Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology

Autor/inn/en	Ariely, Moriah; Nazaretsky, Tanya; Alexandron, Giora
Titel	Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology
Quelle	In: International Journal of Artificial Intelligence in Education, 33 (2023) 1, S.1-34 (34 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Alexandron, Giora)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-021-00283-x
Schlagwörter	Artificial Intelligence; Algorithms; Natural Language Processing; Hebrew; Scoring; Biology; Scoring Rubrics; Formative Evaluation; Automation; Learning Analytics; Science Process Skills + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Algorithm; Algorithmus; Natürliche Sprache; Bewertung; Biologie; Scoring formulas; Auswertungsbogen
Abstract	Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology according to fine-grained analytic grading rubrics that were developed for formative assessment. The experimental results show that our algorithms achieve a high-level of agreement with human experts, on par with previous work on automated assessment of scientific explanations in English, and that [approximately]500 examples are typically enough to build reliable scoring models. The main contribution is twofold. First, we present a conceptual framework for constructing analytic grading rubrics for scientific explanations, which are composed of dichotomous categories that generalize across items. These categories are designed to support automated guidance, but can also be used to provide a composite score. Second, we apply this approach in a new context -- Hebrew, which belongs to a group of languages known as Morphologically-Rich. In languages of this group, among them also Arabic and Turkish, each input token may consist of multiple lexical and functional units, making them particularly challenging for NLP. This is the first study on automatic assessment of scientific explanations (and more generally, of open-ended questions) in Hebrew, and among the firsts to do so in Morphologically-Rich Languages. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01