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研究生: 鄭皓心
Cheng, Hao-Hsin
論文名稱: 基於word2vec的發散思維測驗之自動化評分技術發展
Developing an Automated Scoring Technique for Divergent Thinking Tests Based on word2vec
指導教授: 張國恩
Chang, Kuo-En
宋曜廷
Sung, Yao-Ting
劉子鍵
Liu, Tzu-Chien
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2017
畢業學年度: 105
語文別: 中文
論文頁數: 84
中文關鍵詞: 創造力測驗發散思維測驗語意距離word2vec
英文關鍵詞: creativity test, divergent thinking test, semantic distance, word2vec
DOI URL: https://doi.org/10.6345/NTNU202202694
論文種類: 學術論文
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  • 發散思維測驗藉由評量個人對開放性問題的反應數量與品質來評估個體的創造力潛力,亦可稱是最常用的創造力評量工具,通常以流暢力、獨創力及變通力作為評分指標。傳統的發散思維測驗計分方法有程序繁複、成本高昂等缺點,於是發展自動化評分技術成為一項受到關注的議題,預期藉由電腦計算的方法提供有效且便利的測驗結果。基於語意距離的自動化評分方法常見於創造力相關研究中,然而,基於語意距離的評分方法仍有可改進的空間且缺乏完整的信度、效度研究。本研究以word2vec為計算工具,提出一套基於語意距離的發散思維測驗之自動化評分方法,並檢驗此評分方法之信度、效度。
      研究參與者為493位大學生並施測電腦化圖形之創造思考測驗,其中47人於間隔12個月後進行第二次施測,其測驗資料用於分析再測信度;其中99人另施測新編創造思考測驗圖形作業,其測驗資料用於分析校標關聯效度;其餘研究參與者之測驗資料用於分析常見答案。信度分析結果指出流暢力指標之信度係數可達.83 (p<.01);獨創力指標之信度係數可達.71 (p<.01);變通力指標之信度係數可達.76 (p<.01),由此可知,本研究之自動化評分方法具有良好的再測信度,表示能夠提供穩定且可靠的測驗結果。效度分析方面,本研究以新編創造思考測驗圖形作業為效標,結果得流暢力指標之相關係數可達.60 (p<.01);獨創力指標之相關係數可達.68 (p<.01);變通力指標之相關係數可達.58 (p<.01)。可見得此自動化評分方法具有不錯的效標關聯效度,表示能夠有效的測量個體的創造力潛力。
      本研究之基於word2vec的發散思維測驗之自動化評分方法不僅能夠全自動化地執行發散思維測驗的評分工作,且研究結果顯示具備良好的信度與效度,可見得此自動化評分方法可以提供有效且便利的創造力潛力評量結果。

    Divergent thinking (DT) tests are a commonly used method to assess an individual’s creative potential. Typically, these tests use fluency, originality, and flexibility as score indicators. Traditional scoring methods for DT tests is lengthy and complicated, and the costs are high. Thus, the development of an automated scoring method for DT tests has become a major issue. Automated scoring method based on semantic distance is commonly used in the related studies of creativity. However, scoring method based on semantic distance isn’t good enough, and lack the complete study of reliability and validity. This article reports an automated scoring method for DT tests based on word2vec, and examines reliability and validity of this scoring method. Participants (N=493) used a divergent thinking test, Computerized Creative Association and Figures Test (C-CRAFT), as its primary measure. Of the 493 students, 47 re-took the test 12 months later to assess test-retest reliability, and 99 also took the Wu’s Chinese Version of the Torrance Tests of Creative Thinking (CTTCT) to assess criterion-related validity. The remainder of the responses were used to analyze the commonality of responses from college students. The results show that the automated scoring method for DT tests based on word2vec has good test re-test reliability (r=.63~.83, p<.01) as well as good criterion-related validity (r=.51~.68, p<.01), and thus is a practically applicable method for DT test scoring. Limitation of the present study and directions for future research are offered.

    目次 中文摘要 i 英文摘要 ii 附表目錄 vi 附圖目錄 vii 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與待答問題 5 第三節 名詞釋義 5 第二章 文獻回顧 8 第一節 創造力的涵義 8 第二節 聯結理論 11 第三節 創造力的測量 14 第四節 發散思維測驗 16 第五節 word2vec 24 第三章 系統設計 27 第一節 系統架構 27 第二節 流暢力指標自動化計分模組設計 29 第三節 獨創力指標自動化計分模組設計 30 第四節 變通力指標自動化計分模組設計 35 第四章 研究方法 38 第一節 研究架構與流程 38 第二節 研究參與者 39 第三節 研究工具 39 第四節 資料分析 43 第五章 研究結果 44 第一節 信度分析 44 第二節 效度分析 52 第六章 討論、結論與建議 61 第一節 討論 61 第二節 結論 64 第三節 建議 66 參考資料 68 附錄一 75 附錄二 78 附錄三 81 附錄四 83

    一、中文文獻
    王坤(2015)。基於數據庫的非常規用途測驗計分法(碩士論文)。取自http://cdmd.cnki.com.cn/Article/CDMD-10269-1015352129.htm
    吳靜吉(1998)。新編創造思考測驗研究。教育部輔導工作六年計畫研究報告。
    林緯倫、連韻文、任純慧(2005)。想得多是想得好的前提嗎?探討發散性思考能力在創意問題解決的角色。中華心理學刊,47(3),211-227。
    張春興(1996)。教育心理學。台北:東華。
    陳光華(2012)。圖書館學與資訊科學大辭典。取自http://terms.naer.edu.tw/detail/1679016/

    二、英文文獻
    Acar, S., & Runco, M. A. (2014). Assessing associative distance among ideas elicited by tests of divergent thinking. Creativity Research Journal, 26(2), 229-238. doi: 10.1080/10400419.2014.901095
    Altszyler, E., Sigman, M., & Slezak, D. F. (2016). Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database. Retrieved from http://arxiv.org/abs/1610.01520
    Beaty, R. E., Silvia, P. J., Nusbaum, E. C., Jauk, E., & Benedek, M. (2014). The roles of associative and executive processes in creative cognition. Memory & Cognition, 42(7), 1186-1197. doi: 10.3758/s13421-014-0428-8
    Beketayev, K., & Runco, M. A. (2016). Scoring Divergent Thinking Tests by Computer With a Semantics-Based Algorithm. Europe's Journal of Psychology, 12(2), 210-220. doi:10.5964/ejop.v12i2.1127
    Benedek, M., & Neubauer, A. C. (2013). Revisiting Mednick's model on creativity‐related differences in associative hierarchies. Evidence for a common path to uncommon thought. The Journal of Creative Behavior, 47(4), 273-289. doi: 10.1002/jocb.35
    Benedek, M., Könen, T., & Neubauer, A. C. (2012). Associative abilities underlying creativity. Psychology of Aesthetics, Creativity, and the Arts, 6(3), 273-281. doi: 10.1037/a0027059
    Bossomaier, T., Harré, M., Knittel, A., & Snyder, A. (2009). A semantic network approach to the creativity quotient (CQ). Creativity Research Journal,21(1), 64-71. doi: 10.1080/10400410802633517
    Chang, T. H., Sung, Y. T., & Lee, Y. T. (2012). A Chinese word segmentation and POS tagging system for readability research. Paper presented at 42nd annual meeting of the Society for Computers in Psychology (SCiP 2012), Minneapolis, MN.
    Cheung, P. C., & Lau, S. (2010). Gender differences in the creativity of Hong Kong school children: Comparison by using the new electronic Wallach–Kogan creativity tests. Creativity Research Journal, 22(2), 194-199. doi: 10.1080/10400419.2010.481522
    Cheung, P. C., Lau, S., Chan, D. W., &Wu, W. Y. H. (2004). Creative potential of school children in Hong Kong: Norms of the Wallach–Kogan Creativity Tests and their implications. Creativity Research Journal, 16, 69-78. doi: 10.1207/s15326934crj1601_7
    Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407
    Dumas, D., & Dunbar, K. N. (2014). Understanding Fluency and Originality: A latent variable perspective. Thinking Skills and Creativity, 14, 56-67. doi: 10.1016/j.tsc.2014.09.003
    Forster, E. A., & Dunbar, K. N. (2009, July). Creativity evaluation through latent semantic analysis. In N. A. Taatgen & H. van Rijn (Eds), Proceedings of the 31th Annual Conference of the Cognitive Science Society (pp. 602-7). Austin, TX: Cognitive Science Society.
    Green, A. E., Cohen, M. S., Kim, J. U., & Gray, J. R. (2012). An explicit cue improves creative analogical reasoning. Intelligence, 40(6), 598-603. doi: 10.1016/j.intell.2012.08.005
    Guilford, J. P. (1956). The structure of intellect. Psychological Bulletin, 53(4), 267-293. doi: 10.1037/h0040755
    Guilford, J. P. (1966). Measurement and creativity. Theory Into Practice, 5(4), 185-189. doi: 10.1080/00405846609542023
    Guilford, J. P. (1967). The nature of human intelligence. New York: McGraw-Hill.
    Hass, R. W. (2015). Feasibility of online divergent thinking assessment. Computers in Human Behavior, 46, 85-93. doi: 10.1016/j.chb.2014.12.056
    Hass, R. W. (2016). Tracking the dynamics of divergent thinking via semantic distance: Analytic methods and theoretical implications. Memory & Cognition, 1-12. doi: 10.3758/s13421-016-0659-y
    Kaufman, J. C., & Beghetto, R. A. (2009). Beyond big and little: The four c model of creativity. Review of General Psychology, 13(1), 1-12. doi: 10.1037/a0013688
    Kerr, B., & Gagliardi, C. (2003). Measuring creativity in research and practice. In S. J. Lopez, & C. R. Snyder (Eds.), Positive psychological assessment: A handbook of models and measures (pp. 155-169). Washington, DC: American Psychological Association.
    Kwon, M., Goetz, E. T., & Zellner, R. D. (1998). Developing a Computer‐Based TTCT: Promises and Problems. The Journal of Creative Behavior, 32(2), 96-106.
    Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240. doi: 10.1037/0033-295X.104.2.211
    Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259-284. doi: 10.1080/01638539809545028
    Lau, S., & Cheung, P. C. (2010a). Creativity assessment: Comparability of the electronic and paper-and-pencil versions of the Wallach–Kogan Creativity Tests. Thinking Skills and Creativity, 5(3), 101-107. doi: 10.1016/j.tsc.2010.09.04
    Lau, S., & Cheung, P. C. (2010b). Developmental trends of creativity: What twists of turn do boys and girls take at different grades?. Creativity Research Journal, 22(3), 329-336. doi: 10.1080/10400419.2010.503543
    Lehman, H. C. (1953). Age and achievement. Princeton, NJ: Princeton.
    Luo, J., Wang, Q., & Li, Y. (2014, July). Word clustering based on word2vec and semantic similarity. In Proceedings of the 33rd Chinese Control Conference (pp. 517-521). Piscataway, NJ: IEEE. doi: 10.1109/ChiCC.2014.6896677
    MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp. 281-297). Berkeley, University of California Press.
    Mednick, S. A. (1962). The associative basis of creative process. Psychological Review, 69, 220-232.
    Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space. In Proceedings of workshop at ICLR.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In Proceedings of neural information processing systems (pp. 3111-3119).
    Mikolov, T., Yih, W.-t., & Zweig, G. (2013c). Linguistic regularities in continuous space word representations. In Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 746-751). Association for Computational Linguistics.
    Palaniappan, A. K. (2012). Web-based Creativity Assessment System. International Journal of Information and Education Technology, 2(3), 255-258.
    Pásztor, A., Molnár, G., & Csapó, B. (2015). Technology-based assessment of creativity in educational context: the case of divergent thinking and its relation to mathematical achievement. Thinking Skills and Creativity, 18, 32-42. doi: 10.1016/j.tsc.2015.05.004
    Plucker, J. A., & Renzulli, J. S. (1999). Psychometric approaches to the study of human creativity. In R. J. Sternberg (Eds.), Handbook of creativity (pp. 35-61). New York: Cambridge University Press.
    Prabhakaran, R., Green, A. E., & Gray, J. R. (2014). Thin slices of creativity: Using single word utterances to assess creative cognition. Behavior Research Methods, 46(3), 641-659. doi: 10.3758/s13428-013-0401-7
    Pretz, J. E., & Link, J. A. (2008). The Creative Task Creator: A tool for the generation of customized, Web-based creativity tasks. Behavior Research Methods, 40(4), 1129-1133. doi: 10.3758/BRM.40.4.1129
    Ranjan, A., & Srinivasan, N. (2010). Dissimilarity in creative categorization. The Journal of Creative Behavior, 44(2), 71-83.
    Runco, M. A. (2007). Creativity: theories and themes: research, development, and practice. Boston, MA : Elsevier Academic.
    Runco, M. A., & Acar, S. (2012). Divergent thinking as an indicator of creative potential. Creativity Research Journal, 24(1), 66-75. doi: 10.1080/10400419.2012.652929
    Smith, K. A., Huber, D. E., & Vul, E. (2013). Multiply-constrained semantic search in the Remote Associates Test. Cognition, 128(1), 64-75. doi: 10.1016/j.cognition.2013.03.001
    Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11-21.
    Sung, Y.T., Chang, T.H., Lin, W.C., Hsieh, K.S., and Chang, K.E. (2016). CRIE: An automated analyzer for Chinese texts. Behavior Research Methods, 48(4):1238-1251. doi:10.3758/s13428-015-0649-1
    Torrance, E. P. (1962). Guiding creative talent. Englewood Cliffs, NJ: Prentice-Hall.
    Torrance, E. P. (1974). Torrance Tests of Creative Thinking: Norms-Technical Manual. Lexington, MA: Ginn.
    Torrance, E. P. (1988). The nature of creativity as manifest in its testing. In R. J. Sternberg (Eds.), The nature of creativity (pp. 43-75). Cambridge: Cambridge University Press.
    Wallach, M. A., & Kogan, N. (1965). Modes of thinking in young children. New York: Holt, Rinehart, & Winston.
    Wang, P., Xu, B., Xu, J., Tian, G., Liu, C. L., & Hao, H. (2016). Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification. Neurocomputing, 174, 806-814. doi: 10.1016/j.neucom.2015.09.096
    Xue, B., Fu, C., & Shaobin, Z. (2014). A study on sentiment computing and classification of sina weibo with word2vec. In 2014 IEEE International Congress on Big Data bigdatacongress Big Data (BigData Congress) (pp. 358-363). Piscataway, NJ: IEEE. doi: 10.1109/BigData.Congress.2014.59
    Zhang, D., Xu, H., Su, Z., & Xu, Y. (2015). Chinese comments sentiment classification based on word2vec and SVM perf. Expert Systems with Applications, 42(4), 1857-1863. doi: 10.1016/j.eswa.2014.09.011

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