Improving Mathematics Assessment Readability: Do Large Language Models Help?

Autor/inn/en	Patel, Nirmal; Nagpal, Pooja; Shah, Tirth; Sharma, Aditya; Malvi, Shrey; Lomas, Derek
Titel	Improving Mathematics Assessment Readability: Do Large Language Models Help?
Quelle	In: Journal of Computer Assisted Learning, 39 (2023) 3, S.804-822 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Patel, Nirmal)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0266-4909
DOI	10.1111/jcal.12776
Schlagwörter	Mathematics Tests; Readability; Word Problems (Mathematics); Mathematics Achievement; Reading Skills; Grade 3; Grade 4; Grade 5; Elementary School Mathematics; Artificial Intelligence + Suchen Sie Ihr Suchwort? Lesbarkeit; Textaufgabe; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Reading skill; Lesefertigkeit; School year 03; 3. Schuljahr; Schuljahr 03; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05; Elementare Mathematik; Schulmathematik; Künstliche Intelligenz
Abstract	Background: Readability metrics provide us with an objective and efficient way to assess the quality of educational texts. We can use the readability measures for finding assessment items that are difficult to read for a given grade level. Hard-to-read math word problems can put some students at a disadvantage if they are behind in their literacy learning. Despite their math abilities, these students can perform poorly on difficult-to-read word problems because of their poor reading skills. Less readable math tests can create equity issues for students who are relatively new to the language of assessment. Less readable test items can also affect the assessment's construct validity by partially measuring reading comprehension. Objectives: This study shows how large language models help us improve the readability of math assessment items. Methods: We analysed 250 test items from grades 3 to 5 of EngageNY, an open-source curriculum. We used the GPT-3 AI system to simplify the text of these math word problems. We used text prompts and the few-shot learning method for the simplification task. Results and Conclusions: On average, GPT-3 AI produced output passages that showed improvements in readability metrics, but the outputs had a large amount of noise and were often unrelated to the input. We used thresholds over text similarity metrics and changes in readability measures to filter out the noise. We found meaningful simplifications that can be given to item authors as suggestions for improvement. Takeaways: GPT-3 AI is capable of simplifying hard-to-read math word problems. The model generates noisy simplifications using text prompts or few-shot learning methods. The noise can be filtered using text similarity and readability measures. The meaningful simplifications AI produces are sound but not ready to be used as a direct replacement for the original items. To improve test quality, simplifications can be suggested to item authors at the time of digital question authoring. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01