Survey of Computerized Adaptive Testing: A Machine Learning Perspective

Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance. Widely adopted across diverse fields like education, healthcare, sports, and sociology, CAT has revolutionized testing practices. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing method. By examining the test question selection algorithm at the heart of CAT's adaptivity, we shed light on its functionality. Furthermore, we delve into cognitive diagnosis models, question bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing

Mussel byssus-inspired dual-functionalization of zirconia dental implants for improved bone integration

Zirconia faces challenges in dental implant applications due to its inherent biological inertness, which compromises osseointegration, a critical factor for the long-term success of implants that rely heavily on specific cell adhesion and enhanced osteogenic activity. Here, we fabricated a dual-functional coating that incorporates strontium ions, aimed at enhancing osteogenic activity, along with an integrin-targeting sequence to improve cell adhesion by mussel byssus-inspired surface chemistry. The results indicated that although the integrin-targeting sequence at the interface solely enhances osteoblast adhesion without directly increasing osteogenic activity, its synergistic interaction with the continuously released strontium ions from the coating, as compared to the release of strontium ions alone, significantly enhances the overall osteogenic effect. More importantly, compared to traditional polydopamine surface chemistry, the coating surface is enriched with amino groups capable of undergoing various chemical reactions and exhibits enhanced stability and aesthetic appeal. Therefore, the synergistic interplay between strontium and the functionally customizable surface offers considerable potential to improve the success of zirconia implantation