政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/155452
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 114393/145446 (79%)
Visitors : 53036037      Online Users : 644
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/155452


    Title: 互動式大學課程資訊分析工具之設計與實現
    Design and Implementation of an Interactive Tool for University Course Information Analysis
    Authors: 黃紹禎
    Huang, Shao-Zhen
    Contributors: 李蔡彥
    Li, Tsai-Yen
    黃紹禎
    Huang, Shao-Zhen
    Keywords: 主題建模
    Anchored CorEx
    資訊視覺化
    課程資訊分析
    Topic Modeling
    Anchored CorEx
    Information Visualization
    Course Information Analysis
    Date: 2025
    Issue Date: 2025-02-04 15:43:54 (UTC+8)
    Abstract: 隨著教育部高等教育深耕計畫[1]的推動,全國大專校院在各領域開設了豐富且多元的課程,提供學生修習,相關課程資料皆收錄於大學暨技專校院課程資源網[2]。此網站不僅方便社會大眾快速查詢各校院的課程內容,亦提供相關數據作為參考。然而,對課程研究者而言,這些大量而繁雜的資訊難以直接進行分析與詮釋,進而觀察特定議題或脈絡,導致課程資料的潛在價值未能充分發揮。為解決上述挑戰,本研究提出一套基於主題建模技術的互動式大學課程資訊分析工具。透過自定義方式,該工具能引導課程分群,並從多維度進行交互式課程資料分析,結合視覺化呈現,協助使用者從不同角度挖掘課程資訊的潛在價值。在「跨校」層面,此系統能整合各校離散的課程資訊,建立相互關聯;在「跨年度」層面,使用者則可透過系統觀察不同學年間課程的變化模式。本研究分兩階段進行實驗以驗證系統的有效性與實用性。結果顯示,該工具在精準度與數據洞察力方面具顯著優勢,並透過互動式設計提升用戶體驗與分析效率。此工具為課程研究提供了一個靈活且高效的分析框架,對教育領域的課程政策規劃與資源分配提出全新的解決方案。
    With the Ministry of Education's Higher Education Deep Cultivation Project[1], universities nationwide offer diverse courses, compiled in the University and Technical College Course Resource Network[2]. This platform facilitates public access to course information and provides data for reference. However, the complexity of this data poses challenges for researchers in analyzing and interpreting it effectively, limiting its potential value.
    To address this, we propose an interactive analysis tool for university course data based on topic modeling. This tool enables customizable course clustering, multidimensional interactive analysis, and visualized insights, helping users uncover hidden values. At the "inter-institutional" level, it integrates dispersed course data to establish correlations, while at the "inter-annual" level, it reveals patterns of change across years.
    Experiments conducted in two phases validate the tool's accuracy, data insights, and show that the user experience and efficiency of analysis has been improved through interactive design. The tool offers a flexible, efficient framework for curriculum research, providing innovative solutions for course policy planning and resource allocation.
    Reference: [1] 高等教育司,<高等教育深耕計畫正式啟動>,檢索自:https://reurl.cc/Xq38GM。
    [2] 國立雲林科技大學,<大學暨技專校院課程資源網>,檢索自:https://course-tvc.yuntech.edu.tw/default.aspx。
    [3] C. Fischer, Z. A. Pardos, R. S. Baker, J. J. Williams, P. Smyth, R. Yu, S. Slater, R. Baker, and M. Warschauer, “Mining big data in education: Affordances and challenges,” Review of Research in Education, vol. 44, no. 1, pp. 130-160, 2020.
    [4] H. Aldowah, H. Al-Samarraie and W. M. Fauzy, “Educational data mining and learning analytics for 21st century higher education: A review and synthesis,” Telematics and Informatics, vol. 37, pp. 13-49, 2019.
    [5] C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Trans. Syst. Man Cybern. C Appl. Rev., vol. 40, no. 6, pp. 601-618, 2010.
    [6] International Educational Data Mining Society, “educationaldatamining.org,” https://educationaldatamining.org/.
    [7] C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 3, pp. e1355, 2020.
    [8] R. S. Baker, T. Martin, and L. M. Rossi, “Educational data mining and learning analytics,” The Wiley Handbook of Cognition and Assessment: Frameworks, Methodologies, and Applications, pp. 379-396, 2016.
    [9] S. Slater, S. Joksimović, V. Kovanovic, R. S. Baker and D. Gasevic, “Tools for educational data mining: A review,” J. Educ. Behav. Stat., vol. 42, no. 1, pp. 85-106, 2017.
    [10] J. Zheng, J. Wang, Y. Ren and Z. Yang, “Chinese sentiment analysis of online education and internet buzzwords based on BERT,” J. Phys. Conf. Ser., vol. 1631, no. 1, 2020.
    [11] J. M. Markel, S. G. Opferman, J. A. Landay and C. Piech, “GPTeach: Interactive TA training with GPT-based students,” Proc. 10th ACM Conf. Learn. @ Scale, pp. 226-236, 2023.
    [12] C. Ware, Information Visualization: Perception for Design. Morgan Kaufmann, 2019.
    [13] A. M. Tervakari, K. Silius, J. Koro, J. Paukkeri, and O. Pirttilä, “Usefulness of information visualizations based on educational data,” in Proceedings of the 4th IEEE Global Engineering Education Conference (EDUCON), pp. 142-151, 2014.
    [14] J. Heer, M. Bostock and V. Ogievetsky, “A tour through the visualization zoo,” Communications of the ACM, vol. 53, no. 6, pp. 59-67, 2010.
    [15] M. A. A. Dewan, W. M. Pachon and F. Lin, “A review on visualization of educational data in online learning,” Proc. Int. Symp. Emerg. Technol. Educ., vol. 12511, pp. 15-24, 2021.
    [16] V. P. Bresfelean, M. Bresfelean, N. Ghisoiu and C. A. Comes, “Determining students’ academic failure profile founded on data mining methods,” ITI 30th Int. Conf. Inf. Technol. Interfaces, pp. 317-322, 2008.
    [17] A. Dutt, M. A. Ismail and T. Herawan, “A systematic review on educational data mining,” in IEEE Access, vol. 5, pp. 15991-16005, 2017.
    [18] D. M. Blei, A. Y. Ng and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.
    [19] J. M. Rouly, H. Rangwala and A. Johri, “What are we teaching? Automated evaluation of cs curricula content using topic modeling,” Proceedings of the Eleventh Annual International Conference on International Computing Education Research, pp. 189-197, 2015.
    [20] S. R. Kallem, “Model for analyzing course description using LDA topic modeling,” The University of North Carolina at Greensboro, Greensboro, 2022.
    [21] X. Yan, J. Guo, Y. Lan and X. Cheng, “A biterm topic model for short texts,” Proc. of the International Conference on World Wide Web, pp. 1445-1456, 2013.
    [22] R. J. Gallagher, K. Reing, D. Kale and G. Ver Steeg, “Anchored correlation explanation: Topic modeling with minimal domain knowledge,” Trans. Assoc. Comput. Linguistics, vol. 5, pp. 529-542, 2017.
    [23] K. Zhou, J. Wang, B. Ashuri, and J. Chen, “Discovering the research topics on construction safety and health using semi-supervised topic modeling,” Buildings, vol. 13, no. 5, p. 1169, 2023.
    [24] Vikash Singh, “Welcome to GuidedLDA’s documentation!,” https://guidedlda.readthedocs.io/en/latest/, 2017.
    [25] R. Egger and J. Yu, “A topic modeling comparison between LDA, NMF, Top2Vec, and BERTopic to demystify twitter posts,” Front. Sociol., vol. 7, 2022.
    [26] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” Proc. Adv. Neural Inf. Process. Syst., pp. 556-562, 2001.
    [27] D. Angelov, “Top2Vec: Distributed representations of topics,” arXiv:2008.09470, 2020.
    [28] M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” arXiv:2203.05794, 2022.
    [29] T. K. Moon, “The expectation-maximization algorithm,” in IEEE Signal Processing Magazine, vol. 13, no. 6, pp. 47-60, 1996.
    [30] N. Friedman, O. Mosenzon, N. Slonim and N. Tishby, “Multivariate information bottleneck,” arXiv:1301.2270, 2013.
    [31] 國立政治大學,<政大課程地圖>,檢索自:https://cis.nccu.edu.tw/coursemap/students/GenEdu.aspx。
    [32] 逢甲大學,<課程地圖(112學年度起適用)>,檢索自:https://reurl.cc/jW07ND。
    [33] 靜宜大學通識教育中心,<110學年度新制通識涵養課程架構>,檢索自:https://gec.pu.edu.tw/p/404-1051-22379.php?Lang=zh-tw。
    [34] Maarten Grootendorst, “Guided Topic Modeling,” https://reurl.cc/OGD98g, 2024.
    [35] D. Mimno, H. M. Wallach, E. Talley, M. Leenders and A. McCallum, “Optimizing semantic coherence in topic models,” Proc. Conf. Empirical Methods Natural Lang. Process., pp. 262-272, 2011.
    [36] J. Brooke, “SUS-A quick and dirty usability scale,” Usability Evaluation in Industry, vol. 189, no. 194, pp. 4-7, 1996.
    [37] T. S. Tullis and J. N. Stetson, “A comparison of questionnaires for assessing website usability,” Usability Professional Association Conference, pp. 1-12, 2004.
    [38] R. Likert, “A technique for the measurement of attitudes,” Arch. Psychol., vol. 140, pp. 5-55, 1932.
    [39] A. Bangor, P. Kortum and J. Miller, “Determining what individual SUS scores mean: Adding an adjective rating scale,” J. Usability Studies, vol. 4, no. 3, pp. 114-123, 2009.
    Description: 碩士
    國立政治大學
    資訊科學系
    111753115
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111753115
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

    Files in This Item:

    File Description SizeFormat
    311501.pdf6805KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback