國內大學學習預警資訊系統研究：發展現況與侷限

Students' Early Alert Systems in Taiwanese Universities: A Study of Current Uses and Restrictions

10.6209/JORIES.202012_65(4).0006

謝佩珊(Pei-Shan Hsieh)；石美倫(Meilun Shih)

高危險群學生 ； 學習預警 ； 學習預警資訊系統 ； 學習輔導 ； at-risk student group ； academic alert ； early alert system ； intervention

教育科學研究期刊

65卷4期（2020 / 12 / 01）

171 - 201

繁體中文

學生學習預警資訊系統被認為是評量學生學習成效、預測學習困擾、輔導高危險群學生的有力機制，尤其近年來新興資訊技術的進步，使學習預警資訊系統在規模和作法上都有所突破。國內各大學發展學習預警資訊系統已超過10年，普及率也相當高，但是相較於國外既有的深入性和檢討性研究，目前針對國內現有學習預警機制與系統運作的著述仍然頗為零散，而該系統在功能上的敏感性及資料上的隱密性，也讓各校之間缺乏相關資訊交流。因此，本研究主要透過文獻探討、訪察和訪談等方法，針對國內目前56所公私立一般大學學習預警資訊系統的現行運作情況進行概括性調查。研究發現，目前各校多已建立了學期間、即時和特殊預警等不同機制，系統使用率在許多大學也都達到半數課程以上，但在實際使用上常面臨教師反彈和後續追蹤不易等問題。建議未來各校應以加強跨單位合作、提升系統使用成效評估，以及提供更多元的預警指標為方向，進一步落實學習預警資訊系統之功能及使用效益。

The development of students' early alert systems (EAS), or students' learning alarm systems, has long been discussed by administrators and researchers in higher education. They are considered a powerful mechanism for evaluating students' learning effectiveness, predicting potential learning difficulties, and identifying academically at-risk students. The rapid growth of information technology has made the EAS advanced in scale and practice. However, although most universities in Taiwan have established similar preventive systems or mechanisms, few studies have focused on exploring actual uses of and possible restrictions on existing EAS. Therefore, the main purpose of this study was to outline the current situation of EAS in Taiwanese universities. We explored the following questions: (1) What types of EAS have been established in universities in Taiwan? (2) What are the main functions and mechanisms of the current EAS? (3) What are the actual uses of EAS in universities? (4) What are the possible restrictions on and solutions for using EAS? Data were gathered from 56 public and private universities in Taiwan. Experts familiar with or in charge of EAS design, development, and use in these universities were interviewed by telephone or face-to-face. We found that although the utility rate of EAS in most universities tended to be high, faculty resistance and difficulty in follow-up assessments on EAS utilization were the two common restrictions that occurred across campuses. To enhance the use effectiveness of EAS, faculty and administrators from both departmental and institutional levels should be involved. In addition, this study suggested that in the future, universities must attempt to strengthen collaboration among units related to EAS, improve the evaluation of EAS effectiveness, and provide additional early alert indicators of students' academic performance.

1. 周汎澔, F.-H.,歐秀慧, H.-H.,林志隆, C.-L.,鐘育志, Y.-J.,陳鵬元, P.-Y.,楊瑞成, R.-C.(2010)。醫學大學學生期中成績預警與課業輔導之實施成效。醫學教育，14(3)，197-208。
   連結：
2. 陳慧娟, H.-J.,簡洧晴, W.-C.(2020)。大學生選課後悔與動機干擾研究：檢驗後悔因應策略的中介效果與社會支持的調節效果。教育科學研究期刊，65(2)，277-312。
   連結：
3. 陳鏗任, K.-Z(2014)。大學院校應用學習分析之概況。教育資料與圖書館學，51(4)，597-636。
   連結：
4. 羅孟彥, M.-Y.(2013)。支援教育品質管理之整合式資訊系統建構與研究。教育科學研究期刊，58(4)，69-101。
   連結：
5. Arnold, K. E.,Pistilli, M. D.(2012).Course signals at Purdue: Using learning analytics to increase student success.Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK’12,Vancouver, Canada:
6. Bentham, C.(2017).Department of Child, Family, and Community Sciences, College of Education and Human Performance, University of Central Florida.
7. Cai, Q.,Lewis, C. L.,Higdon, J.(2015).Developing an early-alert system to promote student visits to tutor center.The Learning Assistance Review,20(1),61-72.
8. Chang, C.-C.,Chou, P.-N.,Liang, C.(2018).Using ePortfolio-based learning approach to facilitate knowledge sharing and creation of college students.Australasian Journal of Educational Technology,34(1),30-41.
9. Daniel, B. K.(Ed.)(2017).Big data and learning analytics in higher education.Cham, CH:Springer.
10. Dietz-Uhler, B.,Hurn, J. E.(2013).Using learning analytics to predict (and improve) student success: A faculty perspective.Journal of Interactive Online Learning,12(1),17-26.
11. Dwyer, L. J.,Williams, M. R.,Pribesh, S.(2019).Impact of early alert on community college student persistence in Virginia.Community College Journal of Research and Practice,43(3),228-231.
12. Ferguson, R.(2012).Learning analytics: Drivers, developments and challenges.International Journal of Technology Enhanced Learning,4(5/6),304-317.
13. Goldstein, P.,Katz, R.(2005).,未出版
14. Grush, M. (2011). Monitoring the PACE of student learning: Analytics at Rio Salado College. Campus Technology. Retrieved from https://campustechnology.com/articles/2011/12/14/monitoring-the-pace-of-student-learning-analytics-at-rio-salado-college.aspx
15. Harkins, S.(2016).Response to intervention for student success in higher education: Is it possible?.Journal of Instructional Research,5,79-82.
16. Hung, J.-L.,Wang, M.-C.,Wang, S.,Abdelrasoul, M.,Li, Y.,He, W.(2017).Identifying at-risk students for early interventions - A time-series clustering approach.IEEE Transactions on Emerging Topics in Computing,5(1),45-55.
17. Jayaprakash, S. M.,Lauría, E. J. M.(2014).Open academic early alert system: Technical demonstration.Proceedings of the Fourth International Conference on Learning Analytics and Knowledge - LAK’14,Indianapolis, IN:
18. Jayaprakash, S. M.,Moody, E. W.,Lauría, E. J. M.,Regan, J. R.,Baron, J. D.(2014).Early alert of academically at-risk students: An open source analytics initiative.Journal of Learning Analytics,1(1),6-47.
19. May, T. A.(2011).Analytics, university 3.0, and the future of information technology.Educause review,September/October
20. Purnell, K.,McCarthy, R.,McLeod, M.(2010).Student success at university: Using early profiling and interventions to support learning.Studies in Learning, Evaluation, Innovation and Development,7(3),77-86.
21. Tsao, N.-L.,Kuo, C.-H.,Guo, T.-L.,Sun, T.-J.(2017).Data consideration for at-risk students early alert.2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI),Hamamatsu, Japan:
22. Villano, R.,Harrison, S.,Lynch, G.,Chen, G.(2018).Linking early alert systems and student retention: A survival analysis approach.Higher Education,76(5),903-920.
23. Yu, L.-C.,Lee, C.-W.,Pan, H.-L.,Chou, C.-Y.,Chao, P.-Y.,Chen, Z.-H.,Lai, K.-R.(2018).Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments.Journal of Computer Assisted Learning,34(4),358-365.
24. 王瑋婷, W.-T.,黃國倫, G.-L.(2016)。學生課程出席率對學習成效之影響─以國立臺北藝術大學為例。「TANET2016 臺灣網際網路研討會」論文集，花蓮縣=Hualien, Taiwan:
25. 余奕憲, Y.-H.(2018)。桃園市=Taoyuan, Taiwan，國立中央大學=National Central University。
26. 邱宏彬, H.-P.,許依宸(2011)。資料採礦在學生流失偵測上之應用。資訊管理研究，11，83-99。
27. 唐慧慈, H.-C.,郭玟杏, W.-X.(2017)。臺灣經驗：校務研究及對大學校院發展之影響。評鑑雙月刊，65，24-26。
28. 國立臺灣大學（2016）。國立臺灣大學學士班學生學業成績預警作業要點。取自 http://www.aca.ntu.edu.tw/reg/law/%E6%88%90%E7%B8%BE%E9%A0%90%E8%AD%A6%E4%BD%9C%E6%A5%AD%E8%A6%81%E9%BB%9E.pdf 【National Taiwan University. (2016). National Taiwan University academic alert guidelines for undergraduate student. Retrieved from http://www.aca.ntu.edu.tw/reg/law/%E6%88%90%E7%B8%BE%E9%A0%90%E8%AD%A6%E4%BD%9C%E6%A5%AD%E8%A6%81%E9%BB%9E.pdf】
29. 教育部（2012）。教育部補助獎勵大學教學卓越計畫及區域教學資源中心計畫實施要點。取自http://www.admin.ltu.edu.tw/Public/Upload/files/%E6%9C%83%E8%A8%88%E5%AE%A4/%E6%95%99%E5%8D%93%E8%A8%88%E7%95%AB%E5%AF%A6%E6%96%BD%E8%A6%81%E9%BB%9E.pdf【Ministry of Education. (2012). Implementation directions for program of promoting teaching excellence of universities and program for Teaching and Learning Resource Center. Retrieved from http://www.admin.ltu.edu.tw/Public/Upload/files/%E6%9C%83%E8%A8%88%E5%AE%A4/%E6%95%99%E5%8D%93%E8%A8%88%E7%95%AB%E5%AF%A6%E6%96%BD%E8%A6%81%E9%BB%9E.pdf】
30. 溫瑞烘, J.-H.(2000)。校務資訊系統研究─退學預警系統之研製。中華技術學院學報，20，22-37。
31. 劉宇潔, Y.-J.,韋小滿, X.-M.(2012)。干預─反應（RtI）模型：美國教育政策理念架構的新趨勢。比較教育研究，34(11)，86-90。
32. 蕭玉真, Y.-C.(2011)。提升學習成效：淺談大學教學卓越計畫。評鑑雙月刊，31，29-33。