WCET Leadership Summit - SmarterMeasure & Noncognitive Readiness Data
D R . M A C A D K I N SP R E S I D E N T , S M A R T E R S E R V I C E SNoncognitive Readiness Data
Feel Your Pain I understand your need not just to collect data, but tomake meaning in actionable ways from the data. Ph.D. in Higher Education Leadership, AuburnUniversity, 1995 Served as a Dean at two regionally accredited universities Served as a data analyst in and institutional effectiveness officefor three years
Types of Data Used To Predict Learner SuccessWHATAcademic Success –GPA, Standardized TestsDemographic – Incomelevel, First generation collegestudentLearning Pathways – BlackboardEngagement - StarfishWHYSmarterMeasureLearning ReadinessIndicatorData about“WHY”a student doessomething isactionable.
SmarterMeasureLearning Readiness Indicator A 124-item online skills test and attributesinventory that measures a student’s level ofreadiness for studying online Used by over 500 Colleges and Universities Since 2002 taken by over 2,225,000 students
What Does The Assessment Measure?INTERNALINDIVIDUALATTRIBUTESMotivationProcrastinationTime ManagementHelp SeekingLocus of ControlLEARNING STYLESVisual / VerbalSocial / SolitaryPhysical / AuralLogicalEXTERNALLIFE FACTORSAvailability of TimeDedicated PlaceReasonSupport from FamilySKILLSTECHNICALTechnology UsageLife ApplicationTech VocabularyComputing AccessTYPINGRateAccuracyON-SCREENREADINGRateRecall
Noncognitive Data is a Valid Indicatorof Student Success & RetentionMiddlesex Community College Before SmarterMeasure™ was implemented, 6% to13% more students failed online courses thanstudents taking on-ground courses. After theimplementation, the gaps were narrowed: 1.3% to5.8% more online students failed than on-groundstudents.
Academic Success Rates010203040506070Skills Resources TimeHigh ScoreLow ScoreLess than 10% of students with low scoresexperienced academic success.
Statistically Significant RelationshipsAcademicAchievementEngagement RetentionIndividualAttributesX X XTechnicalKnowledgeX X XLearningStylesX XLife Factors X XTechnicalCompetencyX
Using Discriminant Analysis a composite using thescales of Life Factors, IndividualAttributes, Technical Competency, TechnicalKnowledge, and Learning Styles was created topredict levels of engagement and satisfaction with93% classification accuracy.
Statistically significant correlations were found betweenSmarterMeasure scores and student satisfaction, retention andacademic success.SatisfactionRetentionSuccessTechnicalMotivationTime