Transitioning to Online Courses in Higher Education Free

University administration is encouraging our college of education to embrace newer technologies in course delivery. Increasing competition from more versatile institutions has been systematically drawing students away from our more traditional program. Evidence points to increasing numbers of colleges moving in this direction (Crawford-Ferre & Wiest, 2012; Young, 2002).

Additionally, there has been increasing pressure from our undergraduate and graduate students to move portions of their required coursework online. Many of the students who attend our university work full-time and the convenience of online course delivery is appealing. Students being drawn to programs with more flexible formats is evidenced by increasing enrollments in classes we already moved to an online or hybrid format. We also understand that by offering more content online we may increase rates of degree completion (Shea & Bidjerano, 2014). Our decision, thus, to move to delivering content over the Internet was one of pragmatism and survival.

Faculty from our department of special education decided cooperatively that the introductory course, which routinely serves about 75 students per semester, would be an ideal course to target for change. The course has a fixed curriculum, dictated primarily by the state accreditation agency. In addition, due to the large enrollment of the course and thus large class size, when compared to smaller courses the face-to-face experience is probably not as fulfilling for students.

Hybrid courses “combine technologies of distance delivery with face-to-face interaction” (Soules, 2000). The general idea is also referred to in the literature as “blended learning” (see Albrecht, 2006; Halverson, Graham, Spring, Drysdale, & Henrie, 2014). In our pilot move from 100% in-class instruction, we decided to alter the course in three major ways. First, while we kept the traditional introductory textbook, all supporting content was moved into the Blackboard course management system; this included PowerPoint slides summarizing each chapter, related professional literature, and various chapter related websites and video resources. Second, all assignments (10), all course quizzes (10), and discussions (4) were moved online. Third, 50% of the class meetings were delivered in an online classroom, with synchronous text-based chat, whiteboard, and audio support. Recordings of online classes were also made available to students, on demand. In many respects our choices represent a classic hybrid delivered class. The course was cotaught by the first two authors.

A major concern for us were the students for whom technology skills and/or technology access posed a barrier that might lead difficulties not otherwise present in the face-to-face class. Inevitably, these barriers would cause students to fall behind with course requirements. This concern was the driving force in creating our research questions. Despite evidence pointing to the indifferent impact of modes of course delivery (e.g., Bailey & Jaggars, 2010, Bowen, Chingo, Lack, & Nygren, 2014; Pascarella & Terenzini, 2005; Russell, 1999) we were nonetheless apprehensive about our own capability to administer the new class with its online component at the same degree of quality as our traditional in person course. We are technologically literate, but giving over our content to a somewhat uncertain medium gave us reason to be cautious.

To gauge the degree to which we were making good decisions during our hybrid move, we decided to collect data through a pre- and postsurvey model. The survey was composed of 48 selected-response questions. All students were required to take the survey prior to the first online class meeting and again before the final examination at the end of the semester. Because we were concerned primarily with barriers students might face given the course’s new hybrid delivery model, we focused on understanding the impact of general technology skills, classroom- specific technology proficiencies, and access to technologies away from and at home. The five surveyed categories aligned with our research questions and are summarized in Table 1. Data collected in categories A through D represent independent variables, and category E asked questions to collect data for dependent variables. Students were also given opportunities throughout the survey to add personal thoughts and comments. To add richness our decisions, we also solicited informal student feedback periodically through the course.

Our study focused on answering the following general questions:

1. How much of the course did students want delivered online? Did this perception change after participating in the hybrid class?

2. Did the degree to which students want the course to be online depend on (a) general technology skill, (b) classroom related technology proficiency, or (c) access to technology? Did this perception change after participating in the hybrid class?

By gathering evidence to answer these questions we hoped to understand how our students perceived the course changes, and better understand what factors might impact those perceptions.

The university is located in the southwest United States and has a large (> 40,000) and diverse (~70% minority) student body. We developed and delivered our online survey in the fall semester and 67 completed both the pre- and postcourse survey. Not all students answered all questions; however, when possible, data for these students were also included in calculations. Typical for this class, the majority of students were under the age of 30 (81%) and female (83%). The survey was developed in part by a colleague (B. Dodge, personal communication) in our educational technology department, and refined to suit our specific purpose. The survey was delivered over the Internet with Survey Monkey, a commercial online survey creation tool. Students participated in the survey outside of class, and participation was monitored remotely. Reminder e-mails were sent to students three times before the survey closed. Frequencies and descriptive statistics along with student comments were used to answer our research questions. Group means were compared using one-way within-subjects ANOVA and the relationship between predictor variables (i.e., general technology skill, specific classroom technology skill, and level of access to technology) and degree of preference for an online class was calculated with linear regression. Results that follow are presented in the order in which research questions were posed.

How much of the class did students want delivered online? On the survey this questions was worded as, “How much of this class do you think should be delivered online?” The distribution of responses is illustrated in Figure 1: The plot on the left depicts precourse student responses and on the right postcourse responses.

At the beginning of the semester 61% of the students felt the course should be delivered 50% or more online. After the course, this number rose to 71%. Before and after course delivery, nearly 25% of all respondents indicated they would have liked the course to be completely online. Only one respondent indicated the desire to have the class delivered 0% online, and that student did not change from beginning to end. The mean preference of the class grew from wanting 61.3% (SD = 2.8) online before the course began to 70.6% (SD = 2.7) at the end of the course, a statistically significant growth of nearly 10% (F(1, 66)=14.6, p < 0.05).

Does the degree to which students want the course online depend on (a) general technology skills, (b) proficiency with specific classroom technologies, or (c) access to technology at and away from home? To isolate these variables we asked 12 questions about student general technology skill level, five questions about classroom specific technology proficiencies, and 18 questions about student access to technology. In each area—general technology, classroom technology, and technology access—we aggregated responses into single variables so we had a single measure of each and could more easily manage statistical analyses. These aggregated measures were converted to percentages. The impact of technology skill is presented first, followed by classroom technology proficiency, and then student access.

On the survey, student technology skill was measured in many different ways. On both the pre- and postcourse survey 12 questions asked students to rate themselves in an array of skill areas, from basic e-mail to web authoring. In each question students were asked to rate themselves on a scale from 1 (little) to 4 (expert). The mean skill level on the precourse survey was 67.1% (SD = 10.5). On the postcourse survey, this grew to 71.0% (SD = 10.1).

The degree to which that general technology skill predicted preference for an online class was calculated with linear regression, for the precourse data and post-course data. Before the course began self reported computer skill significantly predicted the way students reported their preference for online class, F(62, 1) = 7.7, p < 0.05, r² = 0.10. After the course ended, self-reported computer skill also significantly predicted the way students reported their preference for online class, F(62,1)=6.1, p < 0.05, r² = 0.09. These results point to very little change over the duration of the class in the relationship between student skill level and preference for online class. High student skill level was consistently associated with a preference for online class, both before and after the course.

On the survey the impact of student classroom technology proficiency was measured with five questions. On both the pre- and postcourse survey, questions asked students to rate themselves in classroom specific technologies like class related email, Blackboard, and online discussions. Each question asked students to rate themselves on a scale from one (not proficient) to four (very proficient). The mean classroom technology proficiency level on the precourse survey was 90.5% (SD = 9.4). On the postcourse survey this grew to 92.4% (SD = 12.0).

The degree to which classroom technology proficiency predicted preference for an online class was calculated with linear regression, for the precourse data and postcourse data. Before the course began self reported classroom technology skill significantly predicted the way students reported their preference for online class, F(65, 1) = 11.1, p < 0.05, r² = 0.15. After the course ended, however, self reported classroom technology proficiency did not significantly predict the way students reported their preference for online class, F(66, 1) = 3.6, p = 0.06. In other words, before the class began, student perception of their own classroom-specific technology proficiency was highly associated with their preference for having the class delivered online, but after the class this relationship diminished.

On the survey the impact of our students’ access to technology was addressed in many different ways. On both the pre- and postcourse survey 18 questions asked students to rate themselves in an array of skill areas, from access to old and new computers, to access to GPS and mp3 players. Of the 18 questions, nine asked about access from home, and nine about access to the same technologies away from home, for example, at a library or on campus. In each question students were asked to report whether they had (yes) or did not have (no) access to a specific technology. The mean access to technology on the precourse survey was 55.8% (SD = 17.9). On the postcourse survey the mean rose to 62.4 (SD = 21.2). Plots of the distributions suggested that over the duration of the course students developed better access to technology, the distribution moved from close to normal to a somewhat negatively skew. Not surprisingly, students had better access to technology at home than away.

The degree to which level of technology access predicted preference for an online class was calculated with linear regression, for the precourse data and postcourse data. Before the course began, selfreported access to technology did not significantly predict the way students reported their preference for online class, F(57, 1) = 1.8, p = 0.18. Similarly, after the course ended, self-reported access to technology did not significantly predict preference for online class, F(59, 1) = 3.3, p = 0.07. These results seem to point to very little relationship between access to technology and preference for having an online class for our students.

This discussion is organized around the research questions, from the perspective of the classroom decisions with which we were faced. We begin with a discussion about student preference for online courses, in general, and then discuss how student skills and proficiencies seemed to influence those preferences. We close with our thoughts on how this study might have been improved or elaborated upon, and a brief description of our experiences teaching the hybrid class.

Did our students want the class to be online? Our decision to pilot the course with greater technology integration was influenced by our awareness that both our students and university administrators felt it might be good practice. Evidence we gathered from the survey confirmed that the majority of the class wanted some form of hybrid class. After the course finished, nearly three quarters of 67 students reported that they would like the course to be at least 50% online. One student wrote:

This sentiment was reassuring to us, as we made the move to hybridizing, and not surprising given our suspicion that the convenience of having much of the class available online would appeal to students. However, based on the mixed feeling of students, it appears neither completely face-to-face nor completely online would be ideal for our class. While one quarter of the class consistently indicated a preference for a 100% online class, the rest preferred some mix—the hybrid class. Survey responses indicated that about 70% delivered online might be close to ideal.

Only one student maintained throughout the class that a 100% face-to-face class would have been preferable. After the class this student wrote, “For me, actually going to a lecture and having paper homework or exams would’ve benefited me immensely.” This brings to light the simple fact that some students have learning styles not suited for this kind of hybrid class, but, nevertheless, the vast majority of students preferred some online content. In addition, participating in the hybrid course seemed to generate increased preference for online instruction. Data trends in this study pointed to student growth in their technology understanding, perhaps simply as a result of participating in the hybrid class. Clearly, no statement about true causation can be made from this kind of survey, but it seems at least plausible that delivering the class partially online moved students toward being more accepting of online delivery.

Did general technology skill impact student preference? In our survey, the higher a student’s technology skill, the more likely he or she wanted the course to be delivered online. Of all the independent variables we attempted to measure, this was the clearest predictor of whether a student would want the class to be online. This is an important finding because many of the skills captured in this aggregate variable might be considered beyond the scope of our program: skills like using word processors, the Internet, spreadsheets, and database software. One student put it well: “I have learned how to be more comfortable with online classes by taking this class and it was nice to be able to see people communicating and sharing ideas, if only I knew how to address glitches.” In our teacher training program we have a single course dealing with K-12 technology issues, but the class does not attempt to develop preservice teacher technology skills, and instead focuses on technologies used by their students. Perhaps we would do well to consider the addition of such a course.

It should be noted that while general technology skills seemed to be a statistically significant predictor of preference for participating in an online class, it would be a stretch to call it a strong predictor. Only 10% of the variance could be accounted for with the relationship we modeled here. The unexplained variance suggests to us that other variables may also be at play— but we hold nonetheless that general technology skill is one that cannot be ignored.

Did specific classroom technology proficiencies impact student preference? Our survey suggested that students thought of themselves very highly in the area of specific classroom technology skills, like using email and Blackboard for class. The mean classroom technology proficiency level before the course started was about 90%, and this rose by about 2 points after the course ended. As the class progressed, it may have been the case that students became more practiced at these skills, which might account for the slight elevation in classroom specific proficiencies.

Before the class began, student perception of their own classroom specific technology proficiency was also predictive of their preference for having the course delivered online, but after the course this relationship diminished. This might be explained in a few ways. First, in general preference for having an online class grew from the pre- to post-course survey. Thus the dependent variable in this case, preference for online class, was a moving target. Another possible explanation is the ceiling effect manifested in the relatively high proficiency rates students gave themselves. With a mean proficiency of about 92% after the course, there was little room for upward change. The instrument used in the survey may not have been sensitive enough to higher levels of proficiency, in effect constraining the variance.

Did access to technology impact student preference? If moving our course to more online delivery meant a number of students would be left out due to limited technology access, then the move was not acceptable to us. Results from the survey, however, suggest that access to technology both at home and away was not predictive of student preference for online course delivery. Very few students indicated they could not access the online material and, perhaps spurred on by the class requirements, students access to technology improved during the course. On the other hand, technical difficulties were reported by a significant number of students over the duration of the course. A student remarked,

This sentiment was echoed by many other students, and like the results presented about deficiencies in general technology skills, suggests to us that additional technology supports be in place for students.

We realize that with a survey like this we are not measuring actual skill, but instead are measuring self-reported skill, a proxy for the real construct of interest. This selfreported level of skill, access, or proficiency can be confounded by overarching individual issues such as self-efficacy, readiness to learn, or motivation, among others. With a large enough sample size and randomization these factors can better be controlled, but in our survey study none of this took place. This will, naturally, lead to a second phase of our research: random assignment of students to online and face- to-face sections of the same class.

This study makes the inference that student preference be the driving factor in whether or not to make the move to being online. This, also, needs careful consideration. One student wrote, “I believe that technology can increase students’ learning but I don’t believe that having classes delivered online increases learning. I feel that when it comes to classroom learning, online classes take away a lot of the benefits.” Perhaps the dependent variable in our study should have been student learning; however, without randomization it would be impossible to control for all factors associated with whether or not individual students learn. In this study we rested on the idea that students knew what was best for them; in the future when we use randomization in our design we can also employ student test scores as dependent variables.

We consider results in light of specific program and classroom decisions we need to make. Is hybridizing a sound decision from the student perspective? We believe yes. Access to technology did not appear to present a barrier, and almost all students preferred some degree of hybridization. This conclusion might be generalized to decisions around hybridizing classes in any field or discipline, when the student population is similar. From the instructor’s perspective the transition was not without problem, but in general was also positive. Like the students, the instructors experienced technical glitches along the way, but overall using the technology was not a barrier. We are looking forward to continuation of this course in a hybrid format.

In our study, it appeared that experience with the hybrid course also led to more favorable perceptions of the hybrid course. Preference increased over the duration of the class for all students, no matter the level of general skill, classroom technology proficiency, or access to technology. Although not always statistically significant, the growth was small and present.

Based on what we thought was good pedagogy, it was our intention to keep at least part of the class face-to-face. Evidence from the student survey seems to support this intention. On average, the class wanted about 70% of the course delivered online, and of those only 15% said they would like class to be completely online. This confirms our faculty’s perception that people like some face-to-face contact. The question of how much to hybridize our class is not completely answered, but we do know now that both 0% and 100% are not ideal options for everyone.

Access to technology did not appear to be a barrier for most students. More than this, however, we suspect hybridizing also minimizes more traditional barriers like getting to class on time, finding parking, changing work schedules, and finding childcare (see Horspool & Lange, 2012). These hidden benefits may explain why student are anxious to see more courses move to the hybrid model. We also appreciate the potential benefits for faculty, such as more flexibility in our teaching schedule and the potential benefits of creating innovative interactive material to deliver online. Perhaps the move to online learning can be beneficial to all involved.