Supervised diagnostic classification of cognitive attributes using data augmentation.

Over recent decades, machine learning, an integral subfield of artificial intelligence, has revolutionized diverse sectors, enabling data-driven decisions with minimal human intervention. In particular, the field of educational assessment emerges as a promising area for machine learning applications, where students can be classified and diagnosed using their performance data. The objectives of Diagnostic Classification Models (DCMs), which provide a suite of methods for diagnosing students' cognitive states in relation to the mastery of necessary cognitive attributes for solving problems in a test, can be effectively addressed through machine learning techniques.

The Cyclic Value-Context Reinforcement Model of Problematic Internet Use: Empirical Validation Using a Thematic Analysis of Children's Counseling Data.

Background: Research on problematic internet use has focused on devising diagnostic criteria or describing the factors that influence internet overuse. However, a paradigm shift is necessary in studying the phenomenon of increased internet use not just from a pathological point of view but also from a developmental point of view that considers children's behavior of adapting to a technology-oriented society.

Objective: In this paper, we propose the Cyclic Value-Context Reinforcement Model (CVCRM) to understand problematic internet use behavior.

Classification of problematic Internet usage types by motives and contexts with elementary and secondary school-aged counseling clients.

Background And Aims: Past research on the classification of problematic Internet use (PIU) has focused on symptom-based severity and usage motive in order to understand its mechanism. Recently, usage context, such as family or social relationships, has been identified as a key influencing factor of PIU. Therefore, we extended the classification of PIU to include usage context in addition to symptom-based severity and usage motive.

Phenomenological model for the normal-state angle-resolved photoemission spectroscopy line shapes of high-temperature superconductors.

Providing a full theoretical description of the single-particle spectral function observed for high-temperature superconductors in the normal state is an important goal, yet unrealized. Here, we present a phenomenological model approaching towards this goal. The model results from implementing key phenomenological improvement in the so-called extremely correlated Fermi-liquid model.

Extremely correlated Fermi-liquid description of normal-state ARPES in cuprates.

The normal-state single particle spectral function of the high temperature superconducting cuprates, measured by the angle-resolved photoelectron spectroscopy (ARPES), has been considered both anomalous and crucial to understand. Here, we report an unprecedented success of the new extremely correlated Fermi liquid theory by one of us [B. S.