The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education

arXiv:2404.02444v1 Announce Type: new
Abstract: Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that mostly focuses on low-inference instructional practices on a singular basis, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two …

abstract arxiv cs.ai cs.cl education expertise feedback however improvement language language models quality teachers type