A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

Autor/inn/en	Anglin, Kylie L.; Wong, Vivian C.; Boguslav, Arielle
Titel	A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity
Quelle	In: AERA Open, 7 (2021) 1, (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2332-8584
Schlagwörter	Natural Language Processing; Program Implementation; Measurement Techniques; Intervention; Fidelity; Reliability; Preservice Teachers; Coaching (Performance) + Suchen Sie Ihr Suchwort? Natürliche Sprache; Messtechnik; Reliabilität
Abstract	Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01