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Factors that Predict Full-TimeCommunity College Faculty Engagementin Online InstructionDuane Akroyd a , Bess Patton a & Susan Bracken aa Department of Leadership , Policy, Adult and Higher Education,North Carolina State University , Raleigh , North Carolina , USAPublished online: 18 Jan 2013.

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Factors that Predict Full-Time Community CollegeFaculty Engagement in Online Instruction

Duane Akroyd, Bess Patton, and Susan Bracken

Department of Leadership, Policy, Adult and Higher Education,North Carolina State University, Raleigh, North Carolina, USA

This study is a secondary quantitative analysis of the 2004 National Study of Postsecondary Faculty

(NSOPF) data. It examines the ability of human capital, intrinsic rewards, extrinsic rewards, and

gender=race demographics to predict full-time community college faculty teaching on-line courses.

Findings indicate that those faculty with higher degree attainment were three times more likely to

teach online. Faculty members who felt the institution supported teaching were 8% more likely to

teach online, and faculty who taught general education courses were 25% less likely to teach online

than their occupational=vocational counterparts. This data offers an important baseline for future

work. Online course offerings trend upwards, with 50% of all online enrollments at two year

institutions (Allen & Seaman, 2008).

Community colleges are taking a leading role in using technology for instruction in attempting to

reach students who may not always be able to attend all classes in a traditional format. Developing

a distance course generally takes more time than developing a face-to-face course (Keramidas,

Ludlow, Collins, & Baird, 2007; Zhao, Alexander, Perrault, Waldman, & Truell, 2009) and also often

entails a significant learning curve to match technology with instructional techniques for faculty not

familiar with delivering technology enhanced instruction. Many community colleges lack formal pro-

fessional development for faculty members (Eddy, 2007), and what little money exists does not cover

all faculty (Akroyd, Jaeger, Jackowski, & Jones, 2004).In a recent faculty survey, 88% of faculty

members felt that the use of technology in instruction is essential; yet, there isn’t enough financial

or professional development support of faculty know-how to do things right (Garza Mitchell, 2010).

Although it has been reported that faculty are the key to successful implementation and out-

comes of distance education, Jones, Lindner, Murphy, and Dooley (2002) cite faculty resistance

to instructional technology as a primary barrier to the continued growth of distance education

programs. The data from the National Study of Postsecondary Faculty (Heuer et al., 2004) indi-

cate that only 20% of all full-time faculty at public community colleges taught an online course.

While the use of online instruction is becoming more prevalent, only a small percentage of com-

munity college faculty actually teach an online course. Olcott and Wright (1995) have observed

that many faculty resist participation in distance education. Faculty are concerned about the

Address correspondence to Duane Akroyd, Professor, Department of Leadership, Policy, Adult and Higher

Education, North Carolina State University, Box 7801, College of Education, Raleigh, NC 27695. E-mail: duane_

akroyd@ncsu.edu

Community College Journal of Research and Practice, 37: 185–195, 2013

Copyright # Taylor & Francis Group, LLC

ISSN: 1066-8926 print=1521-0413 online

DOI: 10.1080/10668926.2013.739512

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learning curve to effectively use technology, the time intensiveness of utilizing technology to

create and manage courses, and the lack of release time and instructional support (Ellis,

2000; Hayes & Jamrozik, 2001; Rockwell, Schauer, Fritz, & Marx, 2000). Community college

faculty are in an interesting position as they see their work being transformed in ways that intro-

duces technology that can make their work more efficient in ways. But technology also adds the

possibility of a steep learning curve in training skills for the development, design, and, in some

cases, ongoing provision of courses. This brings challenges not only to faculty but to institutions

as they seek to support and administer the instructional processes and training and support that

accompany these efforts (Barber, 2011).

As community colleges continue their trend of increased utilization of distance education, faculty

resistance must be addressed and resolved. As Surry and Land (2000) note, ‘‘to increase the utiliza-

tion of technology on campus, administrators will have to understand technological change from the

faculty’s perspective and develop strategies for encouraging faculty to use technology’’ (p. 149).

THEORETICAL FRAMEWORK

Equity theory addresses the perceived relative fairness of rewards resulting from a person’s job

performance and accomplishments. Equity theory considers the employee’s inputs (e.g., experi-

ence, education, efforts, skills, abilities) and outcomes (e.g., salary, bonuses, promotions, recog-

nition) relative to a comparison employee (Borkowski, 2005). Equity theory, which is sometimes

referred to as expectancy theory, addresses the perceived relative fairness of rewards resulting

from a person’s job performance and accomplishments. Expectancy is described as the person’s

perception that effort (i.e., action, input) will lead to performance and subsequent outcome (i.e.,

FIGURE 1 Measurement model for full-time faculty member teaching online course.

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reward, outcome). Rewards may be intrinsic (generated within the individual) and=or extrinsic(generated by the organization); although some rewards that are offered by the institution that

relate to the primacy of teaching may also be considered intrinsic because they impact teaching

and learning (Cohen & Brawer, 2008). The employee’s view of various aspects of their job is

proportional to the perceived amount and equitability of rewards (Borkowski, 2005).

Human capital in the literature indicates that individuals invest in their human capital (e.g., edu-

cation, training, experience) because they expect future returns (i.e., rewards) to result from

improved performance and additional work contributions (Langelett, 2002; Lubinski, Benbow,

Webb, & Bleske-Rechek, 2006). Job satisfaction and subsequent performance is impacted accord-

ing to the match=mismatch between human capital investments and expected rewards (Allen &

van der Velden, 2001; Langelett, 2002). The measurement model for this research (see Figure 1)

is a modified version of equity=expectancy theory that attempts to capture the nature of community

college faculty work using secondary data from the National Study of Postsecondary Faculty

(NSOPF:04). In this model, the independent variables will be grouped into four areas: human capi-

tal investments, intrinsic rewards, extrinsic rewards, and demographic variables. Each of the four

general areas is comprised of specific variables to measure those constructs.

RESEARCH QUESTION

The following is the research question for this study: What is the ability of human capital

(highest degree, discipline, and years in profession); intrinsic rewards (institutional support

for teaching, and fair treatment of women and minorities); extrinsic rewards (satisfaction with

financial aspects of job, student faculty ratio, union membership); and demographic variables

(gender and race) to predict full-time community college faculty teaching online courses?

Logistic regression is the primary analysis used to answer the above question.

METHODOLOGY

Design and Sample

Using Johnson’s (2001) typology, the design used for this research is a nonexperimental, quan-

titative, cross-sectional predictive design utilizing secondary analysis of the NSOPF:04) dataset,

a nationally representative survey sponsored by the U.S. Department of Education’s National

Center for Education Statistics (NCES). NSOPF:04 was a comprehensive nationwide study of

the characteristics, workload, and career paths of postsecondary faculty and instructional staff.

The study was based on a nationally representative sample of all full- and part-time faculty

and instructional staff at public and private not-for-profit two- and four-year degree-granting

institutions in the United States. TheNSOPF:04 sample consisted of 26,110 responding faculty

and instructional staff selected from 980 sampled institutions (290 were public community

colleges) in the 50 states and the District of Columbia (Heuer et al., 2004).

For this study, the full NSOPF:04 sample was reduced to include only full-time community

college faculty who were employed at public two-year institutions whose principle activity was

teaching credit courses and who were not considered administrators. Application of the above

criteria yielded a sample of approximately 2,133 respondents used for analysis in this study.

FACULTY ENGAGEMENT IN ONLINE INSTRUCTION 187

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Some responses on the demographic information in this study did vary slightly from the above

number due to a very limited number of nonresponses to a few of the demographic questions.

Instrumentation

Specific questions have been selected from the NSOPF:04 survey to gather responses related to

human capital, work rewards (intrinsic and extrinsic), and demographics. Previous research has

been used to guide the selection of appropriate proxy variables to use from the NSOPF:04 data

for this study.

To establish construct validity of the Likert scale questions related to intrinsic and extrinsic

rewards, an exploratory factor analysis was conducted. Responses to 11 questions were exam-

ined via the factor analysis using squared multiple correlations as prior communality estimates.

The factors were initially extracted using the maximum likelihood method. Following extraction,

the factors were rotated using a promax oblique rotation (see Table 1). For interpretation of the

rotated factor pattern, items were determined to load for a factor if the factor loading was .40 or

greater for that factor and less than .40 on all other factors.

Using these criteria, three questions loaded on the first factor, labeled Institutional Support for

Teaching (support teach). See Table 1 for questions loading on this factor. Two items loaded for

the second factor labeled Fair Treatment of Vulnerable Populations (fairtrmt). Three items

loaded for the third factor labeled extrinsic organizational financial (ex org fin). The three

retained factors demonstrate simple structure, and each factor accounts for more than 10% of

the variance (69% factor 1, 18% factor 2, and 13% factor 3). Each of the three factors displayed

eigenvalues greater than 1.00, suggesting that they be retained.

The three items that failed to load on any factor: Q61A-Satisfaction with authority to make

decisions; Q82A-Opinion that teaching is rewarded; and, Q82B-Opinion that part-time faculty

are treated fairly; were dropped. The above factor analysis provides the evidence of validity

TABLE 1

Survey Items, Corresponding Factor Loadings and Final Communality Estimates (h2)

Factor 1 Factor 2 Factor 3Code Survey item a¼ .75 a¼ .77 a¼ .70 h2

Q61A Satisfaction with authority to make decisions 31 13 12 0.22

Q61B Satisfaction with technology-based activities 81 �2 �6 0.59

Q61C Satisfaction with equipment=facilities 70 �5 �3 0.43

Q61D Satisfaction with institutional support for teaching improvement 61 4 12 0.50

Q82A Opinion: teaching is rewarded 34 19 21 0.37

Q82B Opinion: part-time faculty treated fairly 30 29 3 0.28

Q82C Opinion: female faculty treated fairly 1 82 �1 0.67

Q82D Opinion: racial minorities treated fairly �1 77 �2 0.58

Q62B Satisfaction with salary �5 �2 83 0.63

Q62C Satisfaction with benefits 3 �3 67 0.45

Q62A Satisfaction with workload 26 6 40 0.38

Factor 1¼ Institutional support for teaching (support teach); Factor 2¼Fair treatment of vulnerable populations

(fairtrmt); Factor 3¼Extrinsic organizational financial (ex org fin).

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and reliability for the three additive (Likert) scales of support for teaching (Alpha ¼.75), fair

treatment of vulnerable groups (Alpha ¼.77) and extrinsic organizational financial considera-

tions (Alpha ¼.70).

Dependent Variable

The dependent variable selected for this study is a question that asked respondents if they

taught an online course (online; 1¼ yes, 0¼ no). This variable constitutes the binary (categ-

orical) dependent variable for the regression model. It is appropriate to use logistic regression

when the dependent variable is binary (or categorical) and there are a variety of independent

variables that may be continuous and=or categorical.

Human Capital

Proxy measures for the independent variables related to human capital from the NSOPF:04

questionnaire are highest degree (high deg; 0¼ bachelors or less and 1¼masters or higher),

years respondent has been teaching (yrs teach exper), and discipline (discplin; 0¼ vocational=occupational, 1¼ general education).

Intrinsic Rewards

There were two variables related to intrinsic rewards. Both were derived from the factor

analysis previously discussed (see Table 1). The first was Institutional Support for Teaching

(support teach) and the second was Fair Treatment of Vulnerable Populations (fairtrmt).

Extrinsic Rewards

There were three factors related to extrinsic organizational factors that could impact faculty work.

First, three Likert questions related to benefits, salary, and workload were combined from the

factor analysis (see Table 2) to measure extrinsic organizational finances (ex org fin). The second

extrinsic factor was student faculty ratio (st fac ratio) of the institution measured by a question

taken from the institutional data. The third factor was union membership (union member), which

was a question asking each respondent if there was a recognized union that bargained for faculty at

that institution (0¼ no and 1¼ yes).

Demographics

Two demographic variables were added to the model to control for any possible effects of

gender (0¼ female and 1¼male) and race (0¼minority and 1¼majority).

Analysis

Logistic regression was used to examine the predictive value of the independent variables on the depen-

dent variable (taught an online course, 1¼ no, 0¼ yes). It is appropriate to use logistic regression when

FACULTY ENGAGEMENT IN ONLINE INSTRUCTION 189

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the dependent variable is binary (or categorical) as in this research. Logistic regression will identify (a)

the significance of the model; (b) which independent variable(s) contribute to the dependent variable;

and (c) the odds ratio for each significant independent variable (Allison, 1999).

The 2004 National Study of Postsecondary Faculty (NSOPF:04) sampling design was a strati-

fied two-stage design. Because of this complex sampling design, statistical analyses should be

conducted using software packages that properly account for the employed survey design

through use of survey weights. Thus, SAS 9.2 was used with the SURVEYREG procedure to

address the issue using Taylor series expansion of the variance estimations.

RESULTS

Demographics

The sample of full-time community college faculty used for analysis consisted of 2,144 respon-

dents, of which women constituted 43% (921) of the sample while men were 57% (1,223).

TABLE 2

Description of Variables used in Study

Variable description (SAS variable name) How variable used in analysis

Dependent variable

Have you taught an online course in the fall

semester (online)

0¼ taught a course online

1¼ did not teach an online course

NOTE: (SAS modeled on 0¼ taught an online course)

Highest degree earned (highdeg) 0¼ bachelors degree or less

1¼Masters degree or higher

Years respondent has been a faculty member

at all institutions (yrs teach exper)

Number

Academic discipline: each respondent listed

their discipline (discplin)

Responses were categorized into the following discipline areas;

0¼ those in occupational=vocational areas

1¼ those in the liberal arts=general education

Institutional support for teaching

(support teach)

Cumulative score for survey questions

Q61B Satisfaction with technology-based activities

Q61C Satisfaction with equipment=facilities

C61D Satisfaction with institutional support teaching improvement

Fair treatment of vulnerable populations

(fairtrmt)

Cumulative score for survey questions

Q82C Opinion: female faculty treated fairly

Q82D Opinion: racial minorities treated fairly

Extrinsic organizational finances (ex org fin) Cumulative score for survey questions

Q62B Satisfaction with salary

Q62C Satisfaction with benefits

Q62A Satisfaction with workload

Student faculty ratio (st fac ratio) Number

Union membership (union member) 0¼ no

1¼ yes

Gender 0¼ female

1¼male

Race 0¼minority

1¼majority=Caucasian

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Minorities made up 30% (633) of the sample, and the remainder were White (1,511). Faculty

members affiliated with a labor union constituted 57% (1,223) of the sample and the remainder

(43%, or 921) were not members of a union.

The majority of the faculty (83%, or 1,776) possessed a masters degree or higher while 17%(368) held a baccalaureate degree or less. The majority of full-time faculty (1,714, or 80% of the

sample) taught face-to-face courses while only 430 or 20% of the full-time faculty members

reported teaching at least one class online. Approximately 994 (47%) respondents reported

teaching occupational=vocational courses while 1,139 (53%) reported teaching general edu-

cation classes (humanities, math, sciences, arts, literature, etc.). Table 3 provides information

on the demographic information for the full-time faculty members in the sample.

For the 364 full-time faculty members with a baccalaureate degree or less, 324 (89%) taught occu-

pational courses while 40 (11%) taught general education courses. Only 28 (9%) of the 324 faculty

members teaching occupational classes taught an online course while themajority, 296 (91%), taught

in a face-to-face traditional classroom. The remaining 40 faculty members with a baccalaureate

degree or less taught general education courses: two taught in the online learning environment while

38 faculty members provided instruction in a traditional, face-to-face classroom.

Table 4 provides the means and standard deviations for the five variables measured on the

interval scale. The faculty in this sample had a wide range of years in the classroom

(Yrs Teach exper) with some faculty members participating in their first year of teaching and

others having spent up to 48 years teaching. The average teaching experience among the survey

respondents was 15 years.

Faculty members perceived a range of institutional support for teaching in the online learning

environment (support teach) with a low of 3, a high of 12, and a mean of 9.35. The faculty

TABLE 3

Demographic Information for Full-time Community College Faculty Members

in the Sample

Frequency Percent

Gender

. Female 921 42.96

. Male 1223 57.04

Race

. Minority 633 29.52

. Caucasian 1511 70.48

Union membership

. No 919 42.86

. Yes 1225 57.14

Highest degree

. Baccalaureate or less 368 17.16

. Master or doctoral 1776 82.84

Teaching discipline

. Occupational ed. 994 46.60

. General education 1139 53.40

Teaches online

. Yes 430 20.06

. No 1714 79.94

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members reported a strong perception that their institution treated women and minority popula-

tions fairly (fairtrmt), which ranged from 2 to 8, with a mean of 7. Faculty members’ satisfaction

with their salary, benefits, and workload, measured in the construct, extrinsic organizational

finance (ex org fin), ranged from 3 to 12, with a mean of 9.17. Instructors faced a wide range

in classroom size with the student-to-faculty ratios (st fac ratio) ranging from 4 to 86 students in

a class. Most classes were of a manageable size with an average of 18 students. For online

instruction, class averages are usually less than traditional face-to-face classes. Mupinga and

Maughan (2008) reported the average online class size to be about 25, although there was con-

siderable variance.

Logistic Regression

The results of the logistic regression model for factors that predict full-time community college

faculty teaching an online course are found in Table 5. The model was significant (Pr< .0001,

chi square¼ 2233, df¼ 10). The logistic regression analysis revealed that 3 of the 10 predictor

variables had significant effects on the odds of faculty teaching online courses. Faculty members

with a master’s degree or higher were more likely to teach online than full-time faculty with a

bachelor’s degree or less (highdeg, Pr< .0001, odds ratio¼ 2.96). Thus, faculty with a master’s

TABLE 4

Means, Standard Deviations, Minimum and Maximum Values for Variables Measured on Interval Scale

Variable Number Mean Standard deviation Minimum value Maximum value

Yrs. teach exper 2144 15.19 10.58 0 48

Support teach 2144 9.36 2.14 3 12

Fairtrmt 2144 7.03 1.26 2 8

Ex-org-fin 2144 9.17 2.05 3 12

Student-fac-ratio 2144 17.80 8.76 4 86

TABLE 5

Logistic Regression Result for Factors that Predict Online Teaching for Full-time Faculty

Parameter df Estimate Error Chisquare Pr>ChiSq Odds ratio

Highdeg 0 1 1.0859 0.229 122.4627 <0.0001� 2.96

Discplin 0 1 �0.2926 0.1476 3.9272 0.0475� 0.75

Yearsteach 1 0 0.013 0.0058 0.0501 0.8230 1.00

Supprt teach 1 0.0722 0.0332 4.7385 0.0295� 1.08

Fairtrmt 1 �0.0876 0.0463 3.5748 0.0687 0.92

Ex org fin 1 �0.0569 0.0352 1.7681 0.1836 0.95

St fac ratio 1 �0.0128 0.0124 1.0595 0.3033 0.99

Union member 0 1 �0.1609 0.1332 1.4591 0.2271 0.85

Gender 0 1 �0.0499 0.1228 0.1652 0.6844 0.95

Race 01 0.1795 0.1334 1.8113 0.1784 1.20

�p< .05.

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degree or higher are almost three times more likely to teach online courses than their counter-

parts with a bachelor’s degree or less.

Additionally, faculty teaching general education courses were 25% less likely to teach online

courses than those in the occupational or vocational areas (discplin, Pr¼ .04, odds ratio ¼.746).

Faculty who were satisfied that the institution supported teaching via equipment, technology,

and infrastructure were 8% more likely to teach online courses than those who felt there was

less instructional support (Instrinsic suppt teac, Pr¼ .03, odds ratio¼ 1.075).

CONCLUSIONS

It should be noted that of the entire sample (N¼ 2,144) only 20% (430) actually taught an online

course while 80% did not. Thus, the majority of full-time community college faculty in this sam-

ple did not teach any online courses. This figure is increasingly changing as community colleges

are turning to distance education. The variable that appears to have the greatest effect on teach-

ing online were those faculty with a masters degree or higher. Faculty with a master’s degree or

higher (the vast majority of this group were faculty that held a master’s degree) were approxi-

mately three times more likely to teach online than those with a bachelor’s degree or less. In

breaking down the degree demographic, only 8% of faculty with a bachelor’s degree or less

taught an online course.

There are several things to note here. First, a large percentage of faculty do not teach online

(only 20% of the sample do). Second, there is a very low percentage of online teaching by

faculty with a bachelor’s degree or less. This may be a function of a number of contextual factors

that may be related to technology use and skills, faculty development, skills in online course

design, technical support, and institutional and organizational policies that promote or inhibit

the delivery of online instruction. Although one may expect that full-time faculty who have a

master’s degree may be more aware of, or possibly have more online teaching pedagogical skills

that enable them to utilize educational technology to be involved in online courses, this skill may

be a function of disciplinary credentials in certain practice-based fields.

The theme of promoting an institutional infrastructure supporting online instruction and

providing faculty with the time, training, skills, and technology to teach online line courses is

not new. Tabata and Jonsrud (2008) examined faculty attitudes toward distance education. They

found that factors related to the skill of faculty in using technology and their beliefs toward both

technology and distance education were some of the things that influenced their behaviors about

participation in distance education.

Another study finding supporting the above is that faculty who felt that the institution

supported teaching were 8% more likely to teach online than those who felt that the institution

did not support teaching. The previous variable was made up of the summative score of faculty

responses to questions related to satisfaction with technology based activities, satisfaction with

equipment and facilities, and institutional support of teaching improvement. This variable was

factor analyzed to provide evidence of validity and reliability. Our findings provide some

evidence to the argument that institutions attempting to provide a climate for supporting instruc-

tional activities may be better at the promotion of their online course offerings.

Mars and Ginter (2007) in their study to connect organizational environments with instruc-

tional technology practices of community college faculty found that organizational environments

FACULTY ENGAGEMENT IN ONLINE INSTRUCTION 193

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were highly influential in how—and to what degree—faculty integrated technology into their

instructional practices. Faculty was more likely to embrace and utilize instruction technology at

colleges purposefully centering technology within the core of the institutional mission. For institu-

tions seeking to promote the use of technology in instruction, they must provide the appropriate

training and technology while creating a culture that rewards and recognizes faculty use of it.

Certainly, the first step is to ensure that all faculty members have Internet access at work. A study

by Jackowski and Akroyd (2010), using a national sample of community college faculty, found that

only 41% of part-time faculty had access to the Internet at work. Given the wide use of part-time

faculty by community colleges, it is imperative community college administration ensures all faculty

have access to the Internet because this is often the first criteria in offering online instruction.

The Instructional Technology Council (ITC) is one of the American Association of Com-

munity College councils, and it addresses issues of online and distance learning and supports

the professional development of faculty engaged in distance education. Even with that increase

in centralized support, too much of the national discussion is centered upon access, cost, and

infrastructure rather than adequately including pedagogical issues and faculty teaching support

(Garza Mitchell, 2010). Further, research by Smith (2010) indicates that faculty members,

including those at community colleges, are accustomed to functioning as autonomous profes-

sionals. The tendency to automate or look for high-yield efficiency practices in online instruction

due to financial constraints, therefore, leads to faculty members unbundling work in ways that

move them away from their focus on content and pedagogy. This, in turn, leads to a loss of pro-

fessional identity and functioning in more mechanized, uniform ways that are not pedagogically

sound nor desirable in the long run.

There are substantial increases in community college online education. Therefore, the

findings that faculty who receive or perceive institutional support for their teaching efforts

and those with higher credentials are more likely to teach online, offer a preliminary glimpse

of the factors that influence the choice to teach or being satisfied with teaching online. Further

research is necessary to better understand how community college faculty members perceive and

make decisions about whether and how to approach teaching credit courses online.

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