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Title: 整合微觀與鉅觀之即時回饋系統對於深化線上討論成效之影響研究
An Online Discussion System with Instant Micro and Macro-viewpoints Feedback to Facilitate Discussion Effectiveness
Authors: 黃慧君
Huang, Huei-Chun
Contributors: 陳志銘
Chen, Chih-Ming
Huang, Huei-Chun
Keywords: 線上討論
Online discussion
Meso-viewpoints instant feedback system
Micro-viewpoints instant feedback system
Macro-viewpoints instant feedback system
Knowledge construction
Behavior model
Learning process analysis
Quantitative content analysis
Lag sequential analysis
Date: 2021
Issue Date: 2021-08-04 16:44:34 (UTC+8)
Abstract: 討論學習的關鍵在於鏈結了人與人之間的互動,而互動來自於學習者之間持續性的對話。在線上討論的學習情境中,學習者之間的互動更是一個複雜的學習過程,須透過適當的討論活動設計與輔助工具的應用,建構一個好的線上討論環境,讓學習者得以在過程中闡述、分享、反饋、評估彼此的見解,進而共同建構新的知識。因此,本研究從社會網絡的思維角度,提出微觀即時回饋系統、鉅觀即時回饋系統,以及綜觀即時回饋系統等三種具不同觀點特色的系統工具輔以線上討論,從觀點分析的角度引導學習者在討論過程中發掘輔助工具所提供之觀點資訊,進而促進討論學習成效。
The key purpose of learning activity with discussion is to link the interaction between learners, and the interaction comes from the continuous dialogue between learners. In the learning situation of online discussion, the interaction between learners is a very complex learning process. It is necessary to construct a good online discussion environment through appropriately designing discussion activities and using assisted tools. By doing so, learners can explain, share, feedback and evaluate each other's insights, and then collaboratively construct new knowledge in an online discussion process. Therefore, from the perspective of social network, this research proposes three online discussion tools with different viewpoints and characteristics, which are the meso-viewpoints instant feedback system, micro-viewpoints instant feedback system, and macro-viewpoints instant feedback system, respectively. The three online discussion tools were integrated with the online discussion board of Moodle e-learning platform to assist learners’ online discussion. According to the perspective of viewpoint analysis, learners can discover the opinions and information provided by these assisted tools more easily and efficiently to promote their discussion effectiveness.
This research adopted the true experimental research method to examine the research questions. A total of 78 university students were recruited from a national university in Chiayi County as the research subjects. And they were randomly assigned to three groups assisted by three different tools for online discussion. The three groups are the experimental group assisted with meso-viewpoints instant feedback system for online discussion, the control group A assisted with micro-viewpoints instant feedback system, and the control group B assisted with macro-viewpoints instant feedback system. The research examines whether there are significant differences in the effectiveness of online discussion among the learners of three groups. Furthermore, the levels of prior knowledge and computer-mediated communication (CMC) ability were also considered as background variables to examine whether there are significant differences in the effectiveness of online discussions among the learners with different background variables of three groups. In addition, through the learning behavior recorder embedded in the Moodle e-learning platform, the contents of the posts and system operation behavior patterns of the learners during online discussion processes were recorded. With these data, the quantitative content analysis and the sequence analysis of behavior patterns based on the lag sequential analysis (LSA) were performed. Finally, supplemented by semi-structured interviews, the research conclusions were summarized.
The research result shows that the learners who used the meso-viewpoints instant feedback system had significantly better performance than those who used the micro-viewpoints instant feedback system and the macro-viewpoints instant feedback system in terms of entire discussion effectiveness, complexity, and multiple perspectives. Among different background variables, a significant discussion effectiveness difference was found in the entire discussion effectiveness and complexity of the learners with high prior knowledge who use the meso-viewpoints instant feedback system, a significant discussion effectiveness difference was found in the entire discussion effectiveness of the learners with high computer-mediated communication skills, and a significant discussion effectiveness difference was found in the entire discussion effectiveness and complexity of the learners with low computer-mediated communication skills. In addition, through the coding analysis of the discussion posts, this research also found that learners who used different instant online discussion feedback systems have different levels of knowledge construction. Finally, from the operational behavior analysis of the meso-viewpoints instant feedback system group, an effective discussion behavior model for the learners with high discussion effectiveness was also deduced.
Finally, based on the research results of this study, several suggestions for the applications of the three instant online discussion feedback systems in teaching scenarios, system improvement, as well as further research directions were proposed in this study. Overall speaking, this study found that different instant online discussion feedback systems are appropriate to be applied for different types of online discussion subjects. This study can be a useful reference for teachers to choose asynchronous online discussion tools for supporting digital learning. It would contribute to facilitating online discussion in e-learning environments.
Reference: 中文文獻
侯惠澤 (2008)。線上知識分享討論活動與其行為模式探究:以教師/學生社群為例 (博士論文)。國立臺灣師範大學,台北市。檢自:
榮泰生 (2013)。UCINET在社會網絡分析(SNA)之應用 (初版. ed.)。臺北市:臺北市:五南圖書出版,2013民102。
蔡毓智 (2008)。社會網絡:社會學研究的新取向。思與言:人文與社會科學期刊,46(1),1-33。
蔡曉婷 (2019)。基於語義分析網路即時回饋系統輔助線上討論成效之有效行為模式研究 (碩士論文)。國立政治大學,台北市。檢自:

Adrianson, L., & Hjelmquist, E. (1999). Group processes in solving two problems: Face-to-face and computer-mediated communication. Behaviour & Information Technology, 18(3), 179-198.
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.
Anderson, L. W., & Bloom, B. S. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives: Longman.
Asterhan, C. S., & Bouton, E. (2017). Teenage peer-to-peer knowledge sharing through social network sites in secondary schools. Computers & Education, 110, 16-34.
Bahati, B. (2015). Extending student discussions beyond lecture room walls via Facebook. Journal of Education and Practice, 6(15), 160-171.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis: Cambridge university press.
Bannan-Ritland, B. (2002). Computer-mediated communication, elearning, and interactivity: A review of the research. Quarterly Review of Distance Education, 3(2), 161-179.
Berlanga, A. J., & García-Peñalvo, F. J. (2008). Learning design in adaptive educational hypermedia systems. J. UCS, 14(22), 3627-3647.
Bernard, H. R. (2017). Research methods in anthropology: Qualitative and quantitative approaches: Rowman & Littlefield.
Borgatti, S. P. (2005). Centrality and network flow. Social networks, 27(1), 55-71.
Brass, D. J. (2003). A social network perspective on human resources management. Networks in the Knowledge Economy, Oxford University Press, New York, NY, 283-323.
Bubaš, G. (2005). Competence in computer-mediated communication: An evaluation and potential uses of a self-assessment measure. Paper presented at the 56th Annual Conference of the International Communication Association, Dresden, Germany, On-line Program.
Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293-332.
Chen, C.-M., Li, M.-C., Chang, W.-C., & Chen, X.-X. (2020). Developing a Topic Analysis Instant Feedback System to Facilitate Asynchronous Online Discussion Effectiveness. Computers & Education, 104095.
Chen, C.-M., Li, M.-C., & Huang, Y.-L. (2020). Developing an instant semantic analysis and feedback system to facilitate learning performance of online discussion. Interactive learning environments, 1-19.
Chen, C.-M., & Tsao, H.-W. (2020). An Instant Perspective Comparison System to Facilitate Learners’ Discussion Effectiveness in an Online Discussion Process. Computers & Education, 104037.
Chua, Y. P., & Chua, Y. P. (2017). Do computer-mediated communication skill, knowledge and motivation mediate the relationships between personality traits and attitude toward Facebook? Computers in Human Behavior, 70, 51-59.
Chun, D. M. (1994). Using computer networking to facilitate the acquisition of interactive competence. System, 22(1), 17-31.
Dochy. (1992). Assessment of prior knowledge as a determinant for future learning: The use of prior knowledge state tests and knowledge profiles: Centre for Educational Technology and Innovation, Open University.
Dochy, De Rijdt, C., & Dyck, W. (2002). Cognitive prerequisites and learning: How far have we progressed since Bloom? Implications for educational practice and teaching. Active Learning in Higher Education, 3(3), 265-284.
Dochy, Moerkerke, G., & Martens, R. (1996). Integrating assessment, learning and instruction: Assessment of domain-specific and domaintranscending prior knowledge and progress. Studies in educational evaluation, 22(4), 309-339.
Dochy, Segers, M., & Buehl, M. M. (1999). The relation between assessment practices and outcomes of studies: The case of research on prior knowledge. Review of Educational research, 69(2), 145-186.
Dubovi, I., & Tabak, I. (2020). An empirical analysis of knowledge co-construction in YouTube comments. Computers & Education, 103939.
Emirbayer, M. (1997). Manifesto for a relational sociology. American journal of sociology, 103(2), 281-317.
Emirbayer, M., & Goodwin, J. (1994). Network analysis, culture, and the problem of agency. American journal of sociology, 99(6), 1411-1454.
Eryilmaz, E., Thoms, B., & Canelon, J. (2018). How design science research helps improve learning efficiency in online conversations. Communications of the Association for Information Systems, 42(1), 21.
Gao, F., Zhang, T., & Franklin, T. (2013). Designing asynchronous online discussion environments: Recent progress and possible future directions. British Journal of Educational Technology, 44(3), 469-483.
García-Peñalvo, F. J. (2020). Learning analytics as a breakthrough in educational improvement. In Radical Solutions and Learning Analytics (pp. 1-15): Springer.
Garrison, D. R. (1991). Critical thinking and adult education: A conceptual model for developing critical thinking in adult learners. International Journal of Lifelong Education, 10(4), 287-303.
Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying online social networks. Journal of Computer-Mediated Communication, 3(1), JCMC313.
Gerosa, M. A., Filippo, D., Pimentel, M., Fuks, H., & Lucena, C. J. (2010). Is the unfolding of the group discussion off-pattern? Improving coordination support in educational forums using mobile devices. Computers & Education, 54(2), 528-544.
Gittell, J. H., & Weiss, L. (2004). Coordination networks within and across organizations: A multi‐level Framework. Journal of management studies, 41(1), 127-153.
Gunawardena, C. N., Flor, N. V., Gómez, D., & Sánchez, D. (2016). Analyzing social construction of knowledge online by employing interaction analysis, learning analytics, and social network analysis. Quarterly Review of Distance Education, 17(3), 35.
Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4), 397-431.
Haythornthwaite, C. (2005). Social networks and Internet connectivity effects. Information, Community & Society, 8(2), 125-147.
Haythornthwaite, C. (2019). Learning, connectivity and networks. Information and Learning Sciences.
Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of ‘useful’digital technology in university teaching and learning. Studies in Higher Education, 42(8), 1567-1579.
Henri, F. (1992). Computer conferencing and content analysis. In Collaborative learning through computer conferencing (pp. 117-136): Springer.
Hernández-Lara, A. B., Perera-Lluna, A., & Serradell-López, E. (2019). Applying learning analytics to students’ interaction in business simulation games. The usefulness of learning analytics to know what students really learn. Computers in Human Behavior, 92, 600-612.
Herring, S. C. (1996). Computer-mediated communication: Linguistic, social, and cross-cultural perspectives (Vol. 39): John Benjamins Publishing.
Holtshouse, D. (1999). Ten knowledge domains: model of a knowledge-driven company? Knowledge and process management, 6(1), 3-8.
Hou, H.-T., Chang, K.-E., & Sung, Y.-T. (2008). Analysis of problem-solving-based online asynchronous discussion pattern. Journal of Educational Technology & Society, 11(1), 17-28.
Hou, H.-T., Sung, Y.-T., & Chang, K.-E. (2009). Exploring the behavioral patterns of an online knowledge-sharing discussion activity among teachers with problem-solving strategy. Teaching and Teacher Education, 25(1), 101-108.
Hou, H.-T., Wang, S.-M., Lin, P.-C., & Chang, K.-E. (2015). Exploring the learner’s knowledge construction and cognitive patterns of different asynchronous platforms: comparison of an online discussion forum and Facebook. Innovations in education and Teaching International, 52(6), 610-620.
Hrastinski, S. (2009). A theory of online learning as online participation. Computers & Education, 52(1), 78-82.
Jeong, A., & Joung, S. (2007). Scaffolding collaborative argumentation in asynchronous discussions with message constraints and message labels. Computers & Education, 48(3), 427-445.
Kayler, M., & Weller, K. (2007). Pedagogy, self-assessment, and online discussion groups. Journal of Educational Technology & Society, 10(1), 136-147.
Kerr, D. S., & Murthy, U. S. (2004). Divergent and convergent idea generation in teams: A comparison of computer-mediated and face-to-face communication. Group decision and Negotiation, 13(4), 381-399.
Kintsch, W., & Van Dijk, T. A. (1978). Toward a model of text comprehension and production. Psychological review, 85(5), 363.
Kintsch, W., & Walter Kintsch, C. (1998). Comprehension: A paradigm for cognition: Cambridge university press.
Koh, J. H. L., Herring, S. C., & Hew, K. F. (2010). Project-based learning and student knowledge construction during asynchronous online discussion. The Internet and Higher Education, 13(4), 284-291.
Koszalka, T. A., Pavlov, Y., & Wu, Y. (2021). The informed use of pre-work activities in collaborative asynchronous online discussions: The exploration of idea exchange, content focus, and deep learning. Computers & Education, 161, 104067.
Kwon, K., Shin, S., & Park, S. J. (2018). Effects of graphic organizers in online discussions: comparison between instructor-provided and student-generated. Educational Technology Research and Development, 66(6), 1479-1503.
Lerís, D., & Sein-Echaluce, M. L. (2011). La personalización del aprendizaje: Un objetivo del paradigma educativo centrado en el aprendizaje. Arbor, 187(Extra_3), 123-134.
Lerís, D., Sein-Echaluce, M. L., Hernández, M., & Bueno, C. (2017). Validation of indicators for implementing an adaptive platform for MOOCs. Computers in Human Behavior, 72, 783-795.
Lewis, C., & Fabos, B. (2005). Instant messaging, literacies, and social identities. Reading Research Quarterly, 40(4), 470-501.
Lin, Y., Dowell, N., Godfrey, A., Choi, H., & Brooks, C. (2019). Modeling gender dynamics in intra and interpersonal interactions during online collaborative learning. Paper presented at the Proceedings of the 9th international conference on learning analytics & knowledge.
Loncar, M., Barrett, N. E., & Liu, G.-Z. (2014). Towards the refinement of forum and asynchronous online discussion in educational contexts worldwide: Trends and investigative approaches within a dominant research paradigm. Computers & Education, 73, 93-110.
Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
Lucas, M., Gunawardena, C., & Moreira, A. (2014). Assessing social construction of knowledge online: A critique of the interaction analysis model. Computers in Human Behavior, 30, 574-582.
Mayer, R. E. (2005). Cognitive theory of multimedia learning. The Cambridge handbook of multimedia learning, 41, 31-48.
Mishler, W., & Rose, R. (2001). What are the origins of political trust? Testing institutional and cultural theories in post-communist societies. Comparative political studies, 34(1), 30-62.
Mpungose, C. B. (2020). Is Moodle or WhatsApp the preferred e-learning platform at a South African university? First-year students’ experiences. Education and Information Technologies, 25(2), 927-941.
Mutlu-Bayraktar, D., Cosgun, V., & Altan, T. (2019). Cognitive load in multimedia learning environments: A systematic review. Computers & Education, 141, 103618.
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of management review, 23(2), 242-266.
Newman, D. R., Webb, B., & Cochrane, C. (1995). A content analysis method to measure critical thinking in face-to-face and computer supported group learning. Interpersonal Computing and Technology, 3(2), 56-77.
O'Riordan, T., Millard, D. E., & Schulz, J. B. (2016). How should we measure online learning activity? Research in Learning Technology, 24, 1-28.
Paivio, A. (1990). Mental representations: A dual coding approach: Oxford University Press.
Pala, F. K., & Erdem, M. (2020). Development of a participation style scale for online instructional discussions. Educational Technology Research and Development, 1-21.
Renkl, A., & Scheiter, K. (2017). Studying visual displays: How to instructionally support learning. Educational Psychology Review, 29(3), 599-621.
Richter, T., Naumann, J., & Groeben, N. (2000). Attitudes toward the computer: Construct validation of an instrument with scales differentiated by content. Computers in Human Behavior, 16(5), 473-491.
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.
Rosé, C. P., & Ferschke, O. (2016). Technology support for discussion based learning: From computer supported collaborative learning to the future of massive open online courses. International Journal of Artificial Intelligence in Education, 26(2), 660-678.
Rose, E. (2016). Reflection in asynchronous online postsecondary courses: a reflective review of the literature. Reflective Practice, 17(6), 779-791.
Ruthotto, I., Kreth, Q., Stevens, J., Trively, C., & Melkers, J. (2020). Lurking and participation in the virtual classroom: The effects of gender, race, and age among graduate students in computer science. Computers & Education, 151, 103854.
Sadler, T. D. (2004). Informal reasoning regarding socioscientific issues: A critical review of research. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 41(5), 513-536.
Sadler, T. D., Barab, S. A., & Scott, B. (2007). What do students gain by engaging in socioscientific inquiry? Research in Science Education, 37(4), 371-391.
Sadler, T. D., & Zeidler, D. L. (2005). Patterns of informal reasoning in the context of socioscientific decision making. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 42(1), 112-138.
Salopek, J. J., & Dixon, N. M. (2000). Common knowledge: How companies thrive by sharing what they know. Training & Development, 54(4), 63-64.
Sayan, H. (2016). Affecting higher students learning activity by using WhatsApp. European Journal of Research and Reflection in Educational Sciences Vol, 4(3), 88-93.
Scardamalia, M. (2002). Collective cognitive responsibility for the advancement of knowledge. Liberal education in a knowledge society, 97, 67-98.
Scardamalia, M. (2004). CSILE/Knowledge forum®. Education and technology: An encyclopedia, 183, 192.
Shaughnessy, J. J., Zechmeister, E. B., & Zechmeister, J. S. (2000). Research methods in psychology: McGraw-Hill.
Shwartz-Asher, D., Chun, S., Adam, N. R., & Snider, K. L. (2020). Knowledge sharing behaviors in social media. Technology in Society, 63, 101426.
Simonneaux, L. (2008). Argumentation in socio-scientific contexts. Argumentation in science education: Perspectives from classroom-based research, 179-199.
Smith, B. (2003). The use of communication strategies in computer-mediated communication. System, 31(1), 29-53.
Spitzberg, B. H. (2006). Preliminary development of a model and measure of computer-mediated communication (CMC) competence. Journal of Computer-Mediated Communication, 11(2), 629-666.
Sun, J. C.-Y., & Chen, A. Y.-Z. (2016). Effects of integrating dynamic concept maps with Interactive Response System on elementary school students' motivation and learning outcome: The case of anti-phishing education. Computers & Education, 102, 117-127.
Thoms, B., & Eryilmaz, E. (2015). Introducing a twitter discussion board to support learning in online and blended learning environments. Education and Information Technologies, 20(2), 265-283.
UNESCO. (2020). School closures caused by Coronavirus (Covid-19). Retrieved from
van Heijst, H., de Jong, F. P., Van Aalst, J., De Hoog, N., & Kirschner, P. A. (2019). Socio-cognitive openness in online knowledge building discourse: does openness keep conversations going? International Journal of Computer-Supported Collaborative Learning, 14(2), 165-184.
Volet, S., Summers, M., & Thurman, J. (2009). High-level co-regulation in collaborative learning: How does it emerge and how is it sustained? Learning and instruction, 19(2), 128-143.
Wang, Y., & Liu, Q. (2020). Effects of online teaching presence on students' interactions and collaborative knowledge construction. Journal of Computer Assisted Learning, 36(3), 370-382.
Wassenberg, A. F. (1982). Neo-corporatism and the quest for control: The cuckoo game: Interuniversitaire interfaculteit bedrijfskunde.
Wegerif, R., McLaren, B. M., Chamrada, M., Scheuer, O., Mansour, N., Mikšátko, J., & Williams, M. (2010). Exploring creative thinking in graphically mediated synchronous dialogues. Computers & Education, 54(3), 613-621.
Wise, A. F., & Cui, Y. (2018). Learning communities in the crowd: Characteristics of content related interactions and social relationships in MOOC discussion forums. Computers & Education, 122, 221-242.
Witherby, A. E., & Carpenter, S. K. (2021). The rich-get-richer effect: Prior knowledge predicts new learning of domain-relevant information. Journal of Experimental Psychology: Learning, Memory, and Cognition.
Wu, Y. T., & Tsai, C. C. (2011). High school students’ informal reasoning regarding a socio‐scientific issue, with relation to scientific epistemological beliefs and cognitive structures. International Journal of Science Education, 33(3), 371-400.
Yeo, T.-M., & Quek, C.-L. (2014). Scaffolding high school students’ divergent idea generation in a computer-mediated design and technology learning environment. International Journal of Technology and Design Education, 24(3), 275-292.
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