Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming…

Argumentation, a key scientific practice presented in the "Framework for K-12 Science Education," requires students to construct and critique arguments, but timely evaluation of arguments in large-scale classrooms is challenging. Recent work has shown the potential of automated scoring systems for open response assessments, leveraging…

In many performance assessments, one or two raters from the complete rater pool scores each performance, resulting in a sparse rating design, where there are limited observations of each rater relative to the complete sample of students. Although sparse rating designs can be constructed to facilitate estimation of student achievement, the…

Manually scoring and revising student essays has long been a time-consuming task for educators. With the rise of natural language processing techniques, automated essay scoring (AES) and automated essay revising (AER) have emerged to alleviate this burden. However, current AES and AER models require large amounts of training data and lack…

Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score…

In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This…

Automated essay scoring (AES) has emerged as a secondary or as a sole marker for many high-stakes educational assessments, in native and non-native testing, owing to remarkable advances in feature engineering using natural language processing, machine learning, and deep-neural algorithms. The purpose of this study is to compare the effectiveness…

Studies on automatic scoring systems in writing assessments have also evaluated the relationship between human and machine scores for the reliability of automated essay scoring systems. This study investigated the magnitudes of indices for inter-rater agreement and discrepancy, especially regarding human and machine scoring, in writing assessment.…

Scoring procedures for many rater-mediated performance assessments include score resolution procedures in which a third rater adjudicates discrepancies between two raters' ratings of the same performance. There are numerous approaches for calculating resolved scores that involve different combinations of the original and third ratings. Using data…

The advent of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, has significantly impacted the educational landscape, offering unique opportunities for learning and assessment. In the realm of written assessment grading, traditionally viewed as a laborious and subjective process, this study sought to…

Purpose: This study builds a new system for automatically assessing learners' speech elicited from an oral discourse completion task (DCT), and evaluates the prediction capability of the system with a view to better understanding factors deemed influential in predicting speaking proficiency scores and the pedagogical implications of the system.…

Purpose: The aim of this study was to advance the use of structured, monologic discourse analysis by validating an automated scoring procedure for core lexicon (CoreLex) using transcripts. Method: Forty-nine transcripts from persons with aphasia and 48 transcripts from persons with no brain injury were retrieved from the AphasiaBank database. Five…

Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our…

Managing crowded classes in terms of classroom assessment is a difficult task due to the amount of time which needs to be devoted to providing feedback to student products. In this respect, the present study aimed to develop an automated essay scoring environment as a potential means to overcome this problem. Secondarily, the study aimed to test…

Recent advances in machine learning have made automated scoring of learner speech widespread, and yet validation research that provides support for applying automated scoring technology to assessment is still in its infancy. Both the educational measurement and language assessment communities have called for greater transparency in describing…