有許多科學領域的專家們致力於開發人類行為之計算模型，並且已經成為相當具前瞻性的跨學科研究。本論文與國家教育研究院(NAER)之研究人員合作，共同開發應用於候用校長即席演講之自動評分系統。此即席演講資料來源自國家教育研究院為候用校長們所舉辦之儲訓(認證)計畫。本研究提出短時距且高密度之特徵計算方法，搭配全時整合編碼算法，用以描述演講過程中校長多模態行為資訊。此外設計了具備初步辨識能力之行為計算框架，並且延伸討論實驗兩個面向之可能性。首先，受到心理學文獻對人類內心決策、評斷機制研究的啟發，本論文利用認知極端之樣本集訓練分類學習模型，並計算置信度得分作為系統對測試演講之數值評分。其次，有鑑於多任務學習在許多研究領域的成功應用，本論文整合來自於儲訓計畫各個面向之訓練任務。利用任務之間潛在的關聯性獲取更重要的行為特徵資訊，使得評分系統獲得更佳的學習效果。所有實驗與結果分析均展示此系統應用於高層次與主觀屬性之可行性。

Developing computational models of human behaviors for experts in many science fields has been at the forefront of several interdisciplinary research. In this work, we collaborate with researchers from National Academy for Educational Research (NAER) to develop an automatic scoring system for pre-service principals’ impromptu speech at the certification program. We propose a dense unit-level feature extraction and session-level encoding methods to characterize principals’ multimodal behavior. Moreover, we extend the framework by two direction. First, with inspiration from the psychological evidence in human’s decision-making mechanism, we assign confidence scores outputted from classifier as the predicted scores to all the speech. Secondly, as recent works on multi-task learning have been successfully utilized in many fields, we leverage other training tasks of the certification program to incorporate information about these new targets and achieve better performance of our scoring system. All the experiments demonstrate that our framework indeed has capability in handling high-level and subjective attributes.

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