000			03508nam a2200409Ii 4500
001			9781789738995
003			UtOrBLW
005			20210303084833.0
006			m o d
007			cr un|||||||||
008			190715t20192019enk ob 001 0 eng d
020			_a9781789738995 (e-book)
040			_aUtOrBLW _beng _erda _cUtOrBLW
050		4	_aLB2341 _b.M69 2019
072		7	_aJN _2bicssc
072		7	_aEDU015000 _2bisacsh
080			_a378
082	0	4	_a378.101 _223
100	1		_aMoye, John N., _eauthor.
245	1	2	_aA machine learning, artificial intelligence approach to institutional effectiveness in higher education / _cJohn N. Moye.
264		1	_bEmerald Publishing Limited,
300			_a1 online resource (xiii, 232 pages) ; _ccm
504			_aIncludes bibliographical references and index.
505	0		_aPrelims -- 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 -- Afterword -- References -- Index.
520			_aThe 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.
588	0		_aPrint version record
650		0	_aEducation, Higher _xManagement.
650		0	_aOrganizational effectiveness _xMeasurement.
650		7	_aEducation _xHigher. _2bisacsh
650		7	_aEducation. _2bicssc
776			_z9781789739008
856	4	0	_uhttps://www.emerald.com/insight/publication/doi/10.1108/9781789738995
999			_c29737 _d29737