Temperament, Learning Styles and Demographic Predictors of Student Satisfaction in a Blended Learning Environment

Maribeth FergusonCECS 5610

Dr. G. Knezek

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

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

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

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

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

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

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

Data Collection: Instruments

The Keirsey Temperament Sorter II: A personality survey: guardian,

artisan, idealist, or rational

The Index of Learning Styles:sensory/intuitive, visual/ verbalactive/reflective, sequential/global

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

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

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

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

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

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

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

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)

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

Assumption

The target sample would be representative of the institution

And the total student population involved in blended learning environments at the postsecondary level

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

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

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

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

Data Analysis

Responses to each satisfaction statement with blended learning environment were reported by using frequencies and percentages for each indicator level

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

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

Data Analysis

Variables that emerge as predictors of satisfaction were also compared to the individual satisfaction item responses to identify possible relationships

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

Results

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

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

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

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