While mathematics achievement continues to decline across the United States, minimal research has been conducted on the use of CT skills to increase proficiency. This explanatory, nonexperimental quantitative study focused on the self-assessment of teachers' content knowledge in computational thinking skills and the relationship to students'…

The primary purpose of this study is to investigate the degree to which register knowledge, register-specific motivation, and diverse linguistic features are predictive of human judgment of writing quality in three registers--narrative, informative, and opinion. The secondary purpose is to compare the evaluation metrics of register-partitioned…

This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…

Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…

This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a mathematics learning platform used by middle and high school students in several African countries. Using this dataset, we conducted two experiments to evaluate the use of large language models (LLM) for grading particularly challenging…

With recent improvements in machine translation (MT) accuracy, MT has gained unprecedented popularity in second language (L2) learning. Despite the significant number of studies on MT use, the effects of using MT on students' retention of learning or secondary school students' use of MT in L2 writing has rarely been researched. The current study…

This study explored the lexical coverage of corpus-based vocabulary lists (general, academic, and content-specific) across several million tokens gathered from digital science resources (DSR) for middle school (6-8 grade) students in the United States. The goal was to estimate the extent to which a combination of well-known word lists, mostly…

This study explores the use of Large Language Models (LLMs), specifically GPT-4, in analysing classroom dialogue--a key task for teaching diagnosis and quality improvement. Traditional qualitative methods are both knowledge- and labour-intensive. This research investigates the potential of LLMs to streamline and enhance this process. Using…

This study examines the impact of the College, Career, and Community Writers Program (C3WP) on upper-elementary students' first draft writing quality, addressing the challenges teachers face in providing timely and effective feedback on extensive student writing, particularly in spelling, grammar, mechanics, and cohesion. The hypothesis posits…

Purpose: The aim of this study was to advance the analysis of written language transcripts by validating an automated scoring procedure using an automated open-access tool for calculating morphological complexity (MC) from written transcripts. Method: The MC of words in 146 written responses of students in fifth grade was assessed using two…

This academic article has objectives were 1) to study the techniques in data analyzing of speech recognition systems techniques to detect reading aloud skills in Thai tonal conjugation for Primary Education Students, and 2) to study the effect of speech recognition systems techniques to detect reading aloud skills in Thai tonal conjugation for…

Large language models have recently been able to perform well in a wide variety of circumstances. In this work, we explore the possibility of large language models, specifically GPT-3, to write explanations for middle-school mathematics problems, with the goal of eventually using this process to rapidly generate explanations for the mathematics…

An automated essay scoring (AES) program is a software system that uses techniques from corpus and computational linguistics and machine learning to grade essays. In this study, we aimed to describe and evaluate particular language features of Coh-Metrix for a novel AES program that would score junior and senior high school students' essays from…

This study, conducted in France, sought to describe the organization of the content of the social representations that high school students in transition construct of work and their own future, taking into account two variables: their type of secondary school and the anticipated length of their post-secondary education. For this purpose, 669…

This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…