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Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. [1]
Often, two or more species co-adapt and co-evolve as they develop adaptations that interlock with those of the other species, such as with flowering plants and pollinating insects. In mimicry , species evolve to resemble other species; in mimicry this is a mutually beneficial co-evolution as each of a group of strongly defended species (such as ...
Changing conditions trigger the execution of an adaptation. Many characteristics might be taken into account as catalysts for such an adaptation process. They can be clustered into three main categories: inter-individual, intra-individual and environmental differences.
Humans did not evolve from either of the living species of chimpanzees (common chimpanzees and bonobos) or any other living species of apes. [174] Humans and chimpanzees did, however, evolve from a common ancestor. [175] [176] This most recent common ancestor of living humans and chimpanzees would have lived between 5 and 8 million years ago. [177]
Renaissance Learning, Inc. (also known simply as Renaissance) is a software as a service and learning analytics company that makes cloud-based, Pre-K–12 educational software and adaptive assessments. Renaissance employs about 1,000 employees in nine U.S. cities and subsidiaries in Canada, the United Kingdom, Korea, and Australia.
A learning management system (LMS) or virtual learning environment (VLE) is a software application for the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, materials or learning and development programs. [1] The learning management system concept emerged directly from e ...
Domain adaptation is a specialized area within transfer learning. In domain adaptation, the source and target domains share the same feature space but differ in their data distributions. In contrast, transfer learning encompasses broader scenarios, including cases where the target domain’s feature space differs from that of the source domain(s).