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Our study explored the prospects and limitations of using machine-learning software to score introductory biology students’ written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using student responses at one university function effectively at another university? 2) How many human-scored student responses are needed to build scoring models suitable for cross-institutional application? 3) What factors limit computer-scoring efficacy, and how can these factors be mitigated? To answer these questions, two biology experts scored a corpus of 2556 short-answer explanations (from biology majors and nonmajors) at two universities for the presence or absence of five key concepts of evolution. Human- and computer-generated scores were compared using kappa agreement statistics. We found that machine-learning software was capable in most cases of accurately evaluating the degree of scientific sophistication in undergraduate majors’ and nonmajors’ written explanations of evolutionary change. In cases in which the software did not perform at the benchmark of “near-perfect” agreement (kappa > 0.80), we located the causes of poor performance and identified a series of strategies for their mitigation. Machine-learning software holds promise as an assessment tool for use in undergraduate biology education, but like most assessment tools, it is also characterized by limitations. 相似文献
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Lee Jun-Ki Aini Rahmi Qurota Sya’bandari Yustika Rusmana Ai Nurlaelasari Ha Minsu Shin Sein 《Science & Education》2021,30(2):293-316
Science & Education - The conception of racial categories from a biological perspective is unconsciously embedded in the individual’s cognition. This is true even among university... 相似文献
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Transforming Biology Assessment with Machine Learning: Automated Scoring of Written Evolutionary Explanations 总被引：4，自引：4，他引：0
Ross H. Nehm Minsu Ha Elijah Mayfield 《Journal of Science Education and Technology》2012,21(1):183-196
This study explored the use of machine learning to automatically evaluate the accuracy of students’ written explanations of
evolutionary change. Performance of the Summarization Integrated Development Environment (SIDE) program was compared to human
expert scoring using a corpus of 2,260 evolutionary explanations written by 565 undergraduate students in response to two
different evolution instruments (the EGALT-F and EGALT-P) that contained prompts that differed in various surface features
(such as species and traits). We tested human-SIDE scoring correspondence under a series of different training and testing
conditions, using Kappa inter-rater agreement values of greater than 0.80 as a performance benchmark. In addition, we examined
the effects of response length on scoring success; that is, whether SIDE scoring models functioned with comparable success
on short and long responses. We found that SIDE performance was most effective when scoring models were built and tested at
the individual item level and that performance degraded when suites of items or entire instruments were used to build and
test scoring models. Overall, SIDE was found to be a powerful and cost-effective tool for assessing student knowledge and
performance in a complex science domain. 相似文献
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Elizabeth P. Beggrow Minsu Ha Ross H. Nehm Dennis Pearl William J. Boone 《Journal of Science Education and Technology》2014,23(1):160-182
The landscape of science education is being transformed by the new Framework for Science Education (National Research Council, A framework for K-12 science education: practices, crosscutting concepts, and core ideas. The National Academies Press, Washington, DC, 2012), which emphasizes the centrality of scientific practices—such as explanation, argumentation, and communication—in science teaching, learning, and assessment. A major challenge facing the field of science education is developing assessment tools that are capable of validly and efficiently evaluating these practices. Our study examined the efficacy of a free, open-source machine-learning tool for evaluating the quality of students’ written explanations of the causes of evolutionary change relative to three other approaches: (1) human-scored written explanations, (2) a multiple-choice test, and (3) clinical oral interviews. A large sample of undergraduates (n = 104) exposed to varying amounts of evolution content completed all three assessments: a clinical oral interview, a written open-response assessment, and a multiple-choice test. Rasch analysis was used to compute linear person measures and linear item measures on a single logit scale. We found that the multiple-choice test displayed poor person and item fit (mean square outfit >1.3), while both oral interview measures and computer-generated written response measures exhibited acceptable fit (average mean square outfit for interview: person 0.97, item 0.97; computer: person 1.03, item 1.06). Multiple-choice test measures were more weakly associated with interview measures (r = 0.35) than the computer-scored explanation measures (r = 0.63). Overall, Rasch analysis indicated that computer-scored written explanation measures (1) have the strongest correspondence to oral interview measures; (2) are capable of capturing students’ normative scientific and naive ideas as accurately as human-scored explanations, and (3) more validly detect understanding than the multiple-choice assessment. These findings demonstrate the great potential of machine-learning tools for assessing key scientific practices highlighted in the new Framework for Science Education. 相似文献
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Automated computerized scoring systems (ACSSs) are being increasingly used to analyze text in many educational settings. Nevertheless, the impact of misspelled words (MSW) on scoring accuracy remains to be investigated in many domains, particularly jargon-rich disciplines such as the life sciences. Empirical studies confirm that MSW are a pervasive feature of human-generated text and that despite improvements, spell-check and auto-replace programs continue to be characterized by significant errors. Our study explored four research questions relating to MSW and text-based computer assessments: (1) Do English language learners (ELLs) produce equivalent magnitudes and types of spelling errors as non-ELLs? (2) To what degree do MSW impact concept-specific computer scoring rules? (3) What impact do MSW have on computer scoring accuracy? and (4) Are MSW more likely to impact false-positive or false-negative feedback to students? We found that although ELLs produced twice as many MSW as non-ELLs, MSW were relatively uncommon in our corpora. The MSW in the corpora were found to be important features of the computer scoring models. Although MSW did not significantly or meaningfully impact computer scoring efficacy across nine different computer scoring models, MSW had a greater impact on the scoring algorithms for naïve ideas than key concepts. Linguistic and concept redundancy in student responses explains the weak connection between MSW and scoring accuracy. Lastly, we found that MSW tend to have a greater impact on false-positive feedback. We discuss the implications of these findings for the development of next-generation science assessments. 相似文献
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从进步史观和“自下而上”历史观两个视角出发,可以清晰地理解霍布斯鲍姆史学思想体系中以社会历史观为核心的历史本体论思想。一方面,霍布斯鲍姆进步历史观念的内涵变化轨迹明显;另一方面,基于“自下而上”的历史观,霍布斯鲍姆与其他英国马克思主义史学家共同开创了一种关于社会历史研究的新模式和方法论。 相似文献
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Sein Shin Arif Rachmatullah Fenny Roshayanti Minsu Ha Jun-Ki Lee 《International Journal for Educational and Vocational Guidance》2018,18(2):203-231
The purpose of this study was to understand the career motivation of secondary students in science, technology, engineering, and mathematics (STEM) by comparing Korean and Indonesian students. Effects of gender and educational level on students’ STEM career motivation were also examined. To test for differences, we used Rasch analysis, 3-way ANOVA, correlation analysis, and multiple group path analysis. STEM career motivation was found to be significantly affected by interactions between country, gender, and educational level. Overall, Indonesian students had more STEM career motivation than Korean students. Korean students showed larger gender differences in STEM career motivation than Indonesian students. 相似文献
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Cognitive foundations for science assessment design: Knowing what students know about evolution 总被引：1，自引：0，他引：1
To improve assessments of academic achievement, test developers have been urged to use an “assessment triangle” that starts with research‐based models of cognition and learning [NRC (2001) Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press]. This approach has been successful in designing high‐quality reading and math assessments, but less progress has been made for assessments in content‐rich sciences such as biology. To rectify this situation, we applied the “assessment triangle” to design and evaluate new items for an instrument (ACORNS, Assessing Contextual Reasoning about Natural Selection) that had been proposed to assess students' use of natural selection to explain evolutionary change. Design and scoring of items was explicitly guided by a cognitive model that reflected four psychological principles: with development of expertise, (1) core concepts facilitate long‐term recall, (2) causally‐central features become weighted more strongly in explaining phenomena, (3) normative ideas co‐exist but increasingly outcompete naive ideas in reasoning, and (4) knowledge becomes more abstract and less specific to the learning situation. We conducted an evaluation study with 320 students to examine whether scores from our new ACORNS items could detect gradations of expertise, provide insight into thinking about evolutionary change, and predict teachers' assessments of student achievement. Findings were consistent with our cognitive model, and ACORNS was revealing about undergraduates' thinking about evolutionary change. Results indicated that (1) causally‐central concepts of evolution by natural selection typically co‐existed and competed with the presence of naïve ideas in all students' explanations, with naïve ideas being especially prevalent in low‐performers' explanations; (2) causally‐central concepts were elicited most frequently when students were asked to explain evolution of animals and familiar plants, with influence of superficial features being strongest for low‐performers; and (3) ACORNS scores accurately predicted students' later achievement in a college‐level evolution course. Together, findings illustrate usefulness of cognitive models in designing instruments intended to capture students' developing expertise. © 2012 Wiley Periodicals, Inc. J Res Sci Teach 49: 744–777, 2012 相似文献
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山东省的济南、潍县与周村自主开埠以后,在城市的规划、管理与公共基础设施建设中呈现新的特点,具有新的发展。理性抗外是这些新特点的核心内容,而地方当局的主观努力则是造成三个开埠城市建设成就差异的主要原因。随着中外交往的深入,西文外来文化对开埠城市传统文化产生极大冲击,社会各阶层普遍产生了移风易俗、趋新思变的精神动力,城市居民的思想观念也因此发生显著变化。 相似文献