期刊專區/ Periodical Section

國小高年級學童線上數位閱讀認知負荷量表編製

Cognitive Load Scale of Digital Reading for Elementary School Students

所屬期刊：	第12卷第4期 「教育心理、輔導與測評」 主編：國立臺灣師範大學教育心理與輔導學系兼任教授 陳秉華
系統編號：	vol047_01
主題：	測驗與評量
出版年份：	2016
作者：	陳新豐
作者(英文)：	Shin-Feng Chen
論文名稱：	國小高年級學童線上數位閱讀認知負荷量表編製
論文名稱(英文)：	Cognitive Load Scale of Digital Reading for Elementary School Students
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語文別：	中文
論文頁數：	22
中文關鍵字：	位閱讀、認知負荷、結構方程模式、量表編制
英文關鍵字：	cognitive load, digital reading, scale development, structural equation modeling
服務單位：	屏東大學教育學系
稿件字數：	14248
作者專長：	測驗評量、教育統計、電腦化測驗、試題等化、測驗組卷
投稿日期：	2016/10/13
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摘要(中文)：	本研究旨在編制國小高年級學童數位閱讀認知負荷量表（Digital Reading Cognitive Load Scale, DRCLS），建立信度及效度資料。研究工具採用自編「國小 高年級學童數位閱讀認知負荷量表」，命題內涵依任務／環境、學習者特性（認 知能力、認知風格、先備知識與經驗）、以及環境與學習者特性的交互作用等3 個向度，再區分為心智的努力（mental efforts）以及心理的負荷（mental load）等 2 個構面，採用李克特5 點量尺的設計方式，總共25 題。研究分二個階段進行， 第一階段先編擬試題，並商請四位學科領域專家及二位國小教師來進行題項審查， 具有良好的內容效度指數（Content Validity Index, CVI），繼而就387 位預試樣本 進行施測與蒐集資料分析，根據預試之量表信度與效度資料進行修正。第二階段， 分層比率隨機抽取35 個班級，針對728 位國小高年級學童進行線上量表施測。測 量信度方面，具有良好的內部一致性信度，在構念效度方面，以結構方程模式對 二群隨機樣本群組分別進行理論模式和複核效度的驗證，結果具良好的模式適配 度以及模式穩定度，在區辨和幅合效度方面，心智的努力與心理的負荷等二個構 面分量表均可接受。綜上所述，DRCLS 在相關研究以及數位閱讀實務上具有應用 價值。
摘要(英文)：	This study aims to establish a Digital Reading Cognitive Load Scale (DRCLS) for elementary school high-grade students in order to form a reliable and valid database. The DRCLS consists of three components: (1) task environment, (2) learner characteristic (cognitive capability, cognitive style, prior knowledge and experience) assessment, and (3) an interactive effect component to measure learner’s engagement within the task environment. The interactive effect component measures a learner at two levels: mental effort and mental load. Twenty-five Likert-type scale items were produced for the DRCLS. The entire study was divided into two steps. Step 1 included developing the items and having them validated by four subject field experts and two elementary school teachers. Validated items were statistically analyzed using 387 pre-test samples. The 25 items were further refined according to the results from the pre-test data analysis. At the Step 2 data were collected from a stratified proportional sampling of 35 classes, which included 728 elementary school high-grade students. These students responded to the 25 items in a Web-based online environment. Analysis on data collected from these students revealed good internal consistency reliability. Structural equation modeling (SEM) was developed to measure two scale components: mental effort and mental load. The theory model was cross-validated using two random sample groups. Analysis of the SEM model to data revealed a good model fit to the data, good construct validity, and good divergent and convergent construct validity for both scales’ components. The education value of using DRCLS to promote digital reading practice among students is also forwarded.
參考文獻：	左台益、呂鳳琳、曾世綺、吳慧敏、陳明璋、譚寧君（2011）。以分段方式降 低任務複雜度對專家與生手閱讀幾何證明的影響。教育心理學報，43，291- 314。 林巧敏（2011）。臺灣國小學童數位閱讀興趣與行為之調查分析。國家圖書館館 刊，100（2），30-59。 施俊名、吳裕益（2008）。「大學生身心健康量表」概念效度驗證之究。教育研 究與發展期刊，4（4），201-230。 涂金堂（2012）。應用認知負荷理論的數學解題教學實驗。屏東教育大學學報－ 教育類，38，227-256。 Blunch, N. J. (2008). Introduction to structural equation modelling using SPSS and AMOS. London, England: Sage. Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychology, 38(1), 53-61. Chall, J.S. (1996). Stages of reading development (2nd Ed.). Fort Worth, TX: Harcourt Brace. Clark, R.C., Nguyen, F., & Sweller, J. (2006). Efficiency in learning: evidence-based guidelines to manage cognitive load. San Francisco, CA: Pfeiffer. DeLeeuw, K. E., & Mayer, R. E.(2008). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100(1), 223-234. Gerjets, P., Scheiter, K., & Cierniak, G. (2009). The scientific value of cognitive load theory: A research agenda based on the structuralist view on theories. Educational Psychology Review, 22, 43-54. Hadie, S. N. H., & Yusoff, M. S. B., (2016).Assessing the validity of the cognitive load scale in a problem-based learning setting. Journal of Taibah University Medical Sciences, 11(3), 194-202. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis ( 7th ed.). Upper Saddle River, NJ: Prentice Hall. Hart, S. G., & Staveland, L. E. (1988). Development of a multi-dimensional workload rating scale: Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139-183). Amsterdam, Netherlands: Elsevier. Hill, J., & Hannafin, M. (2001). Teaching and learning in digital environments: The resurgence of resource-based learning. Educational Technology Research and Development, 49(3), 37-52. Hoelter, J. W. (1983). The analysis of covariance structures goodness-of-fit indices. Sociological Methods & Research, 11(3), 325-344. Hwang, G.-J., & Chang, H.-F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023-1031. Kline, R. B. (2011). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford. Leppink, J., Paas, F., van der Vleuten, C.P.M., van Gog, T., & van Merrienboer, J.G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058-1072. Marsh, H. W., Ellis, L. A., Parada, R. H., Richards, G., & Heubeck, B. G. (2005). A short version of the self-description questionnaire II: Operationalizing criteria for short-form evaluation with new applications of confirmatory factor analysis. Psychological Assessment, 17(1), 81-102. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309-326. Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429-434. Paas, F., Tuovinen, J. E., Tabbers, H., & van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63-71. Paas, F., van Merrienboer, J. J. G., & Adam, J. J. (1994). Measurement of cognitive loadin instructional research. Perceptual Motor and Skills, 79, 419-430. Paas, F. G. W. C., & van Merrienboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-Load Approach. Journal of Educational Psychology, 86(1), 122-133. Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489-497. Polit, D.F., Beck, C. T., & Owen, S. V. (2007).Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Research in Nursing & Health, 30(4), 459-467. Reid, G., & Nygren, T. E. (1988).The subjective workload assessment technique: A scaling procedure for measuring mental workload. Advances in psychology, 52, 185-218. Rose, D. H., & Dolan, R. P. (2006). Implications of universal design for learning for classroom assessment. In D. H. Rose & A. Meyer (Ed.), A practical reader in universal design for learning (pp.73-83). Cambridge, MA: Harvard Education Press. Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling. New York, NY: Routledge. Shapiro, A. M., & Niederhauser, D. S. (2004). Learning from hypertext: Research issues and findings. In D. H. Johassen (Ed.), Handbook of Research on Educational Communications and Technology (2nd ed.)(pp. 605-620). Mahwah, NJ: Lawrence Erlbaum Associates. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257-285. Sweller, J., van Merrienboer, J. J.G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10,251-296. Wiley, J., & Schooler, J. W. (2001). The mental web pedagogical and cognitive implications of the net. In C. R. Wolfe (Ed.), Learning and Teaching on the World Wide Web (pp. 243-257). San Diego, CA: Academic. Wouters, P., Tabbers, H. K., & Paas, F. (2007). Interactivity in video-based models.