The purpose of this study was to explore the frequency of interaction between instructors and students in discussion boards in online courses and if this interaction is related to student satisfaction to better understand and set a baseline for this institution and others for understanding regular and substantive interaction. We report the results of our inquiry in the following section.
Demographics of the courses
A total of 415 online courses, taught over a single academic year, across six schools and colleges, were identified for the study (see Table 5). Most of the courses were taught in the College of Liberal Arts and Sciences (44.6%) which serves not only a diverse student population but offers a diverse number of online programs at the university. The other schools and colleges made up the remaining 55% of the courses in the study.
Tenure-track vs non-tenure-track instructors
In the study, 82% of the instructors were non-tenure-track, thus less than 20% of the instructors were tenure-track. However, the Business School (27%), the College of Arts and Media (23%), and the School of Education and Human Development (26%) had slightly higher percentages of tenure-tracked faculty teaching online courses compared to the other schools and colleges.
The distribution of course levels is shown in Table 5. Courses were categorized as lower division, upper division, and graduate. For this study, 27.71% (N = 115) of the courses were lower-level undergraduate courses, 38.07% (N = 158) were upper level undergraduate courses, and 34.22% (N = 142) were graduate level courses.
Instructors, TAs, and students
Courses in the study had only one instructor and no teaching assistants (TA). This decision was made to eliminate courses with multiple instructors or a TA. Courses with TAs were also removed since a TA can have a combination of roles in a course, from designer to facilitator, to teacher. The number of students in a course, though, ranged from five to 79 students (N = 415, M = 25.43, SD = 11.3).
Number of discussion boards
To better understand how instructors and students interact in discussion boards, it was important to analyze the number of discussions in a course. The total number of discussion boards in a course ranged from 0 to 140. There were 23 courses with no discussions. These courses were removed from further analysis since these courses did not use discussion boards. Therefore, 392 courses were included in the remaining analysis.
Total posts refer to the total number of posts per course to any discussion board in the course. A post is a reply to the discussion topic or another post. A post can be made by the instructor or a student. This number is used to describe the amount of interaction in a course because a post in a discussion board is like a face-to-face discussion where students and instructors exchange ideas through taking turns speaking. The minimum number of posts in a course was two and the maximum number of posts was 2468 with an average of 503.21 (SD = 447.2) posts per course.
Research question 1: Instructor interaction
Research question one was, “How do instructors interact in asynchronous discussions in online courses?” Results answering this question provides baseline data regarding frequency of discussion board posts as well as the rate of interaction for instructors in online courses. It is not possible to determine whether the instructor or students created the initial discussion board in the data set. However, regardless of who created the discussion, interaction occurs through a series of posts, or replies between the instructor and students. The number of posts by an instructor ranged from 0 to 347, with the average instructor posting 32.90 times throughout a course.
An instructor post would be in response to either the initial discussion board or a student in the course. We found that 63.7% (or 250 out of 392 courses) of courses had the instructor post less than 32 times (the mean in this sample) during the semester. Of those 250 courses, 28.8% of the courses had no instructor posts at all.
It is important to note that the total number of posts an instructor makes in an online course provides only a glimpse into their interactions with students during a course. While it is helpful to know if an instructor is posting below the average number of posts for the institution (e.g., to identify absentee instructors), the number does not take into account situational factors, such as class size. For instance, we contend that the effect of 32 posts by an instructor is more impactful with a course with 25 students versus a course with 75 students. Thus, researchers and practitioners need a way to better understand how active instructors are in a course. One method was created by Bliss and Lawrence (2009a, b). In this method, the calculation of instructor participation is the total number of instructor posts divided by the number of students in the course. This means that in a course with five students and an instructor who posted 80 times during the semester would have an average interaction rate of 16 posts per student. While a course with 25 students and an instructor who posted 80 times during the semester would have an average interaction rate of 3.2 posts per student.
Instructor interaction rate was calculated for each course in the study. Instructor interaction ranged from 0 to 18.9 with a mean of 1.49 and a standard deviation of 2.33. These results indicate a varied approach to discussion boards. A closer look at the distribution (see Fig. 1) shows that although most courses had an average instructor interaction rate of less than one post per student, there was a large spread with some instructors having an interaction rate of over ten posts per student. This spread could indicate varied approaches by the instructors. For instance, some instructors may post less frequently in discussions, but have other strategies or methods of communication, like summarizing discussions after each week, sending individual emails or using synchronous forms of communication. The wide variety of tools available within and outside the learning management system means that interaction is not limited to discussion boards only. With this in mind, though, based on the data from Canvas, instructors in this study posted an average of 1.49 times a semester for every student in their class.
Research question 2: Student interaction
Research question two was “How do students interact in asynchronous discussions in online courses?” Results answering this question provides baseline data about student use of discussion boards in online courses. In an online course, discussion boards serve as a primary opportunity for person-to-person interaction (Lieberman, 2019). When a student posts to a discussion board, makes a reply to a discussion board, or another person’s post, it is meant to simulate a conversation in a face-to-face classroom. Descriptive statistics were used to analyze the number of posts by students. The total number of student posts per course ranged from 0 to 2438 (N = 392, M = 470.31, SD = 432.8).
When assessing the shape of the distribution (see Fig. 2), almost half of the courses in the study had over 350 student posts (N = 194) throughout the semester. However, 48 courses (12.2%) had less than 0 student posts.
Since each course has a variable number of students, it is difficult to determine from total posts alone whether a course has a lot of interaction. Therefore, it was important to look at the average number of posts per student, in addition to total numbers. Our analysis revealed that the average number of total posts per student was 19.9 per student per course (SD = 18.1). This means that on average, a student posted in the discussion boards approximately 19 times per semester. Given that the semester is 15 weeks, plus final weeks, this averages out to each student posting a little more than once a week.
We also found that 25% of courses had an average of less than 5 posts per student (N = 98). Based on these results, students who post more than 20 times per semester have an above average number of posts. This information could be used by instructors or administrators looking to identify students who may need additional support or encouragement in order to fulfil the requirement of regular interaction. In this case, an instructor may identify students who have posted only a few times during the first two weeks of the semester. Then, the instructor could reach out to those students regarding the expectation of regular interaction.
Research question 3: Weekly interaction
Research question three was “How do students and instructors interact each week in asynchronous discussions in online courses?” Results answering this question provides baseline data for discussion board activity in online courses. This data could be used to identify courses early in the semester who have low levels of discussion board interaction. An instructor or administrator may wish to identify students or instructors who have low levels of interaction to promote regular learner-instructor interaction. To answer this research question, weekly totals of discussion posts were calculated. For each week, the number of student posts and instructor posts were reported for each of the courses. The courses in the data set were offered over fall or spring semester; the courses were assumed to have followed the university’s traditional 15-week schedule, plus finals week. All courses are expected to take part in finals week, either by giving an exam or fulfilling two contact hours of instruction. Table 6 shows the weekly totals of posts for all courses as well as the totals for instructors and for students. Additionally, the average number of posts per course was calculated along with the percentage of overall posts for each week.
Based on the data set, most interaction happened in the discussion boards during the first two weeks of a semester. This was true for both students and instructors. After that, there was a steady decrease in the number of discussion board posts. The least amount of interaction in the discussion boards happened during finals week and spring or winter break (depending on the semester). Further, it is worth pointing out that the last few weeks of the semester have about a third of the interaction as the first week (see Table 6).
As discussed previously, class size can influence interaction. Therefore, using the average class size of the courses in the study (M = 25.43), average instructor interaction rate and average posts per student were calculated each week. These numbers provide a baseline measure which could be used to identify courses with low interaction rates. Since this data could be particularly helpful during the first few weeks of the semester to encourage participation from students and ensure that instructors are practicing regular interaction, Table 7 shows the average instructor interaction rate and average posts per student for the first four weeks of the semester. After that, average interaction drops off.
Based on the average instructor interaction rate and average posts for students, instructors should possibly attempt to post an average of once, per every three students in their class and a student should post at least twice. During week two, an instructor should post an average of once per every seven students in their class and a student should post at least once. Using the average instructor interaction rate and average posts per students, these numbers could help assist instructors on setting targets numbers which they can use to help ensure they are maintaining regular interaction with their students.
The two semesters used in the study showed similar results for interaction. Term 1 had 207 courses and term 2 had 185 courses. Fig. 4.9 shows the total posts by term. As shown in Fig. 3, posts for both students and instructors decrease from the first week of the semester to the last week. This decrease in posts may indicate a reduction in interaction throughout the semester. However, additional research would need to be done to determine if interaction was occurring in different ways at different points in the semester.
Research question 4: Correlation testing
Research question 4 was, “Is there a relationship between asynchronous discussion interaction measures and student satisfaction?” This research question focuses on whether there is a correlation between total posts (i.e., interaction) in a course and student satisfaction. It is important to understand if the total posts in an online course is associated with student satisfaction. If a correlation was found, course design and delivery methods could be modified to increase student satisfaction. For the variable, total posts, from the 392 courses with discussions, the total number of posts ranged from two to 2468 posts, with a mean of 503.21 (SD = 447.2). For the variable, student satisfaction, from the 392 courses with discussions, student satisfaction ranged from 2.625 to 6.0 with a scale from zero to six. The mean was 4.96 (SD = 0.5).
To determine the appropriate statistical technique, a test of normality was used to assess the distribution of the scores (Pallant, 2013). Results of the Kolmogorov-Smirnov and Shapiro Wilk provided the Sig. value of .000 for both total posts and student satisfaction, suggesting violation of the assumption of normality. An inspection of the normal probability plots confirmed a non-normal distribution for both variables. Several attempts were made to normalize the data. This included removing outliers and transforming the variables. Since student satisfaction was already a new variable introduced by averaging the scores from eight questions from the end-of-course evaluation, it felt excessive to transform that variable. In addition, there is “considerable controversy” concerning transforming variables (Pallant, 2013, p. 96). When removing outliers, results from correlation testing produced similar results as when not removing outliers. Therefore, a non-parametric technique was selected. Non-parametric tests are useful in cases where the assumption required for parametric tests are not met (Pallant, 2013, p. 221). Therefore, a Spearman’s Rho correlation was selected to measure the relationship between the two variables. A Spearman’s rank-order correlation was run to assess the relationship between student satisfaction score and total posts in a course. 392 courses were used in the analysis. Preliminary analysis showed the relationship to be non-monotonic, as assessed by visual inspection of a scatterplot. There was no statistically significant correlation between student satisfaction scores and total posts, rs = −.060, p = .240 (see Table 8).