MOOC Data – all personal identifying info removed.
If anyone is interested in the data that I collected for my microethnography then these sheets contain the raw figures. My analysis went on to about a dozen sheets where I looked at this raw data in a number of different ways.
Created February 28, 2020 at 11:24AM
Folder path: /MScEDC Spreadsheets
I’ve seen a few people using Pocket, so I had a look at it. Seems like quite a useful app (especially since it can be used for content anywhere, not just content already on one site). Unfortunately it doesn’t seem to embed very well on WordPress and relies on people copying a link into a new tab 🙁
I tried swapping out the shortened IFTTT link for the longer one that it has if you look up the IFTTT applet history. It gives this effect where the title is there instead of a link, and it does link to the right article. However, it doesn’t open in a new tab, so that’s annoying…
Okay, so posting the shortened url for the post using IFTTT gives just the shortened link, which is dull and requires people to copy it into their browser to see the post – no dice.
Using the full recipe suggested by IFTTT posts the content of the Reddit post, and a shortened link, which is just as annoying.
Going into the instance of the recipe being activated, and copying the entire pre-shortened link gives us a post like this, which looks illegible on this background. It would also require me going in to each post from Reddit to this blog and messing about with the url…
Changing my theme to light seems to have worked in terms of making this legible. Perhaps I can do something with the Custom version – hate writing on a white background 🙁
Unfortunately Custom just allows me to change the header colour. I’ve gone back to the Dark version of the theme on the basis that I can live without using Reddit on my Lifestream if trying to use it makes everything look terrible. Also Reddit is a pain to find anything useful on.
From MOOCs to TikTok: The Unexpected Impact of the MOOC Hype
Submitted January 15, 2020 at 07:39PM by dhawal
In an attempt to add more feeds to my Lifestream, I’m going to experiment with a few different sites to see what I can add. I think Reddit might be a good one to try, but so far I’m finding it a nightmare to navigate 🙁 I’ll probably give LinkedIn a try and maybe even Pinterest (though I’m not sure how useful that one may be). Has anyone found a way to share things from any audio sites like Soundcloud (other than just pasting in a link)? When I’m looking for things on a subject I usually just Google them rather than trawling specific sites, so setting up a range of feeds is a little bit alien to me…
Lots of fun trying to draw conclusions from the sheets and sheets of data I gathered from the MOOC I'm doing. Not sure how successful I was in finding the community aspect… #mscedchttps://t.co/9DdfD8xlA4
So, what did my intrepid expedition into the AI for Healthcare: Equipping the Workforce For Digital Transformation MOOC unearth in the first couple of weeks? As mentioned before, I’m using Week 1 of the MOOC for my microethnography. The MOOC runs over 5 weeks, so taking a snapshot of the first week, using data collected at the end of that week seems the fairest way to look at how the community started off.
In terms of community it is difficult to judge just from my own experiences of the MOOC. I can see that people Like and reply to some of my posts, and I’ve left replies for some others. However, the format doesn’t lend itself to prolonged conversations. To see if my experience is broadly similar to others’ I have collected a list of every known user (those who have posted or replied to a post in week 1 or on the initial Hello thread). I have then collected data on how many posts each known person has made on each of the topic threads, how many replies where made on each post and by whom, and how many Likes each person received on their posts and comments. I know that this methodology is a crude way of measuring community. It doesn’t take into account the nature or usefulness of the posts or replies (for instance, replies that just state “well said” or “I agree”), but hopefully we can get a rough idea of how much the students interact through these figure. There is also no way of looking at Lurkers (people on the MOOC who never interact) as there is no way of finding out the total number of students on the MOOC. I would suggest that there is a subcategory of Lurker – the Like Lurker, who only interacts with others through Liking posts or comments. I can’t prove the existence of these elusive people, as there is no way of knowing who has awarded a Like, but I think it is a reasonable assumption based on my experience of Social Media.
Known Users (Week 1 and Hello thread) = 240
Total number of Posts = 514
Total number of replies = 74
Total number of Likes = 503
Users who posted at least once during Week 1 = 183 (76%)
Users who received replies = 51 (21%)
Users who left replies = 47 (19%)
Users who received Likes = 123 (51%)
Users who replied but never posted = 6 (3%)
Users who’s posts never received Likes or replies = 59 (25%)
Users who received replies but never replied to others (except on their own posts) = 38 (16%)
Users who gave replies to others but never received any for their posts = 28 (12%)
These figures tell a story. While most known users (76%) posted in Week 1, relatively few replies where made, with most interaction taking place through Likes.
I decided to drill a bit further into the data to see if there where any patterns. For instance, are posts, replies and Likes spread evenly through the Known Users set, or are some students more engaged in forum activities than others? It could be that we have a small group of highly engaged students who largely spend their time on the MOOC Posting, replying to and Liking each other’s contributions, with a larger group showing some engagement and then the pool of Lurkers?
Analysis of Post numbers
As we can see, the majority of people made relatively few posts. 90% of students posted 5 times or less across the 12 topics looked at. So, 205 of the 514 total posts (40%) of the posts where made by 25 people (10%). This tends to support the idea that there is a core group of students who are more heavily engaged with the MOOC and that the emerging community may be driven by the comments made by this group of “influencers” who have a lot of control over the direction of conversation.
Analysis of replies and Likes
For this analysis I thought it would be useful to look at not just the number of replies each person received, but also how many they received per post. This should give us an idea of how successful our “influencers” are in controlling the conversation. A similar analysis of Likes was also carried out as this could be a better measure of how many people agree with the posts made by each person, while replies would tend to examine people disagreeing.
As we can see there are a couple of peaks in these graphs. Looking at the posts in question, one was a post that was “pinned” to the top of a thread by the course leaders. Since the posts are usually displayed in order of most recent, this blip could be caused simply by exposure, or it could be caused by the post being particularly insightful (which would reasonably be why it was pinned in the first place). The smaller anomaly was a post referencing a particular source of potential funding for projects, so it was naturally of great interest to the learners from within the NHS who would need to fund any AI applications that they intend to develop. I’ve reproduced the two graphs without these peaks so that we can look more closely at the trends.
As we can see, other than a small peak further along, the number of replies per post is relatively small and concentrated to those people who post often. However, the pattern for Likes is more uniform with those who post less frequently receiving a similar number of Likes per post to those who post often. In fact there seems to be a trend where those who post less frequently receive more Likes per post. There could be several reasons for this. Perhaps the people who post often are posing questions. Perhaps their views are contentious. Perhaps those within our proposed “influencer” group comment on each other’s posts, seeking them out to add to. Without considerable data gathering it is not possible to see which of these is correct, or if it is a combination. What we can see is that students are clearly reading the contributions left by their peers (remember, we don’t know if Likes definitely come from those who post, or if they also come from Lurkers). Not only can we see that students are reading a lot of the posts, but we can see that they are slightly more inclined to Like posts from those who are less prolific. There could be many reasons for this. Perhaps those who post less often only do so when they have a good point to make. Perhaps the usual way that posts are presented chronologically means that some posts get more exposure (on the front page) during times when more people are using the MOOC. Again, further data would need to be collected to arrive at any solid conclusions.
So what can I conclude from my examination of Week 1? It would seem that the MOOC has a large group of students who are engaged in the threads (although we can’t know this as a percentage of the number of users on the MOOC as we don’t know the total number of users). Within this group, there is a proportion (10%) who are heavily engaged in the threads (posting often and receiving replies and Likes in line with the number of posts). There is a larger group on the fringes of this community who involve themselves more rarely, but who’s contributions are either equally or slightly more highly regarded by the majority of users. The reason why we have this split is not clear, it could be down to personality type, experience of other online learning and MOOCs, familiarity with other online communities (for example social media), or it could be due to their reasons for attending the MOOC – it was designed for use by those within the NHS, and is very focussed on that organisation. Those attending who work for the NHS will naturally have more of an opinion on how the subject matter relates to their role, and may have insights that those attending the MOOC for personal interest do not. It would be interesting to see how these patterns play out in later weeks of the MOOC, or to see how they compare to a different type of digital course. It would also be interesting to see if there is any pattern in community engagement based on the background of the students (some data was collected on those users who are part of an NHS organisation or who have a medical background from the Hello thread before Week 1). I think that given the time constraints and the hours that it took to collect the data set so far, I will follow up on the last one of these ideas and look at patterns based on student history.