不同思考風格與遊戲經驗的玩家眼動分析-以數獨為例

Abstract

摘 要 人們的學習過程，主要是依賴經由眼球所進入大腦的訊息，透過眼動行為的觀察，將提供我們瞭解人們學習歷程及注意力分佈。數位式遊戲學習不僅對個人心智成長有幫助也提升學習至更深層的階段。遊戲過程中，開始的時段為玩家學習歷程的重要關鍵，本研究主題在探討不同思考風格與遊戲經驗是否影響此時段的眼動行為。 研究對象為北部高三學生共五十九名。用眼動儀蒐集在數獨遊戲情境中的眼動資料，並以思考風格量表及自編問卷來測量玩家思考風格及遊戲經驗。本研究分析的眼動指標是不同興趣區的總凝視時間( Dwell Time )、凝視次數( Fixation Count )及返回次數( Run Count )。將數獨遊戲畫面分成七個興趣區域( Area of Interest )，研究時間區間為遊戲開始後一分鐘內，每十秒分成一個時段。以單因子變異數及迴歸進行統計分析，主要研究目的在探討不同興趣區及不同時段的眼動行為是否受玩家個人特質(思考風格與遊戲經驗)的影響。 研究結果如下: 一. 玩家而言:眼動行為中的「凝視時間」、「凝視次數」和「返回次數」會因興趣區域不同，有顯著差異。玩家在「遊戲主畫面」的眼動指標顯著高於其他區域。可知數獨遊戲是玩家的主要任務。不重要的訊息比較容易被忽略，如:「標題區」、「提示區」及「計時區」。「規則區」在不同時段的眼動行為不同，遊戲初期的「返回次數」顯著高於其他時段。結果顯示玩家在遊戲開始時會傾向先建立規則。 二. 思考風格而言:分析發現，行政型玩家在「規則區」的「返回次數」較少，而司法型的返回次數較高，行政型玩家由於規則建立後即不再懷疑，故較少返回看此區域 ，而司法型玩家仍不斷多元思考分析規則，因此重複返回此區域。至於「立法型」對「返回次數」無影響，因其具有創新的特質，但是數獨遊戲較嚴謹，可能導致沒有創新的空間可發揮。 三. 遊戲經驗而言:結果發現，在遊戲初期有經驗的玩家凝視整個畫面的頻率較高，顯示有遊戲經驗玩家搜尋時間較少，可能原因是較早進入遊戲分析思考階段。，有遊戲經驗玩家在「提示區」的「返回次數」較高。因為遊戲經驗豐富的玩家在遊戲初期即已進入遊戲，所以在「提示區」有訊息出現，且有遊戲經驗玩家對介面變化較敏感，故在玩家遊戲經驗可能會影響「提示區」的「返回次數」。

Abstract

People learn mainly through acquiring information with visual organs. A thorough observation on eye movement behaviors may gain our comprehension on how the attenation is distributed in learning processes. Learning by playing digital games can improve individual mental progress, and elevate learning to a higher stage as well. During gaming, the initial stage serves as a predominate role relating to players' learning process. The present research aims to investigate whether different thinking styles and game experience may have impact on eye movement behaviors. Fifty-nine third-grade senior high school students from northern Taiwan participated in the study. Eye movement data in sudoku games were collected using an eye tracker. Thinking style and game experience were measured using questionnaires. The eye movement indicators include DWELL TIME, FIXATION COUNT, and RUN COUNT. The screen of sudoku game is split into 7 areas of interest and the interest of period is one minute after players started playing, by each ten seconds. We conducted a series of one-way ANOVAs and regression alalysis to analyze the eye movement data. The results are as follows: 1. As far as the players are concerned, we revealed significant difference on the DWELL TIME, FIXATION COUNT, and RUN COUNTwith respect to different areas of interest between players. In addition, we found significantly higher DWELL TIME, FIXATION COUNT, and RUN COUNT on the 9 square areas than all the other areas, which suggested that the sudoku game should be the players' major mission. Minor information, such as the title area, icon area, and the timer tends to be ignored. Regarding the rule area, RUN COUNT in the first period was significantly higher than all the other periods of interest, which indicated that the players would set up the rules at the initial stage of the game. 2. In the aspect of thinking styles, we found that executive players had lower RUN COUNT, while implied that judicial players had higher RUN COUNT. The executive players returned to check the rule area less often because they were less likely to doubt after the rules were set; by contrast the judicial players keeped focusing on analyzing the rules from different perspectives, resulting in repeatedly returning to check the area. In addition, the legislate players performed little reaction to RUN COUNT, for their property of innovation might not be properly elaborated in a closed-system sudoku game. 3. Within the criteria of game experience, the regression analysis indicated that in the first period of interest, experienced players gazed at the whole screen with a higher frequency, inferring that game experience and search time could be negatively correlated; one of the possible causes might be that experienced players were able totapped in the analytical thinking stage sooner. As to the second period of interest, experienced players had higher RUN COUNT in the icon area, owing that they might have to make proper responses to the information in the icon area so that they tendedto perform better in the game.