Search results for ""Author John N. Moye, Ph.D.""

The Institutional Research profession is currently experimenting with many strategies to assess institutional effectiveness in a manner that reflects the letter and spirit of their unique mission, vision, and values. While a "best-practices" approach to the measurement and assessment of institutional functions is prevalent in the literature, a machine learning approach that synthesizes these parts into a coherent and synergistic approach has not emerged. A Machine Learning, Artificial Intelligence Approach to Institutional Effectiveness in Higher Education presents a practical, effective, and systematic approach to the measurement, assessment, and sensemaking of institutional performance. Included are instruments and strategies to measure and assess the performance of Curriculum, Learning, Instruction, Support Services, and Program Feasibility as well as a meaningful Environmental Scanning method. The data collected in this system are organized into assessments of institutional effectiveness through the application of machine learning data processes that create an artificial intelligence model of actual institutional performance from the raw performance data. This artificial intelligence is visualized through five organizational sensemaking approaches to monitor, demonstrate, and improve institutional performance. Thus, this book provides a set of tools that can be adopted or adapted to the specific intentions of any institution, making it an invaluable resource for Higher Education administrators, leaders and practitioners.

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As the complexity of 21st century disciplines has increased it has become necessary to rethink the design and delivery of learning to more effectively serve the diverse needs and learning styles of students in higher education. While instructional design and instructional technologies have advanced quickly, the processes of curriculum design have fallen behind. This book presents a comprehensive, systematic approach to the development of curricula in higher education in which each component is configured to optimize learning. The approach is based in an analysis of the psychophysics of the learner and employs theories of learning, instruction, and environment to design each component. The result of this process is the development of curricula that demonstrate optimal design attributes, remove barriers to learning, and expedite the learning. By presenting methods that provide practical, effective and efficient strategies to ensure an inclusive curriculum for all learners, this book will prove invaluable for higher education leaders, practitioners, and curriculum designers.

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The Psychophysics of Learning presents a learning system design approach that is formulated by the strategies and techniques the brain uses to process external information and make sense of that information to the learning ecology of all learners. The psychophysics of sensation, perception, and cognition provide the research information, which is used to formulate the learning system framework. These processes are inherent to all individuals and result in a model that promotes access, learning, and academic success for all learners. This information is applied to the design of the learning engagement, learning experience, and learning environment dimensions of a learning system. The psychophysics of sensation are applied to the design of the learning engagement strategies to ensure that all learners can intellectually access and comprehend the information presented as inputs to the learning system. The psychophysics of cognition are applied to the development of learning environments that integrate and internalize the external learning into the unique cognition of each learner. The resulting system creates a learning system design that is aligned with the natural learning processes of the brain.

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