دورية أكاديمية
Automatic Story and Item Generation for Reading Comprehension Assessments with Transformers
| العنوان: | Automatic Story and Item Generation for Reading Comprehension Assessments with Transformers |
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| اللغة: | English |
| المؤلفون: | Bulut, Okan (ORCID |
| المصدر: | International Journal of Assessment Tools in Education. 2022 9:72-87. |
| الإتاحة: | International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://ijate.net/index.php/ijate |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| تاريخ النشر: | 2022 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Descriptors: | Reading Comprehension, Natural Language Processing, Artificial Intelligence, Automation, Open Source Technology, Models, Algorithms |
| تدمد: | 2148-7456 |
| مستخلص: | Reading comprehension is one of the essential skills for students as they make a transition from learning to read to reading to learn. Over the last decade, the increased use of digital learning materials for promoting literacy skills (e.g., oral fluency and reading comprehension) in K-12 classrooms has been a boon for teachers. However, instant access to reading materials, as well as relevant assessment tools for evaluating students' comprehension skills, remains to be a problem. Teachers must spend many hours looking for suitable materials for their students because high-quality reading materials and assessments are primarily available through commercial literacy programs and websites. This study proposes a promising solution to this problem by employing an artificial intelligence (AI) approach. We demonstrate how to use advanced language models (e.g., OpenAI's GPT-2 and Google's T5) to automatically generate reading passages and items. Our preliminary findings suggest that with additional training and fine-tuning, opensource language models could be used to support the instruction and assessment of reading comprehension skills in the classroom. For both automatic story and item generation, the language models performed reasonably; however, the outcomes of these language models still require a human evaluation and further adjustments before sharing them with students. Practical implications of the findings and future research directions are discussed. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| رقم الأكسشن: | EJ1372092 |
| قاعدة البيانات: | ERIC |
| تدمد: | 2148-7456 |
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