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The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. 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 applyingdoi:10.1016/j.compedu.2008.01.006 fatcat:bvo43pmetrgbhfvciwar4pcnjy