In response to the increasing demand for convenience by non-traditional students and to stay competitive in the marketplace, colleges and universities are incorporating online learning into their teacher education programs (Smith, Smith, & Boone, 2000). In 2001, over 3 million students were involved in online learning (Waits & Lewis). As this trend continues, online enrollment continues to increase and as a result, pertinent program selection criteria will be important to institutions looking to attract, maintain, and retain students.

The flexibility and lack of constraints offered by online courses continues to be a chief attraction for many. Christensen, Anakwe & Kessler define constraints as “the barriers faced by students in their educational pursuits” (2001). Today’s students struggle with continuously balancing multiple commitments. Global accessibility of the internet allows for participation from anywhere at any time. This and the compatibility with work and family schedules appeals to a majority of students (Gaytan & McEwen, 2007; Pastore & Carr-Chellman, 2009).

Reputation of the course, professor and institution also influence student’s decision to enroll in online courses. Shiao-Chuan conducted a survey of the factors affecting willingness to participate in distance learning. Females and students with full-time employment gave more importance to the reputation of the institution and the instructor. In addition, students 23 years of age and older deemed the program’s reputation to be more critical than did students age 20 and below (2002). Reputation was also found to be positively related with the general receptivity of distance learning. (Christensen, Anakwe & Kessler, 2001).

Students also appreciate the ability to monitor their work and development throughout the course. The asynchronous environment provides the opportunity for reflection on past assignments and discussion with peers, which can allow for valuable insight into the course (Browne, 2003). The literature also demonstrates both students and instructors perceive online learning is equally as effective as traditional face-to-face methods. (Liaw, Huang, & Chen, 2007; Lim, 2005).

As online learning opportunities continue to be introduced and integrated in the academy, the viability and veracity of online learning continues to be debated (Song, Singleton, Hill, & Koh, 2003). Online education will continue to flourish; but it is not without its detractors. Online learning is generally defined as instruction that is fully online and has received criticism for its lack of human interaction (So & Brush, 2008). Much of the research on online learning is not conclusive and best practices are still being developed (Schrum, Burbank, and Capps, 2007). The recent DOE study points out that one of the contributors of student learning in the online environment may be the result of more “time on task” required by the online environment (Means et al., 2009). As the veracity of online education is researched and debated, the demand for online learning in higher education continues. As a result, designing effective learning environments and developing strategies to achieve student learning outcomes continue to be important factors in educational institutions in the online sphere. Understanding the needs of adult learners will allow course designers to construct courses that provide an optimal learning experience and engage the adult learner.

Instructional design is one of the single most critical factors in successful online learning (Desai, Hart, and Richards, 2008). A study of adults identifying online course features and instructional design goals listed options, personalization, self-direction, variety and creation of a learning community as valuable to their online experience. Personal relevance, connection of learning outcomes to real-world needs, self-directed learning, and creation of a community for learning and professional growth were deemed important to the online learner (Ausburn, 2004). Effective design of instruction provides optimal learning environments, learner satisfaction, and achievement of learning outcomes in any learning environment. In a meta-analysis of the empirical literature incorporating 232 studies in distance education compared to classroom instruction, the systematic design of instruction was a predictor of positive attitude outcomes (Bernard et al., 2004). Managing cognitive load is a critical factor in providing an environment of success for an online student. Providing scaffolding that allows learners to increasingly build upon knowledge throughout a course in both the academic and technological components of the course are essential elements in providing learners with a successful online experience and decreasing extraneous cognitive load (Merrienboer, Kirschner, and Kester, 2003).

Whether face-to-face or online, the learning process contains similar components—content delivery, facilitation, syllabus, assessment, outcomes, etc. The environment determines the best methodology to achieve the desired outcome. The emerging technology of Web 2.0 is gaining increased attention from the academy (Huang, 2010) and it is essential for the academy to sift through the milieu of collaborative offerings to provide learners with tools that have demonstrated effectiveness in promoting learning. Utilization of the online environment is no longer the sole domain of distance education. Face-to-face classes are increasingly using the internet to post syllabi and blogs often serve as a secondary discussion forum.

In an online environment, designing courses that foster collaboration promote active learning and students view the coursework more favorably (Meyer, 2003; Bernard et al., 2004). A report from Athabasca University found online students experience greater cognitive and explanatory learning as a result of increased participation in course communication where students exchanged between 80 and 100 messages, which is far more than in the classroom (Meyer, 2003). A Web 2.0 technology that is gaining increased usage is the wiki. It is known for promoting collaborative efforts and knowledge construction (Goodwin-Jones, 2003). Unlike blogs, which are usually arranged in topical fashion, wikis are created by all group participants (Engstrom and Jewett, 2005) and are constructivist in nature.

Effective design offers learners courses that enable them to slowly adapt to the learning environment and build on their knowledge while gaining success and confidence in their learning and ability to navigate in the online environment. Adult learners often possess a degree of anxiety of the use of new technology and understanding where the fears exist and their perceived ability to overcome these fears will assist the academic institution in guiding learners through their experience effectively. The continued growth of the online sector of higher education has implications for course designers as they continue to attract students to this growing market (Granitz and Green 2003).

As online learning continues to grow, its role in preparing educators will continue to be implemented in colleges and universities to prepare leaders in education. Technology in the classroom, convenience, and the technologically savvy students in the educational system require teachers to learn how to use technology (Fabry & Higgs, 1997). Furthermore, the utilization of appropriate instructional technology has been shown to favorably influence academic performance, and today’s students are primed to incorporate new technologies in their studies (Saeed, Yun & Sinnappan, 2009).

When students and teachers identify the practical applications of the available technology, they are more apt to employ it. The perceived usefulness of technology is positively related to the general receptivity of distance learning and technology acceptance (Gibson, Harris & Colaric, 2008; Christensen, Anakwe & Kessler, 2001). This is of particular importance when considering Raaij & Schepers findings that the success of online educational programs considerably depends on the acceptance and use of the presented technologies (2008).

Individuals who believe they possess a greater aptitude with technology are typically at ease with online curriculum. In 2001, enrollment in online courses led to higher reported computer self-efficacy for a number of participants (Piccoli, Ahmad, & Ives) and technology familiarity was positively and significantly correlated with the general receptivity of distance learning (Christensen, Anakwe & Kessler). This seems to allude to a circular relationship between these two variables. Despite this, the association between self-perceived computer competence and student’s intentions to continue taking online courses remains small (Calvin & Freeburg, 2010).

Studies have also suggested that technological self-efficacy is not correlated with student performance or satisfaction of online courses (Puzziferro, 2008). Calvin and Freedburg (2010) found no direct relationship between perceived computer competence and student success. Participants in their study still reasoned that technical training or tutoring might contribute to more successful course completion.

Adult learners tend to be more autonomous and self-directed in their learning, have accumulated life experiences that they wish to connect with their learning, and have little patience for assignments or courses that they do not feel relate to their life or their goals (Lorenzetti, 2003; Bennet et al., 2007). A survey of 318 students representing cohorts in four countries (America, Mexico, Israel, and Japan) asked for ranking of 10 items generally considered critical elements to successful online learning. Respondents ranked the items in the following order:

Responding to questions of elements that influenced satisfaction in the online experience, Western and Japanese students listed course content and organization/design as the dominant element of satisfaction in their online experience while Israeli and Mexican respondents listed convenience and community as most influential (Beaudoin, Kurtz, and Eden, 2009).

Adult learners perceive their online education as a social activity where they are active participants in the practices of social communities and constructing identities in relation to these communities (Brown, 2001). Unlike individual participants on the internet, where participants can feel a sense of anonymity, there is no anonymity in online learning, one must commit and be present (Wallace, 1999). Motivating learners to continue to study and enjoy learning is one of the critical factors in distance education (Liao, 2009).

Participants were 36 graduate students in the first cohort of the Leadership of Learning in the Master of Education program at Abilene Christian University. Abilene Christian University is a small (approximately 4500-5000 students) private, Christian institution. Fifty-nine percent of the participants were female. The mean student age was situated in the 30 – 39 age range. All participants had previously completed an undergraduate program in which the majority of classes were taken in a face-to-face setting. Of the 36 students participating in the study, ten had previously taken an online course. Four students matriculated from the program in the two year period of the study. All four had previously taken an online course prior to enrolling in the program. For use in the study, 6 of the 32 students who completed the post survey had participated in an online course.

The survey asked 3 demographic and 45 perception questions (Appendix A). A 5-point Likert scale was used to ask instructional design, program selection, personal qualities and technology skill questions. Each of the four areas asked between 10 – 13 questions comparing, contrasting, or seeking perception data on some aspect of the online and/or online versus face-to-face learning environment. Personal qualities and instructional design questions employed a 5-point Likert Scale (1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree). Program selection questions employed a 5-point Likert Scale (1=not important, 2=little importance, 3=somewhat important, 4=important, 5=very important) and technology skills questions employed a 5-point Likert Scale (1=no experience, 2=below average, 3=average, 4=above average, 5=superior).

A purposive sample of all 36 participants in the cohort were sent a survey. All 36 participants completed and returned the pretest in Fall 2006. In the final semester of the cohort’s program (Fall 2008), the students were sent the same survey. All 32 of the program’s remaining graduating students completed the post-test. The four students who matriculated from the program were not calculated in the data of this research.

Analysis of the pretest/posttest data was completed using SPSS. A one-way ANOVA was used to measure mean differences between gender and age across the four sections of the survey. The survey data was entered into the program and a repeated measures ANOVA was performed to identify responses with statistical significance (p<.05).

Gender was a statistically significant factor in several areas of the survey. Female students had statistically significant higher perceptions of their ability to use a word processor, keyboard, and participate effectively in an online discussion group. Their perceptions were higher than males in effect of background and experience, ability to be self-directed in their learning, and self-discipline in allocating study time (see Table 1). Males had statistically higher perceptions than females in perception of an online course as being more motivating than a face-to-face course and the belief that it would be easier to be discourteous in an online course (see Table 1).

There were no statistically significant differences found between age groups. In the amount of hours per week spent on coursework (p<.059), the 50+ age group had self-reported spending more time on task (M=3.0, SD=.81) than the 40-49 (M=2.33, SD=.86), the 30-39 (M=2.62, SD=.71), and the 20-29 (M=1.85, SD=.37).

Results demonstrated all four areas of the survey demonstrated statistically significant change in the repeated measures ANOVA (Appendix B). Of the four areas surveyed, instructional design demonstrated significant statistical change in 9 of 10 questions. Program selection demonstrated the least change (3 of 13 questions). Personal qualities demonstrated change in 4 of 10 questions and technology skills demonstrated change in 5 of 12 questions.

The instructional design component of the survey contained 10 questions (Appendix B: 4 - 13). The 10 questions were divided into questions of motivation (4,11), learning factors (5,6,7,8,10) and perceived ability to obtain help (9,12,13). Both questions of motivation were statistically significant (p=.001, .000). In response to the learning in an online course as more motivating than a face-to-face class (#4), pretest results demonstrated 22% of students agreed or strongly agreed with this statement. Following their experience, 50% agree/strongly agreed that they were more motivated in an online course.

When asked if learning was the same in a face-to-face course as in an online course (#5, p=.000), 61% in the pretest disagreed or strongly disagreed. In the posttest, this decreased to 37%. In the pretest, 22% agreed or strongly agreed learning was the same in an online class as in a face-to-face environment. In the posttest, 46% agreed or strongly agreed. This accounted for a 24% decrease in negative perception and a 24% increase in positive perception that learning is the same in a face-to-face and online environment.

The other learning environment questions (6,7,8,10) compared the academic rigor (p=.000), necessity of face-to-face contact (p=.000), instructional material (p=.000) and perceived social interaction of face-to-face and online learning (p=.045). Perception of academic rigor increased from 75% (agree or strongly agree) in the pretest to 85% (agree or strongly agree) in the posttest. Face-to-face contact with a teacher as necessity to learning demonstrated a 28% decrease in negative perception. The perception of instructional material as superior in online environment compared to face-to-face instructional material increased positively by 10%. When asked if they would miss the social interaction of a face-to-face environment, 34% selected agree or strongly agreed in the pretest, while only 9% agreed in the posttest.

On the pretest, 37% agreed or strongly agreed that it would be difficult to obtain help with a problem in an online course (#12, p=.000). On the posttest survey, only 3% agreed that it was difficult. This represents a 34% decrease in the perception that the online environment makes it difficult to obtain help.

Overall, the students participating in a two year online program increased their perception of the online environment as providing a quality and rigorous learning environment. Students viewed the courses as academically rigorous, socially satisfying and an environment in which they could satisfactorily access the teacher and receive assistance when needed.

Program selection contained 13 questions (Appendix C: 14 – 26). Three questions demonstrated statistical change in the repeated measures ANOVA (21,p=.000; 22,p= .000; 25,p=.021); however, the researchers concluded that the student’s perception of the program selection criteria were factors used as criterion in selecting the program. Much of what was an important factor upon entering the program was still an important factor upon completion of the program.

The importance of the program being based on national standards, the selection of the institution as an iTunes University by Apple, Inc., and the university’s status as a “Best College” were factors that the students became exposed to while students. The researchers surmised that the students may have been unaware or the importance of these factors was not evident prior to enrollment. These factors demonstrated statistically significant change; however, their utility for other universities or institutions in planning an online offering is minimal. Not having a commute and flexibility offered by online courses were selected in the agree or strongly agree category by 90% of respondents. While not significant statistically, it is important to note that these ranked high on both the pre and posttest and are important considerations for students in their selection of an online program.

Ten questions in this section described the respondent’s ability to self-manage and interact with others in the online learning environment (Appendix D). Four of the 10 questions demonstrated significant change (30, 31, 32, 34).

Two of the significant questions addressed a student’s ability to self-manage and set goals. Of interest to the researchers was the decrease in perception of student ability to effectively and easily complete assignments on time (#30, p=.001). On the pretest, 47% strongly agreed they could self-manage. On the posttest, only 25% characterized themselves as able to effectively self-manage their online workload. While participating in an online program the percentage of students who set goals (#31, p=.001) increased from 36% to 53% (strongly agreed). Student perception of their ability to self-manage decreased 22% and their perception of themselves as goal setters increased by 17%.

Two of the questions (32, p=.002, 34, p=.02) addressed perception of working with others online. On the pretest, 52% reported they enjoyed working with others in groups (44% agreed, 8% strongly agreed). On the posttest, 87% reported they enjoyed working with others in groups (53% agreed, 34% strongly agreed). This represented a 35% increase in perception of enjoyment in working with others online. On the pretest, 67% (61% agreed, 6% strongly agreed) perceived the possibility to interact successfully with classmates. On the posttest 88% (50% agree, 38% strongly agree) perceived themselves as having successful interactions with classmates. Students' perception of difficulty in accurately expressing their ideas online on the pretest was 14% (agree/strongly agree). On the posttest students marked 0%.

Students demonstrated a decrease in their perception of themselves appropriately managing study time and completing assignments on time. Their perceived ability to set goals decreased. Students demonstrated an increase in their perceived success and enjoyment of working in an online group and found it to be an environment.

The 12 questions in this section provided a self-rating on functionality of specific technology skills- perceived ability to use email, PowerPoint, search engines, keyboards, spreadsheets, MP3 players, and online interactions (Appendix E: questions 37 - 48).

It seemed reasonable to assume that over a two-year period, most adults would improve in their ability to use various applications. However, there were 5 areas that demonstrated significant change PowerPoint ability, use of MP3 player, use of online discussion groups, comfort communicating online, and frustration with technology glitches (37, p=.001; 34, p=.000; 44, p=.000; 46, p=.004; 48, p=.000). Their perceived ability to work in an online group (increased from 25% agree and 0% strongly agree) on the pretest to 71% on the posttest (59% agree, 12% strongly agree). Students may find that as their skill increases their enjoyment of the online experience increases.

Their frustration with the use of technology decreased from the pretest score of 57% (43% agree, 14% strongly agree) to a posttest score of 25 % agree and 0% strongly agree. Implications for the effective use of instruction design and training prior to a student commencing an online program may be factors for institutions to consider in achieving student satisfaction in their online experience.