以解構式計畫行為理論探討大學生使用Web2.0平台於網路學習行為之研究

本研究旨在探討大學生使用Web2.0平台於網路學習行為意圖，主要了解大學生使用WEB2.0之現況以及解構式計劃行為理論變項對大學生使用Web2.0平台之行為意圖預測情形，並根據研究結果提出相關建議，作為教育相關單位及學校推動大學生使用Web2.0平台於網路學習之參考。本研究之研究工具係參考國外相關文獻所編製之結構式問卷，問卷內容包含基本背景資料、解構式計劃行為理論量表。研究對象為台灣的大學生，採便利抽樣調查，共發出問卷435份、回收412份、回收率為89.7%，所得問卷以SPSS18.0軟體進行描述性統計、Pearsom積差相關分析、迴歸分析等方法，進行資料的分析，根據分析結果，歸納出以下結論：一、大學生使用Web2.0平台於網路學習之情形 (一)大學生對於Web2.0平台瞭解程度以未曾聽過居多，其次為聽過但不了解 (二)大學生平均每日上網時數平均數高於12歲以上國人上網時數 (三)大學生日常上網行為以搜尋、瀏覽資訊、社群使用、線上影片觀賞居多 (四)大學生使用Web2.0平台種類以YouTube與社交網絡居多 (五)大學生使用Web2.0平台目的以社交互動與友情聯繫居多二、大學生應用Web2.0平台於網路學習之相關分析 (一)研究結果表示相關係數皆表示高度相關 (二)相關係數以同儕影響與主觀規範最高；資源輔助狀態與知覺行為控制、科技輔助狀態與知覺行為控制共列次高三、大學生應用Web2.0平台於網路學習之預測分析 (一) 解構式計劃型為理論之各變項對於大學生使用Web2.0平台於網路學習行為意圖有顯著的預測力 (二) 「知覺有用性」、「知覺易用性」、「相容性」構面對「行為態度」可有效預測，整體預測力為53% (三)「同儕影響」、「上級影響」構面對「主觀規範」可有效預測，整體預測力為25% (四) 「自我效能」、「資源輔助狀態」、「科技輔助狀態」構面對「知覺行為控制」可有效預測，整體預測力為55% (五) 「行為態度」、「主觀規範」、「知覺行為控制」構面對「行為意圖」可有效預測，整體預測力為70% (六)「行為意圖」構面在迴歸分析結果下之預測力最佳

關鍵字

Web2.0 ；網路學習；解構式計劃行為理論

並列摘要

The purpose of this study was to investigate college students’ behavioral intentions in using Web2.0 technologies for E-learning by. The study analyzing the patterns of students using Web2.0 technologies. The Decomposed Theory of Planned Behavior (DTPB ) was used to understand and to predict college students’ behavioral intentions. Furthermore, according to the results of the study, some relevant suggestions are offered for higher education institutions. The research employed a structural questionnaire that contained a demographic data sheet and Decomposed Theory of Planned Behavior (based on both overseas and domestic researches.). A total of 435 questionnaires were distributed to college students in Taiwan, 412 copies were returned, for a valid response rate of 89.7% . Statistical analyses were performed using SPSS 18.0 statistical software, with results including descriptive statistic percentage, means, standard, Pearson’s product-moment correlations and multiple regressions. The empirical results were as follows: 1. College students’ backgrounds in using Web2.0 technologies (1) The majority of college students who responded to the questionnaire had never heard of Web2.0 technologies. Most part of minorities had heard it and with little knowledge of it. (2) Average College students spent more time on surfing the Internet each day than aged above 12 in Taiwan. (3) The three major types of daily Internet usage reported by the students were searching or browsing for information, using social networks and watching videos online. (4) YouTube and social networks were the types of Web 2.0 technologies the students mainly reported using. (5) The main purposes reported by the students for using Web 2.0 technologies were social interactions and maintaining friendships. 2. Correlation analysis of college students utilizing Web2.0 technologies for E-learning. (1) The research results indicated that all the correlation coefficients were highly correlated (2) Peer influence and subjective norms stood the highest in canonical coefficient (.93); both “resources facilitating conditions and perceived behavioral control” and “technology facilitating conditions and perceived behavioral control” rated second with a value of 0.88. 3. Prediction Analysis of students’ applications of using web2.0 technologies (1) The DTPB could significantly predict students’ behaviors with regard to using web2.0 technologies. (2) Regression results confirmed that three factors, namely, perceived usefulness, ease of use, and compatibility, were significantly indicative of attitude toward learning, which regression coefficient beta values were 0.26, 0.41, and 0.21 respectively. Its whole explanatory power was 53%, adjusted by R². (3) Regression results confirmed that two factors, peer influence and superior’s influence, could predict subjective norms, with regression coefficient beta values of 0.19 and 0.39, respectively. Its whole explanatory power was 25%, adjusted by R². (4) Regression results confirmed each of the three factors, self-efficacy, resources facilitating conditions, technology facilitating conditions, which regression coefficient beta value were .40,.07, and .37 respectively, indicated that it had a significant effect on perceived behavioral control . Its whole explanatory power was 55% (adjusted R²). (5) Regression results confirmed that three factors, namely, attitude, subjective norm, and perceived behavioral control, which had regression coefficient beta values of .36, .09, and .50, respectively, had significant effects on behavioral intention., which whole explanatory power was 70% (adjusted R²). (6) According to the results of regression analysis, behavioral intention had greatest ability to predict DTPB.