運用三維動作分析輔助戲曲教學之研究
Research on the Teaching and Learning of Using 3D Motion Capture Analysis on Chinese Opera Performance
中文摘要
本論題研究的初創重要性，在傳統戲曲歷經中華文化八百多年的時代意義甚為重大，表演者的技藝尤其重要，每一位學習者的訓練歷程都有一個特定的模式可尋，而模式裡則可能探索出優秀表演者的訓練建構過程，到底是經由何種量或質之屬性混合的模組，因而最終可以在舞臺上除了展現戲曲的傳統表演形式外，並且能夠保有自我獨特的藝術風格。然而戲曲的程式概念及分類上，其符號象徵意境的表演程式被分類為二種，一為直接在表演內容裡於視覺和聽覺可感受之「有形程式」，另一種為以程式化思維來安排戲曲劇情和內容表演之「無形程式」。
研究方法共分為二個階段，階段一為戲曲表演動作教與學之異同分析，主要以三維擷取系統分析專家與初學者，在戲曲動作教學現場教學成效的表現水準，實驗方法以「前實驗設計(pre-experimental design)」為架構，資料蒐集方法則主要採用「單組後測設計(one-shot case study)」。階段二為三維擷取系統實驗戲曲動作教學，主要以分析和驗證戲曲動作是否因三維擷取系統介入，在教學現場因而能夠提升表現水準，實驗方法以「真實驗研究設計(true-experimental design)」為架構，資料蒐集方法則主要採用「隨機化實驗控制組前後測設計(randomized control-group pretest-posttest design)」。
實驗結果在階段一的量化與質性互為探索裡，以科技輔助驗證教師和學生在教與學的過程裡，師生之間動作異同的相關分析。並且確立了更為需要探索除了科技能否輔助教學成效之外的質的不確定性– 戲曲有形的程式和無形的程式屬性。而在階段二的質性與量化互為探索過程，原先所設定的二個虛無假設可以依據分析後的顯著差異予以拒絕，一為戲曲表演動作使用影片媒體教學的方式，在實際的戲曲教學現場早已是師生在教與學之間，除了親身傳授表演動作外的重要教學媒體選項。某種程度上影片教學是會對學生在教學現場學習成效上產生差異，但其差異係為前後改變量上的分別而無法確知因果關係為何。
但是就另一個可以拒絕的虛無假設而論，戲曲表演動作使用三維動作擷取系統輔助教學的方式，則有很高的機率會對學生在教學現場學習成效上產生差異，因為教與學的前後測改變量，是可以明確的得知由何因子所帶來的高度關聯影響力，無論是易於說明和理解的物理屬性項目，或者是較為廣泛概念的重點式項目，更甚至是戲曲專業領域裡數百年來所發展形成的程式性邏輯之表演屬性，本論文的研究歷程和所建立以科技輔助教學的新工具，確信在過程裡探索出前所未有的成果，並在新的研究取徑上完成戲曲教學現場從未有過的探究歷程。本研究所設計和形成的模式將可以概化到其他同質性的樣本，甚或是未來可以進一步的研究戲曲科班學生的教與學之間的現象。
未來將應用「三維動作擷取 (3D Motion Capture)」數位工具，進行存儲戲曲表演動作之數據，於教學或數位策展 (Digital Curation) 循環應用機制，如此將能輔助戲曲教育訓練，亦將能夠在教育、學習、創作、研究、產學合作等各方面，能夠以更為全面性的人、機、環境等統合整體規劃與設計，則一股如同文藝復興時期跨領域範疇的新思維力量，將逐漸的在人工智慧的雲端時代之大數據裡演繹著。
Abstract
The difference of performance movements are a frequent reality for training Chinese opera performers. Many aspects of the acting training make performers particularly susceptible to being different with each other’s. Thus, the process of 3D motion capture could investigate the data of movements in between the instructor and students.
The purpose of this study focuses on a comparison of Chinese opera performance movements’ difference for the professionals and students in training place. For the training issues, the “Eight Attributes” are the foundation of performing skills in Chinese opera. It concerns the manner of hands, eyes, head, feet, legs, and body which present the movements of a Chinese opera performer’s physical capabilities. Therefore, the aim of this paper is directly towards one of three types of disciplines, which can be describe as “Basic Exercises” (Ji-ben-gong). It is the most important foundation in the training for traditional Chinese Opera curriculum.
For the methods, the reseacher use PERCEPTION NEURON, a motion capture equipment, for capturing the movements of instructor and participants. A Neuron houses an Inertial Measurement Unit also known as an IMU, with a gyroscope, accelerometer, and magnetometer. The experimental processes consists of two phases. The first phase of experiment are using pre-experimental design in one-shot case study. The second phase of experiment are using true-experimental design in randomized control-group pretest-posttest design.
The subjects and conditions randomly and voluntarily participated. Furthermore, an electronic data analysis program are used to facilitate analysis. The main program used are SPSS 18 because of its versatility in statistical analysis. For the calculate methods, the bivariate correlation, paired sample T-test, ANOVA, IPA (Importance-Performance Analysis), and multiple regrasion are used at a two-tailed significance level of .05 for comparison of the movements of instructor and participants variables such as right hand, left hand, hips, right foot, left foot, and head.
Therefore, 3D motion capture can use for the future application of identifying detailed factors through a live demonstration involving visual blind spots and a rotating 3D doll perspective, to enhance the accuracy of movement observation. Analysed data from 3D motion capture can benefit training. A database analysis could applied to quantify and qualify cross-examination; individualized guidance can enable professionals and students to mitigate the problems of differences. Hopefully, the database of Chinese opera performance movements will be established and not only supported teaching and learning in the real training field but also applied for VR (virtual reality), AR (augmented reality), and MR (mixed reality) in the near future.
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