A Machine Learning, Artificial Intelligence Approach to Institutional Effectiveness in Higher Education

John N. Moye Ph.D.
Performance Learning Technologies, USA

Product Details
29 Jul 2019
Emerald Publishing Limited
248 pages - 152 x 229mm


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.
Chapter 1. Defining, Measuring, and Assessing Effectiveness 
Chapter 2. Creating Shared Mission, Vision, and Values 
Chapter 3. Measuring and Assessing Program Structure: Intended Performance 
Chapter 4. Measuring and Assessing Instruction: Intended Performance 
Chapter 5. Measuring and Assessing Support Services: Intended Performance 
Chapter 6. Functional Data Modeling: Identifying the Drivers and Constraints of Actual Performance 
Chapter 7. Institutional Data Modeling: Looking Beyond the Data 
Chapter 8. Continuous Quality Improvement
John N. Moye is an effectiveness consultant at Performance Learning Technologies, USA. He holds a Ph.D. from Florida State University, where he researched the field of psychophysics and performance and has held effectiveness positions with numerous institutions of higher learning in the US. His current work focuses on the research and development of systematic assessments to measure the effectiveness of unique institutions.

You might also be interested in..

« Back