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Maribeth Ferguson CECS 5610 Dr. G. Knezek

Temperament, Learning Styles and Demographic Predictors of Student Satisfaction in a Blended Learning Environment. Maribeth Ferguson CECS 5610 Dr. G. Knezek. Purpose.

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Maribeth Ferguson CECS 5610 Dr. G. Knezek

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  1. Temperament, Learning Styles and Demographic Predictors of Student Satisfaction in a Blended Learning Environment Maribeth Ferguson CECS 5610 Dr. G. Knezek

  2. Purpose The purpose of this study was to identify predictors of student satisfaction in undergraduate college students at a mid-sized southern university enrolled in courses with a blended learning environment

  3. Purpose • A mid-sized southern university states that 25% of students who enroll in traditional large-enrollment course do not finish the course • The university plans to conduct research to compare learner satisfaction and learner outcomes between the two learning environments

  4. Quality Enhancement Plan Quality Enhancement Plan: • To improve student learning outcomes and student experience in large-enrollment undergraduate courses • A component of the Southern Association of Colleges and Schools reaffirmation and accreditation process

  5. Online Education • The separation of teachers and learners • The influence of an educational organization • The use of a computer network to present and distribute some educational content • The provision of two-way communication via a computer network, may benefit from communication with each other, teachers and staff

  6. Instructional Delivery • Adult learners present a wide range of individual differences including: differences in orientation to learning and readiness to learn • No assumptions should be made about adult’s preferences for instructional delivery simply because they are adults

  7. Changes in Higher Education • Distance learning is an increasing important component of higher education • Studies have been conducted on the effects of learner satisfaction in an online learning environment • However, few research studies have focused on improving learner satisfaction through a blended learning environment

  8. Recent Research • Recent research can be classified generally into four categories: interaction, active learning, student perceptions, and learning outcomes • The quality of online education has also prompted the attention of higher education accreditation associations

  9. Data Collection: Instruments The Keirsey Temperament Sorter II: • A personality survey: guardian, artisan, idealist, or rational • The Index of Learning Styles: sensory/intuitive, visual/ verbal active/reflective, sequential/global

  10. Data Collection: Instruments The Student Satisfaction Questionnaire: • 16 statements; the scores range from: the least satisfaction scoring 16 to the most satisfaction scoring 80 • The degree of satisfaction was recoded as unsatisfied to satisfied with the median score as the determinant for the categories

  11. Forward Selection • Forward selection starts with an empty model • The random/independent variable with the smallest P value, when it is the only predictor in the regression equation, was placed in the model

  12. Forward Selection • Each subsequent step adds the variable that has the smallest P-value in the presence of the predictors already in the equation • Variables were added one-at-a-time as long as their P-values were small enough, typically less than 0.05 or 0.10

  13. P-Value • P value—the probability that any particular outcome would have arisen by chance • Small P-values suggest that the null hypothesis is unlikely to be true • The smaller it is, the more convincing is the rejection of the null hypothesis

  14. Logical Regression • Regression analysis is any statistical method where the mean of one or more random/independent variables is predicted on other response/dependent variables • Random variables: Temperament, Learning Styles, Demographic Characteristics

  15. Multiple Linear Regression • Multiple linear regression aims is to find a linear relationship between a response variable and several possible predictor variables (Easton, Hall, & Young 1997) • Response/Dependent Variable: Student Satisfaction • Predictor/Independent Variables: temperament, learning styles, demographic characteristics

  16. Logistic Regression • Logistic Regression is a regression method used when the random/independent variable is dichotomous • The Index of Learning Styles: sensory/intuitive, visual/ verbal, active/reflective, and sequential/global

  17. Logistic Regression • Logistic regression is used to predict the likelihood (the odds/ratio) of the outcome based on the predictor/independent variables • The significance of the logistic regression can be evaluated by …a Chi-square test, evaluated at the p < .05 level (Lani, 2006)

  18. Assumptions • The students enrolled in the five blended learning courses had the technical skills necessary to participate in a partially Web-based course • The students would understand and answer the surveys honestly

  19. Assumption • The target sample would be representative of the institution • And the total student population involved in blended learning environments at the postsecondary level

  20. Limitations • This study’s generalizability of the data is limited • The target sample involved undergraduate college students from only one institution in the southern United States

  21. Limitations • Additionally, the data is collected at only one point in time • If independent samples are taken repeatedly from the same population • And a confidence interval calculated for each sample • Then a certain percentage (confidence level) of the intervals will include the unknown population parameter

  22. Limitations • Confidence intervals are more informative than the simple results of hypothesis tests, where we decide 'reject H0' or 'don't reject H0‘, since they provide a range of plausible values for the unknown parameter

  23. Data Analysis • The SSQ was recorded as interval, ordinal and nominal data • Descriptive statistics were used to report the temperament, learning styles and demographic characteristics of the target sample

  24. Data Analysis • Responses to each satisfaction statement with blended learning environment were reported by using frequencies and percentages for each indicator level

  25. Data Analysis • Each predictor/independent variable was correlated with the criterion/dependent variable, determining the rating of satisfied or unsatisfied • Two levels of experience were considered in the analysis, novice and intermediate users; and the proficient users

  26. Data Analysis The regression equation: • indicated whether or not a significant effect from the predictor/independent variables on satisfaction existed • and offered the probably of a correct prediction of satisfaction for the set of predictors/independent variables

  27. Data Analysis • Variables that emerge as predictors of satisfaction were also compared to the individual satisfaction item responses to identify possible relationships

  28. Expectations • The participation should be high since the suveys are require assignments • The grade classification characteristic should be mostly lower classmen • The student experience with blended learning environments should be low

  29. Results • Other studies have found gender and lnternet experience to be the only significant predictors of student satisfaction in digital learning environment

  30. Research Question Are temperament, learning styles, and demographic characteristics of college students predictors of student satisfaction in a blended learning environment? • Females were more likely to be satisfied with digital learning environments than are males • More experienced Internet users reported more satisfaction than the less experienced users

  31. Research Significance • The significance of this study is in the independent variables that did not show significance as predictors of student satisfaction • Sometimes knowing what does not work is just as important as know what does work

  32. References • Paulson, Morton F. (2002). Online Education Systems: Discussion and Definition of Terms. Retrieved on 13, 2006 from http://www.nettskolen.com/forskning/Definition%20of%20Terms.pdf. • Sage, N.A. (2001). Elements of a research study [WWW document]. URL: http://www.psy.pdx.edu/PsyTutor/Tutorials/Research/Elements. • Felder, Richard, "Reaching the Second Tier: Learning and Teaching Styles in College Science Education."J. College Science Teaching, 23(5), 286-290 (1993). • Garson, David G. (2006). Logistical Regression. Retrieved on April 28, 2006 from http://www2.chass.ncsu.edu/garson/PA765/logistic.htm. • Greenhalgh, Trisha. (1997) How to Read a Paper: Statistics for the Non-Statistician.II: “Significant Realtions and their Pitfalls. Retrieved on April 28, 2006 from http://bmj.bmjjournals.com/cgi/content/full/315/7105/422. • Hiltz, R., & Coppola, N.,& Rotter, N.,& Turoff, M., & Benbunan-Fich, R., (2000). Measuring the Importance of Collaboration Learning for the Effectiveness of ALN: A Multi-Method Approach. Retrieved on April, 1, 2006 from http://www.aln.org/publications/jaln/v4n2/pdf/v4n2_hiltz.pdf.

  33. References • McAllister, C. and Ting, E. (2001). Analysis of Discussion Items by Males and Females in Online College Courses. Seattle, WA: The Annual Meeting of the Ammerican Educational Research Association. (ERIC Document Reproduction Services No ED 458-237) • Paulson, Morton F. (2002). Online Education Systems: Discussion and Definition of Terms. Retrieved on 13, 2006 from http://www.nettskolen.com/forskning/Definition%20of%20Terms.pdf. • Schwarz, Carl J. (1998) Scales of Measurement. Retrieved on April 1, 2006 from http://www.math.sfu.ca/~cschwarz/Stat-301/Handouts/node5.html • Spiceland, David J. (2002). The Impact of Learning of an Asynchronous Active Learning Course Format. Retrieved on April 1, 2006 from http://www.nettskolen.com/forskning/Definition%20of%20Terms.pdf. • Stokes, Suanne P., (2003). Temperament, Learning Styles, and Demographic Predictors of College Student Satisfaction in a Digital Learning Environment. Biloxi, MS: The Annual Meeting of the Mid-South Educational Research Association. (ERIC Document Reproduction Service No. ED 482-454).

  34. References • Wegner, Scott P. (1999). The Effects of Internet-Based Instruction on Student Learning. Retrieved on April 1, 2006 from http://www.sloan-c.org/publications/jaln/v3n2/v3n2_wegner.asp • Yang, Yi, and Cornelius, Linda. (2004). Students’ Perceptions Towards the Quality of Online Education: A Qualitative Approach. Retrieved on April 1, 2006 from http://www.nettskolen.com/forskning/Definition%20of%20Terms.pdf

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