How Well Can You Articulate that Idea? Insights from Automated Formative Assessment
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| Format: | Preprint |
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2024
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| _version_ | 1866911844817436672 |
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| author | Karizaki, Mahsa Sheikhi Gnesdilow, Dana Puntambekar, Sadhana Passonneau, Rebecca J. |
| author_facet | Karizaki, Mahsa Sheikhi Gnesdilow, Dana Puntambekar, Sadhana Passonneau, Rebecca J. |
| contents | Automated methods are becoming increasingly integrated into studies of formative feedback on students' science explanation writing. Most of this work, however, addresses students' responses to short answer questions. We investigate automated feedback on students' science explanation essays, where students must articulate multiple ideas. Feedback is based on a rubric that identifies the main ideas students are prompted to include in explanatory essays about the physics of energy and mass, given their experiments with a simulated roller coaster. We have found that students generally improve on revised versions of their essays. Here, however, we focus on two factors that affect the accuracy of the automated feedback. First, we find that the main ideas in the rubric differ with respect to how much freedom they afford in explanations of the idea, thus explanation of a natural law is relatively constrained. Students have more freedom in how they explain complex relations they observe in their roller coasters, such as transfer of different forms of energy. Second, by tracing the automated decision process, we can diagnose when a student's statement lacks sufficient clarity for the automated tool to associate it more strongly with one of the main ideas above all others. This in turn provides an opportunity for teachers and peers to help students reflect on how to state their ideas more clearly. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_11682 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Well Can You Articulate that Idea? Insights from Automated Formative Assessment Karizaki, Mahsa Sheikhi Gnesdilow, Dana Puntambekar, Sadhana Passonneau, Rebecca J. Computation and Language Automated methods are becoming increasingly integrated into studies of formative feedback on students' science explanation writing. Most of this work, however, addresses students' responses to short answer questions. We investigate automated feedback on students' science explanation essays, where students must articulate multiple ideas. Feedback is based on a rubric that identifies the main ideas students are prompted to include in explanatory essays about the physics of energy and mass, given their experiments with a simulated roller coaster. We have found that students generally improve on revised versions of their essays. Here, however, we focus on two factors that affect the accuracy of the automated feedback. First, we find that the main ideas in the rubric differ with respect to how much freedom they afford in explanations of the idea, thus explanation of a natural law is relatively constrained. Students have more freedom in how they explain complex relations they observe in their roller coasters, such as transfer of different forms of energy. Second, by tracing the automated decision process, we can diagnose when a student's statement lacks sufficient clarity for the automated tool to associate it more strongly with one of the main ideas above all others. This in turn provides an opportunity for teachers and peers to help students reflect on how to state their ideas more clearly. |
| title | How Well Can You Articulate that Idea? Insights from Automated Formative Assessment |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.11682 |