00835nam0-22003011i-450-99000678408040332120001010000678408FED01000678408(Aleph)000678408FED0100067840820001010d--------km-y0itay50------baitay-------001yyCommunications and the "Third World"Geoffrey ReevesLondon-New YorkRoutledge1993.XIV, 278 p.21 cmStudies in culture and communication302Reeves,Geoffrey251483ITUNINARICAUNIMARCBK990006784080403321VI D 14721893FSPBCFSPBCCommunications and the "Third World"634970UNINAGEN0103843nam 2200541I 450 991096979200332120190715103526.09781789739015178973901297817897389951789738997(CKB)4100000008730934(MiAaPQ)EBC5833987(UtOrBLW)9781789738995(Perlego)882957(EXLCZ)99410000000873093420190715h20192019 uy 0engurun|||||||||txtrdacontentcrdamediacrrdacarrierA machine learning, artificial intelligence approach to institutional effectiveness in higher education /John N. Moye1st ed.Bingley, England :Emerald Publishing,2019.©20191 online resource (247 pages)9781789739008 1789739004 Includes bibliographical references and index.Prelims -- 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.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. Education, HigherManagementOrganizational effectivenessMeasurementEducationHigherbisacshEducationbicsscEducation, HigherManagement.Organizational effectivenessMeasurement.EducationHigher.Education.378.101Moye John N.1806225UtOrBLWUtOrBLWBOOK9910969792003321A machine learning, artificial intelligence approach to institutional effectiveness in higher education4359827UNINA