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Cathy Cavanaugh; Bryn Humphrey; Paige Pullen – International Journal on E-Learning, 2024
To address needs in one US state to provide a professional development micro-credential for tens of thousands of educators, we automated an assignment scoring workflow in an online course by developing and refining an AI model to scan submitted assignments and score them against a rubric. This article outlines the AI model development process and…
Descriptors: Artificial Intelligence, Automation, Scoring, Microcredentials
Chen, Dandan; Hebert, Michael; Wilson, Joshua – American Educational Research Journal, 2022
We used multivariate generalizability theory to examine the reliability of hand-scoring and automated essay scoring (AES) and to identify how these scoring methods could be used in conjunction to optimize writing assessment. Students (n = 113) included subsamples of struggling writers and non-struggling writers in Grades 3-5 drawn from a larger…
Descriptors: Reliability, Scoring, Essays, Automation
Alice Barana; Marina Marchisio Conte – Journal on Mathematics Education, 2024
Ensuring equity in education is a goal for sustainable development. Among the factors that hinder equity, socioeconomic status (SES) has the highest impact on learning Mathematics. This paper addresses the issue of equity at the secondary school level by proposing an approach based on adopting automatic formative assessment (AFA). Carefully…
Descriptors: Equal Education, Sustainable Development, Socioeconomic Status, Mathematics Achievement
Younes-Aziz Bachiri; Hicham Mouncif; Belaid Bouikhalene; Radoine Hamzaoui – Turkish Online Journal of Distance Education, 2024
This study examined the integration of artificial intelligence-powered speech recognition technology within early reading assessments in Morocco's Teaching at the Right Level (TaRL) program. The purpose was to evaluate the effectiveness of an automated speech recognition tool compared to traditional paper-based assessments in improving reading…
Descriptors: Foreign Countries, Artificial Intelligence, Speech Communication, Identification
L. Hannah; E. E. Jang; M. Shah; V. Gupta – Language Assessment Quarterly, 2023
Machines have a long-demonstrated ability to find statistical relationships between qualities of texts and surface-level linguistic indicators of writing. More recently, unlocked by artificial intelligence, the potential of using machines to identify content-related writing trait criteria has been uncovered. This development is significant,…
Descriptors: Validity, Automation, Scoring, Writing Assignments
Vitale, Jonathan M.; Lai, Kevin; Linn, Marcia C. – Journal of Research in Science Teaching, 2015
We present a new system for automated scoring of graph construction items that address complex science concepts, feature qualitative prompts, and support a range of possible solutions. This system utilizes analysis of spatial features (e.g., slope of a line) to evaluate potential student ideas represented within graphs. Student ideas are then…
Descriptors: Scoring, Graphs, Scientific Concepts, Prompting
Ben-Simon, Anat; Bennett, Randy Elliott – Journal of Technology, Learning, and Assessment, 2007
This study evaluated a "substantively driven" method for scoring NAEP writing assessments automatically. The study used variations of an existing commercial program, e-rater[R], to compare the performance of three approaches to automated essay scoring: a "brute-empirical" approach in which variables are selected and weighted solely according to…
Descriptors: Writing Evaluation, Writing Tests, Scoring, Essays