3844.
Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers & Education, 113, 226-242.
Angeli, C., Valanides, N., & Bonk, C. J. (2003). Communication in a web‐based conferencing system: the quality of computer‐mediated interactions. British Journal of Educational Technology, 34(1), 31-43.
Angrist, J. D., & Lang, K. (2004). Does school integration generate peer effects? Evidence from Boston′s Metco Program. American Economic Review, 94(5), 1613-1634.
Askew, M. (2012). Transforming primary mathematics. Routledge.
Ayub, M., Toba, H., Yong, S., & Wijanto, M. C. (2017). Modelling students’ activities in programming subjects through educational data mining. Global Journal of Engineering Education, 19(3), 249-255.
Balakrishnan, V., Liew, T. K., & Pourgholaminejad, S. (2015). Fun learning with Edooware–A social media enabled tool. Computers & Education, 80, 39-47.
Barczyk, C. C., & Duncan, D. G. (2012). Social networking media: An approach for the teaching of international business. Journal of Teaching in International Business, 23(2), 98-122.
Bhattacharyya, P., Garg, A., & Wu, S. F. (2009). Social network model based on keyword categorization. Proceedings of 2009 International Conference on Advances in Social Network Analysis and Mining (pp. 170-175). Athens, Greece: IEEE.
Bhattacharyya, P., Garg, A., & Wu, S. F. (2011). Analysis of user keyword similarity in online social networks. Social Network Analysis and Mining, 1(3), 143-158.
Boguslaw, J. (2017). The key player in disruptive behavior: Whom should we target to improve the classroom learning environ ment. Unpublished manuscript.
Bond, R. M., Chykina, V., & Jones, J. J. (2017). Social network effects on academic achievement. The Social Science Journal, 54(4), 438-449.
Borgatti, S. P. (2006). Identifying sets of key players in a social network. Computational & Mathematical Organization Theory, 12(1), 21-34.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies.
Brechwald, W. A., & Prinstein, M. J. (2011). Beyond homophily: A decade of advances in understanding peer influence processes. Journal of Research on Adolescence, 21(1), 166-179.
Brewe, E., Kramer, L., & Sawtelle, V. (2012). Investigating student communities with network analysis of interactions in a physics learning center. Physical Review Special Topics-Physics Education Research, 8(1), 010101.
Brinton, C. G., & Chiang, M. (2014). Social learning networks: A brief survey. Paper presented at the 2014 48th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA.
Calle-Alonso, F., Botón-Fernández, V., de la Fuente, D., Sánchez, C. J. P., Vega-Rodríguez, M. A., & de la Mata Lara, D. (2018). Graphs and Key Players in an Educational Social Network. Proceedings of the 10th International Conference on Computer Supported Education (pp. 523-527). Funchal, Madeira-Portugal.
Cañas, A. J., & Novak, J. D. (2014). Concept mapping using CmapTools to enhance meaningful learning. Knowledge cartography (pp. 23-45). Springer.
Carter, M. (2009). Visible learning: A synthesis of over 800 meta‐analyses relating to achievement. Routledge.
Cela, K. L., Sicilia, M. Á., & Sánchez, S. (2015). Social network analysis in e-learning environments: A preliminary systematic review. Educational Psychology Review, 27(1), 219-246.
Chang, B., Shih, Y.-A., & Lu, F.-C. (2018). Co-construction concept through cloud-based social network platform design, implementation, and evaluation. International Review of Research in Open and Distributed Learning, 19(5), 238-253.
Chang, C.-C., Liu, G.-Y., Chen, K.-J., Huang, C.-H., Lai, Y.-M., & Yeh, T.-K. (2017). The effects of a collaborative computer-based concept mapping strategy on geographic science performance in junior high school students. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 5049-5060.
Chen, B., & Huang, T. (2019). It is about timing: Network prestige in asynchronous online discussions. Journal of Computer Assisted Learning, 35(4), 503-515. doi:10.1111/jcal.12355
Chen, R.-C., Chen, S.-Y., Fan, J.-Y., & Chen, Y.-T. (2012). Grouping partners for cooperative learning using genetic algorithm and social network analysis. Procedia Engineering, 29, 3888-3893.
Chiou, C.-C., Tien, L.-C., & Tang, Y.-C. (2020). Applying structured computer-assisted collaborative concept mapping to flipped classroom for hospitality accounting. Journal of Hospitality, Leisure, Sport & Tourism Education, 26, 100243.
Christakis, N. A., & Fowler, J. H. (2009). Connected: The surprising power of our social networks and how they shape our lives. Little, Brown Spark.
Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715-4729.
Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317-332.
Currarini, S., Matheson, J., & Vega-Redondo, F. (2016). A simple model of homophily in social networks. European Economic Review, 90, 18-39.
Dohn, N. B., Cranmer, S., Sime, J.-A., Laat, M. d., & Ryberg, T. (2018). Networked learning. Springer International Publishing.
Downey, C. (2020). Identifying key actors in informal collaborative networks in schools. Paper presented at the International Congress for School Effectiveness and Improvement 33rd Annual Conference.
Dringus, L. P., & Ellis, T. (2005). Using data mining as a strategy for assessing asynchronous discussion forums. Computers & Education, 45(1), 141-160.
Durak, G., Cankaya, S., Yunkul, E., & Ozturk, G. (2017). The effects of a social learning network on students′ performances and attitudes. European Journal of Education Studies, 3(3), 312-333.
Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5, 15991-16005.
Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D. (1993). Development during adolescence: The impact of stage–environment fit on young adolescents’ experiences in schools and in families. American Psychologist, 48(2), 90-101.
Edmunds, S., & Brown, G. (2010). Effective small group learning: AMEE Guide No. 48. Medical Teacher, 32(9), 715-726.
Erlin, B., Yusof, N., & Rahman, A. A. (2009). Analyzing online asynchronous discussion using content and social network analysis. Proceedings of 2009 Ninth International Conference on Intelligent Systems Design and Applications (pp. 872-877). Pisa, Italy: IEEE.
Eveland, W. P., & Kleinman, S. B. (2013). Comparing general and political discussion networks within voluntary organizations using social network analysis. Political Behavior, 35(1), 65-87.
Farrokhnia, M., Pijeira-Díaz, H. J., Noroozi, O., & Hatami, J. (2019). Computer-supported collaborative concept mapping: The effects of different instructional designs on conceptual understanding and knowledge co-construction. Computers & Education, 142, 103640.
Fratamico, L., Conati, C., Kardan, S., & Roll, I. (2017). Applying a framework for student modeling in exploratory learning environments: comparing data representation granularity to handle environment complexity. International Journal of Artificial Intelligence in Education, 27(2), 320-352.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239.
Friedkin, N. E. (1991). Theoretical foundations for centrality measures. American Journal of Sociology, 96(6), 1478-1504.
Gao, R., Hao, B., Bai, S., Li, L., Li, A., & Zhu, T. (2013). Improving user profile with personality traits predicted from social media content. In Q. Yang, I. King, Q. Li, P. Pu, & G. Karypis (Eds.), Proceedings of the 7th ACM Conference on Recommender Systems (pp. 355-358). Hong Kong, China: ACM.
Gehlbach, H., Brinkworth, M. E., King, A. M., Hsu, L. M., McIntyre, J., & Rogers, T. (2016). Creating birds of similar feathers: Leveraging similarity to improve teacher–student relationships and academic achievement. Journal of Educational Psychology, 108(3), 342-352.
Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18-26.
Grobelnik, M., Mladenic, D., & Jermol, M. (2002). Exploiting text mining in publishing and education. Proceedings of the ICML-2002 Workshop on Data Mining Lessons Learned (pp. 34-39). Sydney, Australia.
Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE-Life Sciences Education, 13(2), 167-178.
Hammouda, K., & Kamel, M. (2007). Data mining in e-learning. E-Learning Networked Environments and Architectures (pp. 374-404). Springer.
Hara, N., Bonk, C. J., & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28(2), 115-152.
Harris, A. L., & Rea, A. (2009). Web 2.0 and virtual world technologies: A growing impact on IS education. Journal of Information Systems Education, 20(2), 137-144.
Haythornthwaite, C., & De Laat, M. (2010). Social networks and learning networks: Using social network perspectives to understand social learning. Proceedings of the 7th International Conference on Networked Learning (pp. 183-190) Lancaster University Aalborg, Denmark.
He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1), 90-102.
Hew, K. F., & Cheung, W. S. (2011). Higher-level knowledge construction in asynchronous online discussions: An analysis of group size, duration of online discussion, and student facilitation techniques. Instructional Science, 39(3), 303-319.
Hoffer, T. B. (1992). Middle school ability grouping and student achievement in science and mathematics. Educational Evaluation and Policy Analysis, 14(3), 205-227.
Holmes, S. C. (2019). Examining the effect of group assignment on upper elementary students′ experiences in a technology-mediated collaborative compositional activity. (Doctoral dissertation). Georgia State University, Retrieved from https://scholarworks.gsu.edu/mse_diss/76
Huang, J. J., Yang, S. J., Huang, Y.-M., & Hsiao, I. Y. (2010). Social learning networks: Build mobile learning networks based on collaborative services. Journal of Educational Technology & Society, 13(3), 78-92.
Huda, M., Maseleno, A., Atmotiyoso, P., Siregar, M., Ahmad, R., Jasmi, K., & Muhamad, N. (2018). Big data emerging technology: Insights into innovative environment for online learning resources. International Journal of Emerging Technologies in Learning, 13(1), 23-36.
Jackson, D., & Temperley, J. (2007). From professional learning community to networked learning community. Paper presented at the International Congress for School Effectiveness and Improvement (ICSEI) Conference 2006
Jacquemin, S. J., Smelser, L. K., & Bernot, M. J. (2014). Twitter in the higher education classroom: A student and faculty assessment of use and perception. Journal of College Science Teaching, 43(6), 22-27.
Johnson, D. W., Johnson, R. T., & Holubec, E. J. (1994). Cooperative learning in the classroom. Association for Supervision and Curriculum.
Junco, R., & Clem, C. (2015). Predicting course outcomes with digital textbook usage data. The Internet and Higher Education, 27, 54-63.
Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27(2), 119-132.
Kabilan, M. K., Ahmad, N., & Abidin, M. J. Z. (2010). Facebook: An online environment for learning of English in institutions of higher education? Internet and Higher Education, 13(4), 179-187.
Khodorchenko, M., & Butakov, N. (2018). Developing an approach for lifestyle identification based on explicit and implicit features from social media. Procedia Computer Science, 136, 236-245.
Khoza, S. B., & Biyela, A. T. (2019). Decolonising technological pedagogical content knowledge of first year mathematics students. Education and Information Technologies, 1-15.
Kooloos, J. G., Klaassen, T., Vereijken, M., Van Kuppeveld, S., Bolhuis, S., & Vorstenbosch, M. (2011). Collaborative group work: Effects of group size and assignment structure on learning gain, student satisfaction and perceived participation. Medical Teacher, 33(12), 983-988.
Kossinets, G., & Watts, D. J. (2009). Origins of homophily in an evolving social network. American Journal of Sociology, 115(2), 405-450.
Kulik, J. A., & Kulik, C.-L. C. (1992). Meta-analytic findings on grouping programs. Gifted Child Quarterly, 36(2), 73-77.
Kuznetcova, I., Glassman, M., & Lin, T.-J. (2019). Multi-user virtual environments as a pathway to distributed social networks in the classroom. Computers & Education, 130, 26-39.
Leach, G. (2019). Strength based grouping: A call for change. Paper presented at the Annual Meeting of the Mathematics Education Research Group of Australasia, Perth, Western Australia.
Lee, L.-F., Liu, X., Patacchini, E., & Zenou, Y. (2020). Who is the key player? A network analysis of juvenile delinquency. Journal of Business & Economic Statistics. doi:10.1080/07350015.2020.1737082
Liang, H., & Shen, F. (2018). Birds of a schedule flock together: Social networks, peer influence, and digital activity cycles. Computers in Human Behavior, 82, 167-176.
Liu, C.-C., Chen, Y.-C., & Tai, S.-J. D. (2017). A social network analysis on elementary student engagement in the networked creation community. Computers & Education, 115, 114-125.
Liu, C., Kim, J., & Wang, H.-C. (2018). ConceptScape: Collaborative concept mapping for video learning. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12). Montreal QC Canada: Association for Computing Machinery.
Lobel, I., & Sadler, E. (2016). Preferences, homophily, and social learning. Operations Research, 64(3), 564-584.
Magsino, S. L. (2009). Applications of social network analysis for building community disaster resilience. Washington, DC: The National Academies Press.
Maker, C. J., & Zimmerman, R. H. (2020). Concept maps as assessments of expertise: Understanding of the complexity and interrelationships of concepts in science. Journal of Advanced Academics, 31(3), 254-297.
Mamas, C., Schaelli, G. H., Daly, A. J., Navarro, H. R., & Trisokka, L. (2020). Employing social network analysis to examine the social participation of students identified as having special educational needs and disabilities. International Journal of Disability, Development and Education, 67(4), 393-408.
Mansur, A. B. F., & Yusof, N. (2013). Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning. Computers & Education, 63, 73-86.
Marks, R. (2012). How do pupils experience setting in primary mathematics. Mathematics Teaching, 230, 5-8.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415-444.
Merriënboer, J. J. G. v., & Paas, F. (2003). Powerful learning and the many faces of instructional design: Toward a framework for the design of powerful learning environments. Powerful learning environments: Unravelling basic components and dimensions. (pp. 3-20). Oxford, England: Pergamon/Elsevier Science Ltd.
Miflin, B. (2004). Small groups and problem-based learning: are we singing from the same hymn sheet? Medical Teacher, 26(5), 444-450.
Mollica, K. A., Gray, B., & Trevino, L. K. (2003). Racial homophily and its persistence in newcomers’ social networks. Organization Science, 14(2), 123-136.
Mpungose, C. B. (2020). Are social media sites a platform for formal or informal learning? Students’ experiences in institutions of higher education. International Journal of Higher Education, 9(5), 300-311.
Nguyen, C. D., Vo, K. D., Bui, D. B., & Nguyen, D. T. (2011). An ontology-based IT student model in an educational social network. Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services (pp. 379-382). Ho Chi Minh City, Vietnam.
Nick, B., Lee, C., Cunningham, P., & Brandes, U. (2013). Simmelian backbones: Amplifying hidden homophily in facebook networks. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 525-532). Canada: IEEE.
Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in Science Teaching, 27(10), 937-949.
Oberlander, J., & Nowson, S. (2006). Whose thumb is it anyway? Classifying author personality from weblog text. Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions (pp. 627-634). Sydney, Australia.
Ortiz-Arroyo, D. (2010). Discovering sets of key players in social networks. In A. Abraham, A.-E. Hassanien, & V. Sná¿el (Eds.), Computational Social Network Analysis (pp. 27-47). Springer.
Pang, N. S.-k., & Miao, Z. (2017). The roles of teacher leadership in shanghai education success. Paper presented at the Annual International Conference of the Bulgarian Comparative Education Society (BCES) (15th) and the International Partner Conference of the International Research Centre (IRC) "Scientific Cooperation", Borovets, Bulgaria.
Parker, D. C., & Brown, H. (2012). Foundational methods: Understanding teaching and learning. Pearson Custom Pub.
Penuel, W. R., Sussex, W., Korbak, C., & Hoadley, C. (2006). Investigating the potential of using social network analysis in educational evaluation. American Journal of Evaluation, 27(4), 437-451.
Pfister, H. R., & Oehl, M. (2009). The impact of goal focus, task type and group size on synchronous net‐based collaborative learning discourses. Journal of Computer Assisted Learning, 25(2), 161-176.
Philip, T., & Garcia, A. (2013). The importance of still teaching the iGeneration: New technologies and the centrality of pedagogy. Harvard Educational Review, 83(2), 300-319.
Rabbany, R., Elatia, S., Takaffoli, M., & Zaïane, O. R. (2014). Collaborative learning of students in online discussion forums: A social network analysis perspective. In A. Peña-Ayala (Ed.), Educational Data Mining (pp. 441-466). Cham: Springer.
Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13(3), 134-140.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146.
Saint-Charles, J., & Mongeau, P. (2018). Social influence and discourse similarity networks in workgroups. Social Networks, 52, 228-237.
Salloum, S. A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2017). A survey of text mining in social media: Facebook and Twitter perspectives. Advances in Science, Technology and Engineering Systems Journal, 2(1), 127-133.
Salloum, S. A., Al-Emran, M., & Shaalan, K. (2017). Mining social media text: Extracting knowledge from Facebook. International Journal of Computing and Digital Systems, 6(02), 73-81.
Salloum, S. A., Mhamdi, C., Al-Emran, M., & Shaalan, K. (2017). Analysis and classification of Arabic Newspapers’ Facebook pages using text mining techniques. International Journal of Information Technology and Language Studies, 1(2), 8-17.
Sanchiz, M., Lemarié, J., Chevalier, A., Cegarra, J., Paubel, P.-V., Salmerón, L., & Amadieu, F. (2019). Investigating multimedia effects on concept map building: Impact on map quality, information processing and learning outcome. Education and Information Technologies, 24(6), 3645-3667.
Sandbulte, J., Kropczynski, J., & Carroll, J. M. (2019). Using Key Player Analysis as a Method for Examining the Role of Community Animators in Technology Adoption. Retrieved from https://arxiv.org/abs/1902.05630
Sathik, M. M., & Rasheed, A. A. (2009). A centrality approach to identify sets of key players in an online weblog. International Journal of Recent Trends in Engineering, 2(3), 85.
Scheuerell, S. (2010). Virtual Warrensburg: Using cooperative learning and the internet in the social studies classroom. The Social Studies, 101(5), 194-199.
Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to e-learning students. Computers & Education, 51(4), 1744-1754.
Scott, J. (1988). Social network analysis. Sociology, 22(1), 109-127.
Shih, Y.-A., Chang, B., & Chin, J. Y. (2020). Data-driven student homophily pattern analysis of online discussion in a social network learning Journal of Computers in Education, 7, 373–394.
Slavin, R. E. (1996). Research on cooperative learning and achievement: What we know, what we need to know. Contemporary educational psychology, 21(1), 43-69.
Sleeman, J., Lang, C., & Dakich, E. (2020). Social media, learning and connections for international students: The disconnect between what students use and the tools learning management systems offer. Australasian Journal of Educational Technology, 36(4), 44-56.
Smirnov, I., & Thurner, S. (2017). Formation of homophily in academic performance: Students change their friends rather than performance. PLOS ONE, 12(8).
Smith, S., Van Tubergen, F., Maas, I., & McFarland, D. A. (2016). Ethnic composition and friendship segregation: differential effects for adolescent natives and immigrants. American Journal of Sociology, 121(4), 1223-1272.
Sun, B., Wang, M., & Guo, W. (2018). The influence of grouping/non-grouping strategies upon student interaction in online forum: A social network analysis. Proceedings of 2018 International Symposium on Educational Technology (ISET) (pp. 173-177). Osaka, Japan: IEEE.
Sundararajan, B. (2010). Emergence of the most knowledgeable other (mko): Social network analysis of chat and bulletin board conversations in a CSCL system. Electronic Journal of e-Learning, 8(2), 191-208.
Tane, J., Schmitz, C., & Stumme, G. (2004). Semantic resource management for the web: an e-learning application. Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters (pp. 1-10).
Teclehaimanot, B., & Hickman, T. (2011). Student-teacher interaction on Facebook: What students find appropriate. TechTrends, 55(3), 1-12.
Thomson, D., & Mitrovic, A. (2009). Towards a negotiable student model for constraint-based ITSs. Proceedings of 17th International on Conference Computers in Education (pp. 83-90). Hong Kong: University of Canterbury. Computer Science and Software Engineering.
Tien, L.-C., & Chen, Y.-C. (2019). Applying Structured Computer-Assisted Collaborative Concept Mapping to Flipped Classroom. Proceedings of 2019 International Symposium on Educational Technology (ISET) (pp. 120-123). Hradec Kralove Czech Republic: IEEE.
Tucker, C., Pursel, B. K., & Divinsky, A. (2014). Mining student-generated textual data in MOOCs and quantifying their effects on student performance and learning outcomes. The ASEE Computers in Education Journal, 5(4), 84-95.
Vandamme, J.-P., Meskens, N., & Superby, J.-F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110.
Weber, H., Schwenzer, M., & Hillmert, S. (2020). Homophily in the formation and development of learning networks among university students. Network Science, 8(4), 469-491.
Welser, H. T., Gleave, E., Fisher, D., & Smith, M. (2007). Visualizing the signatures of social roles in online discussion groups. Journal of Social Structure, 8(2), 1-32.
Wenger, E., McDermott, R. A., & Snyder, W. (2002). Cultivating communities of practice: A guide to managing knowledge. Harvard Business Press.
Wikipedia. (2019, December 26, 2019). Social learning network. Retrieved from https://en.wikipedia.org/wiki/Social_learning_network
Wilcox, P., Winn, S., & Fyvie‐Gauld, M. (2005). ‘It was nothing to do with the university, it was just the people’: the role of social support in the first‐year experience of higher education. Studies in Higher Education, 30(6), 707-722.
Wimmer, A., & Lewis, K. (2010). Beyond and below racial homophily: ERG models of a friendship network documented on Facebook. American Journal of Sociology, 116(2), 583-642.
Xu, K., Qi, G., Huang, J., Wu, T., & Fu, X. (2018). Detecting bursts in sentiment-aware topics from social media. Knowledge-Based Systems, 141, 44-54.
Xu, X., Yin, X., & Chen, X. (2019). A large-group emergency risk decision method based on data mining of public attribute preferences. Knowledge-Based Systems, 163, 495-509.
Yi, J. S., Kang, Y.-a., Stasko, J. T., & Jacko, J. A. (2008). Understanding and characterizing insights: how do people gain insights using information visualization? Proceedings of the 2008 Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization (pp. 1-6). Florence, Italy: ACM.
Young, J. T. (2011). How do they ‘end up together’? A social network analysis of self-control, homophily, and adolescent relationships. Journal of Quantitative Criminology, 27(3), 251-273.
Yunkul, E., & Cankaya, S. (2017). Students’ attitudes towards Edmodo, a social learning network: A scale development study. Turkish Online Journal of Distance Education, 18(2), 16-29.
Zenou, Y. (2016). Key players. Y. Bramoullé, A. Galeotti, & B. W. Rogers Eds. The Oxford Handbook of the Economics of Networks.
Zevenbergen, R. (2003). Ability grouping in mathematics classrooms: A Bourdieuian analysis. For the Learning of Mathematics, 23(3), 5-10.
Zhao, Y. (2013). Analysing twitter data with text mining and social network analysis. Proceedings of 11th Australasian Data Mining & Analytics Conference (pp. 41-47). Canberra, Australia.
Zhu, C. (2012). Student satisfaction, performance, and knowledge construction in online collaborative learning. Journal of Educational Technology & Society, 15(1), 127-136.
指導教授 張立杰(Ben Chang) 審核日期 2021-3-23
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明