博碩士論文 102423023 詳細資訊




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姓名 林怡均(Yi-chun Lin)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 運用資料探勘技術於建置招生 決策支援系統之研究
(Development of Higher Education Enrollment Decision Support System Using Data Mining Technology)
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摘要(中) 高等教育發展於人才培育中是不可或缺的角色,且為了符合聯合國教育科學文化組織所提出教育政策,應滿足公平、適切和卓越三項原則。各大專院校系所自行制訂該系所的入學標準,以適切的入學標準、公平審核學生資格,並期望錄取之學生能有卓越表現。因此,本篇論文旨在探討入學標準是否符合系所特色,且欲了解學生潛質對於系所課程表現的影響,進而了解學生潛質是否符合該系所之特色,以達成適性揚才之目標。本研究利用資料探勘中分類、屬性選擇及關聯規則之技術,來發現影響學業表現因子,並且歸納、建立規則模型,然後依據此模型建置協助招生及決策人員所使用之決策支援系統並能給予建議,以作為提供招生委員會修改入學標準之建議。
摘要(英) In higher education, the selection of future students are critical to the success of education. Every universities establish their own admission criteria. Using the relevant admission criteria and equally examine applicants’ qualification, hoping to enroll the applicant which has excellent performance. Therefore, this research aims to establish a model for determine the suitable admission criteria for the features of the department. In order to understand the influence between the potential capability of student and specific subject, and further comprehend whether capability of student correspond to the features of the department or not.This paper apply data mining techniques including classification, attribute selection and association to discover the factors of affecting study performance and establish the model. The Decision Support Systems is built based on this model. It support admission committee to enroll students and moderfy the admission criteria.
關鍵字(中) ★ 高等教育
★ 招生條件
★ 商業智慧
★ 資料探勘
★ 決策樹
★ 關聯規則
關鍵字(英) ★ Higher education
★ Admission criteria
★ Business intelligence
★ Data mining
★ Decision tree
★ Association rule
論文目次 目錄
摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與問題 2
1.3 研究目的 3
1.4 研究假設 4
1.5 論文架構 4
第二章 文獻探討 6
2.1 大學招生及遴選 6
2.2 學業表現預測 7
2.3 商業智慧與資料探勘 8
2.3.1 資料探勘之概述 9
2.3.2 資料探勘之技術 10
2.3.3 資料探勘之工具介紹 11
2.4 應用資料探勘於預測學業表現之相關研究 13
第三章、研究方法 14
3.1 研究架構圖 14
3.2 模型步驟介紹 16
3.2.1 步驟一:輸入資料 16
3.2.2 步驟二:彙整資料 17
3.2.3 步驟三:資料探勘 17
3.2.4 步驟四:輸出結果 20
3.2.5 步驟五:維護系統 20
第四章 研究展示 21
4.1 資料描述 22
4.1.1 原始資料 22
4.1.2 資料合併 23
4.2 資料探勘 25
4.2.1 資料預處理 25
4.2.2 資料結構描述 37
4.2.3 預測學生四年表現之規則 39
4.2.4 學測成績與入學後學業表現關聯 44
4.2.5 建立系統 48
第五章 系統展示 49
5.1 遴選系統關鍵人員 49
5.2 系統架構以及情境說明 49
5.3 系統操作步驟 51
5.4 系統分析結果 60
第六章 討論與結論 61
6.1 研究結果 61
6.1.1 學生潛能關聯性 61
6.1.2 修改訂定入學標準 63
6.1.3 其他相關討論 63
6.2 研究貢獻 64
6.3 研究限制 65
6.4 未來展望 65
6.5 實際採用本系統 66
參考文獻 67
參考文獻 [1] 袁梅宇(2015)。王者歸來WEKA機器學習與大數據聖經。佳魁資訊。
[2] 蘇進棻(2007)。台灣高等教育發展與未來挑戰。教育人力與專業發展,第24卷第1期,p125-132。
[3] 吳清山(2011)。我國高等教育革新的重要課題與未來發展之分析。長庚人文社會學報,第四卷第二期,p241-280。
[4] 李俊憲(2007)。人格特質、生活型態、星座類型三者對消費者決策型態差異之研究,大葉大學休閒事業管理學碩士班,彰化。
[5] 黃怡瑜(2011)。星座、人格特質、工作特性、工作適應性與工作表現關聯性之研究─以國際觀光旅館業員工為例,明新科技大學服務事業管理研究所碩士論文,新竹。
[6] 顏博文(2003)。應用資料探勘技術分析學生選課特性與學業表現。中原大學資訊管理研究所碩士論文,桃園。
[7] 楊琇媛(2003)。利用資料倉儲與資料探勘技術於招生策略與學生特質分析之研究。中原大學資訊管理研究所碩士論文,桃園。
[8] 溫侑柯(2005)。應用資料探勘之關聯法則探討大學入學成績對在學成績的影響─以資管系為例,南華大學資訊管理研究所碩士論文,嘉義。
[9] 王培倫(2003)。星座對於消費者在購物傾向上之影響─以大台北地區大學生為例,國立政治大學廣告研究所碩士論文,台北。
[10] 大學入學考試中心。測驗考試。2015年1月28日,取自:http://www.ceec.edu.tw/
[11] 教育部(2013)。教育部人才培育白皮書。2015年1月29日,取自:http://www.edu.tw/userfiles/url/20131209094223/%E6%95%99%E8%82%B2%E9%83%A8%E4%BA%BA%E6%89%8D%E5%9F%B9%E8%82%B2%E7%99%BD%E7%9A%AE%E6%9B%B81.pdf
[12] 教育部統計處(2014)。103學年大專校院新生註冊率概況。2015年1月31日,取自:https://stats.moe.gov.tw/files/brief/103%E5%AD%B8%E5%B9%B4%E5%A4%A7%E5%B0%88%E6%A0%A1%E9%99%A2%E6%96%B0%E7%94%9F%E8%A8%BB%E5%86%8A%E7%8E%87%E6%A6%82%E6%B3%81.pdf
[13] 教育部綜合規劃司(未知)。高等教育現況。2015年1月29日,取自:http://www.edu.tw/userfiles/url/20140530154937/9.%E9%AB%98%E7%AD%89%E6%95%99%E8%82%B2.pdf
[14] 中央大學教務處。招生名額規畫表。2014年10月27日,取自:http://pdc.adm.ncu.edu.tw/admission/adminfo.asp?roadno=66
[15] Ayesha, S., Mustafa, T., Sattar, A.R., Khan, M.L. (2010). Data mining model for higher education system. Europen Journal of Scientific Research, 43(1), 24-29.
[16] Baker, R. S. J. D., & Yancef, K. (2009). The state of educational data mining in 2009: a review and future visions. Journal of Educational Data Mining, 1(1), 3-16.
[17] Baradwaj, B. K., & Pal, S. (2011). Mining education data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2(6), 63-69.
[18] Bhise, R.B., Thorat, S. S., & Supekar, A. K. (2013). Importance of data mining in higher education system. Journal of Humanities and Social Science, 6(6), 18-21.
[19] Bill, C. H., Rick L. W., & Kent A. W. (1994). Predicting graduate student success: a comparison of neural networks and traditional techniques. Journal of Computer & Operations research, 21(3), 249-263.
[20] Bresfelean, V. P. (2007, June). Analysis and predictions on students’ behavior using decision trees in WEKA environment. Proceeding of the ITI 29th Int. Conf. on International Technology Interface (pp. 51-56).
[21] Chung, H. M., Gray, P., Mannino, M. (1998, January). Introduction to data mining and knowledge discovery. System Sciences (pp. 244-246). IEEE.
[22] Clarke, D., Gabriels, T., & Barnes, J. (1996). Astrological Signs as Determinants of Extroversion and Emotionality: An Empirical Study. The journal of psychology, 130(2), 131-140.
[23] Delors, J.(1998).Learning: The Treasure Within, Report to UNESCO of the International Commission Pocket Edition, p254
[24] Dennis, C.,Marsland, D., & Cockett, T. (2011). Data mining for shopping centres-customer knowledge-management framework. Journal of Knowledge Management, 5(4), 368-374.
[25] Fang, N., Lu, J. (2009, October). Work in progress- a decision tree approach to predicting student performance in a high-enrollment, high-impact, and core engineering M2F(pp. 1-3). IEEE.
[26] Fourie, D. P. (1984). Self-attribution theory and the sun-sign. The journal of social psychology, 122(1), 121-126.
[27] Frank, E., Hall, M., Trigg, L., Holmes, G., & Witten, I. H. (2004). Data mining in bioinformatics using weka. Bioinformatics, 20(15), 2479-2481.
[28] Garton, B. L., Dyer, J. E., King, B. O. (2000). The use of learning styles and admission criteria in predicting academic performance and retention of college freshmen. Journal of agricultural education, 41(2), 46-53.
[29] Geiser, S., & Santelices, M. V. (2007). Validity of school grades in predicting student success beyond the freshman year: high-school record vs. standard tests as indicators of four-year college outcomes. Center for studies in higher education.
[30] Gnanasundari, S., & Narendran, P. (2014). Analysis of different feature selection methods in intrusion detection system. International Journal of Research in Computer Application and Robotics, 2(8), 119-125.
[31] Goebel, M., & Gruenwald, L. (1999). A survey of data mining knowledge discovery software tools. ACM SIGKDD Explorations Newsletter, 1(1), 20-33.
[32] Guruler, H., Istanbullu, A., & Karahasan, M. (2010). A new student performance analyzing system knowledge discovery in higher educational databases. Computer & Education, 55, 247-254.
[33] Hall, M.A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining, IEEE Transactions on Knowledge and Data Engineering. 15(6), 1437-1447.
[34] Han, J., & Kamber, M.Data Mining.(2001). Data Mining:Concepts and Techniques. Morgan Kaufmann Publisher.
[35] Hoefer, P., & Gould, J. (2000). Assessment of admission criteria for predicting students’ academic performance in graduate business programs. Journal of Education for Business, 75(4), 225-229.
[36] HolmesPiedade, M. B., & Santos, M. Y. (2010, June). Business intelligence in higher education. Information Systems and Technologies (pp. 1-5). IEEE.
[37] Homlmes, G., Donkin, A., & Witten, I. H. (1994, December). Weka: a machine learning workbench. Intelligent Information Systems (pp. 357-361). IEEE.
[38] Hornik, K., Buchta, C., & Zeileis, A. (2009). Open-source machine learning: R meets Weka. Journal of Computational Statics, 24(2), 225-232.
[39] Höschl, C., & Kožený, J. (1997). Predicting academic performance of medical student: the first three year. American Journal of Psychiatry, 154(6), 87-92.
[40] Hsieh, Y. C., & Hu, B. (2005). Assessment of admission criteria for predicting hotel management students’ academic performance. Journal of Teaching in Travel & Tourism, 5(4), 1-14.
[41] Ismail, N. H., Ahmad, F., & Aziz, A. A. (2013, September). Implementing WEKA as a data mining to analyze students’ academic performances using Naïve Bayes Classifier. UniSZA Postgraduate Research Conference (pp. 855-863).
[42] Kabakchieva, D. (2013). Predicting Student Performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61-72.
[43] Kiss, G. (2010, October). Using data mining tools in analyzing undergraduate paper results in Computer Science at Obuda University. Intelligent Computing and Integrated System (pp. 954-956). IEEE.
[44] Kotsiantis, S., Pierrakeas, C., & Pintelas, P.(2004). Predicting students’ performance in distance learning using machine learning techniques. Applied artificial intelligence, 18, 411-426.
[45] Kovacic, Z. J. (2010, June). Early prediction of student success: mining students enrolment data. Proceeding of Informing Science & IT Education Conference (pp. 647-665).
[46] Kovacic, Z. J.(2012). Predicting student success by mining enrolment data. Research in Higher Education Journal, 15, 1-20.
[47] Kumari, P. M., Nabi, SK. A., & Priyanka, P. (2014). Educational data mining and its role in educational field. International Journal of Computer Science and Information Technologies, 5(2), 2458-2461.
[48] Kuncel, N. R., & Hezlett, S. A. (2007, February 23). Standardized tests predict graduate students’ success. Science, 315(5815), 1080-1081.
[49] Li, P. S., & Li, L., & Zong, L. (2007). Postgraduate educational aspiration and policy and implications: A case study of university in western China. Journal of higher education, 29(2), 143-158.
[50] Liautaud, B., & Hammond, M.(2000). E-Business Intelligence: Turning Information into Knowledge into Profit. McGraw-Hill.
[51] Lobur, M., Stekh, Y., Kernytsky, A., & Sardieh, F. M. E. (2008, May). Some trends in knowledge discovery and data mining. Perspective Technologies and Methods in MEMS Design (pp. 95-97). IEEE.
[52] Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: a comparison with K-means. Canadian Journal of Marketing Research, 20(1), 36–43.
[53] Marjoribanks, K. (2003). Family background, individual and environmental influences, aspirations and young adults’ educational attainment: A follow-up study. Educational Studies, 29(2–3), 233–242.
[54] Marjoribanks, K. (2005). Family background, adolescents educational aspirations, and Australian young adults’ education attainment. International Education Journal, 6(1), 104–112.
[55] Markov, Z., & Russell, I.(2006, June). An introduction to the WEKA data mining system. Conference on Innovation and Technology in Computer Science Education (pp. 367-368).
[56] Mayo, J., White, O., & Eysenck, H. J. (1978). An empirical study of the relation between astrological factors and personality. The journal of social psychology, 105, 229-236.
[57] Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003, November). Predicting student performance: an application of data mining method with the educational web-based system LON-CAPA. ASEE/IEEE Frontiers in Education Conference (pp.1-6). IEEE.
[58] Negash, S. (2004). Business intelligence. Communications of the association for information systems, 13(15), 177-195.
[59] Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Journal of Economics and Business, 1(1), 3-12.
[60] Pandey, U.K., Pal, S. (2011). Data Mining: A prediction of performer or underperformer using classification. (IJCSIT) International Journal of Computer Science and Information Technology, 2(2), 686-690.
[61] Romeo, C., & Ventura, S. (2013). Data mining in education. Journal of Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
[62] Romero, C. & Ventura, S. (2007). Educational data mining: a survey from 1995 to 2005. Expert System with Application, 33(1), 135-146.
[63] Rooij, J. J. F., Brak, M. A., & Commandeur, J. J.F. (2002). Introversion-Extraversion and Sun-Sign. The journal of psychology, 122(3), 275-278.
[64] Santoso, S., Lamoree, J. D. (2000, July). Power quality data analysis: from raw data to knowledge using knowledge discovery approach. Power Engineering Society Summer meeting. IEEE.
[65] Satyanarayana, A. (2013, Fall). Software tools for teaching undergraduate data mining course. American Society for Engineering Education MidAtlantic Conference.
[66] Sawon, K., Pembroke, M., Wille, P.(2012). An analysis of student characteristics and behavior in relation to absence from lecture. Journal of higher education policy and management, 34(6), 575-596.
[67] Suljic, M., & Osmanbegovic, E.(2012). Data mining approach for predicting student performance. Journal of economics and business, 5(1), 3-12.
[68] Suman, & Mittal, P. (2014). A comparative study on role of data mining techniques in education: a review. International Journal of Emerging Trends & Technology in Computer Science, 3(3), 65-69.
[69] Thai-Nighe, N., Drumond, L., Krohn-Grimberghe, A., Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1(2), 2811-2819.
[70] Tovar, E., Soto, O. (2010 October). The use of competences assessment to predict the performance of first year student. ASEE/IEEE Frontiers Conference F3J (pp.1-4).IEEE.
[71] Trybus, E. K., & Trybus, G. (2001, August). A brief introduction to the field of data mining. Management of Engineering and Technology. IEEE.
[72] Vandamme, J., Meskens, N., & Superby, J. (2007). Predicting academic performance by data mining methods, Education Economics, 15(4), 405-419.
[73] Vipin Kummar K S, Pramod V K, K C Paul, & Manoj N. (2013, December). Effect of educational reforms and new policies on school education. MOOC Innovation and Technology in Education (pp.417-420).IEEE
[74] Vranic, M., Pintar, D., & Skocir, Z. (2007, June). The use of data in education environment. International Conference on Telecommunications (pp. 243-250).
[75] Watson, H. j., Wixom, B. H. (2007). The current state of business intelligence. IEEE computer society, 40(9), 96-99.
[76] Witten, I. H., Frank, E., Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques(3rd ed.). Morgan Kaufmann Publishers.
[77] Wook, M., Wahab, N., Awang, N. F., Yahaya, Y. H., Isa, M. R. M., & Seong, H. Y. (2009, December). Predicting NDUM student’s academic performance using data mining. Second International Conference on Computer and Electrical Engineering (pp. 357-361). IEEE.
[78] Yadav, S.K., & Pal, S. (2012). Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. (WCSIT) World of Computer Science and Information Technology Journal, 2(2), 51-56.
[79] Yang, B., Lu, D. R. (2001). Predicting academic performance in management education: an empirical investigation of MBA success. Journal of Education for Business, 77(1), 15-20.
[80] Zhang H. Y. (2011 August). A short Introduction to data mining and its application. Management and Service Science (pp. 1-4). IEEE.
[81] SPSS。Data mining application in higher education。2015年2月5日,取自:http://www.spss.ch/upload/1122641492_Data%20mining%20applicatio
ns%20in%20higher%20education.pdf
指導教授 陳仲儼、許文錦(Chung-yang Chen Wen-chin Hsu) 審核日期 2015-7-8
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