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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/75061


    Title: Assessing creative problem-solving with automated text grading
    Authors: Li, Tsai-yen
    李蔡彥
    Chang, Chun-yen
    Wang, Hao-chuan
    Contributors: 資科系
    Keywords: Computer-aided assessment;Automated grading;Creative problem-solving;Science learning assessment;Machine learning application
    Date: 2008
    Issue Date: 2015-05-08 16:08:43 (UTC+8)
    Abstract: The work aims to improve the assessment of creative problem-solving in science education by employing language tech- nologies and computational-statistical machine learning methods to grade students` natural language responses automat- ically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students` constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evalu- ated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students` natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen`s Kappa = .65-.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson`s r = .92). The reliable performance in automatic concept identification and numeric grading demon- strates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.
    Relation: Computers & Education , vol. 51, no. 4, pp. 1450-1466
    Data Type: article
    DOI link: http://dx.doi.org/10.1016/j.compedu.2008.01.006
    DOI: 10.1016/j.compedu.2008.01.006
    Appears in Collections:[Department of Computer Science ] Periodical Articles

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