measuring grit do non-cognitive attributes impact academic success, engagement, satisfaction and...
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Measuring Grit
Do Non-Cognitive Attributes Impact Academic Success, Engagement, Satisfaction and Retention?
Dr. Mac Adkins, President SmarterServices
Provided by
Measuring Grits
• We recommend a glass beaker for measuring grits. We are from Alabama, we know about these things.
What is Grit?
• Why does one student who had straight A’s in high school drop out of college after one year, and another one excel?
• Why does one single mom with three children graduate Summa Cum Laude and another one drop out?
What is Grit?
• Grit is that elusive quality that prompts one student to stick with it while others quit.
• For over ten years we have measured levels of grit in over 2,300,000 students at over 500 colleges and universities.
• Today I want to share with you the results of research related to the impact that grit has on student success.
Types of Data Used To Predict Learner Success
APTITUDE ATTITUDE SITUATION
Importance of Retention
Some Students Seem To Have More Grit Than Others
Three Approaches to Measuring Grit
• Stick your head in the sand.• Use a brief, non-prescriptive survey.• Use SmarterMeasure Learning Readiness
Indicator.
Is Distance Learning For Me?
SmarterMeasure Learning Readiness Indicator
• A 124-item online skills test and attributes inventory that measures a student’s level of readiness for studying online
• Used by over 500 Colleges and Universities• Since 2002 taken by over 2,300,000 students
What Are The Ingredients In Our Grits?
What Does The Assessment Measure?INTERNAL
INDIVIDUAL ATTRIBUTES
MotivationProcrastination
Time ManagementHelp Seeking
Locus of Control
LEARNING STYLES
VisualVerbalSocial
SolitaryPhysical
AuralLogical
EXTERNAL
LIFE FACTORS
Availability of TimeDedicated Place
ReasonSupport from Family
SKILLS
TECHNICAL
Technology UsageLife Application
Tech VocabularyComputing Access
TYPING
RateAccuracy
ON-SCREEN READING
RateRecall
WCET Survey, May 2013
How Do You Spot a Bad Grit?
Adjusting Readiness Ranges
Adjusting the cut points can make the reporting a more accurate predictor of success.
How Do Schools Use It?
• Orientation Course• Enrollment Process• Information Webinar• Public Website• Class Participation• Facebook• 68% of client schools administer the
assessment to all students, not just eLearning students
Thermometer Analogy
• More important than taking your child’s temperature is taking appropriate action based on their temperature.
• More important than measuring student readiness is taking appropriate action based on the scores.
Measuring the Grits
Predictive
Correlation
Comparison
Descriptive
Student Service
Progression of SmarterMeasure Data Utilization
Research Ideas on the Research Page of the Website
Internally
Conducted
Company
Assisted
Professionall
y Assiste
d
Approaches to Research Projects
Middlesex Community College
• 6% to 13% more students failed online courses than on-ground courses.
• Intervention Plan- Administer SmarterMeasure- Identify which constructs best predicted success- Provide “Success Tips” as identified
Distributed by website, email, orientation course, records office, library, posters, and mail
Research Findings
• Analyzed 3228 cases over two years• Significant positive correlation between
individual attributes and grades
GradesImpactsMotivatio
n
Results of Middlesex Research
Before SmarterMeasure™ was implemented, 6% to 13% more students failed online courses than students taking on-ground courses. After theimplementation, the gaps were narrowed: 1.3% to 5.8% more online students failed than on-ground students.
Results of Middlesex ResearchFailure rates reduced by as much as 10%
Action Plan
• Empower eLearning staff, faculty advisors, and academic counselors with student data
MotivationSelf
Discipline
Time Managemen
t
Three areas of
focus
Project Summary
“In summary, the implementation of SmarterMeasure has helped students to achieve better academic success by identifying their strengths and weaknesses in online learning.”
In essence, with various strategies implemented to promote SmarterMeasure™, a “culture” was created during advising and registration for students, faculty, and support staff to know that there is a way for students to see if they are a good fit for learning online.
CEC - The Need
• We need to know which students to advise to take online, hybrid or on-campus courses.
• We need to know which students to direct to which student services to help them succeed.
• We need to know how to best design our courses so that new students are not overwhelmed.
The Analysis
• What is the relationship between measures of student readiness and variables of:– Academic Success - GPA– Engagement – Survey (N=587)– Satisfaction – Survey (Representative Sample
based on GPA and number of courses taken per term)
– Retention – Re-enrollment data
The Analysis
• Phase One – Summer 2011– Included data from all three delivery systems – online, hybrid
and on-campus– Analyzed data at the scale level
• Phase Two – Fall 2011– Focused the research on online learners only– Analyzed data at the sub-scale level
• A neutral, third-part research firm (Applied Measurement Associates) used the following statistical analyses in the project:– ANOVA, Independent Samples t-tests, Discriminant Analysis,
Structural Equation Modeling, Multiple Regression, Correlation.
The Findings
• Academic Achievement– The scales of Individual Attributes, Technical
Knowledge, and Life Factors had statistically significant mean differences with the measures of GPA.
The Findings
• Retention– The measure of Learning Styles produced a
statistically significant mean difference between students who were retained and those who left. • A 73% classification accuracy of this retention
measure was achieved.
– The scales of Individual Attributes and Technical Knowledge were statistically significant predictors of retention as measured by the number of courses taken per term.
The Findings
• Engagement– The scales of Individual Attributes and Technical
Competency had statistically significant relationships with the four survey items related to Engagement.
– The scales of Life Factors, Individual Attributes, Technical Competency, Technical Knowledge, and Learning Styles were used to correctly classify responses to the survey questions related to engagement and satisfaction with up to 93% classification accuracy.
The Findings
• Satisfaction– Structural equation modeling was used to create a
hypothesized theoretical model to determine if SmarterMeasure scores would predict satisfaction as measured by the survey.
– Results indicated that prior to taking online courses, student responses to the readiness variables were statistically significant indicators of later student satisfaction.
– Therefore, the multiple SmarterMeasure assessment scores are a predictor of the Career Education survey responses.
The Findings
• Statistically Significant RelationshipsAcademic Achievement
Engagement Retention
Individual Attributes
X X X
Technical Knowledge
X X X
Learning Styles
X X
Life Factors X X
Technical Competency
X
The Findings
• Student Categorizations– Enrollment Status
• Positive – active/graduated (34.3%)• Negative – withdrew/dismissed/transfer (65.7%)
– Academic Success Status• Passing – A, B or C (48.9%)• Failing – D, F or Other (21.1%)
– Transfer Credit – (21.8%)– Not reported – (8.2%)
The Findings - Correlates
Readiness Domain Readiness Domain Subscales
Positive vs. Negative Pass vs. Fail
Life Factor Place, Reason, and Skills Place
Learning Styles
Socialand
LogicalN/A
Personal Attributes
Academic, Help Seeking, Procrastination, Time Management, and Locus of Control
Time Management
Technical Competency
Internet CompetencyInternet Competency
andComputer Competency
Technical Knowledge
Technology Usageand
Technical VocabularyTechnical Vocabulary
The Findings - Predictors
Readiness Domains GPA F p
Life Factor Place and Skills 12.35 .0001
Learning Styles Verbal a and Logical 3.95 .02
Personal Attributes
Help Seeking, Time Management, and Locus of
Control
21.11
.0001
Technical Competency
Computer and Internet Competency
22.75
.0001
Technical Knowledge
Technology Vocabulary
38.76
.0001
The Findings - Predictors
Readiness Domains Credit Hours Earned F p
Life Factor Place 12.37 .0001
Learning Styles Visual 6.81 .01
Personal Attributes
Academic Attributes, Help
Seeking, and Locus of Control
13.40
.0001
Technical Competency
Computer Competencyand Internet Competency
12.23
.0001
Technical Knowledge
Technology Usage and Technology Vocabulary
26.97
.0001
The Recommendations
• We need to know which students to advise to take online, hybrid or on-campus courses.– A profile of a strong online student is one who:
• Has a dedicated place to study online• Possesses strong time management skills• Demonstrates strong technical skills• Exhibits a strong vocabulary of technology terms
The Recommendations
• We need to know which students to direct to which student services to help them succeed.– An online student who should be directed toward
remedial/support resources is one who:• Has a weak reason for returning to school• Has weak prior academic skills• Is not likely to seek help on their own• Is prone to procrastinate• Has low, internal locus of control• Has weak technology skills
The Recommendations
• We need to know how to best design our courses so that new students are not overwhelmed.– Limit advanced technology in courses offered early in
a curriculum– Foster frequent teacher to student interaction early in
the course– Require milestones in assignments to prevent
procrastination– Clearly provide links to people/resources for
assistance
Argosy University
• Required in Freshman Experience course• Students reflect on scores and identify
areas for improvement in their Personal Development Plan
• Group reflection with others with similar levels of readiness
Argosy University - COMPARE
• Compared the traits, attributes, and skills of the online and hybrid students.
• Substantial differences between the two groups existed. • Changes were made to the instructional design process
for each delivery system.
Online
Hybrid
Argosy University - EXPLORE
• Correlational analysis between SmarterMeasure scores and student satisfaction, retention, and academic success
SatisfactionRetentionSuccess
Technical
Motivation TimeStatistically Significant
Factors:
Technical Competency Motivation
Availability of Time.
Argosy University - TREND
• Aggregate analysis of SmarterMeasure data to identify mean scores for students.
• Comparison made to the national mean scores from the Student Readiness Report.
National Scores
Argosy Scores
Argosy University - APPLY
• Findings were shared with the instructional design and student services groups and improvements in processes were made.
For example, since technical competency scores increase as the students take more online courses, the instructional designers purposefully allowed only basic forms of technology to be infused into the first courses that students take.
J. Sargeant Reynolds Community College
• Required as admissions assessment
• Integral part of their QEP• Computed correlations
with grades and SmarterMeasure sub-scales of over 4000 students.
• P
Grades
Findings• Statistically significant correlations:
Scores
- Dedicated place, support from employers and family, access to study resources, and academic skills (Life Factors)
- Tech vocabulary (Technical Knowledge)
- Procrastination (Individual Attributes)
Academic Success Rates
Skills Resources Time0
10
20
30
40
50
60
70
High Score
Low Score
Less than 10% of students with low scores experienced academic success.
Five Schools
What is the relationship between measures of online student readiness and measures
of online student satisfaction?
Methodology
Data from 1,611 students who completed both the SmarterMeasure Learning Readiness Indicator and the Priority Survey for Online Learners were analyzed.
Incoming vs Outgoing
Findings• There were statistically significant
relationships between factors of readiness and satisfaction.
Comparison to Compass Scores
North Central Michigan College - Petoskey, MI
National Data
• 2013 Student Readiness Report• Data from 639,324 students from 275
colleges and universities
Online Learner Demographics
• 69% were female• 54% were Caucasian/White• 54% had never taken an online course before• 40% were traditional aged college students • 53% were students at an associate’s level
institution
Online Learner Demographics
• Dominant Social learning style• Highly motivated• Moderate reading skills• Pressed for time• Increasing technical skills
Profile of a Successful Online Student
• Four demographic variables have had a statistically significant higher mean for five years in a row.
Females higher in Individual Attributes, Academic Attributes, and Time Management.
Males higher in Technical Knowledge.
Profile of a Successful Online Student
• Caucasians have had the highest means for five years in Technical Knowledge.
• Students who have taken five or more online courses have had the highest means for five years in Individual Attributes, and Technical Knowledge.
Conclusion
• Statistically significant relationships exist between measures of online student readiness and measures of academic success, engagement, satisfaction and retention.
Readiness Impacts Satisfaction
Conclusion
• Students individually benefit and schools collectively benefit from measuring learner readiness and appropriately responding.
Schools and Students Benefit
SmarterMeasure.com
Middlesex, Argosy, J. Sargeant Reynolds: http://smartermeasure.com/research/research-results/
Noel Levitz Study: https://www.noellevitz.com/papers-research-higher-education/2011/2011-adult-and-online-learner-satisfaction-priorities-reports
Student Readiness Report: http://smartermeasure.com/smartermeasure/assets/File/Online-Student-Readiness-Report.pdf
SmarterMeasure References
“Live as if you were to die tomorrow. Learn as if you were to live forever.”
Mahatma Gandhi