升學路徑對大學生自我調節學習特質與學業表現之影響－繁星推薦、個人申請、考試分發多元入學管道之比較

Do Students Admitted to Universities Through Different Channels Have Differences in Self-Regulated Learning and Academic Performance? Comparison of Students From the Multi-Star Project, Individual Application Channel, and Exam-Based Channel

10.6251/BEP.202106_52(4).0002

鄭夙珍(Nellie S. Cheng)；王金龍(Jin-Long Wang)

多元入學方案 ； 自我調節學習 ； 學業表現 ； multi-channel entrance system ； self-regulatory learning ； academic performance

教育心理學報

52卷4期（2021 / 06 / 01）

757 - 779

繁體中文

大學多元入學方案的實施，破除單一考試方式，予以考生選擇適合自己升學管道的機會。三種入學管道（繁星推薦、申請入學、考試分發）之升學路徑的差異，影響學生之學習經驗。過去多元管道的比較多探討管道間學業表現之差異，本研究加入自我調節學習特質之影響，有助於理解學業表現差異的原因。由於繁星推薦管道入學標準取決於高中學業表現，而考試分發管道應考時間最長，本研究假設繁星推薦與考試分發兩種管道學生的升學經驗較有助於培養自我調節學習特質，因而也有較佳大學學業表現；另一方面，由於申請入學管道最具有適性揚才的特徵，申請入學生相較於其餘兩管道應有較佳學業動機。本研究以Sitzman與Ely（2011）自我調節學習精簡架構的九變項為探究基礎，首先進行內部因素結構，再據以探討（1）三類學生之自我調節學習對學業表現影響之模式差異，及（2）三類學生在自我調節學習及學業表現之差異。本研究於個案大學學生事務系統蒐集資料，樣本包括繁星推薦513人、個人申請1929人、考試分發2100人，共4542人。研究結果顯示四項自我調節學習建構（目標動能、調節機制、學業動機、投入時間）中，目標動能及投入時間對學業表現有預測力，此一預測模式有跨類別的一致性；而目標動能及投入時間也具有群組差異，呈現出繁星推薦及考試分發優於申請入學管道的結果；就學業表現而言，繁星推薦優於考試分發、而考試分發又優於申請入學。與假設不符的是，學業動機對學業表現無預測力，也未有群組差異。本研究提出研究結果對多元入學方案之實務意涵。

For decades, the Taiwanese university admission system consisted of only one channel-the Joint College Entrance Examination-for admitting students into universities. This single-channel system received widespread criticism; for example, it was criticized for imposing enormous stress on students and for preventing students from exploring their interests and alternative career paths. Consequently, the multichannel entrance system was launched in 2002 to replace the single-channel paradigm and provide multiple options for students to be admitted into universities. Among the available admittance channels, most high school students are admitted into universities through three main channels: the multistar project, individual application channel, and exam-based channel. This study focused on these channels. These three channels were developed to meet distinct policy goals, and each challenge has unique procedures and criteria. The multistar project was designed to reduce the urban-rural gap and promote the communization of senior high schools. Therefore, students with good academic performance, regardless of the standing of their high school, have favorable chances of entering the university of their choosing. The individual application channel involves a similar application process to that in the United States and European countries. This channel requires students to prepare a personal portfolio supporting an application to a specific program. In-person interviews are usually mandatory during the process to facilitate proper consideration by the program faculty; compared with the other two channels, this channel is considered more effective at matching students' interests and competences with a department's selection criteria. Finally, the exam-based channel largely duplicates the old system of the Joint College Entrance Examination in which students list their desired programs and the exam score is the only criterion used to determine whether a student is qualified. All students are required to take the General Scholastic Ability Test in late January or early February; however, for students who choose the exam-based channel, an additional and considerably more difficult exam (Advanced Subjects Test, AST) is required in early July. Students who choose the multistar project channel are informed of their results in late March. The interview process for the individual application channel takes place in April, and students are informed of their results in May. For students who use the exam-based channel, the admission results are announced in early August. This study used self-regulated learning characteristics to interpret differences in academic performance. Among the three entrance channels, the multistar project channel emphasizes the high school GPA (grade point average), requiring applicants to maintain a high level of academic performance. The exam-based channel requires students to prepare for an additional and more difficult exam, requiring students to maintain their study skills and efforts longer. During this preparation time, students who choose the two other channels already know their admission results and are relieved of this burden. The researchers hypothesized that students who choose these two channels (multistar and exam-based) would have better self-regulated learning characteristics and academic performance in college. However, because the individual application channel is the most effective at matching students and programs, the researchers also hypothesized that students who choose the individual application channel would have stronger academic motives for their chosen majors in college. The study sample comprised 4542 college students, of whom 513 were admitted through the multistar project channel, 1929 were admitted through the individual application channel, and 2100 were admitted through the exam-based channel. Student data were collected through the student affairs system of a single university in Taiwan. The researchers adopted the nine variables of the framework of self-regulated learning proposed by Sitzman and Ely (2011). The researchers first explored the internal structure of the variables and used them to derive four self-regulatory factors, namely goal momentum, self-regulatory mechanism, academic motivation, and engagement time, which were then used to test the hypotheses. Goal momentum, including self-efficacy, goal setting, and metacognitive strategies, represents the process of planning, executing, and monitoring progress toward goals. Self-regulatory mechanism, including time management, learning environment structure, attention, and attribution, reflects an individual's self-regulating actions during the learning process. Because academic motivation and engagement time could not be categorized into a common factor, they were added to the regression analysis individually. Hierarchical regression analysis was used to derive a predictive model of how these self-regulatory factors predict academic performance (GPA in college). Finally, analysis of variance (ANOVA) was applied to test the hypotheses: first, that students from the multi-star project and exam-based channels would outperform students from the individual application channel in terms of self-regulatory characteristic variables (except academic motivation) and academic performance, and second, that students from the individual application channel would have more academic motivation than would students from the two other channels. The results demonstrated that among the four self-regulatory learning factors (goal momentum, self-regulatory mechanism, academic motivation, and engagement time), goal momentum and engagement time significantly predicted academic performance; this pattern appeared to be consistent across the three groups of students. Goal momentum and engagement time differed among the three groups. Specifically, as hypothesized, students admitted through the multistar project and exam-based channels outperformed those admitted through the individual application channel. Students admitted through the multistar project channel had higher academic performance than did those admitted through the exam-based channel, and in turn, students admitted through the exam-based channel had higher academic performance than did those admitted through the individual application channel. However, academic motivation (including the evaluation of their values and interest for their majors) did not differ among the three groups. The individual application channel aims to enhance self-exploration among students, and in theory, it best matches students and programs; nevertheless, the results of this study do not support this proposition. In addition, the study revealed that students admitted through the individual application channel had a slightly higher rate of changes in majors (p ＜ .01 in chi-square analysis) than did those admitted through the two other channels. Thus, contrary to the policy goal of matching students and programs, the students admitted through the individual application channel did not have stronger academic motivation, and a higher percentage of these students changed their majors. The individual application channel is the most commonly employed channel to admit students into universities in the United States and European countries; the Ministry of Education in Taiwan intended for this to be the dominant or even only channel for future admissions. The results of this study did not support the superiority of the individual application channel in the Taiwanese higher education context as measured by GPA; instead, the results for this channel were less favorable regarding students' self-regulatory characteristics and academic performance. The establishment of the multichannel entrance system was revolutionary and upheld a noble goal of both allowing students to better tailor their self-development and career paths and giving universities more power to choose suitable students for their departments. However, the revision of public policies to meet the needs of society takes time and multiple adjustments. A new version of the multichannel entrance system will be implemented in 2022; a notable improvement is being negotiated based on negative feedback regarding the current version. For example, students who choose the multistar project and individual application channels have nearly 3 to 4 months of ＂nonstudying time＂ because their application procedure ends earlier. The new version has shortened this non-studying time by revising the timetable. For any policy to improve, it must include a dynamic feedback and revision process. A policy aimed at correcting one problem may create a new set of problems. Although some channels are designed to match students with applied programs, the traditional approach of students choosing programs based not on their interests but on the reputation of the university might still be common. This unexpected behavior during application to programs might have affected the channels through which students enter a program. Additional qualitative studies aimed at identifying the means through which students select an entrance channel are required to further determine effective means of designing policy to better persuade students to choose a major based on their own interests and competence.

1. 王秀槐, H.-H.,黃金俊, C.-C.(2010)。擇其所愛、愛其所擇：從自我決定理論看大學多元入學制度中學生的科系選擇與學習成果。教育科學研究期刊，55(2)，1-27。
   連結：
2. 田芳華, F.-H.,傅祖壇, T.-T.(2009)。大學多元入學制度：學生家庭社經背景與學業成就之比較。教育科學研究期刊，54(1)，209-233。
   連結：
3. 吳宥葶, Y.-T.,孫之元, J.-C.,李威儀, W.-I.(2013)。大專院校開放式課程學習者之自我調節問卷研發與編製。國立臺灣科技大學人文社會學報，9(3)，189-208。
   連結：
4. 林進隆, J.-L.(2011)。網路輔助教學模式對運動技能學習之自我調整策略的影響。臺大體育學報，20，1-15。
   連結：
5. 曾正宜, J.-Y.,謝小芩, H.-C.(2019)。多元入學方案的系統性矛盾與統合：以辯證認識觀解析 T 大經驗。教育研究集刊，65(2)，77-116。
   連結：
6. 黃政仁, C.-J.,黃偉婷, W.-T.(2017)。家庭資源、學習態度、多元入學管道與學習成效關聯性之研究：以臺灣某大學為例。教育科學研究期刊，62(4)，117-143。
   連結：
7. 銀慶貞, C.-C.,陶宏麟, H.-L.,洪嘉瑜, C.-Y.(2015)。由大學多元入學者的個人背景與滿意度評估多元入學的成效。應用經濟論叢，98，1-53。
   連結：
8. Astorne-Figari, C.,Speer, J. D.(2019).Are changes of major major changes? The roles of grades, gender, and preferences in college major switching.Economics of Education Review,70,75-93.
9. Bandura, A.(1977).Self-efficacy: Toward a unifying theory of behavioral change.Psychological Review,84,191-215.
10. Bembenutty, H.(2011).New directions for self-regulation of learning in postsecondary education.New Directions for Teaching and Learning,126,117-124.
11. Brown, K. G.(2001).Using computers to deliver training: Which employees learn and why?.Personnel Psychology,54,271-296.
12. Carini, R.,Kuh, G.,Klein, S.(2006).Student engagement and student learning: Testing the linkages.Research in Higher Education,47(1),1-32.
13. Carver, C. S.,Scheier, M. F.(2000).On the structure of behavioral self-regulation.Handbook of self-regulation
14. Cattell, R. B.(1966).The scree test for the number of factors.Multivariate Behavioral Research,1,245-276.
15. Chen, X.,Soldner, M.(2013).,未出版
16. Chickering, A. W.,Reisser, L.(1993).Education and Identity.Jossey-Bass.
17. Cohen, J.(1988).Statistical power analysis for the behavioral sciences.Lawrence Erlbaum Associates.
18. DeSimone, J. A.,Harms, P. D.,DeSimone, A. J.(2015).Best practice recommendations for data screening.Journal of Organizational Behavior,36(2),171-181.
19. Flavell, J. H.(1979).Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry.American Psychologist,34,906-911.
20. Harackiewicz, J. M.,Barron, K. E.,Tauer, J. M.,Carter, S. M.,Elliot, A. J.(2000).Short-term and long-term consequences of achievement goals: Predicting interest and performance over time.Journal of Educational Psychology,92,316-330.
21. Heikkilä, A.,Niemivirta, M.,Nieminen, J.,Lonka, K.(2011).Interrelations among university students’ approaches to learning, regulation of learning, and cognitive and attributional strategies: A person-oriented approach.Higher Education,61(5),513-529.
22. Kaiser, H. F.(1974).An index of factorial simplicity.Psychometrika,39(1),31-36.
23. Karoly, P.(1993).Mechanisms of self-regulation: A systems view.Annual Review of Psychology,44,23-52.
24. Kelly, W. E.(2009).Criterion validity and temporal stability of the Robert Morris Attention Scale.Individual Differences Research,7(2),105-112.
25. Kitsantas, A.,Zimmerman, B. J.(2009).College students’ homework and academic achievement: The mediating role of self-regulatory beliefs.Metacognition and Learning,4(2),97-110.
26. Kutner, M. H.,Nachtsheim, C. J.,Neter, J.(2004).Applied linear regression models.Mc-Graw-Hill Irwin.
27. Pintrich, P. R.(2000).The role of goal orientation in self-regulated learning.Handbook of self-regulation
28. Pintrich, P. R.,DeGroot, E. V.(1990).Motivational and self-regulated learning components of classroom academic performance.Journal of Educational Psychology,82,33-40.
29. Pintrich, P. R.,Smith, D. A. F.,Garcia, T.,McKeachie, W. J.(1991).,University of Michigan.
30. Porath, C. L.,Bateman, T. S.(2006).Self-regulation: From goal orientation to job performance.Journal of Applied Psychology,91,185-192.
31. Schunk, D. H.(1991).Self-efficacy and academic motivation.Educational Psychologist,26,207-231.
32. Schunk, D. H.(Ed.),Zimmerman, B. J.(Ed.)(2007).Motivation and self-regulated learning: Theory, research, and application.Erlbaum.
33. Schunk, D. H.,Ertmer, P. A.(2000).Self-regulation and academic learning: Self-efficacy enhancing interventions.Handbook of self-regulation
34. Sitzmann, T.,Ely, K.(2011).A meta-analysis of self-regulated learning in work-related training and educational attainment: What we know and where we need to go.Psychological Bulletin,137(3),421-442.
35. Vancouver, J. B.,Day, D. V.(2005).Industrial and organization research on self-regulation: From constructs to applications.Applied Psychology,54,155-185.
36. Zimmerman, B. J.(2002).Becoming a self-regulated learner: An overview.Theory into Practice,41(2),64-70.
37. Zimmerman, B. J.(2000).Attaining self-regulation: A social cognitive perspective.Handbook of self-regulation
38. Zimmerman, B. J.(Ed.),Schunk, D. H.(Ed.)(2001).Self-regulated learning and academic achievement: Theoretical perspectives.Lawrence Erlbaum Associates.
39. Zimmerman, B. J.,Kitsantas, A.(1997).Developmental phases in self-regulation: Shifting from process to outcome goals.Journal of Educational Psychology,89,29-36.
40. 大學招生委員會聯合會（2018）：《多元入學方案（108-110學年度適用）》。大學招生委員會聯合會。https://www.jbcrc.edu.tw/multi2.html [Joint Board of College Recruitment Commission. (2018). Multi-Channel College Entrance System (Year 2019-2021). Joint Board of College Recruitment Commission. https://www.jbcrc.edu.tw/multi2.html]
41. 王立昇（2015 年 9 月 7 日）︰〈連署書〉。「高中學習要完整有效、大學入學要公平適性」連署網站。https://sites.google.com/site/collegeentrance2015/ [Wang, L.-S. (2015, September 7). Petition.Website of High School Learning Should Be Integrative and Effective; College Entrance Shoud Be Fair and Adaptive. https://sites.google.com/site/collegeentrance2015/]
42. 丘愛鈴, I.-L.(2012)。繁星推薦擴大招生名額政策之問題分析。臺灣教育評論月刊，1(10)，76-78。
43. 余秋芬, C.-F.(2005)。中國文化大學=Chinese Cultural University。
44. 李大偉（計畫主持人）, D.-W.,李建興（計畫主持人）, G.-H.,胡茹萍（計畫主持人）, Z.-P.,黃嘉莉（計畫主持人）, G.-L.(2012)。財團法人國家政策研究基金會委託研究報告財團法人國家政策研究基金會委託研究報告,財團法人國家政策研究基金會=National Policy Research Foundation。
45. 沈姍姍, S.-S.(2010)。英國高等教育入學機制之探討—教育篩選意義、能力訴求與社會公平。教育資料集刊，48，139-168。
46. 邱皓政, H.-C.(2010).量化研究與統計分析：SPSS/PASW 資料分析範例解析.五南=Wu-Nan.
47. 洪泰雄, T.-H.(2004)。國立臺灣師範大學=National Taiwan Normal University。
48. 秦夢群, J.-M.(2004)。大學多元入學制度實施與改革之研究。教育政策論壇，7，59-84。
49. 翁志強, C.-G.,孫瑞霙, J.-I.,廖玲珠, L.-C.(2010)。大學多元入學管道學生學習成效之比較分析：以某私立大學會計系學生為例。經營管理論叢，6(2)，69-92。
50. 教育部（2016）：《「大學招生及入學考試長程調整草案」公聽會會議手冊》。https://recruit.knu.edu.tw/uploads/files/%E6%84%B7%E8%AD%B0%E8%B3%87%E6%96%99.pdf。[Ministry of Education. (2016). The draft of long-term college entrance system adjustment plan (Manual for subregion hearing). https://recruit.knu.edu.tw/uploads/files/%E6%84%B7%E8%AD%B0%E8%B3%87%E6%96%99.pdf]
51. 教育部（2018）：《政策優質化均質化》。十二年國民教育。https://12basic.edu.tw/content.php?ParentNo=2&LevelNo=212。[Ministry of Education. (2018). The optimization and homogenization of policies. 12-year Basic Education. https://12basic.edu.tw/content.php?ParentNo=2&LevelNo=212]
52. 教育部統計處（2018）：《表 A1-16 大專校院招生報考及錄取人數》。https://stats.moe.gov.tw/files/ebook/Education_Statistics/107/107edu_A_1_16.xls [Department of Statistics, Minstry of Education. (2018). Table A1-16 summary of entrance examination for universities, colleges and junior colleges. https://stats.moe.gov.tw/files/ebook/Education_Statistics/107/107edu_A_1_16.xls]
53. 楊淑涵（2016）：〈高中生涯輔導工作實務現況與展望〉。台灣生涯發展與諮詢學會。https://www.tcdca.org/?p=2125 [Yang, S.-H. (2016). The current status and future perspective on high school career counseling practice. Taiwan Career Development and Counseling Association. https://www.tcdca.org/?p=2125]
54. 廖述茂, S.-M.,朱崑中, K.- C.(2000)。大學多元入學新方案對高中生涯輔導的影響及因應之道。臺灣省高級中等學校輔導通訊，63，13-19。
55. 謝宜辰、賴以威（2020 年 2 月 8 日）：〈5 大數據，看看入學管道怎麼變〉。最佳大學指南。https://web.cheers.com.tw/issue/2018/college/article3.php [Hsieh, I.-C., & Lai, I.-W. (2020, February 8). Five numbers to see the changes of multi-channel entrance system. Best Guide to Colleges. https://web.cheers.com.tw/issue/2018/college/article3.php]