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For the purposes of this study, in an online course, an introductory activity was assigned and remained constant over 11 offerings of the course (with multiple sections in single semesters) through multiple instructors who had used a primary version of the course. This introductory activity was a forum where students shared information about themselves in a five-minute (maximum) video and to which a minimum of two responses were required by each student to original posts of their classmates. The consistency throughout multiple terms and with hundreds of students enabled the gathering of a sufficient amount of data to draw statistically viable conclusions.
To initial reviewers of engagement through the course, there was a perceived imbalance in these initial forum posts and the number of responses received depending on the ethnicity and gender of the original poster. After receiving Institutional Review Board (IRB) approval, a review of all online sections of the course was conducted to glean information for the study. The question of the study was finalized as follows:
Do posts in the introductory activity of an institutionally required online course receive different numbers of responses based upon the ethnicity and/or gender of the original poster?
A review of literature in the field regarding the need for community-building in the classroom reveals several studies on the topic. Introductory activities have long been a mainstay in online courses and have served to build community among students [1]. Leveraged through technology such as forums, these activities foster “…reciprocity and cooperation among students” [2, 3]. Brown [4] argues these communities generate the natural making of online friends, and Vesely [5] concludes interaction and respect among community members can be built, but only instructors lead, foster, and model appropriate behaviors in that community.
Community-building, however, can be influenced through many factors, many of which are impacted by a student’s gender and ethnicity. Students describe differing opinions of online instruction (as a pedagogical practice) based upon both race and gender [6]. Fairlie [7], Baker [8], and Casteel [9] correlate that race and ethnicity alter grade and discipline outcomes for students in both traditional and online classrooms. Tynes, referencing Tapscott [10], argues “teens were more intellectually open and inclusive than their predecessors” in the early days of online interaction [11]. Tynes shares (earlier) that race played a part in most interactions of students in a study, with many of those interactions becoming negative [12].
Ethnicity and gender strongly influence classroom dynamics, which would impact the ability to build and foster any sense of community. Students carry these concerns with them even in the initial stages of a course when determining their sense of belonging in a community.
Collecting more than 11 offerings of the course, with class enrollments ranging from 20 to 50, information was tabulated on responses from 417 (sample size n) students. In the introductory activity, students in an online forum were asked to introduce themselves to their classmates for the sake of building community via a five-minute video. Students were required to post about themselves but were also required to respond to at least two posts from student peers. (Instructors' comments were not included in the summarized data.)
References to learning management system (LMS) profiles and student information system (SIS) data (delivered only for specific students) were used to accurately catalog responses based upon students’ self-perceptions. When the study was completed, due to classification in the SIS and the LMS, the students were all self-identified in binary genders (male or female) and as one of a variety of ethnicities. Of the various ethnicities, the two primary groups were African American (comprising roughly 17.3 percent of the enrolled students in the sections), white/Caucasian (comprising 76.7 percent of the enrolled students in the sections), Asian, and Pacific Islander. The number of participants identifying as Asian or Pacific Islander was small in this study (25 or 6.0 percent) and was consequently clustered together. (An abbreviated view of the data is displayed in Table 1.)
Table 1. An abbreviated view of data summarized by number of responses in the introductory forum to original posts. Students were categorized by self-identified data as male (M) or female (F) and as African Americans (AA), white/Caucasians (WC), or Asians and Pacific Islanders (API).
Student |
Gender |
Ethnicity |
No. of Responses |
1 |
F |
WC |
12 |
2 |
M |
WC |
3 |
3 |
F |
WC |
1 |
|
|
|
|
417 |
M |
AA |
1 |
Table 2 summarizes the number and average number (arithmetic mean) of responses received depending upon the binary gender of the original poster.
Table 2. A summary of responses received by students self-identifying according to binary gender (male (M) or female (F)). Additionally, the rightmost column shares the mean number of responses received by identified gender.
Gender |
No. of |
Mean No. of |
F |
252 |
2.82 |
M |
165 |
1.99 |
Similarly, Table 3 offers a summary of the average number of responses received depending upon the ethnicity of the original poster and is sorted by increasing frequency of number of responses. The mean number of responses similarly increases.
Table 3. Sorted by increasing numbers of responses, average numbers of responses are shared according to self-identified ethnicities of the original poster. API is Asian and Pacific Islander, AA is African American, and WC is white/Caucasian.
Ethnicity |
No. of Responses |
Mean No. of Responses |
API |
25 (6.0%) |
1.61 |
AA |
72 (17.3%) |
1.64 |
WC |
320 (76.7%) |
2.76 |
Table 4 offers a tabulation of numbers of responses and mean numbers of responses by ethnicity and gender.
Table 4. Summaries of student responses received by the original poster depending upon both ethnicity and gender as denoted in Tables 2 and 3. Results are sorted by increasing frequency (number of responses).
Ethnicity |
Gender |
No. of Responses |
Mean No. of Responses |
API |
F |
12 |
2.17 |
API |
M |
13 |
1.15 |
AA |
M |
29 |
1.52 |
AA |
F |
43 |
1.67 |
WC |
M |
123 |
2.20 |
WC |
F |
197 |
3.11 |
The original analyses were conducted in MS Excel but were validated against results in R.
The previously detailed scenarios were addressed individually. The One-Way ANOVA (Analysis of Variance) was used to determine statistical significance in the mean number of responses for (each) gender and then ethnicity. The Two-Way ANOVA was employed to ascertain if there were significant differences in the means between both gender and ethnicity. (Both versions of ANOVA can be employed with comparison groups that are not of equivalent size.)
Because ANOVA is based on the F-distribution, we looked for F-values after computing a critical value. If the F-value was greater than the critical value, we rejected the null hypothesis. If not, we did not have enough evidence to reject the null hypothesis [13, 14, 15, 16]
In all scenarios, we performed the analyses using a 95% confidence interval (i.e. α = 0.05). In general, all null hypotheses stated that there were no differences between the means of the studied groups while the alternative hypotheses suggested that a difference existed between at least two examined groups.
The null hypothesis H0 and the alternative hypothesis H1 are stated below.
For the gender comparison, our statistical analysis prompted us to reject the null hypothesis (the test statistic was significantly larger than the critical value). As such, we argued that there was a statistically significant difference between the number of responses that were received by male and female students.
From the results of Table 2, the average number of responses to female posters was higher than that of male posters (2.82 versus 1.99). A one-sided t-test could be performed to verify this observation, but the conclusion seemed obvious.
The null hypothesis H0 and the alternative hypothesis H1 are stated below.
The alternative hypothesis states the claim that at least one of the three means are of significant difference from one of the others. The || operator is used to determine a logical "or" comparison. Although there are three means of comparison, the One-Way ANOVA can still be used for comparison.
Again, our test statistic was larger than our critical value and, as such, we rejected the null hypothesis. We argued that there was a statistically significant difference between the number of responses that were received for at least one of the three broad ethnic distinctions.
To determine where the statistically significant difference was to be found, we could perform three, one-sided t-tests. From casual observation, a reader can ascertain that a significant difference exists between numbers of responses to white/Caucasian posters versus each of the other ethnic groups. However, it is doubtful that there is a statistically significant difference in the means between the African American and Asian/Pacific Islander groups (see Table 3).
The null hypothesis H0 and the alternative hypothesis H1 are stated below.
The alternative hypothesis states the claim that the mean of a gender/ethnicity category may not be equivalent to any of the others. We employ a compound terminology in this instance of the null and alternative hypotheses. For example, the combined designation (F,API) in the first term of the null hypothesis denotes that the value is the mean of females who are of Asian and/or Pacific Islander descent.
Unlike the previous scenarios, statistical analysis directed that we choose not to reject the null hypothesis (with large p-value). That is, there was no evidence of statistical significance to the differences that could be attributed to the interaction of race and gender in this experiment.
Summarized, there was a statistically significant difference in the quantity of replies each gender received; there was a statistically significant difference in the quantity of replies between at least two of the ethnic groups (and potentially as many as three differences); however, the interaction between race, gender, and quantity of replies each subgroup received was not substantiated.
Although there were perceived differences, readers should not rush to assign a motivation such as bias or discrimination to these results. In many regards, the choice to analyze by race and/or gender may have been imposed upon the problem. Athletics, Greek life, dormitories, hometowns, and a multitude of other correlating factors may have impacted these findings.
Similarly, educators of these courses should consider that the initial prompt may be one that promotes students aligning with others of common interests, appearances, and backgrounds. Inadvertently, this may leave those who do not belong to previously designed peer groups (athletics teams, Greek organizations, etc.) or perceived social strata with posts that receive lower numbers of responses.
Given that the initial prompt may have set the tone for the entire course, instructors should have designed a community-building activity that unified students. Although video as part of the introduction may seem personal, it may also create an opportunity for division (the opposite of its intent). Biases on the parts of students may be communicated subtly (or clearly!) in the making of the video. Particularly in online courses, a feeling of protection or anonymity on the part of a viewer may generate feelings of liberation and permit biases to influence responses. The use of universal design principles [17] could have guided the construction of content that was accessible to all enrolled in the course, regardless of ability, ethnicity, or background [13]. As such, attempts to remove displays of privilege and create environments based upon commonalities (such as those highlighted in [18]) could have initiated an environment where students felt less vulnerable. Instructors specifying aspects of the assessment could have further reduced the imbalance between posts of students. For example, instructors could have specified that all backgrounds of videos happen with blurring and that students include transcripts with these videos.
There are several limitations to this study that also generate clear directions for exploration. Because the institution of the study is small, it is likely that several factors (other than just ethnicity or gender) impacted forum engagement. Collaboration with individuals at larger institutions (where students may not be as familiar with each other) could speak to the validity of the findings. Also, this analysis was solely quantitative, and a qualitative analysis of student perceptions of the community could add nuance to the conversation.
In the end, if instructors hope to create an instructional environment where students participate and learn with equity, we should strive to analyze our own courses and remove barriers, whatever they may be. This begins with candid analyses of our own courses via thoughtfully designed experiments and should force us to think about aspects of our courses that can be iteratively improved upon.
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[5] Vesely, P., Bloom, L., and Sherlock, J. Key elements of building online community: comparing faculty and student perceptions. MERLOT Journal of Online Learning and Teaching 3, 3 (2007), 234–246.
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[8] Baker, R., Dee, T., Evans, B., and John, J. Race and gender biases appear in online education. Brown Center Chalkboard. Brookings Institution. April 27, 2018.
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[11] Tynes, B. M., Rose, C.A., Hiss, S., Umaña-Taylor, A. J. , Mitchell, K., and Williams, D. Virtual environments, online racial discrimination, and adjustment among a diverse, school-based sample of adolescents. International Journal of Gaming and Computer-Mediated Simulations 6, 3 (2016), 1–16. https://doi.org/10.4018/ijgcms.2014070101
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[13] Bluman, Allan G. Elementary Statistics: A Step by Step Approach. New York: McGraw-Hill Higher Education, 2009.
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[16] Wall Emerson, R. ANOVA and T-Tests. Journal of Visual Impairment & Blindness 111, 2 (2017), 193–196.
[17] Burgstahler, S. Universal Design in Education: Principles and Applications. DO-IT. 2009.
[18] @AcademicChat, et. al. This is an epic example of how to lead an icebreaker that doesn't ask students to use privilege for status- which can create hierarchy in the classroom. Twitter. 2020.
Jon Ernstberger is an Associate Vice President for Academic Affairs and Professor of Mathematics at LaGrange College. He earned his doctorate in applied mathematics with a computational concentration from NC State University in 2008 and has research interests focused on pedagogy, student success, and computational applications of mathematics.
Justin Fetner received his B.S. in mathematics and M.A.T. at LaGrange College. His interests include various branches of mathematics, sports statistics, engineering, cryptography, accounting, and actuarial science. He currently works as a mathematics instructor and coach with Auburn City Schools.
Kyle Gutowski graduated with a B.S. in mathematics from LaGrange College in 2017. He currently works as a property and casualty consulting actuary.
Patrick J. Riley, M.Ed., is a Student Affairs practitioner in the state of Kentucky, most recently at Bellarmine University (Louisville, KY). His research interests include mentorship within higher education and its connection to the motivation of practitioners as they pursue career fulfillment.
Nick Stavrow graduated from LaGrange College in 2016 with a B.A. in mathematics. He currently works as an actuary at Alliant Health Plans.
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