A decade of OLab3: what do 25 million data points tell us?

Our main demonstration server at https://demo.openlabyrinth.ca has now been up and running for a decade. And all through this time, we have been collecting a steady flow of activity metrics on how our materials are used. Every click, every response, even the thoughtful pauses, have all been recorded. So what does this tell us?

With over 25 million data points, we can sure say that this server is well used. Originally set up as a quick test bed to demonstrate what OpenLabyrinth v3 can do, it has been used by many for all sorts of purposes. We emphasize that this is a demo box with no guarantee of uptime but many authors and groups remain keen to use it. And it has been pretty reliable.

We just published a tech report on the various analytics that we have pulled from its databases. For those who are more technically inclined, feel free to peruse the report which delves into these numbers in more detail, along with some description of how we generated them.

Most of this data, we pulled directly from the SQL database that runs this server. But we have also used data pulled from our Learning Records Store, using xAPI statements.

This has given us a valuable source of orthogonal data to correlate with our SQL reports. This server has recorded nearly half a million user sessions and over 1.5 million clicks. There are  over 2000 maps and over 4000 registered users. And this is just for a single server.

We run 8 servers for PiHPES related services at UofC. Around the world, we are aware of many other installations of OpenLabyrinth but we do not track these. We know that users from around the world enjoy OpenLabyrinth. As noted a few years ago, we looked at the geolocations of some of them:

Now, let’s try to find those maps that we think have been played significantly i.e. 6 or more clicks. Here are the first 30 rows of MostPlayedCaseNamesView. The Cnt column shows how many clicks they have accumulated each. 

map_id name Cnt
624 Angus – IPE case 50172
24 Emil, Palliative Care 45820
23 Mildred Blonde 42034
33 John’s back again 24860
553 Digital Professionalism 22697
21 Chester Angermeier 21812
206 Suturing Session 16621
321 Cathy 1 (CAMH ODT Core Course) 16221
388 Lackadaisical Larry – a virtual learner 12814
933 Náhly pôrod v domácnosti 12706
335 Cathy 2 (CAMH ODT Core Course) 12065
937 GDM 11867
304 Sam (CAMH ODT Core Course) 11607
49 VP on VPs 11389
486 Obs SCT Case Series 10187
578 Rushing to Keep Up 10086
5 Welcome 10084
639 Tehotná ena 9844
940 Starostlivos? o enu pri neefektívnom doj?ení  9605
272 Sarah-Jane Pritchard 9495
771 Virtuele SOLK Patiënt: Mevrouw de Vos 9413
770 Virtuele SOLK Patiënt: Mevrouw de Graaf 9180
1551 TTalk: Allison resolving conflicts 9114
909 Perinatálna strata 8890
489 Ed Nekke 8196
207 Kendal Sweetman 8090
1679 Podpora bondingu po pôrode 8014
1922 Shoulder pain in a tennis player 8008
346 Abdominal Pain for SharcFM 7884
640 Diferenciálna diagnostika ikteru 7711

Each time a user clicks on a Link taking them to another Node, it is recorded, along with the state of Counters, the timestamp, user_id and whether this was part of a Course or Scenario. A single session can generate many thousands of rows in this table for cases like Medical Careers. But in crude terms it is an accurate representation of engagement or interaction with a case. The user cannot generate increased data or the appearance of activity by merely clicking randomly on the screen. Only valid links are recorded.

How much time is spent on a case? This is surprisingly difficult to answer across the board. While we can tell exactly how long each session was, there is such a wide variation, with lots of power law distributions, and it is not meaningful to use things like medium or maximum or standard deviation to describe them. We have lots of sessions where the user drops the case almost immediately — not the Droids they were looking for.

For a more complete look at how we analyzed this, check out our report at OLab3 case analytics tech report on Dataverse. We looked in more depth at cases which saw significant use, and used some approaches to filter out outlying data.

For an initial look at how much time might have been spent productively on the server, we estimate that comes to 10,500 hours of play time. This in itself is not inconsiderable and compares favorably with the activity metrics that we examined for our YouTube Clinisnips series: 100,000 hours: a decade on YouTube.

Which is the most popular case by amount of time spent on there? We found that to be Angus McWhindae, map 624, an interprofessional education case that was designed to be played in parallel by multiple small discussion groups. This case has accumulated more than 232 hours of learner interaction time. 

Let’s try the second one, map 24, Emil, one of our oldest palliative care cases and quite complex. This case has accumulated 151 hours for significant learner interaction. And Mildred ( a case on dizziness that we use in a large number of clinical teaching situations with individual learners), has 6200 sessions. This case has accumulated over 109 hours of interaction time. 

To give you an illustration of the power law curves that we see when looking at how long a user spends per session,

On the x-axis is duration in seconds; on the y-axis is the frequency count of sessions. 

For reference, 6000 seconds which we used for our cutoff, is 100 mins. This was the longest example of a credible, “truly played and engaged” session that we could find. This assessment of “engaged” was based on other metrics from that session, such as user_responses, consistent time spent per Node (no long interval while the user forgot about the case). 

Without filtering for very long or very short duration sessions, the curve is much more exaggerated but also less helpful.

What about ‘case completion’ metrics? As noted in the report, we found this quite difficult to assess. It does raise the higher issue in the minds of some educators as to whether the learner should be given credit for ‘completing’ the case. Indeed, this is foundational to SCORM and most other badge or credit-related systems that purport to assess ‘learning’.

If you have a resource that is essentially linear in style (such as most online course materials, which are just page-turners: a means to send information to the learner), then getting to the last page means that the learner ‘completed’ the lesson. Of course, this means very little educationally. 

If we accept the premise that the majority of our users are experienced learners and are discriminatory in the use of their time, they will not waste time on activities that do not contribute to their learning. There is very little frivolous play of these cases – that aspect is not like YouTube at all. So in relative terms for comparing cases with each other, or learners with each other, these simple metrics would appear to have value. Of course, this should be tested more thoroughly but at least this paradata gives us the tools to measure these factors. 

The purpose of this report was to illustrate, not just the extent to which OLab has been used, but also the wide variety of paradata that can be extracted from this usage. It does allow us to whittle out some weaker areas (of which there are many), and potentially learn from the stronger examples. 

It does provide a range of objective data that is orthogonal to our usual evaluation tools: happiness ratings and similar subjective surveys of learner opinions. The business world decided decades ago that customer satisfaction surveys tell you little but activity streams (who buys/does what and when) are a rich source of data that can be captured without threatening the woman-on-the-street with a clipboard. I think we are all a little tired of surveys.

Chatbots and OLab

Thinking of using a chatbot to improve interactions with students?

Chatbots are getting better. Yes, some of the most powerful are now remarkably good. But what about the chatbot services available to the average educator?

Well, these are steadily improving and we have been testing them out for use with OLab. For more information on what we have found, check out this short article.

As you will see, they are proving to be very beneficial to large and medium-sized companies.But the resources needed to create a good scenario are still a bit beyond the average educator.

Using Chats for spoilers in OLab3

Spoiler Alert

Everyone hates spoilers. But sometimes they have a useful function. We recently came across a very neat way in which they had been used.

This reminded us of our Chats widgets in OLab3, a function that has not seen a lot of use. We created a map here that demonstrates how Chats can be used to selectively reveal spoilers and why you might want to consider them. Use ‘demo‘ to unlock the case.

CURIOS service up and running again

Nice to have some good news in the current Covid outbreak: not a big item but it will help those who are trying to create content for online learning, which is much more needed now.

We have finally managed to fix an annoying bug which was preventing proper use of our CURIOS video mashup service. It is now fully functional again.

For those of you who may have missed it previously, here is more information on how to use the CURIOS video mashup tool.

Servers properly secured again

Our various servers, supporting OpenLabyrinth and the OLab education research platform, had some hiccups for a while, which resulted in our support of https and SSL being rather patchy.

Most people will find these days that many web browsers have conniptions about visiting unsecured sites, so many of you were getting dire warning about the site being insecure or have an invalid security certificate.

I think we have all that fixed now and there should be no more warnings.

Just to reassure you, there was no actual compromise of our sites and the data remains secure.

OLab4 Designer launch

Things have been really quiet over the summer but we are now ready to show you our work on the the OLab4 authoring interface.

Check out a series of upcoming posts on olab.ca which give more information on the OLab4 Designer and other new capabilities.

As before, all the source code will be posted on GitHub.

An Active Repository for OLab scenarios

For several years, we have been looking at different ways to make OpenLabyrinth scenarios more accessible. We think we have found a solution that meets the needs of both consumers and contributors of scenarios: the OLab Dataverse.

Now at first glance, this looks like Yet Another OER. But we think there are a few things that may help this to be more successful. We are working on ways to make it dead easy for OLab authors to upload their best cases directly to the OLab Dataverse which should help with the tedious task of metadata entry.

Because the materials are given a proper citation and DOI by the DataCite service, it means that the scenario becomes a citable reference that can be added to the authors’ CV and makes it easier for them to get academic credit for publishing their cases.

We have created some short notes on how we currently upload OpenLabyrinth maps to the OLab Dataverse, using a template in the meantime.

The OLab Dataverse is hosted at Scholars Portal on Canadian servers. Using a non-USA based service will help to mitigate some of the concerns raised for some jurisdictions and granting agencies.

When we say ‘Active Repository‘, we also plan to make this process more useful in providing activity metrics, using xAPI and a LRS. At present, we can create simple Guestbooks, which help us to track when the datasets are downloaded. But we feel it is equally important to create some activity metrics around the contributions  by faculty members and teachers. Partly, this will be based on the new xAPI Faculty Profile that we are developing and will incorporate into our OLab uploading mechanisms.

It is time we did a better job of looking at how our contributions to open science are used, appreciated and distributed in the world of Precision Education. We just submitted an article to MedEdPublish on why this is so important.

If you are interested in working with us in exploring how we can make these processes more accessible and more rewarding, please contact us.

OIPH Catalyst Grant outputs and metrics

In 2015, we were delighted to receive a Catalyst Grant from the O’Brien Institute of Public Health in support of development of various aspects of OpenLabyrinth as an educational research platform.

We have just reported on what arose as a result of this grant and, in general, we are pretty pleased with what came out of it and where things are headed. OpenLabyrinth continues to be used widely in the educational community and that reach is growing.

But how do we know?

This is more challenging to assess than you might think. Using standard lit search techniques, it is not hard to find journal articles that relate to the ongoing and innovative use of OpenLabyrinth. But that is only a small part of the impact. Now to give credit to OIPH and its reporting template, it is great to see that they want to know about the societal impacts, social media etc. This is something that we strongly agree with in OHMES.

But the actual measurement of such outputs is not so easy. An obvious way to do this would be via Altmetrics, which is revolutionizing how such projects and outputs are seen by the public. It has a powerful suite of tools that allows it to track mentions and reports in various public channels, social media channels, news items etc. Great stuff.

But Altmetric requires items to be assigned to either departments or institutes or faculties. For OHMES and OpenLabyrinth, this creates a significant problem. There is no ability to assign a tag or category which spans the range of groups and organizations that are involved. This is somewhat surprising, given that the general approach in Altmetrics is ontological, rather than taxonomical. (1,2)

In our PiHPES Project, partly as a result of such challenges, we are exploring two approaches to this. Firstly, we are using xAPI and the Learning Records Store (LRS) to directly track how our learners and teachers make use of the plethora of learning objects that we create in our LMSs and other platforms – the paradata, data about how things are used, accessed, distributed.

Secondly, we are looking for ways in which to make such activities and objects more discoverable, both through improved analytics on the generated activity metrics, and in tying the paradata and metadata together in more meaningful ontologies, using semantic linking.


  1. Shirky C. Ontology is Overrated — Categories, Links, and Tags [Internet]. 2005 [cited 2019 May 3]. Available from: http://www.shirky.com/writings/ontology_overrated.html
  2. Leu J. Taxonomy, ontology, folksonomies & SKOS. [Internet]. SlideShare. 2012 [cited 2019 May 3]. p. 21. Available from: https://www.slideshare.net/JanetLeu/taxonomy-ontology-folksonomies-skos