measuring what matters: noncognitive skills - grit
TRANSCRIPT
Measuring What Matters
The Role of Non-Cognitive Factors in Student Success
Dr. Mac Adkins, President SmarterServices
Provided by
Question 1?
• How do you determine who can be enrolled at your school?– Standardized test scores– Prior grade point averages– Admissions exams
Top Admissions Factors
• The National Association for College Admission Counseling rated these factors.
• CONSIDERABLY IMPORTANT– College prep course grades– Strength of high school curriculum– Standardized test scores– Overall GPA
• MODERATELY IMPORTANT– Admissions essay– Letters of recommendation– Demonstrated interest– Class rank– Extracurricular commitment
Question 2Why Do Students Drop Out?
A study funded by the Bill and Melinda Gates Foundation ranked these reasons:
1. Conflict with work schedule
2. Affordability of tuition
3. Lack of support from family – financial and practical support
4. Lack of belief that a college degree is valuable
5. Lack of discipline – too much socializing, not enough studying
http://www.publicagenda.org/pages/with-their-whole-lives-ahead-of-them
Do You See the Disconnect?
Is Your School Measuring
What Matters?
To Find Out What Matters Let’s Ask:
Employers
Colleges
Faculty
National Research Council
US Department of Education
Mothers
Skills that Employer’s
Want
National Association of Colleges and Employers
Survey of Employers
http://www.unl.edu/svcaa/documents/how_employers_see_candidates.pdf
Outcomes Schools WantElements of Mission Statements From 35 Universities
Michigan State University, 2004
1. Knowledge, learning, mastery of general principles2. Continuous learning, intellectual interest, curiosity3. Artistic cultural appreciation 4. Appreciation for diversity5. Leadership6. Interpersonal skills7. Social responsibility, citizenship and involvement8. Physical and psychosocial health9. Career preparation10.Adaptability and life skills11.Perseverance12.Ethics and integrity
Traits Online Faculty Want
WICHE Cooperative for Educational Technologies, 2013
2012 National Research Council
COGNITIVEProblem solvingCritical thinkingSystems thinkingStudy skillsAdaptabilityCreativityMeta-cognitive skills
INTERPERSONALCommunicationSocial IntelligenceTeamworkLeadershipCultural sensitivityTolerance for diversity
INTRAPERSONALAnxietySelf-efficacySelf-conceptAttributionsWork ethicPersistenceOrganizationTime managementIntegrityLife-long learning
US Department of Education
“The test score accountability movement and conventional educational approaches tend to focus on intellectual aspects of success, such as content knowledge. However, this is not sufficient. If students are to achieve their full potential, they must have opportunities to engage and develop a much richer set of skills. There is a growing movement to explore the potential of the “noncognitive” factors — attributes, dispositions, social skills, attitudes, and intrapersonal resources, independent of intellectual ability—that high-achieving individuals draw upon to accomplish success.”
Parents Teach It
Are You Beginning To See The Picture?
• Non-cognitive skills matter– Determine student retention– Determine employer satisfaction– Determine online course success– Federal agencies recognize their importance– They are the mission of many schools– Parents value them
“Years of schooling predicts labor market outcomes — cognitive skills account for only 20%; therefore 80% of the “years of schooling” benefit is due to noncognitive skills” (Bowles, Gintis, & Osborne, 2001)
http://www.umass.edu/preferen/gintis/jelpap.pdf
Types of Data Used To Predict Learner Success
APTITUDE ATTITUDE SITUATION
What Are Non-Cognitive Skills?
Can Non-Cognitive Skills Be Taught?
You can’t change a tiger’s stripes, but you can teach that tiger to hunt in a different environment.
Recommended Uses of Non-Cognitive Skills Measures
1. Optic – A lens through which students can view their strengths and opportunities for improvement
2. Student Service – A tool to guide students toward available resources for support
3. Placement – Developmental / remedial course placement
4. Talking Points – A collection of statements which academic advisors can use to advise their students
5. Early Alert – A list of students who are likely to be benefitted by the instructor reaching out to them early in the course.
6. Predictive Analytic - A set of data which can be analyzed at the individual and aggregate level to project student performance
Methods of Measurement
• Instructor ratings – Time and task intensive for the faculty• Observer records – Expensive and time consuming• Letters of recommendation – Rarely objective• Interviews – Time consuming to conduct and code• Socioeconomic data – Beneficial mostly at the aggregate level due
to exceptions and bias• Self assessment – Yes, there are limitations, but it is the preferred
method.
Construct Comparison Matrix
ACT Engage
ETS Success Navigator
Wonderlic Admissions Risk Profile
SmarterMeasure
Individual Attributes
X X X X
Life Factors X
Learning Styles X
Technical Skills X X
Reading Skills X
Keyboarding Skills X
Custom Questions X
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 2,500,000 students
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
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.
Predictive
Correlation
Comparison
Descriptive
Student Service
Progression of SmarterMeasure Data Utilization
Research Ideas on the Research Page of the Website
Internally Conducted
Company Assisted
Professionally Assisted
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
GradesImpactsMotivation
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
Motivation Self Discipline
Time Management
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
SmarterMeasure.com
How important do you consider non-cognitive skills?
How is your school measuring and using non-cognitive factors?