Diagnostic tests are typically administered in a multiple-choice (MC) format due to their advantages of objectivity and time efficiency. The MC-deterministic input, noisy "and" gate (DINA) family of models, a representative class of cognitive diagnostic models for MC items, efficiently and parsimoniously estimates the mastery profiles of…

Examination dishonesty is a global problem that became particularly critical after the outbreak of the COVID-19 pandemic and the shift to remote learning. Academic research has often examined this phenomenon as only one aspect of a broader concept of academic dishonesty and as a one-dimensional construct. This article builds on existing knowledge…

The core of the educational system is students' academic performance, which demands sensitive measures. In this situation, teacher-made tests (TMTs) are more promising, but they can be susceptible to measurement error if not well designed. Hence, this study aimed to investigate the relationship between the properties of TMTs and students' academic…

In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such…

Scoring high-dimensional assessments (e.g., > 15 traits) can be a challenging task. This paper introduces the multilabel neural network (MNN) as a scoring method for high-dimensional assessments. Additionally, it demonstrates how MNN can score the same test responses to maximize different performance metrics, such as accuracy, recall, or…