Faculty Demographics as Identifiers of Online Faculty Teaching Satisfaction and Motivation

Concurrent Session 8

Brief Abstract

Timing, resources, and training can impact faculty transition from face-to-face to online teaching. The variables discussed are faculty engagement, motivation, and satisfaction. Future findings of this research will help faculty/administrators by showing successes or barriers online faculty face.  Identification of these barriers may increase willingness to transition to online teaching.


Experienced Instructional Designer and Mathematics Instructor with a demonstrated history of working in the higher education industry. Strong education professional while pursing a Doctor of Philosophy (Ph.D.) focused in Instructional Design and Technology from Keiser University. Received her B.A. in Mathematics from Dominican College and a M.S.Ed. in Mathematics Education from SUNY New Paltz. Skilled in instructional design methods, innovative educational tools, learning management system (LMS), VoiceThread certified educator, Microsoft excel, Microsoft word, Google drive, and team building.

Extended Abstract

Dissertation (December 2018) for Ph.D. Instructional Design and Technology

Dissertation Chair: Ashlee Robertson; Keiser University; arobertson@keiseruniversity.edu


Extended Abstract

Description of Context:  There is limited research regarding faculty engagement, job satisfaction, and motivationwithin online instruction.  Faculty are not engaged, satisfied or motivated within online instruction, therefore faculty often struggle with the transition to online instruction versus a traditional face-to-face instruction (Ortagus & Stedrak, 2013).  Online education is growing exponentially so the need for engaging online faculty is a necessity for colleges and universities (Allen, Seaman, Sloan, Babson Survey Research, & Pearson, 2013).

The purpose of this study is to identify potential barriers of faculty engagement, motivation, and satisfaction in online instruction.  These barriers may be heightened depending on background variables including but not limited to; education, employment status, and discipline affiliation.  The potential barriers may include the background variables are access to technical support for faculty, the time is takes to contrast an online course, technological intimidation for faculty to teach online, and the miss conceptions within the value of online education compared to other forms of education.

Faculty job satisfaction contributes to the quality of work performed in employment (Ortagus & Stedrak, 2013).  Faculty support and commitment in an online setting will directly impact student engagement in an online setting.  Research within online instructor satisfaction is extremely limited in the field of higher education (McLawhon & Cutright, 2011).  Previous studies have reported instructors are not satisfied with teaching online (Conceicao, 2006).  There are a few online faculty satisfaction surveys but limited research has been conducted connecting job satisfaction and specific demographics to see if there is a correlation (McLawhon & Cutright, 2011).  An identifiable correlation may lead to a better understanding of why faculty are not willing to teach online and continue to teaching online while being engagement, satisfied, and motivated.

Questions:  Is there a statistically significant difference in online faculty job satisfaction based on specific demographics?

1. Is there a statistically significant difference in online faculty job satisfaction based on discipline affiliation?

2. Is there a statistically significant difference in online faculty job satisfaction based on number of years teaching online?

3. Is there a statistically significant difference in online faculty job satisfaction based on employment status (full-time and adjunct)?

4. Is there a statistically significant difference in online faculty job satisfaction based on highest degree earned?

5. What is the relationship between technical support and job satisfaction to teach online courses? 

6. What is the relationship between perceptions of the value of online education and job satisfaction to teach online courses? 

7. What is the relationship between technological intimidation and job satisfaction to teach online courses? 

8. What is the relationship between self-efficacy and job satisfaction to teach online courses? 

Methods:  The design that is appropriate for the collection and analysis of data is a quantitative correlational design.  A correlational study is a quantitative method of research where two or more quantitative variables from the same group of participants are analyzed to determine/establish the relationship or co-variation between the variables (Leedy & Ormrod, 2010).  The correlational design is an important design because it explores the relationship between variables that may enhance future research and institutional change (Prematunga, 2012).  The advantages of a correlational design are the amount of data that can be collected, results are applicable to real life, results may be the foundation or starting point for future studies, and determines the strength or relationship between variables (Neuman & Robson, 2014).  The disadvantages of a correlation design are it only explains a relationship and does not explain the why, cannot determine which relationship causes the influence, cannot account for another variable being involved (Neuman & Robson, 2014).

Data Collection:  The data collection will be gathered by surveying online faculty at a small private college in New York with a researcher made survey.  The population will be the online faculty community.  This information will be received from the Director of Online Design and Innovation.  Probability sampling will be used to find the sample using systematic sampling.  The electronic survey will be sent out with a request to respond within 7 days from receipt.  The researcher will import the data into SPSS.  The data will be analyzed to insure accuracy and that all questions were answered for each survey.  Surveys that are incomplete will be removed from analysis with identified missing data points for summation.

Data Analysis and Future Results (will be completed in Summer 2018):  The data analysis will start with importing the data into SPSS.  The likert scale data will be analyzed using the mean, standard deviation, Spearman rank-order correlation coefficient (Spearman’s correlation), t-test and regression.  The mean and standard deviation will be calculated for each question.  The top 10 and last 10 will be listed with this statistic.  The relationships between the variables in a scatterplot will be shown to see a monotonic relationship.  If it is monotonic then the Spearman’s correlations will be calculated in this survey and research.  The t-test will be used to compare two averages and show the averages are statistically different.

Conclusion/Outline of the Study to Present:

1.     Review the literature concerning faculty engagement, job satisfaction, and motivation.

2.     Conduct a survey of online faculty regarding engagement and satisfaction, their relation to personal constructs in regard to teaching online, at a four-year private college in central New York State.

3.     Compile and analyze the data from the online faculty survey using Google forms and SPSS.

4.     Write the results of the descriptive statistics.

5.     Summarize the results with a conclusion, limitations and implications for future research to help future stakeholders like administrators, faculty, technologist, and instructional designers.