Background and Context: Computational thinking (CT) has been increasingly added to K-12 curricula, prompting teachers to grade more and more CT artifacts. This has led to a rise in automated CT assessment tools. Objective: This study examines the scope and characteristics of publications that use machine learning (ML) approaches to assess…

Automatic grading of short answers is an important task in computer-assisted assessment (CAA). Recently, embeddings, as semantic-rich textual representations, have been increasingly used to represent short answers and predict the grade. Despite the recent trend of applying embeddings in automatic short answer grading (ASAG), there are no…

Generative Artificial Intelligence (GenAI) is transforming education, with assessment design emerging as a crucial area of innovation, particularly in computer science (CS) education. Effective assessment is critical for evaluating student competencies and guiding learning processes, yet traditional practices face significant challenges in CS…

Educators struggle to assess various aspects of reading in valid and reliable ways. Whether it is comprehension, phonological awareness, vocabulary, or phonics, determining appropriate assessments is challenging across grade levels and student abilities. Also challenging is measuring aspects of fluency: rate, accuracy, and prosody. This article…

Argues that synergy between computer-based instruction and automated assessment is possible because of the common needs in assessment and instruction; outlines a framework for characterizing performance; and examines procedures developed as part of an ongoing project to develop fully automated scoring of architectural design for a licensing exam.…