Do Entry Characteristics of Online Learners
Affect Their Satisfaction?
ERMAN YUKSELTURK Middle East Technical University, Turkey
This study analyzed learner characteristics that affect satis-faction in an online certificate program under two main pur-poses. The first purpose was to examine relationships among selected variables (age, gender, educational level, and online course experience), learners’ initial perceptions (online tech-nology self-efficacy, online learning readiness, locus of con-trol, and prior knowledge about program courses) and learn-er satisfaction. The second purpose was to examine program instructors’ views about the factors that contribute to the learners’ satisfaction in the online certificate program. Sam-ple of the study consisted of four program instructors and 103 voluntary participants who attended this online certificate program in 2005-2007. Both quantitative and qualitative methods were used to collect relevant data in this study. Six online questionnaires were used to gather quantitative vari-ables and semistructured interviews were conducted to gath-er instructors' views. The statistical results indicated that three learner characteristics (educational level, online learning readiness, locus of control) showed a significant relationship with learner satisfaction. Also, the qualitative findings from interviews with instructors generally revealed results comple-mentary to these statistical results.
INTRODUCTION
Distance education has been a rapidly growing part of education and training. Recently, there has been a significant movement towards offering online or blended courses around world (Gunawardena & McIsaac, 2004). As distance education continues to expand, higher education institutions have tried to find ways to evaluate the quality of distance education to improve teaching and learning in courses and programs. One of the elements
of evaluating the quality of learning at a distance is to assess learners’ satis-faction (Meyer, 2002; Sener & Humbert, 2003).
Learner satisfaction is one of the major components in ensuring learning outcome and determining the effectiveness of learning. An examination of learner satisfaction in courses or programs can lead to significant success for learners and program improvements. The previous studies emphasized sev-eral other reasons why analyzing learner satisfaction is important for educa-tors and developers who deal with online courses and programs in the liter-ature (e.g., Biner, Dean, & Mellinger, 1994; Feasley & Olgren, 1998; Kelsey, Lindner, & Dooley, 2002; Schwitzer, Ancis, & Brown, 2001). According to these previous studies, the benefits identified on learner satis-faction could be grouped according to three main items:
• Low student attrition: Students who are satisfied with their classes and programs tend to have lower attrition rates, show high levels of moti-vation, and be committed to their educational goals.
• High commitment to a distance education course: Satisfied students are more likely to enroll in another online course. In other words, high learner satisfaction from previously enrolled students helps increase enrollments.
• Large referrals from enrolled students: Students who are satisfied are more likely to recommend distance courses to family and fellows. Several researchers focused on the importance of satisfaction and exam-ined variables that can predict learner satisfaction in the recent studies. Satis-faction can simply be defined as evaluating learners’ reactions or attitudes towards a program (Feasley & Olgren, 1998). Actually, satisfaction is not just a simple concept, and it depends on a number of factors (Sener & Humbert, 2003). Some of the major factors that might affect learner satisfaction in the literature are motivation, interaction with instructors and other students, sup-port services, course materials, and also learner characteristics (Biner, Dean, & Mellinger, 1994; Swan, 2001; Yukselturk & Bulut, 2007). Learners are the main participants of the learning process, and online learning placed more responsibilities on learners than traditional face-to-face learning (Moore & Kearsley, 2005). Therefore, this study examined learners’ characteristics to see their effects on satisfaction in an online environment.
CHARACTERISTICS OF ONLINE LEARNERS AFFECTING SATISFACTION As online education evolves, educators find the need to understand online learners more in online learning environments. Demographic data available from several studies about online learners showed that the majority are female, married, employed full or part time, and older than the typically tra-ditional students (Thompson, 1998). Therefore, learners who participate in
distance education are different from traditional students. Many of these stu-dents have other responsibilities outside of school (e.g., family, job) that place constraints on their time and their commitment to school (Moore & Kearsley, 2005). The growth of distance education has brought about ques-tions concerning these new characteristics and needs of students, especially successful and satisfied ones. In order to develop high quality distance cation programs, it is important for designers and educators of distance edu-cation courses to understand the characteristics of distance learners and what affects their success and satisfaction (Yukselturk & Bulut, 2007). Some of the essential characteristics that might affect learner satisfaction as an online learner (i.e., gender, age, educational level, online course experience, online technologies self-efficacy, online learning readiness, locus of control, and prior knowledge about program courses) have been investigated in the liter-ature. In this study, the combination of all these learner characteristics was examined to see their effects on learner satisfaction in an online environment. Age and gender are two learner characteristics that have often been the focus of research in distance education. When reviewing studies related to age and gender, the effects of these variables are inconclusive on student sat-isfaction and learning outcomes in distance education. For example, some studies reported that gender differences might have an impact on experience in an online environment, (Taplin & Jegede, 2001; Wang, Kanfer, Hinn, & Arvan, 2001), while others did not (Dille & Mezack, 1991; Lim, 2001; Yuk-selturk & Bulut, 2007). In addition, studies have indicated the distance edu-cation student as being over 27 years of age (Dutton, Dutton, & Perry, 2002) and these older learners are seen as more mature and they tend to value their time, effort, and money (Fender, 1999). On the other hand, some previous studies indicated that the age of the learners was unrelated to satisfaction and learning outcomes in distance courses (Biner, et. al, 1996; Lim, 2001; Yuk-selturk & Bulut, 2007). According to these studies, there are conflicting findings in the literature in regard to relations between age; gender; and dependent variables, such as, success, satisfaction, in the literature.
The effects of learner’s prior knowledge about course content and learn-er explearn-eriences with online courses are also unclearn-ertain in the prediction of learner satisfaction and learning outcomes in distance education environ-ments. Lim (2001) conducted a study to develop a predictive model of learn-er satisfaction in an online course. Numlearn-erous variables wlearn-ere examined, including the learners’ previous experience. Results showed that previous experience was not a significant predictor of student satisfaction. On the other hand, some previous studies also showed that a learners’ prior experi-ence with an online course is a strong predictor of learner satisfaction with the delivery medium (Arbaugh, 2000).
Educational level is seen as another student characteristic in research studies. Watson (2005) stated that the educational backgrounds of online
learners could provide early warning indicators for failure or success in online education courses. To the contrary, educational level has not been shown significantly to predict learning outcomes in a distance education in the literature (Yukselturk & Bulut, 2007). For example, Miller and Pilcher (2000) expressed that there is no significant difference in learner success with regard to academic standing between online students and traditional face-to-face students.
The researchers agreed that locus of control is another variable that might affect student satisfaction. Locus of control is a theoretical construct ground-ed in social learning theory and refers to the belief concerning one’s control over life events. Two types of loci have been defined: internal and external. An internal belief maintains that through ability or effort one has influence over outcomes or success. An external belief maintains that forces outside the control of the individual determine outcomes (Rotter, 1966). Research gener-ally showed that students with an internal locus of control were more likely to be successful than students with an external locus of control in the distance education environment (Parker, 1999; Wang & Newlin, 2000). Also, locus of control is one of the major concepts that provide the foundation for a student seeking to become a self-directed learner. Researchers agreed that being a self-directed learner is a critical success factor in online education (Cennamo & Ross, 2000; Whipp & Chiarelli, 2004; Zimmerman, 2000).
Self-efficacy is also confirmed to be a good predictor of future perfor-mance. It is simply defined as students’ confidence in their abilities to com-plete tasks or reach goals (Bandura, 1986). Many different measures of self-efficacy exist; these include academic self-self-efficacy, math self-self-efficacy, and computer self-efficacy. In distance education, computer-mediated communi-cation technologies, such as e-mail, bulletin boards, newsgroups, and video conferencing, have become the primary modes of interaction, communica-tion, and exchange of information between students and instructors. There-fore, several studies have been conducted on the self-efficacy of students toward distance education or the technology used in online learning. Wang and Newlin (2002) found that self-efficacy beliefs for understanding course content and possessing the technological skills required for successful com-pletion of an online course were statistically significant. Lim (2001) also indicated that self efficacy in computer knowledge was the only statistically significant variable that can help predict achievement.
Researchers indicated that learner success and satisfaction might be relat-ed to the readiness of the participant to engage in online studies (Eastmond, 1994; Gunawardena & Duphorne, 2001). Eastmond (1994) stated that three major factors sequentially influence the student’s learning satisfaction with-in the context of the onlwith-ine experience. The first is a readwith-iness factor, and he mentioned several personal and environmental factors as readiness factors in an online learning. Similarly, Gunawardena and Duphorne (2001) stated that
learners who felt more positively about their readiness to participate in an academic computer conference were more satisfied with the conference.
In summary, numerous studies in the literature have analyzed factors con-tributing to the learners’ satisfaction for designing effective online programs. Likely, this study analyzed the learner characteristics that affect satisfaction in an online certificate program. If there is a relationship between learner char-acteristics and learner satisfaction with online courses or programs, then edu-cators and administrators in higher education can be prepared to address these issues to assure learner success and learner satisfaction. Also, identifying to what extent selected learner characteristics effect satisfaction might help determine new methods and new techniques in online courses and programs.
RESEARCH QUESTIONS
The following two major research questions guided this study:
• What is the extent to which learners’ demographics (age, gender, edu-cational level, online course experience) and learners’ initial percep-tions (online technologies self-efficacy, online learning readiness, locus of control, and prior knowledge about program courses) could account for learner satisfaction in the online certificate program?
• What are the instructors’ views about the characteristics of learners that affect satisfaction in the online certificate program?
In order to examine the first research question, the following hypothesis was formed.
• The eight variables together (age, gender, educational level, online course experience, initial perceptions of online technologies, self-effi-cacy, online learning readiness, locus of control, and prior knowledge about program courses) do not explain a significant amount of variance in learners’ satisfaction in the online certificate program.
RESEARCH DESIGN
A combination of quantitative and qualitative research methods was used in this study. Correlational research methods based on Pearson Product-Moment correlation and Linear Stepwise Regression analysis were utilized for the first research question. According to Gall et al. (2003), correlational research designs refer to studies in which the purpose is to discover rela-tionships between variables by using correlational statistics.
For the second research question, semistructured interviews with instruc-tors were conducted to obtain additional information regarding learner sat-isfaction in the online program. An interview is a purposeful conversation, usually between two people, but sometimes involving more, that is directed
by one in order to get information from the other (Mason, 1998). The inter-view schedules were developed around central themes related to learner characteristics affecting satisfaction in the study.
Consequently, the mixed methods approach used in this study is helpful to capture the best of both quantitative and qualitative approaches. Johnson and Onwuegbuzie (2004) stated that “the goal of mixed methods research is not to replace either of these (quantitative and qualitative) approaches but rather to draw from the strengths and minimize the weaknesses of both in single research studies and across studies” (p. 14–15).
DESCRIPTION OF ONLINE CERTIFICATE PROGRAM
The Online Information Technologies Certificate Program (ITCP) is one of the first Internet-based education projects of Middle East Technical Uni-versity (METU) in Ankara, Turkey. The program includes eight fundamen-tal courses in the Computer Engineering Department. It is comprised of four semesters. Each semester lasts two months, and the program lasts a total of nine months. The courses in the program are taught by the instructors from the Computer Engineering Department. The main aim of the online ITCP is to train the participants in the IT field to meet the demands in the field of computer technologies in Turkey. Furthermore, the online ITCP provides opportunities for people who could not get education in information tech-nologies or computer engineering, but are interested and willing to improve themselves in this area and enthusiastic about making progress in their exist-ing career. The participants include current university students and graduates from two- or four-year university programs. Requirements for enrollment in the program include being computer literate and having at least an interme-diate level of English (Isler, 1998).
Online lecture notes, learning activities, and visual aids are provided for each course in the program. Also a textbook is required for each course. In order to provide interaction between instructors and participants and among participants, courses have an e-mail address, discussion list, and chat ses-sions. In each course, at least three or four assignments are given to the par-ticipants during the semester. At the end of each semester, traditional face-to-face final examinations are given on campus. The participant’s final grade for each course is based on the final examinations, assignments, and atten-dance at chat sessions and in discussion lists. At the end of the program, graduates receive an official certificate approved by the university. The courses given in this program are as follows:
First Semester (lasting two months) • Computer Systems and Structures
Second Semester (lasting two months)
• Data Structure and Algorithms with C Operating Systems with Unix Third Semester (lasting two months)
• Software Engineering
• Database Management Systems Fourth Semester (lasting two months)
• Web Programming
• Software Development Project
SUBJECT OF THE STUDY
The study included 103 ITCP participants and four ITCP instructors at Middle East Technical University (METU) in Ankara, Turkey (October 2005 – June 2007). Originally, 176 students were registered in the program; however, this study included students who volunteered to participate in the study. Table 1 presents the demographic characteristics of the participants. The number of male participants (N= 75) was greater than the number of female participants (N=28), and the participants’ age ranged from 20 to 40 and older. The majority of the participants’ ages were between 20 and 29 (N=80). The number of university graduates was slightly more than the number of undergraduate students.
Eight individual instructors taught the eight courses in this online certifi-cate program. Four of them were randomly selected from each semester and interviewed individually about the factors that affect or contribute to learn-ers’ satisfaction in this study. The instructors were faculty members at the Department of Computer Engineering at the university. All had experiences teaching their online course in this program for over six years.
INSTRUMENTATION
In this study, six online questionnaires and semistructured interviews were used to collect relevant data. The following questionnaires helped collect quantitative data: Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, Prior Knowledge Questionnaire, and Student Satisfaction Questionnaire.
Demographic Survey was used to gather learners’ demographic informa-tion (i.e. age, gender, educainforma-tion level). Online Technologies Self-Efficacy Scale was used to measure learners’ self-efficacy beliefs specific to the online environment. This scale was originally developed by Miltiadou and Yu (2000). It is a 29-item Likert scale with five subscales. The scale was translated into Turkish and standardized on a Turkish sample (n=258) by the researcher before using it. The reliability of the questionnaire was satisfac-tory, with a Cronbach alpha of 0.97.
Readiness for Online Learning Questionnaire was used to assess learners’ readiness for online learning. This questionnaire was originally developed by McVay (2000). The questionnaire is comprised of 13 items, rated by respondents on a Likert scale. The scale was also translated into Turkish and standardized on a Turkish sample (n=258) by the researcher before being used. The Cronbach alpha coefficient was found as 0.76.
The Internal-External Locus of Control Scale was used to measure learn-ers’ locus of control orientation. This scale was originally developed by Rot-ter (1966). It is a 29-item, forced-choice self-report scale with scoring range 0 (internality) to 29 (externality) excluding six buffer items. The scale was translated into Turkish and standardized on a Turkish sample (n=532) by Dag (1991). He found the Cronbach alpha coefficient to be 0.71.
The Prior Knowledge Questionnaire was used to assess learners’ prior knowledge about online program courses. This questionnaire consisted of eight items that were prepared by the researcher with help of expert and pro-gram instructors. Each item was related to each aim of the courses that are given in the program. For each item, participants were asked to indicate their level of knowledge related to program courses. Program course instructors approved these items about their course aims.
A Student Satisfaction Questionnaire was used to collect data on learners’ satisfaction about the program. The questionnaire was developed by Parlak (2004). It is based on four questionnaires in the literature (Instructor and Course Evaluation System, ICES, [University of New Mexico, 2001]; Distance and Open Learning Environment Scale, DOLES, [Jegede, Fraser, and Curtin, 1995]; Class Interaction, Structure and Support, CISS, [Johnson, Aragon, Shaik, and Rivas, 2000]; and Web-Based Learning Environment Inventory, WEBLEI, [Chang, 1996]. The questionnaire has five main subscales:
Table 1
The characteristics of the participants
N P
Gender Female 28 27.2
Male 75 72.8
Age 29 and younger 80 77.7
30-39 16 15.5
40 and older 7 6.8
Education Levels University graduates 56 54.4
Undergraduate students 47 45.6
Previous Online Yes 12 11,7
Course No 91 88.3
1. Learner-learner interaction (3 questions, the Cronbach alpha value is 0.80);
2. Learner-instructor interaction (12 questions, the Cronbach alpha value is 0.93);
3. Course structure (12 questions, the Cronbach alpha value is 0.92); 4. Institutional support (12 questions, the Cronbach alpha value is
0.88); and
5. Flexibility (3 questions, the Cronbach alpha value is 0.59). It consists of 38 five-point Likert-type items, and the overall Cronbach alpha value of the questionnaire is 0.95 (Parlak, 2004).
In addition to these questionnaires, semistructured interviews were con-ducted with program instructors to obtain additional information regarding learner satisfaction in this online program. Interview questions were devel-oped around the six major questions related to the questionnaires used in this study. Some of the major interview questions were as follows:
• What are the learner characteristics in the online program?
• What are the online satisfied learner characteristics in the online program? • What types of learner characteristics that affect satisfaction in the online
program?
• What can we do for increasing number of satisfied learners in the online program?
DATA COLLECTION AND ANALYSIS
The subject was the online certificate program students and instructors in this study. At the beginning of this online program, the face-to-face orienta-tion was organized to explain the program and courses to the participants, help them meet each other and with the instructors, explain how to use the web pages, and also mention about the aim of this study. Students were informed that their participation was voluntary and their responses to the questionnaire were confidential. All students were requested and encouraged to complete the online questionnaires. They were given one week after fin-ishing the orientation program to complete the questionnaires (Demograph-ic Survey, Online Technologies Self-Eff(Demograph-icacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The last online questionnaire (Student Satisfaction Ques-tionnaire) was given to the participants at the end of the program. All responses were automatically recorded in databases digitally.
With the help of these instruments, eight independent variables (three cat-egorical: gender, educational level, online learning experience; and five con-tinuous: age, students’ initial perceptions of online technologies
self-effica-cy, online learning readiness, locus of control, and prior knowledge about program courses) were gathered. The dependent variable, learners’ satisfac-tion, was again obtained by an online questionnaire. Descriptive statistics, such as mean and standard deviations of subjects, were calculated for the scale scores. Correlation and regression tests were used to analyze these quantitative data.
This study employed interviews as the second data collection method to find answers to the second research question. Semistructured interviews were conducted with four program instructors who talked about the subject under investigation at the end of program. A semistructured interview is more flexible and can help interviewees to speak more widely and develop their own thoughts to reach more depth (Bogdan & Biklen, 1998; Mason, 1996). Before each interview took place, the instructors were informed of the purpose of the interview. Each interview took about 20–30 minutes and was tape-recorded with the permission of the instructors. The transcripts of the interviews were analyzed according to the qualitative data analysis process. According to Bogdan and Biklen (1998), the qualitative data analy-sis “involves working with data, organizing them, breaking them into man-ageable units, synthesizing them, searching for patterns, discovering what is important and what is to be learned, and deciding what you will tell others” (p. 157). This process was continuous and iterative throughout the data col-lection and the report writing in this study.
RESULTS Descriptive Statistics
Table 2 shows descriptive statistics (range, min, max, mean, standard deviation) of the learners’ initial perceptions about selected variables, such as online technologies self-efficacy, online learning readiness, locus of control, and prior knowledge about program courses. According to the results in Table 2, learners had high perception about online technologies self-efficacy (mean
Table 2 Descriptive statistics
Predictors N Range Min Max Mean Std.
online technologies self-efficacy 102 79 37 116 103.5 15.6
online learning readiness 102 25 27 52 43.3 5.2
locus of control 103 17 0 17 8.3 3.9
= 103.5, out of 116). Most learners thought that they were ready for online learning (mean = 43.3, out of 52). Furthermore, learner perception of locus of control was generally low (mean =8.3, out of 23), which suggests that partic-ipants perceived the locus of control to be internal. Moreover, most learners stated that they did not have much knowledge about program courses at the beginning of the online certificate program (mean =13.5, out of 32). Results of Testing Hypothesis
The problem of this study was examined by means of its associated hypothesis. The hypothesis was in the null form and tested at a significance level of 0.05. The interrelationships among variables before testing the hypotheses were examined due to the concern about the issue multi-collinearity. Therefore, Pearson Product-Moment correlations were conduct-ed to examine the interrelationships among measures. The correlation matrixes were presented in Table 3. Table 3 shows that predictor variables did not have high correlations among themselves; therefore, we deduced that multicollinearity was not a problem in this study.
As Table 4 indicates, three variables viewed together (educational level, online learning readiness, locus of control) explained the significant amount of variance in learners’ satisfaction p<0.05. 16.8% of the variances are explained by these three variables together; R= 0.41, adjusted R2=0.168, F (3, 102) = 6.67, p=0.000. In other words, only three variables (educational level, online learning readiness, and locus of control) from eight learner characteristics analyzed in this study showed a significant relationship with learner satisfaction.
Table 3
Pearson product moment correlations among measures for all subjects of the study
Variables 2 3 4 5 6 7 8 9 1. Gender -0.05 0.16 0.22 -0.01 0.12 -0.08 -0.04 0.09 2. Age 0.53 -0.06 -0.18 -0.12 -0.13 0.11 0.17 3. EduLevel 0.05 -0.03 0.016 -0.11 -0.02 0.27 4. OnlineExp 0.15 0.02 0.02 0.12 -0.45 5. Self-Efficacy 0.47 -0.02 0.32 0.10 6. Readiness -0.04 0.09 0.23 7. LocusCont -0.18 -0.24 8. PriorKnow 0.04 9. Satisfaction
-As can be seen from Table 5, educational level was the strongest predic-tor of learners’ satisfaction, accounting for 7.5% of the variance, R2change= 0.075, F(1, 101) = 8.229 p=0.005. Online learning readiness accounted for an additional 5.1%, R2 change= 0.051, F(1, 100) = 5.815 p=0.018. Also locus of control accounted for an additional 4.2%, R2change= 0.042, F(1, 99) = 5.001 p=0.028. Age, gender, online course experience, online tech-nologies self-efficacy, and prior knowledge about program courses were excluded from the equation of predicting satisfaction because they did not have a significant contribution to variance in satisfaction (p>0.05).
THE INSTRUCTORS’ VIEWS ABOUT CHARACTERISTICS OF ONLINE LEARNERS AFFECTING SATISFACTION
Interviews with four course instructors showed that distance education with Internet technologies provided several benefits to both instructors and learners. Internet-based education provided an anywhere and anytime learn-ing environment allowlearn-ing instructors to prepare a course asynchronously, synchronously, or as a combination of the two. Also, this type of education provided easier and more convenient access for the learners who were unable to attend face-to-face classes.
Table 4
Linear Stepwise Regression Analysis results Regression Statistics Multiple R 0.410 R Square 0.168 Adjusted R Square 0.143 Standard Error 23.88 df SS MS F Sig F Regression 3 11412.1 3804.03 6.67 0.000* Residual 99 56444.79 570.15 Total 102 67856.89 Table 5
Linear Stepwise Regression Analysis results of predictor variables
Step Variable R2 Std. Error of Estimate
1. (EduLevel) 0.075 24.92
2. (EduLevel, Readiness) 0.126 24.35
In this online education, the instructors expressed that students were dif-ferent from traditional learners in several ways. For example, traditional face-to-face learners had generally similar characteristics regarding age level, education level, and aim and expectation in their class. Their primary priority was to be a student, which means that their primary goal was to attend class, study the course notes, and pass the exams. They were full-time students. On the other hand, online students’ ages were generally over twen-ty, and they had various responsibilities (e.g., works, children, and families) in their lives. They had to cope with all responsibilities simultaneously. According to the instructors, these different characteristics of learners might affect their satisfaction in online education.
All instructors in the interviews emphasized changes in learner responsi-bilities in online education. They agreed that online education placed more responsibilities on learners than traditional education. Thus, online learners should be active learner in their learning process, and they should know how to study by themselves. In other words, the instructors mentioned some nec-essary skills that online learners should have in online education. They should be aware of their responsibilities, and they should be self-disciplined in the fulfillment of their responsibilities. To have these skills, instructors expressed that online learners should be older or be mature enough to han-dle the responsibilities. Interview results showed that online learners should be at least undergraduate students in the universities or university graduates. The instructors stated that university graduate learners were especially more motivated and eager since they noticed that graduate learners had to contin-ue their education to adapt to new environments, methods, or tasks while working in their company. Accordingly, they were satisfied with online courses and programs since they provided them several opportunities that these types of learners needed.
Instructors mentioned that online students’ had misconceptions about online courses. According to the instructors’ views, some learners thought that online education courses or programs might be easier than traditional ones. Furthermore, instructors felt that learners should study more and per-form the course requirements regularly in online education. In addition, instructors stated that this online certificate program consisted of eight com-puter engineering courses given in nine months and these courses were not easily comprehensible; therefore, learners needed to be hard-working to be successful. In conjunction with these statements, instructors also thought that online learner satisfaction and success might be affected by the previous edu-cation, experiences, and interests about course contents in the online courses. Interview results also showed that there were some differences in aims and motivations of online learners while they attended the online programs. According to the instructors, online learners attended this program to learn information technologies or to renew their knowledge about information
technologies. Also, online learners wanted to be more productive in their present jobs or to make progress in their existing careers by taking courses in this program necessary to advanced IT technology integrated in all sys-tems. As a result, the instructors expressed that online learners’ aims and motivation were among the most important factors that affected their satis-faction and success in online education. Highly motivated learners general-ly would be successful and satisfied in the online program. Furthermore, instructors added that these learners would maintain their motivation during the entire program since online learners, especially unsuccessful ones, could lose their motivation easily due to several disadvantages of online learning, such as lack of face-to-face interaction. Thus, those students who were sat-isfied and successful were highly motivated. Furthermore, they maintained their motivation during entire courses and programs.
DISCUSSION AND CONCLUSION
Preparing online educational activities to meet the need of online learn-ers has made it necessary to obtain a much more detailed undlearn-erstanding of learners. In the literature, research shows that online learners who participate in distance education courses are different from traditional face-to-face stu-dents (Moore & Kearsley, 2005; Thompson, 1998). Furthermore, the initial perceptions of online students are different from what they actually experi-ence, and apparently, students have misconceptions before attending online courses and programs (Howland & Moore, 2002). Therefore, this study examined perceptions and characteristics of learners to see their effects on satisfaction in an online certificate program. From eight individual charac-teristics of learners examined, only three of them (educational level, online learning readiness, locus of control) showed a significant relationship with satisfaction. Also, qualitative findings from interviews with instructors gen-erally revealed complementary results to these statistical results.
The results of this study showed that some learner characteristics might affect satisfaction in an online environment even though the findings of studies examining the relation of learner qualities to learner satisfaction are mixed in the literature. For example, the regression analysis of this study showed that education level was one of the predictive characteristics that entered the regression equation and accounted for about 7.5% of the vari-ance in learner satisfaction. Moreover, the interview results emphasized on academic maturity and awareness that are essential in online learning, and so the instructors thought that online learning was more appropriate for graduate participants. Understandably, Mourtos and McMullin (2001) stated that undergraduate student were not mature enough for the demands of online education, which required more independent learning and discipline to keep up with the material being taught. Consequently, it might be
neces-sary to tailor online courses more toward the university graduates.
In addition to educational level, online learning readiness was another predictive variable that related to learner satisfaction (accounted for about 5.1% of the variance in learner satisfaction) in this study. For learner satis-faction, participant readiness is an important prerequisite, just as previous studies have found (Gunawardena & Duphorne, 2001; Kuchinke, Aragon, & Bartlett, 2001). For instance, Kuchinke, Aragon, and Bartlett (2001) sug-gested that participant satisfaction in online courses partially depends upon various readiness factors, including self-directed learning and technical pre-paredness for the online environment. Likewise, interview results showed that online students should be aware of their responsibilities, and they should have some necessary skills while fulfilling their responsibilities. As a result, the implication of this relationship was that learners should be intro-duced to the requisite skills and experiences prior to participating in the online environment.
Online learners cannot be successful or satisfied without the ability to take more responsibility and become more active in their own learning. This ability could be categorized by the term “locus of control.” In the same way, the statistical results showed that an additional 4.2% of the variance in learn-er satisfaction was explained by intlearn-ernal locus of control in this study. This finding was similar to some previous research results (e.g., Parker, 1999; Wang & Newlin, 2000). Furthermore, the interview results proved these findings by demonstrating changes in learners’ responsibilities in online courses. The instructors stated that online learners should be active learners and control their learning process. They should study more and perform the course requirements regularly during online programs. Accordingly, some students, especially with an external locus of control, needed help to think of their responsibility and specific guidance to be successful or be satisfied during online programs.
In addition, instructors agreed in the interview results that motivation plays an important role in student learning. In other words, student motiva-tion is considered to be a determining factor in academic performance and can impact the level of student satisfaction. Similarly, research showed that motivation has a great importance in student success, satisfaction, and con-tinuity in the literature (Fredericksen, Pickett & Shea, 2002; Song, 2000).
Furthermore, several researchers believed that students should monitor their learning motivation, control emotions, and use motivational strategies for active involvement in learning (Bandura, 1986; Pintrich & Schunk, 1996; Whipp & Chiarelli, 2004; Yukselturk & Bulut, 2007; Zimmerman, 2000). As a result, learners should be encouraged to use of motivational strategies in learning, which would help assure learner success in online courses.
In conclusion, the findings of this study revealed some learner character-istics (i.e., educational level, online learning readiness, locus of control, and
motivation) that were significantly correlated with learner satisfaction in online programs. Although the findings could not be generalized for all online programs, these findings suggested that people who deal with online courses and programs (e.g., administrators, chairpersons, deans, and instruc-tors) might be aware of the associated with the level of learner satisfaction with regards to learner characteristics and address these issues when plan-ning, developing, and administering online courses. This type of study could help them orient students to the kind of skills they will need to function well in the online environment. It could also assist them in setting realistic crite-ria for determining who should be admitted to an online course. Students without the characteristics or skills that enhance satisfaction might avoid taking online courses, or, perhaps, the online instructors might provide these students with special attention.
LIMITATIONS OF THE STUDY AND FUTURE DIRECTIONS
One of the limitations of this study relates to the online certificate pro-gram and the participants analyzed. The propro-gram is a bit unique and differs from degree programs in its aims, semesters, curriculum, and total time. Also, participants do not have similar characteristics, in terms of age level, education level (both graduate and undergraduate students), and expectation in the program. Since the scope of the study is limited to the program, the results are tightly tied to the context of this case. Results cannot be, there-fore, widely generalized. For the next study, survey pool (both students and faculty) might be extended to other educational institutions that apply online education in Turkey (or even Greece and Bulgaria).
This study also contains limitations inherent in most quantitative and qualitative research. In fact, the structures of the online certificate program and courses given in this program were not changed, and the researcher did not affect the students or instructors of the course during the study. Howev-er, the variance explained by the regression analysis is small (only 16.8%). Other variables (i.e., learning style, motivational beliefs, and self-regulated learning strategies) that were not analyzed in this study might affect learner satisfaction in the online environment. Much more study is needed on a vari-ety of variables and their effects on learner satisfaction. Additionally, other qualitative methods for gathering data, such as, observations and content analysis of learners’ logs, might be used together to ensure the validity and reliability of studies.
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