期刊目錄列表 - 65卷(2020) - 【教育科學研究期刊】65(4)十二月刊（本期專題：高等教育專業發展與教育創新：回顧與前瞻）
Directory
（專題）以教育大數據分析驅動入學管理機制開設新生銜接課程提升就學穩定度之研究
作者:國立雲林科技大學通識教育中心胡詠翔、國立雲林科技大學企業管理系俞慧芸
卷期：65卷第4期
日期：2020年12月
頁碼：31-63
DOI：10.6209/JORIES.202012_65(4).0002
摘要：
不該只看註冊率，在少子女化衝擊下，維持學生入學後的就學穩定度是學校永續經營須重視的關鍵；尤其，新生必須面對與《十二年國民基本教育課程綱要》和高中學習經驗截然不同的院系本位課程與教材內容，大學端該如何提高大一學習經驗，找出影響新生改變學習旅程規劃的關鍵學科，透過入學管理機制開設銜接課程，以維持就學穩定度，成為未來高等教育的重要命題。近年，有大學嘗試自辦暑期銜接課程以解決問題。本研究首先分析個案學校104至106學年某學院2,135位新生，計22,750筆教育大數據資料，透過決策樹分析找出影響新鮮人休學、退學或轉系之關鍵的大一上學期課程，再辦理暑期小規模非公開遠距課程（含補救教學與讀書會），追蹤效果進行機制評估。結果發現：一、物理（I）與微積分（I）這兩門院必修是關鍵課程，且兩門皆不及格學生的休學、退學或轉系機率是原母體的5.5倍；二、未觀看銜接課程教材者開學後的關鍵課程及格率介於50%～63%間，遠低於其他落在83%～94%，且又以微積分的銜接課程具統計上顯著提升學習準備度效果；三、線上補救教學有助於物理（I）的學業表現，讀書會則有助於微積分（I）的學業表現；四、個案學院就學穩定度較前一年提升48.07%。最後，本研究提出具體建議供後續研究與規劃課程參考。
關鍵詞：入學管理機制、教育大數據分析、就學穩定度、銜接課程
《詳全文》
參考文獻：
- 大專校院校務資訊公開平臺（2019）。資料查詢─學生類。取自https://udb.moe.edu.tw/DetailReportList/%E5%AD%B8%E7%94%9F%E9%A1%9E【Public Information Platform for Colleges and Universities. (2019). Information query-Students. Retrieved from https://udb.moe.edu.tw/DetailReportList/%E5%AD%B8%E7%94%9F%E9%A1%9E】
- 大學招生委員會聯合會（2018）。全國大學先修課程暨認證資訊平台。取自https://cis.ncu.edu.tw/ApcourseSys/home【Joint Board of College Recruitment Commission. (2018). University AP and Certification Platform. Retrieved from https://cis.ncu.edu.tw/ApcourseSys/home】
- 何希慧（2014）。校務研究的新思維：大學校院建立學生入學管理模式。評鑑雙月刊，52，14-18。【Ho, S.-H. (2014). IR’s new thinking: Establishing a college admission management model. Evaluation Bimonthly,52, 14-18.】
- 余民寧、李昭鋆（2018）。補救教學中個別化教學對學生學習成效之影響分析。教育科學研究期刊，63（1），247-271。doi:10.6209/JORIES.2018.63(1).08【Yu, M.-N., & Li, C.-Y. (2018). Impact of individualized instruction on the learning outcomes of low-achieving students who received remedial instruction. Journal of Research in Education Sciences, 63(1), 247-271. doi:10.6209/JORIES.2018.63(1).08】
- 佛光大學（2019）。佛光大學辦理高中職校與大學銜接課程施行要點。取自http://personnel.fgu.edu.tw/xhr/archive/download?file=5da94c490e588f2ef3000072【Fo Guang University. (2019). FGU AP implementation plan. Retrieved from http://personnel.fgu.edu.tw/xhr/archive/download?file=5da94c490e588f2ef3000072】
» 展開更多
- 大專校院校務資訊公開平臺（2019）。資料查詢─學生類。取自https://udb.moe.edu.tw/DetailReportList/%E5%AD%B8%E7%94%9F%E9%A1%9E【Public Information Platform for Colleges and Universities. (2019). Information query-Students. Retrieved from https://udb.moe.edu.tw/DetailReportList/%E5%AD%B8%E7%94%9F%E9%A1%9E】
- 大學招生委員會聯合會（2018）。全國大學先修課程暨認證資訊平台。取自https://cis.ncu.edu.tw/ApcourseSys/home【Joint Board of College Recruitment Commission. (2018). University AP and Certification Platform. Retrieved from https://cis.ncu.edu.tw/ApcourseSys/home】
- 何希慧（2014）。校務研究的新思維：大學校院建立學生入學管理模式。評鑑雙月刊，52，14-18。【Ho, S.-H. (2014). IR’s new thinking: Establishing a college admission management model. Evaluation Bimonthly,52, 14-18.】
- 余民寧、李昭鋆（2018）。補救教學中個別化教學對學生學習成效之影響分析。教育科學研究期刊，63（1），247-271。doi:10.6209/JORIES.2018.63(1).08【Yu, M.-N., & Li, C.-Y. (2018). Impact of individualized instruction on the learning outcomes of low-achieving students who received remedial instruction. Journal of Research in Education Sciences, 63(1), 247-271. doi:10.6209/JORIES.2018.63(1).08】
- 佛光大學（2019）。佛光大學辦理高中職校與大學銜接課程施行要點。取自http://personnel.fgu.edu.tw/xhr/archive/download?file=5da94c490e588f2ef3000072【Fo Guang University. (2019). FGU AP implementation plan. Retrieved from http://personnel.fgu.edu.tw/xhr/archive/download?file=5da94c490e588f2ef3000072】
- 吳柏林（2002）。現代統計學。新北市：前程文化。【Wu, B.-L. (2002). Modern statistics. New Taipei City, Taiwan: Future Career.】
- 技專校院招生策略委員會（2019）。關於四技二專。取自https://techexpo.moe.edu.tw/search/profile_edutype.php?ec=4【Technical College Admissions Strategy Committee. (2019). About four-year institute of technology & two-year junior college. Retrieved from https://techexpo.moe.edu.tw/search/profile_edutype.php?ec=4】
- 亞洲大學（2015）。亞洲大學新生先修暨基礎學科銜接課程實施要點。取自http://ac.asia.edu.tw/ezfiles/32/1032/attach/53/pta_10567_4090784_54593.pdf【Asia University. (2015). Asia University AP implementation plan. Retrieved from http://ac.asia.edu.tw/ezfiles/32/1032/attach/53/pta_10567_4090784_54593.pdf】
- 林安邦、姜驊凌、莊雅婷、張國恩（2019）。以校務研究觀點探討行政管考制度之建構。教育科學研究期刊，64（1），241-265。doi:10.6209/JORIES.201903_64(1).0009【Lin, A.-P., Chiang, H.-L., Chuang, Y.-T., & Chang, K.-E. (2019). The study of developing the administrative performance evaluation system from the perspective of institutional research. Journal of Research in Education Sciences, 64(1), 241-265. doi:10.6209/JORIES.201903_64(1).0009】
- 林晶璟（2009）。基礎數學補救教學對學生學習微積分成效影響之研究。臺北科技大學學報，42（1），103-115。doi:10.29768/JNTUT.200906.0007【Lin, C.-C. (2009). Research of the influence and effect on remedy teaching in basic mathematics about student for Studying Calculus. Journal of National Taipei University of Technology, 42(1), 103-115. doi:10.29768/JNTUT.200906.0007】
- 胡詠翔（2019）。大規模開放線上課程學習分析促進科技學科教學知識之研究。教學實踐與創新，2（1），77-114。doi:10.3966/261654492019030201004【Hu, Y.-H. (2019). Applying learning analytics to enhance the technological pedagogical content knowledge of teachers teaching massive open online courses. Journal of Teaching Practice and Pedagogical Innovation, 2(1), 77-114. doi:10.3966/261654492019030201004】
- 高雄醫學大學（2019）。準大一先修課程。取自https://academic.kmu.edu.tw/index.php/zh-TW/%E6%8B%9B%E7%94%9F%E8%A8%8A%E6%81%AF%E8%88%87%E6%A6%9C%E5%96%AE%E6%9F%A5%E8%A9%A2/%E6%BA%96%E5%A4%A7%E4%B8%80%E5%85%88%E4%BF%AE%E8%AA%B2%E7%A8%8B【Kaohsiung Medical University. (2019). Freshman AP courses. Retrieved from https://academic.kmu.edu.tw/index.php/zh-TW/%E6%8B%9B%E7%94%9F%E8%A8%8A%E6%81%AF%E8%88%87%E6%A6%9C%E5%96%AE%E6%9F%A5%E8%A9%A2/%E6%BA%96%E5%A4%A7%E4%B8%80%E5%85%88%E4%BF%AE%E8%AA%B2%E7%A8%8B】
- 國立政治大學附屬高級中學（2018）。AP課程計畫。引自http://www.ahs.nccu.edu.tw/ischool/publish_page/3/?cid=2443【The Affiliated High School of NCCU. (2018). AP implementation plan. Retrieved from http://www.ahs.nccu. edu.tw/ischool/publish_page/3/?cid=2443】
- 國立高雄科技大學（2018）。國立高雄科技大學大學部新生暑期銜接課程實施細則。取自http://academic.kuas.edu.tw/ezfiles/1/1001/img/889/179994380.pdf【National Kaohsiung University of Science and Technology. (2018). National Kaohsiung University of Science and Technology－ Implementation rules for freshman summer pathway courses. Retrieved from http://academic.kuas.edu.tw/ezfiles/1/1001/img/889/179994380.pdf】
- 國立雲林科技大學（2018）。先修銜接課程。取自https://go.yuntech.edu.tw/course【National Yunlin University of Science and Technology. (2018). Pathway program. Retrieved from https://go.yuntech.edu.tw/course】
- 國立臺灣大學（2019）。基礎學科先修課程及免修認證考試。取自http://apc.aca.ntu.edu.tw/fcc/index.aspx【National Taiwan University. (2019). NTU course certification. Retrieved from http://apc.aca.ntu.edu.tw/fcc/index.aspx】
- 教育部（2017）。大專校院大學1年級學生人數預測分析報告（106～121學年度）。取自http://stats.moe.gov.tw/files/analysis/106higherstudent.pdf【Ministry of Education. (2017). Analysis report on the forecast of the number of first-year students in colleges and universities (from 2017 to 2032 academic year). Retrieved from http://stats.moe.gov.tw/files/analysis/106higher student.pdf】
- 教育部（2018）。107學年度大專校務資訊公開記者會新聞稿。取自https://www.edu.tw/News_Content.aspx?n=9E7AC85F1954DDA8&S=5BD50DE475CEF94A【Ministry of Education. (2018). Press release: Information on college affairs in S.Y. 2018. Retrieved from https://www.edu.tw/News_Content.aspx?n=9E7AC85F1954DDA8&S=5BD50DE475CEF94A】
- 逢甲大學（2019）。大學先修課程。取自http://aps.fcu.edu.tw/credits【Feng Chia University. (2019). Advanced placement. Retrieved from http://aps.fcu.edu.tw/credits】
- 彭耀平、劉峰旗、段盛華（2017）。校務研究資料建置與決策支持應用。教育科學研究期刊，62（4），27-51。doi:10.6209/JORIES.2017.62(4).02【Peng, Y.-P., Liu, F.-C., & Tuan, S.-H. (2017). Database establishment in institutional research and decision-making support applications.Journal of Research in Education Sciences, 62(4), 27-51. doi:10.6209/JORIES.2017.62(4). 02】
- 曾憲雄、蔡秀滿、蘇東興、曾秋榮、王慶堯（2005）。資料探勘。臺北市：旗標。【Tseng, S.-S., Tsai, S.-M., Su, D.-H., Tseng, C.-J., & Wang, C.-Y. (2005). Data mining. Taipei, Taiwan: Flag.】
- 董力華、顏正芳、吳明忠（2006）。某醫學大學不同性別大一新生入學時的適應程度與一年內學業表現之關聯性探討。醫學教育，10（1），16-24。doi:10.6145/jme.200603_10(1).0003【Tung, L.-H., Yen, C.-F., & Wu, M.-J. (2006). Gender differences in the association between adjustment ability and academic performance among freshmen attending a medical university. Journal of Medical Education, 10(1), 16-24. doi:10.6145/jme.200603_10(1).0003】
- 簡禎富、許嘉裕（2014）。資料挖礦與大數據分析。新北市：前程文化。【Chien, C.-F., & Hsu, C.-Y. (2014). Data mining & big data analytics. New Taipei City, Taiwan: Future Career.】
- Abdous, M., He, W., & Yen, C.-J. (2012). Using data mining for predicting relationships between online question theme and final grade. Educational Technology & Society, 15(3), 77-88.
- Arnold, K. E. (2010). Signals: Applying academic analytics. Retrieved from ERIC database. (EJ890465)
- Baker, R. S. J. D. (2010). Data mining. In B. McGaw, P. B. Peterson, & E. Baker (Eds.), International encyclopedia of education (3rd ed., Vol. 7, pp. 112-118). Oxford, UK: Elsevier.
- Baker, R. S. J. D., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17. doi:10.5281/zenodo.3554657
- Braxton, J. M. (2003). Student success. In S. R. Komives& D. B. Woodard, Jr. (Eds.), Student services: A handbook for the profession (4th ed., pp. 317-335). San Francisco, CA: Jossey-Bass.
- Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth.
- Calders, T., & Pechenizkiy, M. (2012). Introduction to the special section on educational data mining. ACM SIGKDD Explorations Newsletter, 13(2), 3-6. doi:10.1145/2207243.2207245
- Clarke, A. E., Bloch, D. A., Danoff, D. S., & Esdaile, J. M. (1994). Decreasing costs and improving outcomes in systemic lupus erythematosus: Using regression trees to develop health policy. Journal of Rheumatology, 21(12), 2246-2253.
- Cooper, A. (2012a). JISCCETIS Analytics Series Vol. 1, No 9: A brief history of analytics. Bolton, UK: The University of Bolton Press.
- Cooper, A. (2012b). JISCCETIS Analytics Series Vol. 1, No 7: A framework of characteristics for analytics. Bolton, UK: The University of Bolton Press.
- Dollinger, M.,& Lodge, J.(2019). What learning analytics can learn from students as partners. Educational Media International, 56(3), 218-232. doi:10.1080/09523987.2019.1669883
- Er, E., Gómez-Sánchez, E., Dimitriadis, Y., Bote-Lorenzo, M. L., Asensio-Pérez, J. I.,& Álvarez-Álvarez, S. (2019). Aligning learning design and learning analytics through instructor involvement: A MOOC case study. Interactive Learning Environments, 27(5-6), 685-698. doi:10.1080/10494820.2019.1610455
- Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304-317. doi:10.1504/IJTEL.2012.051816
- Foster, E.,& Siddle, R.(2019). The effectiveness of learning analytics for identifying at-risk students in higher education.Assessment & Evaluation in Higher Education, 45(6), 842-854. doi:10.1080/02602938.2019.1682118
- García, E., Romero, C., Ventura, S., & de Castro, C. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, 19(1-2), 99-132. doi:10.1007/s11257-008-9047-z
- Hossler, D. (2000). Effective admissions recruitment. In G. H. Gaither (Ed.), Promising practices in recruitment, remediation, and relation: New directions for higher education, No. 108 (pp. 15-30). San Francisco, CA: Jossey-Bass.
- Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J.,& Yang, S. J. H. (2019). Predicting students’ academic performance by using educational big data and learning analytics: Evaluation of classification methods and learning logs. Interactive Learning Environments, 28(2), 206-230. doi:10.1080/10494820.2019.1636086
- International Conference on Learning Analytics and Knowledge. (2011). About. Retrieved from https://tekri.athabascau.ca/analytics/about
- Kay, R., & Kletskin, I. (2012). Evaluating the use of problem-based video podcasts to teach mathematics in higher education. Computers & Education, 59(2), 619-627. doi:10.1016/j.compedu.2012.03.007
- Knight, S., Shum, S. B., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23-47. doi:10.18608/jla.2014.12.3
- Kovacic, Z. (2010). Early prediction of student success: Mining students’ enrollment data. Retrieved from https://proceedings.informingscience.org/InSITE2010/InSITE10p647-665Kovacic873.pdf
- Lacave, C., Molina, A. I.,& Cruz-Lemus, J. A. (2018). Learning analytics to identify dropout factors of Computer Science studies through Bayesian networks. Behaviour & Information Technology, 37(10-11), 993-1007. doi:10.1080/0144929X.2018.1485053
- Lodge, J. (2011). What if student attrition was treated like an illness? An epidemiological model for learning analytics. In G. Williams, P. Statham, N. Brown, & B. Cleland (Eds.), Changing demands, changing directions. Proceedings Ascilite Hobart 2011 (pp. 822-825). Queensland, Australia: Ascilite.
- Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q.,& Yang, S. J. H.(2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Journal Interactive Learning Environments, 25(2), 220-234. doi:10.1080/10494820.2016.1278391
- Lustman, P. J., Sowa, C. J., & O’Hara, D. J. (1984). Factors influencing college student health: Development of the Psychological Distress Inventory. Journal of Counseling Psychology, 31(1), 28-35. doi:10.1037/0022-0167.31.1.28
- Ma, X. (2005). Growth in mathematics achievement: Analysis with classification and regression trees. The Journal of Educational Research, 99(2), 78-86. doi:10.3200/JOER.99.2.78-86
- Mandinach, E. B.,& Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Education Researcher, 42(1), 30-37. doi:10.3102/0013189X12459803
- Muzangwa, J., & Chifamba, P. (2012). Analysis of errors and misconceptions in the learning of calculus by undergraduate students. Acta Didactica Napocensia, 5(2), 1-10.
- New, J. (2013). Higher education’s big (data) bang: Part one. Retrieved from https://www.ecampusnews.com/2013/12/05/big-data-bang-344/
- Nguyen, Q., Rienties, B.,& Richardson, J. T. E. (2020). Learning analytics to uncover inequality in behavioural engagement and academic attainment in a distance learning setting. Assessment & Evaluation in Higher Education, 45(4), 594-606. doi:10.1080/02602938.2019.1679088
- Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology and Society, 17(4), 49-64.
- Redish, E. F., Saul, J. M., & Steinberg, R. N. (1998). Student expectations in introductory physics. American Journal of Physics, 66(3), 212-224. doi:10.1119/1.18847
- Rowell, J., Dawson, C., & Pollard, J. (1993). First year university physics: Who succeeds? Research in Science & Technological Education, 11(1), 85-94. doi:10.1080/0263514930110108
- Saqr, M., Fors, U.,& Tedre, M. (2017). How learning analytics can early predict under-achieving students in a blended medical education course. Medical Teacher, 39(7), 757-767. doi:10.1080/0142159X.2017.1309376
- Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Journal of Educational Technology & Society, 15(3), 3-26.
- Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of Asynchronous Learning Networks, 16(3), 51-61. doi:10.24059/olj.v16i3.275
- Tempelaar, D. (2019). Supporting the less-adaptive student: The role of learning analytics, formative assessment and blended learning. Assessment & Evaluation in Higher Education, 45(4), 579-593. doi:10.1080/02602938.2019.1677855
- Tobias, S., & Hake, R. R. (1988). Professors as physics students: What can they teach us? American Journal of Physics, 56(9), 786-794. doi:10.1119/1.15486
Journal directory listing - Volume 65 (2020) - Journal of Research in Education Sciences【65(4)】December (Special Issue: Professional Development and Educational Innovation in Higher Education: Retrospect and Prospect)
Directory
(Special Issue) Improving Retention Rate Through Educational Data Mining: The Design of Placement Program for Newly Enrolled Students
Author: Yung-Hsiang Hu (Center for General Education, National Yunlin University of Science and Technology), Hui-Yun Yu (Department of Business Administration, National Yunlin University of Science and Technology)
Vol.&No.：Vol. 65, No. 4
Date：December 2020
Pages：31-63
DOI：10.6209/JORIES.202012_65(4).0002
Abstract：
Retention rate is a key indicator of university governance. However, identifying key courses that influence first-year students’ termination of learning and improve their performance during the first semester is critical. In recent years, offering pathway courses during the summer semester has become a common practice for universities. Therefore, this institutional research employed educational data mining analysis and pathway courses to improve retention rate and student success. The data analyses comprised classification and regression trees, the Wilcoxon rank-sum test, k-means clustering, and descriptive statistics. This study first analyzed 22,750 educational big data points from 2,135 freshmen in the sample college from the academic year of 2015 to 2017. Subsequently, decision tree analysis was employed to identify key courses that predicted student suspension, dropping out, or transfer. Thereafter, two pathway courses and remedial teaching were offered to freshmen in the summer to learn online. Finally, this study tracked the success of freshmen and evaluated the effects of the two pathway courses. The major findings suggested the following: (1) Physics (I) and Calculus (I) are key courses, and students who failed both courses were 5.5 times more likely to suspend their studies, drop out, or transfer than was the total student population; (2) The pass rate of formal courses for students who had not watched the audiovisual course was between 50% and 63%, much lower than the total student population rate of 83% to 94%, and only the Calculus (I) gateway course could improve learning readiness; (3) Online supplementary teaching was found to promote the academic performance of freshmen in Physics (I); however, no significant differences were observed in Calculus (I). Moreover, the study group improved students’ academic performance in Calculus (I); however, no significant differences were observed in Physics (I); (4) Compared with the previous year, the retention rate of the sample college increased by 48.07%. Finally, the researchers proposed suggestions for the gateway course’s follow-up application. To conclude, this study may be of importance in explaining the effectiveness of gateway courses, in addition to providing university authorities with a better understanding of how retention rate can be improved through educational data mining and institutional research.
Keywords：enrollment management, educational data mining, retention rate, pathway courses