LEADER 03843nam 2200541I 450 001 9910969792003321 005 20190715103526.0 010 $a9781789739015 010 $a1789739012 010 $a9781789738995 010 $a1789738997 035 $a(CKB)4100000008730934 035 $a(MiAaPQ)EBC5833987 035 $a(UtOrBLW)9781789738995 035 $a(Perlego)882957 035 $a(EXLCZ)994100000008730934 100 $a20190715h20192019 uy 0 101 0 $aeng 135 $aurun||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA machine learning, artificial intelligence approach to institutional effectiveness in higher education /$fJohn N. Moye 205 $a1st ed. 210 1$aBingley, England :$cEmerald Publishing,$d2019. 210 4$aŠ2019 215 $a1 online resource (247 pages) 311 08$a9781789739008 311 08$a1789739004 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aEducation, Higher$xManagement 606 $aOrganizational effectiveness$xMeasurement 606 $aEducation$xHigher$2bisacsh 606 $aEducation$2bicssc 615 0$aEducation, Higher$xManagement. 615 0$aOrganizational effectiveness$xMeasurement. 615 7$aEducation$xHigher. 615 7$aEducation. 676 $a378.101 700 $aMoye$b John N.$01806225 801 0$bUtOrBLW 801 1$bUtOrBLW 906 $aBOOK 912 $a9910969792003321 996 $aA machine learning, artificial intelligence approach to institutional effectiveness in higher education$94359827 997 $aUNINA