Weaving things together: Instructor and learner experiences
To describe experiences supported by the designed environment, below I present two narratives, one by the instructor and the other from a student perspective, to demonstrate use of the environment in a typical week of the class.
The instructor narrative:
We are entering the sixth week of this course, when we will explore concepts related to centrality and centralization in social network analysis. In preparation for this week, I have been finalizing learning goals and collecting online resources that would compliment our course textbook. After exploring a wide range of resources online, I decided to incorporate a slide deck from the University of Washington and a video lecture from a Coursera MOOC in this week's course materials. As I put this week's content together, I started to write in Bookdown, adding my own thoughts to weave different ideas and materials together and explaining course activities for this week.
After having a good handle over next week's plan, I opened up the textbook chapter students annotated last week. Dozens of Hypothesis annotations made by students showed up. I spotted several interesting questions and reactions shared by students since the last time I checked, and decided to address them in this week's course video. When pieces for this week were in place, I launched a free screencast software on my computer and started to record this week's video and uploaded it to my Youtube channel.
After a quick proofread I uploaded the course website hosted on Bookdown. I then sent out an announcement to the whole class on Slack, with a link to this week's webpage. I mentioned everyone (by adding "@everyone") so they will all receive a notification email as well. I also shared the link on Twitter and Facebook for open participants to pick up. Last week when I checked Google Analytics, I spotted dozens of visits to the Bookdown site from the US and several other countries, which was quite exciting.
Within a few hours, a Hypothesis annotation was notified on Slack. A student from the class, one of the "early-birds," spotted a typo on the course website and annotated it using Hypothesis. I temporarily left Slack, where I was chatting with a few students about last week's assignments, and went to correct the typo and re-publish the course website, all in a matter of one minute.
As this week progressed, more annotations were popping up in the Slack channel. Even though I monitored student discussions on both Hypothesis and Slack, I usually let the conversations flow and gave students ample space for sensemaking, sensegiving, and problem-solving among themselves. For questions that remained unanswered for a while on Hypothesis, I decided to respond directly, in some cases pointing to additional web resources or a related annotation shared by another student.
As we move towards the end of this week, a few early-birds started to post their assignments on Slack. This week's assignment involved computing centrality measures using either igraph or Gephi (two popular tools for social network analysis). In their posts, a few students expressed challenges they faced with getting their data into the right format. They spent hours to get the data work. Later on, one student shared her R code, together with her interpretation of measures in her course project. Students who expressed their struggles with this assignment praised her work and refactored some of her code for their own projects, with acknowledgments to the student's inspiration. Two students who were especially struggling with R, self-organized to attend a workshop offered by a group from another college of the university. They reported back to Slack what they learned and shared a bunch of resources with the class. There were a number of back and forth among course participants. They created and shared a variety of artifacts, such as Word documents, network graphs, code snippets, examples of tidied datasets, and links to extra resources they found online. I did not need to participate in many cases, as they are collaboratively figuring things out, or in some cases 'geeking out' over things I didn't think about. So I decided to move ahead to author next week's materials.
A narrative from the student perspective:
It's Monday again, the start date of a new week for the online course. I still feel pumped by last week's course activities as I was able to produce beautiful network visualizations from my project data. I don't care how many times the professor says "social network analysis is much more than those graphs," because I simply like visuals of networks, especially those made by myself. I admit this experience feels quite empowering.
When I opened my email, a Slack notification from the professor appeared. He just posted an announcement about this week. I logged into Slack and quickly read through his post. There is nothing particularly new this week, except for the course content. After the past few weeks, I now feel quite comfortable navigating digital tools used in this course. Using Slack and Hypothesis---instead of Moodle that I've been using for years---was initially challenging for my-non-tech-savvy-self, but I was able to find my ways around after the first two weeks and actually liked this approach to learning.
I followed a link to this week's course webpage and started watching the video posted by the professor. He provided a quick overview of this week's key concepts, discussion activities, and assignments. I started reading the book chapter in our textbook and made a bunch of Hypothesis annotations along the way. Sometimes I also use Hypothesis as a note-taking tool: I make private highlights (only visible to myself) of key terms and interesting viewpoints in the textbook, and then when I finish reading, I will review all the highlights and copy them into my own notebook. I am usually the first few students annotating the textbook, so I would revisit the page later to check other students' annotations. Another student, who is taking two other classes with me this semester, mentioned to me she found my annotations helpful while making the first pass through the readings. She said being able to see each other' annotations made her feel less lonely and more intimate to other classmates, even though she would still prefer reading on paper. I surely agreed. Additional resources provided by the professor were helpful for resolving some confusions left from reading the textbook. I posted some general thoughts on Slack as well.
Compared to reading these course materials, working on the assignment was much more challenging. I chose to do social network analysis using R, even though I had zero experiences in coding. The learning curve was steep and I spent a lot of time to code and debug my code. This week turned out to be easier, as my project data has already been cleaned and readily analyzable. I wrote a few lines of code and was able to compute centrality measures. This is exciting and I shared my results and code on the 'assignments' channel on Slack.
As I thought I was done with this week, another student raised a question about the centrality function I used. It turned out there are a bunch of parameters we could manipulate to achieve different analytical purposes. In the code she shared on Slack, I learned that we can normalize the centrality measure within a network. I didn't know that!! But wait, when should we normalize the measures then? Nobody seemed to know yet. A few of us went on to discuss this problem, using both public Slack channels and private messages. One student shared back a blog post seemingly addressing this question. He annotated the answer using Hypothesis so the whole class got notified as well. I kind of wished the professor could chime in. After two days, I sent him a private message on Slack, and he quickly pointed me to a journal article he found useful.
As reflected by summary statistics, the class collectively made a total of 615 public Hypothesis annotations and sent 2.2K Slack messages. These two narratives provide glimpses into their interactions and course experiences. Overall, discourse taking place in this course was fluid, dynamic, and collaborative. Through the discourse, students were engaged in sensemaking, sensegiving, collaborative problem-solving, artifact creation, and deepening discussion around knowledge artifacts. The integrative system with multiple components was able to provide a coherent discourse experience, regardless of its departure from a traditional LMS.
Future Directions
EDUCAUSE envisions a next generation digital learning environment that would support interoperability; personalization; analytics, advising, and learning assessment; collaboration; and accessibility and universal design \cite{Brown2015-wh}. This design case illustrates an effort towards such a vision. Development in educational technology in the past years has been challenging the concept of "learning management," catalyzing a shift from learning management systems towards learning environments or platforms that foster learning---learning of various kinds \cite{Feldstein2017-ai}. This paper introduces an intended design that focuses on integration of existing tools, developed for drastically different purposes, to synergically support a specific genre of learning---learning through collaborative discourse. This design case especially demonstrates possibilities in supporting the functional domains of interoperability and collaboration advocated in EDUCAUSE' vision. Below I discuss future directions of this work.
First of all, a research effort is ongoing to examine student perceptions of this designed environment and their nuanced collaborative discourse across multiple web spaces. We collected data from several sources including surveys and system logs. The ongoing research project will tackle basic usability and usefulness questions, as well as deeper questions regarding participation patterns, discourse qualities, and learner achievements.
Second, a gap in the current design is an analytic system that can operate as an agent to support sensemaking and wayfinding in such an integrative system \cite{Siemens2011-gg}. Specifically, using log data from Hypothesis and Slack, I envision a suite of discourse analytics \cite{De_Liddo2011-gt} to assist the instructor and students in keeping track of their collaborative discourse and making informed decisions on when and how to contribute.
Finally, as the course is to be offered again, I plan to maintain and reuse these digital spaces to explore cross-community interactions \cite{Zhang2017-zu}. This continued design effort will attempt to challenge the imposed artificial timeframes, so as to continually engage course alumni, nurture a sense of sustained community, and foster boundary-crossing across cohorts of students. When an LMS closes, learning could continue, so could connections formed in or for learning.
Acknowledgments
This paper is supported by an NSF Award (# <Blinded>). I thank learners who have made thoughtful contributions to the course and <blinded for review> for commenting on earlier drafts of this paper.