Publications-Theses
Article View/Open
Publication Export
Google ScholarTM
NCCU Library
Citation Infomation
Related Publications in TAIR
| 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 |
| Creator | 黃紹禎 Huang, Shao-Zhen |
| Contributor | 李蔡彥 Li, Tsai-Yen 黃紹禎 Huang, Shao-Zhen |
| Key Words | 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 |
| Date Issued | 4-Feb-2025 15:43:54 (UTC+8) |
| Summary | 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. |
| 參考文獻 | [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 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#G0111753115 |
| Type | thesis |
| dc.contributor.advisor | 李蔡彥 | zh_TW |
| dc.contributor.advisor | Li, Tsai-Yen | en_US |
| dc.contributor.author (Authors) | 黃紹禎 | zh_TW |
| dc.contributor.author (Authors) | Huang, Shao-Zhen | en_US |
| dc.creator (作者) | 黃紹禎 | zh_TW |
| dc.creator (作者) | Huang, Shao-Zhen | en_US |
| dc.date (日期) | 2025 | en_US |
| dc.date.accessioned | 4-Feb-2025 15:43:54 (UTC+8) | - |
| dc.date.available | 4-Feb-2025 15:43:54 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Feb-2025 15:43:54 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0111753115 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/155452 | - |
| dc.description (描述) | 碩士 | 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) | 111753115 | zh_TW |
| dc.description.abstract (摘要) | 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。 | zh_TW |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.description.tableofcontents | 致謝 i 摘要 ii Abstract iii 目次 iv 表次 vii 圖次 viii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目標 2 1.3 預期貢獻 4 1.4 論文架構 5 第二章 相關研究 6 2.1 教育資訊的管理與分析 6 2.2 教育資訊視覺化 6 2.3 聚類演算法與主題建模 7 2.4 小結 9 第三章 研究方法 10 3.1 系統架構與設計 10 3.2 資料蒐集與整理 11 3.2.1 資料來源 11 3.2.2 資料預處理 13 3.3 核心演算法 14 3.3.1 主題探索（Unsupervised Part） 14 3.3.2 主題引導（Semi-supervised Part） 16 3.4 資料呈現 17 第四章 演算法適用性驗證 21 4.1 實驗資料選取 21 4.2 主題探索部分驗證：模型比較 24 4.3 主題引導部分驗證：錨定詞的影響 27 第五章 系統實作 30 5.1 系統建置 30 5.1.1 資料庫（Database） 30 5.1.2 後端（Backend） 32 5.1.3 前端（Frontend） 34 5.2 系統介面 35 5.2.1 頂部功能欄 37 5.2.2 側邊導覽列與主要操作區 37 5.2.2.1 課程資料選擇 37 5.2.2.2 課程資料內容 39 5.2.2.3 課程分群 40 5.2.2.4 統計分析與視覺化 43 第六章 實驗 48 6.1 實驗流程 48 6.1.1 受試者基本資料 49 6.1.2 SUS題組 50 6.1.3 系統有效性題組 51 6.1.4 開放式問題設計 52 6.2 問卷結構化問題量化結果 53 6.2.1 SUS題組結果 53 6.2.2 系統有效性題組結果 57 6.3 受試者回饋內容整理 59 6.3.1 主要任務訪談回饋 60 6.3.2 系統優點與特色 61 6.3.3 系統待改進建議 63 第七章 結論與未來展望 65 7.1 結論 65 7.2 研究限制與未來展望 66 參考文獻 68 附錄 72 附錄一 實驗任務 72 附錄二 受試者問卷 75 附錄三 問卷填答內容：基本資料 78 附錄四 問卷填答內容：開放式問題 81 | zh_TW |
| dc.format.extent | 6968425 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0111753115 | en_US |
| dc.subject (關鍵詞) | 主題建模 | zh_TW |
| dc.subject (關鍵詞) | Anchored CorEx | zh_TW |
| dc.subject (關鍵詞) | 資訊視覺化 | zh_TW |
| dc.subject (關鍵詞) | 課程資訊分析 | zh_TW |
| dc.subject (關鍵詞) | Topic Modeling | en_US |
| dc.subject (關鍵詞) | Anchored CorEx | en_US |
| dc.subject (關鍵詞) | Information Visualization | en_US |
| dc.subject (關鍵詞) | Course Information Analysis | en_US |
| dc.title (題名) | 互動式大學課程資訊分析工具之設計與實現 | zh_TW |
| dc.title (題名) | Design and Implementation of an Interactive Tool for University Course Information Analysis | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | [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. | zh_TW |