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Using personality type to predict student success in a technology-rich classroom environment

Using personality type to predict student success in a technology-rich classroom environment. Lauren H. Brown, Ed.D. Donna Burton North Carolina State University. Why do this research?. Personal Interest Millennial generation Adviser connections Student Success. The Millennials.

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Using personality type to predict student success in a technology-rich classroom environment

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  1. Using personality type to predict student success in a technology-rich classroom environment Lauren H. Brown, Ed.D. Donna Burton North Carolina State University

  2. Why do this research? • Personal Interest • Millennial generation • Adviser connections • Student Success

  3. The Millennials • Generational Differences • Parental ties • Digital Generation • Prevalence of technology • Class • Communication • Personal life • Faculty/student relationships

  4. Personality Theory • Why personality theory? • Carl Jung • Isabel Myers and Katherine Briggs-Myers • The dimensions • Population differences

  5. Personality Theory in the Classroom • Information gathering • Teaching vs. Learning- dissonance

  6. Technology in the Classroom • Uses • Age differences • Instructor preference • Congruence with students’ learning • North Carolina State University

  7. Personality Theory and Technology in the Classroom • Instructor use • Student use • The internet • Lack of research

  8. The TF and SN Dimensions and Technology • Decision making • Thinking • Feeling • Information gathering • Sensing • Intuiting

  9. The Problem • The new students and advisers/faculty • Personality theory and success in the new environment • Lack of specific, predictive research

  10. Purpose • To further investigate if a student’s personality type, specifically on the Sensing/Intuiting and Thinking/Feeling scales, can be used to predict success in a technology-rich classroom environment.

  11. Research Questions • What is the predictive value of an individual’s preference for Sensing vs. Intuiting on achievement in a model that controls for gender differences and SAT score? • What is the predictive value of an individual’s preference for Thinking vs. Feeling on achievement in a model that controls for gender differences and SAT score?

  12. Conceptual Framework

  13. Methodology • Design • Population and Sample • The MBTI • Validity • Reliability • Data Collection • Data Analysis

  14. Variable Gender Frequency N Mean Percent Standard Deviation Minimum Maximum SAT_T Male 365 660 1179.71 55.3 107.63 830 1490 Female 295 44.7 CH_101 660 2.41 1.16 0 4.33 PREF_SN 660 11.68 8.07 1 30 PREF_TF 660 11.67 7.88 1 30 PREFSN 660 1.997 14.07 -30 30 PREFTF 660 -3.16 13.73 -30 30 Descriptive Statistics

  15. Variable Variable Correlation Coefficient Pr > r CH_101 SAT_T .25 <.0001 GENDER (F=5.01) .026 SN (F=5.10) .024 TF (F=9.28) .002 PREFTF .15 .0002 PREFSN .06 .098 GENDER SAT_T -.12 .002 TF -.33 <.0001 PREFTF -.36 <.0001 SAT_T SN -.22 <.0001 TF .11 .006 PREFSN -.27 <.0001 PREFTF .12 .003 Correlation Statistics

  16. Variable DF Parameter Estimate Standard Error t value Pr > t Standardized Regression Coefficient Intercept 1 -1.59 .50 -3.17 .001 GENDER 1 .36 .09 3.98 <.0001 .16 SAT_T 1 .003 .0004 7.32 <.0001 .28 SN 1 .30 .09 3.35 .0009 .13 TF 1 .30 .09 3.15 .0017 .12 Model 1: DF 4, Sum of Squares 96.09, Mean Square 24.02, F-Value 19.99, Pr>F <.0001 R-Square .11 Regression Analysis- Model 1

  17. Variable DF Parameter Estimate Standard Error t value Pr > t Standardized Regression Coefficient Intercept 1 -1.22 .50 -2.43 .0152 GENDER 1 .39 .09 4.19 <.0001 .17 SAT_T 1 .003 .0004 7.06 <.0001 .27 PREFSN 1 .008 .003 2.40 .02 .10 PREFTF 1 .01 .003 3.77 .0002 .15 Model 2: DF 4, Sum of Squares 98.08, Mean Square 24.52, F-value 20.46, Pr>F <.0001, R-Square .11 Regression Analysis- Model 2

  18. Variable Uniqueness Statistic F-Value Significance Level SAT_T .073 52.14 .001 GENDER .0215 15.36 .001 SN .0153 10.93 .001 TF .0135 9.64 .01 Uniqueness Indices- Model 1

  19. Variable Uniqueness Statistic F-Value Significance Level SAT_T .0677 48.36 .001 GENDER .0239 17.07 .001 PREFSN .0079 5.64 .05 PREFTF .0193 13.79 .001 Uniqueness Indices- Model 2

  20. Thinking Feeling Male N=198 N= 167 Female N=64 N=231 Overall Population N= 398 N=262 Chi-Square Value 72.21, Probability <.0001 Males vs. Females on TF

  21. Sensing Intuiting Male N= 161 N=204 Female N=119 N=176 Overall Population N= 280 N=380 Chi-Square Value .95, Probability .33 Males vs. Females on SN

  22. Variable Mean t-value Pr > t Cohen’s D Effect Size Chemistry 101 Males- 2.32 -2.24 .03 -.17 .09 Females- 2.52 SAT Total Score Males- 1191.1 3.05 .002 .24 .12 Females- 1165.6 PREFTF Males- 1.27 9.87 <.0001 .77 .36 Females- -8.637 PREFSN Males- 1.05 -1.93 .05 -.15 .08 Females- 3.17 Chemistry 101 S- 2.49 -2.26 .02 -.18 .09 N – 2.29 Chemistry 101 T- 2.58 -3.05 .002 -.24 .12 F- 2.30 T-test Results-Comparing Means

  23. Discussion- Results • S vs. N • T vs. F

  24. Discussion- Significance • NC State classroom improvements • Millennials and technology- assumptions • Alternative ways to use standard assessments • Implications- new tool for advisers • Link to past research

  25. Limitations • Chemistry 101 • Class difficulty • Enrollment • Timing • Professor grading styles • Not completely random selection • Course repeat

  26. Suggestions for Future Research • Class comparison • Repeat- why? • Upper-class students • Longitudinal

  27. Summary • Questions? • Thank you for attending. Please fill out the evaluation form before leaving. • Email • lauren_brown@ncsu.edu • donna_burton@ncsu.edu

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