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Dr. Mac Adkins, President SmarterServices

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1 Dr. Mac Adkins, President SmarterServices
Measuring Grit Do Non-Cognitive Attributes Impact Academic Success, Engagement, Satisfaction and Retention? Dr. Mac Adkins, President SmarterServices Provided by

2 Measuring Grits We recommend a glass beaker for measuring grits. We are from Alabama, we know about these things.

3 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?

4 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.

5 Types of Data Used To Predict Learner Success
APTITUDE ATTITUDE SITUATION

6 Importance of Retention

7 Some Students Seem To Have More Grit Than Others

8 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?

9 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

10 What Are The Ingredients In Our Grits?

11 What Does The Assessment Measure?
INTERNAL INDIVIDUAL ATTRIBUTES Motivation Procrastination Time Management Help Seeking Locus of Control LEARNING STYLES Visual Verbal Social Solitary Physical Aural Logical EXTERNAL LIFE FACTORS Availability of Time Dedicated Place Reason Support from Family SKILLS TECHNICAL Technology Usage Life Application Tech Vocabulary Computing Access TYPING Rate Accuracy ON-SCREEN READING Recall

12 WCET Survey, May 2013

13 How Does The Beaker Work?

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20 How Do You Spot a Bad Grit?

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26 Adjusting Readiness Ranges
Adjusting the cut points can make the reporting a more accurate predictor of success.

27 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

28 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.

29 Measuring the Grits

30 Progression of SmarterMeasure Data Utilization
Predictive Correlation Comparison Descriptive Student Service

31 Research Ideas on the Research Page of the Website

32 Professionally Assisted
Approaches to Research Projects Internally Conducted Company Assisted Professionally Assisted

33 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, , orientation course, records office, library, posters, and mail

34 Research Findings Analyzed 3228 cases over two years
Significant positive correlation between individual attributes and grades Motivation Impacts Grades

35 Results of Middlesex Research
Before SmarterMeasure™ was implemented, 6% to 13% more students failed online courses than students taking on-ground courses. After the implementation, the gaps were narrowed: 1.3% to 5.8% more online students failed than on-ground students.

36 Results of Middlesex Research
Failure rates reduced by as much as 10%

37 Action Plan Three areas of focus
Empower eLearning staff, faculty advisors, and academic counselors with student data Motivation Self Discipline Time Management Three areas of focus

38 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.

39 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.

40 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

41 The Analysis Phase One – Summer 2011 Phase Two – Fall 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.

42 The Findings Academic Achievement
The scales of Individual Attributes, Technical Knowledge, and Life Factors had statistically significant mean differences with the measures of GPA.

43 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.

44 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.

45 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.

46 The Findings Statistically Significant Relationships
Academic Achievement Engagement Retention Individual Attributes X Technical Knowledge Learning Styles Life Factors Technical Competency

47 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%)

48 The Findings - Correlates
Readiness Domain Readiness Domain Subscales Positive vs. Negative Pass vs. Fail Life Factor Place, Reason, and Skills Place Learning Styles Social and Logical N/A Personal Attributes Academic, Help Seeking, Procrastination, Time Management, and Locus of Control Time Management Technical Competency Internet Competency Computer Competency Technical Knowledge Technology Usage Technical Vocabulary

49 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 Technical Competency Computer and Internet Competency 22.75 Technical Knowledge Technology Vocabulary 38.76

50 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 Technical Competency Computer Competency and Internet Competency 12.23 Technical Knowledge Technology Usage and Technology Vocabulary 26.97

51 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

52 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

53 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

54 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

55 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

56 Argosy University - EXPLORE
Correlational analysis between SmarterMeasure scores and student satisfaction, retention, and academic success Satisfaction Success Retention Technical Motivation Time Statistically Significant Factors: Technical Competency Motivation Availability of Time.

57 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

58 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.

59 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 Attributes Technical Learning Styles Life Factors

60 Findings Statistically significant correlations:
Dedicated place, support from employers and family, access to study resources, and academic skills (Life Factors) Tech vocabulary (Technical Knowledge) Procrastination (Individual Attributes) Scores Grades

61 Academic Success Rates
Less than 10% of students with low scores experienced academic success.

62 Five Schools What is the relationship between measures of online student readiness and measures of online student satisfaction?

63 Methodology Incoming vs Outgoing
Data from 1,611 students who completed both the SmarterMeasure Learning Readiness Indicator and the Priority Survey for Online Learners were analyzed.

64 Findings There were statistically significant relationships between factors of readiness and satisfaction.

65 Comparison to Compass Scores
North Central Michigan College - Petoskey, MI

66 National Data 2013 Student Readiness Report
Data from 639,324 students from 275 colleges and universities

67 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

68 Online Learner Demographics
Dominant Social learning style Highly motivated Moderate reading skills Pressed for time Increasing technical skills

69 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.

70 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.

71 Readiness Impacts Satisfaction
Conclusion Statistically significant relationships exist between measures of online student readiness and measures of academic success, engagement, satisfaction and retention. Readiness Impacts Satisfaction

72 Schools and Students Benefit
Conclusion Students individually benefit and schools collectively benefit from measuring learner readiness and appropriately responding. Schools and Students Benefit

73 SmarterMeasure.com

74 SmarterMeasure References
Middlesex, Argosy, J. Sargeant Reynolds: Noel Levitz Study: https://www.noellevitz.com/papers-research-higher-education/2011/2011-adult-and-online-learner-satisfaction-priorities-reports Student Readiness Report:

75 “Live as if you were to die tomorrow
“Live as if you were to die tomorrow. Learn as if you were to live forever.” Mahatma Gandhi

76 SmarterServices.com (877) 499-SMARTER
For More Info SmarterServices.com (877) 499-SMARTER


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