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題名：學習動機的動態歷程: 從自我決定論的觀點分析統計課程中的影片觀看投入度
作者：廖晨瑄
作者(外文)：Liao, Chen-Hsuan
校院名稱：國立陽明交通大學
系所名稱：教育研究所
指導教授：吳俊育
學位類別：博士
出版日期：2023
主題關鍵詞：學習動機；學習分析；自我決定論；影片學習；機器學習；結構方程模型；Learning motivation；Learning analytics；Self-determination theory；Video-based learning；Machine learning；Structural equation modeling
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即使擁有豐富的學習資源和充實的相關知識，高等教育中仍有許多學生無法維持其學習動機甚至輟學。在遠距教學中，更依賴學習者的學習自主性才能保證學習成功，也因此造成學習中輟問題在遠距教學中甚至比在實體教學中更為嚴峻。有些學習者在上完課後會更加投入學習，而有些人則可能因為不斷遭遇挫折後逐漸失去熱情，讓學習最終只為了滿足外在規範。因此教學者應該要在學期進行中，時刻地掌握學生的學習動機，並即時對於那些學習動機低落的同學給予必要的輔助和激勵。綜上，本論文於一門實際使用影片教學的研究所統計課程中，分三個研究探討影片觀看投入與學習動機動態歷程之間的關聯。
研究一發展了一份僅有五個題項的短版學習動機量表，使教學者能夠在學期中頻繁的施測。透過探索性和驗證性因素分析，本研究證實了該量表具備良好的信效度。搭配研究一所開發的量表，研究二和研究三進行多模態學習分析，找出學生影片觀看行為和其學習動機之間的關係，協助教師能透過影片學習平台的後台數據與學習分析掌握學生的學習動機的動態歷程。
研究二根據學生在影片平台中的學習模式，使用一整個學期的學習資料，針對影片觀看投入度、學習動機、學習成效建模。本研究從兩個層面收集了26位學生的影片學習操作記錄，兩個層面分別為點擊次數(number-based)以及時間幅度(time-based)。點擊次數代表學生在觀看影片的過程中，點擊影片播放器界面上的功能按鍵，使教學影片倒帶、暫停、跳躍的次數；時間幅度代表學生在觀看影片的過程中，透過拖移影片時間軸上的游標，將影片由當下播放時間點往起點倒帶或往終點跳躍，使教學影片的播放進度發生變化的時間長度，或是暫停播放影片的時間長度。每位學生在每個課程單元中的影片觀看投入都將由2個層面(點擊次數、時間幅度) x 3種行為（倒帶、暫停、跳躍）共6個指標所表示。而整個學期中，不同課程單元的影片觀看投入（共6個指標）將各自取平均，視為每位學生在整個學期中的影片觀看投入度。換句話說，每一位同學在整個學期結束後，將可以使用6個影片觀看投入指標，來表示其在該學期的影片觀看投入度。然而，點擊次數、時間幅度（影片觀看投入的兩個層面）擁有不同的單位，分別為次數與秒數，單位差異將會使後續進行集群分析的過程中，由於各變項對於分群結果的影響力強弱不同，可能導致結果產生偏誤。因此研究二將6個影片觀看投入指標，各自轉換為z分數，消弭單位不同對集群分析所產生的影響，當轉換結果的z分數為零時，代表該位同學的影片觀看投入度與班平均相等，相反地，當轉換結果的z分數大於零或小於零時，代表該位同學的影片觀看投入度相比班平均較多或較少。根據學生在教學影片中不同的操作記錄，研究二使用非監督式機器學習中的k-means將學生進行分組，並透過變異數分析檢驗學習動機和學習成效的組間均值差異。分析結果辨識出三種集群：快速學習者、勤奮學習者和茫然學習者。結果指出茫然學習者的特徵是會在影片時間軸中大幅度地跳躍，這些學生不僅學業表現較差，其課後自覺的受控性動機也高於自主性動機。
為了更動態地掌握學生的學習動機，並探討學習動機在學習歷程中的重要性，研究三收集了84位學生在一門研究所統計課程中，每週觀看影片的投入行為，應用結構方程模型檢驗動機在學習投入行為與學業表現之間是否具有中介效果。研究三的影片觀看投入同樣地也包含了2個層面(點擊次數、時間幅度) x 3種行為（倒帶、暫停、跳躍）共6個指標，不過由於研究三將學生的學習歷程細緻地依照課程單元拆解分析，必須考量每個課程單元的影片時間長短對於點擊次數與時間幅度的影響，因此將每位學生在各個單元的六個觀看投入指標各自除以每個單元影片的時間長度，將其轉換為點擊頻率(frequency-based)與時間比例(time-ratio-based)。與研究二相同，研究三在使用影片觀看投入指標建立結構方程模型之前，必須先將其各自轉換為z分數，消弭點擊頻率與時間比例之間因為資料單位不同，對於模型路徑係數影響力強弱不一的問題。當轉換後的z分數等於零時，代表該位同學在該課程單元的影片觀看投入度與班平均相等；當轉換後的z分數大於零時，代表該位同學在觀看該單元影片時，點擊頻率較班平均為多，或是時間比例較班平均為長；當轉換後的z分數小於零時，代表該位同學在觀看該單元影片時，點擊頻率較班平均為少，或是時間比例較班平均為短。本研究發現教學影片暫停頻率能夠正向預測該週學習後的自主性動機，相對地，倒帶頻率則負向預測自主性動機，而自主性動機在該模型中的影片觀看投入度與課後學習成效之間具有顯著的中介效果，證實自主性學習動機對於高品質學習的重要性。本研究也發現，暫停影片時間比例越大的同學，課後學習成效較不理想。本系列研究結果與自我決定理論的主旨一致，當學生在基本心理需求得到滿足時能夠學得更好。綜合上述研究，本論文提供了一個務實的數據分析框架，使教學者能夠透過有效量測工具與數位學習足跡，以客觀、即時的方式掌握學生在影片學習歷程中的學習動機，同時也針對影片學習平台的設計者，提出了如何提升使用者學習品質的實質建議。
Even with rich resources and sufficient domain knowledge, students in higher education may dropout after losing learning motivation. As whether students keep engaging in distance education primarily relies on their autonomous learning instead of external regulations, the attrition rate is even more serious in distance than in face-to-face education. Irrespective of their initial enthusiasm, some may perceive greater self-determination after class and persist in their academic journey. In contrast, others may gradually lose self-determination after constantly facing difficulties, continuing learning only because of external contingencies. Instructors must trace the learning motivation of their students throughout a course and stimulate those who perceive more controlled than autonomous motivation in a timely manner. Focusing on a graduate-level statistics course that implemented hybrid learning with a video-based learning platform, this dissertation has investigated the dynamics of learning motivation in three consecutive studies.
Study I developed a five-item measurement instrument of learning motivation which empowers instructors to administer it in class frequently. Exploratory and confirmatory factor analysis results in this study validated that psychological measurement has reliable and robust psychometric properties. Based on this questionnaire, Study II and Study III conducted multimodal learning analytics to bridge students' external video-viewing behaviors and internal learning motivation. Identifying these relationships help instructors probe how students feel after class by analyzing the digital footprints on an online video learning platform.
Study II developed a fixed-term model of learning motivation regarding how students learn within the instructional videos throughout the course. This study collected video learning records of 26 graduate students from two perspectives: number-based and time-based. The number-based video-viewing engagement represents how many times that a student clicked the features in the video player to make a video skip backward, pause, or skip forward. The time-based video-viewing engagement represents how far a student dragged the cursor on the timeline in the video player to make a video skip backward or skip forward. It also includes how long a student paused a video. The video-viewing engagement of each student was composed of 2 perspectives (i.e., number-based and time-based) x 3 behaviors (i.e., skippingbackward, pause, skippingforward) = 6 indices in each lesson. These indices were separately averaged throughout a semester for each student. That is to say, after a semester, there were six video-viewing engagement indices for each student. However, because the unit of number-based and time-based indices are different, they were transformed into z-scores before the following cluster analysis. The engagement of a student was equal to the class average when z = 0. Contrary, the engagement of a student was higher or lower to the class average when z was not equal to zero. Subsequently, Study II utilized unsupervised machine learning techniques (i.e., k-means) to classify students in terms of their video-viewing engagement profiles and subsequently utilized ANOVA to examine the mean differences in learning motivation and performance across groups. Three clusters were identified: fast learners, diligent learners, and wandering learners. Specifically, wandering learners tended to take a large step when moving backward or forward in videos and they were at-risk students with worse academic outcomes and less self-determination.
Study III developed a temporal model of learning motivation with their weekly video-viewing engagement by structural equation modeling (SEM). This study collected data of 84 graduate students in a graduate-level statistics course. The video-viewing engagement also included 2 perspectives (i.e., number-based and time-based) x 3 behaviors (i.e., skippingbackward, pause, skippingforward) = 6 indices in each lesson. Unlike Study II, the temporal modeling in Study III investigated weekly video-viewing engagement, and thus needed to consider the influence of different video length in each lesson. The normalization was implemented by dividing video-viewing engagement with the video length for each lesson per student, changing engagement into frequency-based and time-ratio-based. Like Study II, all engagement indices were transformed into z-scores to avoid the influence resulting from unit difference. The engagement of a student was equal to the class average when z = 0; the engagement of a student was more than the class average when z > 0; the engagement of a student was less than the class average when z < 0. The temporal modeling supports instructors in inferring the level to which students perceive autonomous or controlled motivation and then predicting their learning performance. In addition, the modeling examined whether autonomous and controlled motivation mediate the relationship between learning engagement and outcomes. Results revealed that the frequency of pausing instructional videos positively predicted autonomous motivation. In contrast, the frequency of backward skipping the video timeline negatively predicted autonomous motivation. Results also validated multiple statistically significant mediation effects of autonomous motivation, reiterating its importance in high-quality video learning. Results in these studies support the gist of Self-Determination Theory (SDT) that students can achieve more when their basic psychological needs are satisfied. This dissertation presents the dynamic nature of learning motivation and illustrates a practical analytical and theoretical framework enabling instructors to better understand their students through digital footprints in video-based learning. In addition, this dissertation provides suggestions to designers of video-based learning platform about how to enhance the learning quality of users.
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