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研究生:洪懷鴻
研究生(外文):Hung, Haui-Hung
論文名稱:大學生在系統工程課程感受的環境條件能預測心流經驗嗎?一日經驗重建資料及多階層線性模式
論文名稱(外文):Do conditions experienced by college students in system engineering course predict flow? A HLM study with repeated experiences from day reconstruction method
指導教授:林珊如林珊如引用關係
指導教授(外文):Lin, Sunny
口試委員:黃育綸吳俊育鄭朝陽
口試委員(外文):Huang, Yu-LunWu Jiun-YuCheng, Chao-Yang
口試日期:2018-09-07
學位類別:碩士
校院名稱:國立交通大學
系所名稱:教育研究所
學門:教育學門
學類:綜合教育學類
論文種類:學術論文
論文出版年:2018
畢業學年度:107
語文別:中文
論文頁數:77
中文關鍵詞:心流經驗環境條件一日經驗重建法階層線性模型系統工程課程
外文關鍵詞:flowenvironmental conditionsDay Reconstruction Method (DRM)Hierarchical Linear Model (HLM)Systems Engineering courses
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本研究以心流經驗 (Csikszentmihalyi, 1975) 為理論基礎,探討大學生在交通大學電機系創新設計的系統工程四門課程中,所知覺的課室環境條件是否比其他課程的環境條件更有利於投入學習?本研究所指的投入為大學生在課堂中知覺到的心流經驗,是一種不受到外界的影響、專注地投入任務,且在任務結束之後感受到滿足與快樂的經驗 (Csikszentmihalyi, 1975),而這樣的經驗感受有助於學生投入課堂之中,所以本研究採用心流經驗作為大學生是否投入的指標。蒐集大學生上課日一天當中知覺的外部經驗 (時間、環境與活動類別) 與內部經驗 (心流經驗與環境條件),了解大學生上課日一天中知覺心流經驗最高的環境為何?他們從事什麼活動?他們在環境中知覺到的環境條件為何?進而針對大學生知覺心流經驗最高的三個學校環境,分析他們知覺到的「環境條件」與「所在環境」對心流經驗的影響。依據 Shernoff 和 Csikszentmihalyi (2009) 歸納出在學習環境中影響學生投入的兩個環境條件,分別為「學科強度」與「正向情緒回應」,為產生學生投入不可或缺的因素。本研究採用對環境知覺的「挑戰性」和「重要性」作為活動強度和知覺的「主動性」作為正向情緒回應。而 Bandura (2000) 指出,個人的心理功能容易受到環境與所屬團體交互作用的影響,所以本研究採用大學生「所在的環境與從事的活動類別」,作為探討心流經驗的環境因素。對比以往對在課室環境對學習影響的研究,本研究為少數以大學生日常生活經驗作為課堂經驗的對比參照點,而不是以實驗設計的方式將受試者分成實驗組與控制組的研究。
本研究資料取自「大學工程教育革新」的密集長期資料庫 (為林珊如主持之科技部研究計畫蒐集),資料包含2011 ~ 2014年針對四門系統工程課程蒐集的資料,以Cheng (2016) 設計的一日經驗重建法問卷 (Day Reconstruction Method, Kahneman, Krueger, Schkade, Schwarz, and Stone (2004)) 重複蒐集58位參與者一整個學期五波的經驗資料。參與者需填答前一天從起床到就寢經歷過的所有事件 (event),蒐集了3256筆事件,而在每一筆事件中需填寫自己知覺的外部經驗 (時間、環境、從事的活動類別、互動對象等…戳記 (tag)),再填寫在每一事件中知覺的內部經驗 (心流經驗與環境條件等…)。本研究所採用的外部經驗為大學生「所在環境與從事的活動類別」,內部經驗為他們知覺的「環境條件與心流經驗」。由於資料是重複五次量測大學生在學校的經驗,有其獨特性,本研究除了提供詳細的描述統計,並以階層線性模型 (Hierarchical Linear Model, HLM) 進行參數估計,檢驗大學生在知覺心流經驗最高的學校環境,環境條件 (挑戰性、重要性和主動性) 在預測心流經驗上是否受到所在環境 (系統工程課程與非系統工程課程活動) 的調節?亦即內部經驗 (挑戰性、重要性和主動性) 與外部經驗 (系統工程課程與非系統工程課程活動) 之間是否具有跨階層交互作用效果 (cross-level interaction effect)。研究結果如下:
(一) 由描述性統計可知,大學生上課日一天中在系統工程課程回報最多的事件數,他們在系統工程課程中知覺的挑戰性與重要性是所有八種學校環境中最高的,然而,知覺的主動性僅高於其他課程與工作場所。大學生在系統工程課程中知覺的心流經驗是所有環境中最高的,其次為其他課程與電腦前活動,而在系統工程課程中知覺心流經驗最高的活動類別為考試、執行作業或實驗和創意專題。顯示新設計的系統工程四門課程是成功的,但與其他課程的對比並不明顯。
(二) 本研究僅針對大學生在知覺最高心流經驗的三個環境 (系統工程課程、其他課程與電腦前活動),利用HLM分析知覺的環境條件 (挑戰性、重要性與主動性) 與所在環境 (系統工程課程對比非系統工程課程活動) 對於心流經驗的預測效果。為了提供系統工程課程的老師了解創新設計的課程能否比其他課程或電腦活動更明顯提升大學生的心流經驗,本研究合併其他課程與電腦活動成為「非系統工程課程」,作為「系統工程課程活動」的對比。從隨機係數模型發現,大學生知覺的挑戰性、重要性與主動性皆顯著預測心流經驗,如同預期;而他們知覺的挑戰性、重要性和主動性預測心流經驗的人間變異數達顯著水準,表示大學生知覺的挑戰性、重要性和主動性在預測心流經驗時存在個別差異,有些人知覺的挑戰性、重要性和主動性比其他同學更容易提升心流經驗。然而在完整模型中,考量大學生知覺的內部經驗與外部經驗之下,不管在系統工程課程或非系統工程課程活動 (其他課程與電腦前活動)的心流經驗,兩者之間差異未達顯著水準,而心流經驗的人間變異數達顯著水準,表示心流經驗在大學生之間的個別差異是無法忽視的,有些人比其他同學更容易有心流經驗。心流經驗受到環境因素的解釋程度較低,大學生在兩種環境 (系統工程課程對比非系統工程課程活動) 知覺的心流經驗都很高。心流經驗存在很大的個別差異,這樣的個別差異可能來自於大學生自身能力所導致,當自身能力與挑戰之間達成平衡時,便容易產生心流經驗 (Csikszentmihalyi, 1975)。然而,大學生入學時的能力是相當的,為何到了高年級後,能力差異如此之大?可能原因是,一、有些大學生在求學的過程中產生興趣,找尋適合自己的挑戰以提升自己的能力,與沒有興趣的大學生形成能力上的落差。二、有些大學生在求學的過程中自身能力逐漸跟不上挑戰,當自身能力達到極限時,便與跟得上挑戰的大學生形成能力上的落差。三、由於大學老師多半期許學生自發性學習,給予他們自由與自主學習的空間,大學的自由未必使每位學生都自主投入學習,從大一起努力與否導致能力的落差,而且隨年級越高,投入與不投入的學生,落差越來越大。另一個可能性是,本研究採用比較嚴格的參考點 (將其他課程與電腦前活動合併成為「非系統工程課程活動」) 與系統工程課程進行對比,由於大學生在「其他課程」與「電腦活動」知覺的心流經驗也非常高,僅次於系統工程課程,可能導致他們在兩種環境中知覺心流經驗之間的差異甚小。
最後研究者針對研究結果提出探討,進一步提供未來研究的方向和給予系統工程課程的教學者實務建議,並提出本研究的限制。
This study, based on flow (Csikszentmihalyi, 1975), was to explore whether the environmental conditions college students experienced in Systems Engineering courses (of Dept. of Electronic Engineering) were more effectively leading them to engage in learning than the environmental conditions in other courses? Learning engagement is defined by flow theory, meaning that if an individual experiences higher flow, s/he shows higher tendency of learning engagement. In this study, the external coordinates of experience and internal coordinates of campus experience of college students were collected in several school days and were analyzed to understand where (in what place of a campus) and how (doing what kind of activities, feeling what features of environmental conditions) would college students experience the highest flow? For the three environments with the highest flow experiences, the thesis explored the predictive capacity of environmental conditions and the environments on flow, and examined the theoretical model.
Shernoff and Csikszentmihalyi (2009) proposed that two environmental conditions that could predict students’ engagement in learning environments, are academic intensity and positive emotional response. In the study, the researcher selected “challenge” and “relevance” that college students experienced in the campus as the indicators of “activity intensity” and “activeness” as the indicator of “positive emotional response”. Bandura (2000) proposed that people’s psychological function is easily influenced by physical and social environments, so the environments in which college students were and activities which they engaged were selected as the factors to predict the level of flow. In educational studies, researchers usually used experimental design to examine the effectiveness of a new type of course between experimental and control groups, but in this study college students’ daily school experiences were compared (within person) with classroom experiences in the newly designed SE courses.
The data source comes from an intensive longitudinal dataset funded by Ministry of Science and Technology to the advisor (MOST projects of Engineering Education remodeling) research project of Professor Lin, and the data was collected for the four Systems Engineering courses from 2013 to 2015. Used Day Reconstruction Method (Kahneman et al., 2004) designed by Cheng (2016) to repeatedly collect 58 participants’ subjective experience over five weeks in a semester. Participants had to report each event they took part in and provide several information (tags) about the events, such as the time, the place, and the activities as well as the experiences that they felt in each event, such as environmental conditions and flow. The participants were 58 engineering students who took Systems Engineering (SE) courses. Internal coordinates of experience used in the study were “environmental conditions” and “flow” and external coordinates of experience were “the place” and “the activities”. Then, several Hierarchical Linear Modeling (HLM) were conducted to test whether the environmental conditions (i.e., challenge, relevance, and activeness) could predict flow and whether the prediction could be moderated by the place tag of the events (i.e., SE courses versus non-SE courses)? Whether there was a cross-level interaction between internal coordinates of experience (i.e., challenge, relevance, and activeness) and external coordinates of experience (i.e., SE courses versus non-SE courses)? The major findings of the research were summarized as in below.
1. The descriptive results showed that college students reported the highest number of events were taken place in Systems Engineering courses compared with other events in a school day. They experienced the highest “challenge” and “relevance” in SE courses compared with all other places, however, activeness they experienced in SE courses was only higher than “other courses” and “workplaces”. They experienced the highest flow in SE courses which was followed by other courses and computer activity. In SE courses, they experienced the highest flow when they took three activities: exams, working on assignments and experiments, and conducting creative projects.
2. For the three places with the highest flow that college students experienced, several HLM models were conducted to test whether the environmental conditions and the place (a dummy variable for the comparison of SE courses versus non-SE courses which was an integration of the places of other courses and computer activity) could predict flow. The results of the random coefficient model showed that challenge, relevance, and activeness (environmental conditions) all significantly predicted flow, meaning that there was an apparent individual difference. For some college students, environmental conditions they experienced predicted flow more successfully than others. However, in the full model, when external coordinates of experience and internal coordinates of experience were simultaneously considered, flow in SE courses versus in non-SE courses (other courses and computer activity) were not significantly different. In contrast, the between-persons variance of flow was significant, meaning that for flow experience there was a remarkable individual difference among college students. Some of them experienced higher level of flow than others, no matter in SE or non-SE course. Comparatively, higher proportion of flow variance was explained by individual difference than by places. Such individual difference might be accounted for by college students’ abilities. When their abilities matched the challenges (Csikszentmihalyi, 1975), college students tended to experience flow. However, students at the time in entering colleges must perform quite outstanding in order to pass the application screening. Why is there a great discrepant in abilities shown in the senior year in taking SE courses? There were three possible reasons. First, some college students might find SE program is interesting in the process of studying but others might not. The motivated college students might actively look for challenges to enhance their abilities but others might not. The active learning approach might form an ability gap between motivated and not-motivated students. Second, some college students couldn't keep up with the challenges in the process of studying. When their abilities reached the limit, it might form an ability gap between college students who could keep up with the challenges and those could not. Third, because most teachers expected that college students could learn actively and spontaneously, some college students didn’t meet teacher's expectation. It might form an ability gap between college students who learned actively/spontaneously versus those who did not.
The other possible reason was that we used a strict reference of other courses and computer activities (where college students obtained the second highest level of flow) as the comparison of SE courses. The difference of flow experienced in SE courses and non-SE courses was not enough apparent.
Finally, according to the above results, the researcher provided several discussions. Several suggestions for future research and for SE teachers were then given.
中文摘要 i
ABSTRACT iv
誌謝 vii
目錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
第一節 研究背景與動機 1
第二節 重要名詞釋義 5
第二章 文獻探討 9
第一節 心流經驗與環境條件 9
一、心流經驗 9
二、環境條件 11
第二節 系統工程課程設計如何引發心流經驗 15
一、系統工程課程 15
二、系統工程課程如何引發心流經驗 18
第三節 一日經驗重建法 21
第四節 綜合論述與研究問題 26
第三章 研究方法 31
第一節 研究對象 31
第二節 研究工具 31
第三節 施測程序 34
第四節 統計方式 35
第四章 研究結果 38
第一節 大學生上課日一天的外部經驗與內部經驗 38
第二節 心流經驗共識程度與階層線性模型 44
第五章 結論與建議 48
第一節 研究結論與討論 48
第二節 未來建議與研究限制 54
參考文獻 58
附錄一 系統工程課程綱要 64
附錄二 一日經驗重建法問卷 69
附錄三 活動內容分類指標表 70
附錄四 心流經驗共識程度表 74
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