In previous blog, we talked about applying network visualization to course discussion interaction analysis. This blog demonstrates an example of using the visualization to analyze the impact of instructor involvement on student discussion interactions.

The following two graphs show student to student and instructor to student discussion interactions in two courses respectively. The two courses were offered in the same term under the same program and contain roughly the same number of enrollments. The discussion requirements specified in the two courses are identical. The results suggest that:

• Less instructor involvement coincides with more student-to-student interaction
• More instructor involvement coincides with longer student replies
• More instructor involvement coincides with greater student self-reflection

Graph 1: Each node represents a student who either received at least one feedback or provided at least one reply to another student. The size of each node suggests the quantity of interactions associated with the student. The thickness of each arrow line implies the length of a reply.

Graph 1 is presented in ‘kamada.kawai’ layout:

The two graphs above show that although course one students were fairly active in discussion activities, comparing to their counterparts in course two, course one students contributed more equally in terms of the length of replies (word counts of the threads) and the number of replies. In contrast, a few students in course two appear to have a greater quantity of the interaction, and yield a few longer replies.

Now, let’s take a look of instructor’s involvement in both courses. The course two instructors’ presence appear to be more evident than course one instructors, and instructors in course two provided more lengthy replies to their students than course one instructors.

Graph 2: the orange node in the middle represents the instructor who provided at least one reply to students. The size of each node suggests the number of replies made to students. The thickness of the arrow line implies the length of a reply.

Our Canvas discussion data shows that about 20% of courses that are published in Canvas use the Canvas discussion tool. However, little is known as to how students interacted with their peers in Canvas discussions, whether students were actively engaged in discussions, and how instructor involvements shape/facilitate a community of inquiry. To see if network analysis is useful to address some of these questions, we applied network graph approach to visualize discussion interaction data.

For the proof of a concept, we fabricated a small set of discussion interaction data. We converted the discussion data to an edge list. An edge list contains “from” and “to” columns that represent the two nodes connected in a network. Table 1 includes the sample set of discussion interaction data in an edge list form. The values in the first column are discussion feedback providers and the second column includes the feedback receivers.

The Graph 1 was derived from the sample data set and generated in R with the igraph package. Each node represents a discussion participant. The direction/edge arrow indicates a directed interaction from a feedback provider to the feedback receiver – the author of the target thread to which the provider replies. The feedback can be a reply to a new post, or a response to a reply. The size of each node implies the total counts of the directed interactions for the node. The graph reveals interesting elements related to students’ discussion engagement. For instance, we can quickly see that studentA tended to respond to most of his peers, but did not get much feedback from his peers at all. In contrast, studentE received responses from many of his peers, but only initiated one thread to the instructor. Maybe the initial thread that studentE posted was so interesting or debatable that grabbed the attention of other students. studentF appears to be less interactive than his peers, and provided no feedback to peer postings.

Graph 1:The size of each node implies the count/degree of the directed interactions for the node

To further explore the relationship between online discussion behavior and classwork performance, we experimented to add student grades as node attributes. We also added the word count of a reply as weight to each directed interaction.

To experiment with the nodes’ attributes in our analysis, we fabricated students’ grades, assigning them either an above median or below median value. We also added a weight for each unique interaction by counting the number of academic words by excluding English Stopwords in the thread. Graph 2 was generated by adding the weight values and the nodes’ attributes.

Graph 2: The color green means a performance above the median, and red denotes a performance below median. The size of each node represents the amount of the two-way interactions for the node. The thickness of the arrow line implies the number of academic words in each interaction.

With the same edge list, we can apply different igraph layouts to an interaction visualization. For instance, the following three graphs were derived from the same set of data, the circle layout gives us an overview of the students who either provided at least one feedback to their peers, or received at least one reply from their peers. The kamada-kawai layout allows us to quickly identify the students who are less interactive. The sphere layout helps us see the reply threads that contain the most words.

Table 1 includes a sample set of discussion interaction data in an edge list form. The values in the first column are discussion feedback providers and the second column includes the feedback receivers. Table 2 is the edgelist with associated edge values, the weight for each unique interaction, which was used to created a weighted network. Table 3 is the nodes’ attributes representing student performance, above median-Above or below median-Below respectively.

Table1:

Table 2 – nodes’ attributes:

Table 3 – a weighted edgelist:

install.packages(“igraph”)
library(igraph)
#load the edge list and nodes to R
nodes links

Resources: http://kateto.net/network-visualization

Would you like to know when Dartmouth students are likely to submit their assignments via Canvas, and whether the activity is related to the time and date when the assignment is due? If so, how does the assignment due times affect students’ submission activities? If you are interested in learning about the assignment submission facts, please click on the image below to view the 2015 assignment submission analytics.

These results were derived from 2015 course, course assignment info and submission data. Graded discussions, online quizzes and any assignments that have an ‘online’ submission type were included in the analysis. The assignment that does not have an ‘online’ submission type or a due date/time associated with was excluded from the analysis.

After a few outliers were identified and removed, the median submission time before due date is 30 minutes and the median submission time past due is 1.2 hours. The ‘withoutoutlier’ charts also show that the number of before due submissions is much greater than the total number of past due submissions and the variation in past due submission hour is wider than before due submission hour. All of which imply that majority Dartmouth students tend to submit assignments more often before than past the due time and the likelihood assignment submission time is 30 minutes prior to assignment due time.

Taking all four terms in the year of 2015 into consideration, the evening period from 8 pm to 10 pm is a popular time for assignment submissions, and 10 pm is the peak assignment due time (when assignments are due). Some months show some variability. For instance, in November, there is a peak submission time at 11 pm coupled with a 11 pm peak assignment due time. in April, 11 am arises to be another peak time for assignment submissions in addition to the popular evening submission hours. Faculty might consider these behaviors when choosing due times.

The chart below reveals that a number of assignments contain due date/time that were set between midnight and 8am Eastern time, which prompted some students staying up overnight in order to submit the assignments right around due time. Even though the hourly submission chart reveals that there are variability in median submission time for all submissions at a given submission hour, we can conclude that students tend to submit assignments 30 minutes prior to assignment due time more often than past assignment due time. Therefore, we need to suggest faculty to be mindful when choosing assignment due time.