Learning from the Arava Long-Term Socio-Ecological Research workshop

DSCN2472The Eilot region, near Eilat in Israel, is considered locally as a remote part of the Negev desert in Israel (it is about 3.5h drive from the population centres of Tel Aviv). It is an arid desert, with very sparse population – about 4000 people who live in communal settlements – mostly kibbutzim in an area of 2650 sq km (about the area of Luxembourg. This is a very challenging place for Western-style human habitation, in an area with a fragile desert ecosystem. The region and the Arava Institute at the centre of it, provided the stage for a workshop on Long-Term Socio-Ecological Research (LTSER) network with participants from the European network and supported by the eLTER H2020 project. LTER and LTSER are placed-based research activities that are led from the ecological perspective, with the latter integrating social aspects as an integral part of its inquiry and research framework.

The workshop run from 4-8 March on location, which allowed the immersion into the issues of the place, as well as exchanging experiences and views across the different “platforms” (the coordination bodies for the different sites that are used for LTSER research). While the people who are involved in the network were mostly familiar with one another, I was the external guest – invited to provide some training and insight into the way citizen science can be used in this type of research.

Its been over 21 years since I’ve been in this place – which I visited several times from my childhood to my late 20s. With a long experience of living in the UK, I felt like an outsider-insider – I can understand many of the cultural aspects while, at the same time, bringing my thinking and practice that is shaped through my work at UCL over this period. Also in disciplinary terms, I was outsider-insider – I’m interested in ecology, and with citizen science, linked to many people and activities in this area of research – however, I’m not an ecologist (leaving aside what exactly is my discipline). Because of that, I am aware of their framework, research questions, and issues (e.g. limited funding and marginalisation in science and research policy) which helped me in understanding the discussion and participating in it.

Visiting the area, discussing the social and ecological aspects, and progressing on a range of concepts, brought up several reflections that I’m outlining here:

DSCN2468First is the challenge of sustainability and sustainable development in such an area. It was quite telling that the head of the region, who is an active scientist, was pointing out that they want to have progressive development, and not exactly sustainable development. As we visited and travelled through the area, the challenges of achieving sustainability – with a wish for limited demographic growth and economic development that will ensure the high quality of life that the communities carve in a hostile environment can continue. This means attracting younger people who want to be part of the specific kibbutz community (the average age in the current settlement is quite high); bringing in commercial activities that match the characteristics of the area without altering them hugely – such as renewable energy activities (the area is already receiving 70% of energy from renewable energy during the day), agriculture (the area is a large producer of Medjool dates), and tourism (a new airport is about to be finished for flights from Europe); and all this while paying attention to the environmental and natural aspects of the area.

Second, the importance of cultural shaping of human-environment relationship in the area. The social organisation, the focus on agriculture (in addition to the dates there is an important milk dairy in ), and a strong belief in the power of technology to offer solutions to emerging problems stood out as major drivers of the way things happen. Each Kibbutz have a specific culture, which influences its social and operational characteristics so each is making collective decisions according to the specific organisation, and this has an environmental impact – for example, with the increase in heat due to climate change it must be that Yotveta, with a big herd of milk cows that are maintained in the desert conditions, is facing tougher challenges – and we heard from Ketura who made the decision not to maintain their herd. The impact here is an increasing use of water to cool the cows, not to mention that need to bring the feed from outside, I’d guess through Eilat port which is a short drive away. The agriculture is important in both the general ethos of the Kibbutz movement, but also significant economic income – and at the moment the dates are suitable in terms of the income that they provide. The way technological optimism is integrated into this vision is especially interesting and was pointed out by several participants. Several local presenters (some of them decision makers) mentioned that the region wants to be “silicon valley of renewable energy” and there is already rapid development of various solar energy schemes in different settlements, a research centre, and the cadaver of Better Place battery replacement station, but clearly nothing on the scale of say, Masdar Institute or anything similar in terms of the scale and R&D effort, so it is not clear what is standing behind this phrase. It seems more like a beacon of energy independence of some sort, and the provision of energy to the nearby city of Eilat as a source of income. The local presentation and discussions show a strong “frontier” conceptualisation of a personal and collective role, and this comes first in term of the relationship with the environment. The result is odd – organic palm dates which are planted next to fragile sand dunes, and with issues with waste management…

DSCN2431Third, it was not surprising to hear about citizen science activities in the area, including a recent winter bird survey that was initiated by several environmental bodies in the area, and which includes the use of Esri Survey123 forms to collect data in several specific sites, by providing the participants shelter and food during a weekend and which had excellent results. The area is perfect for citizen science activities – it got a highly educated population, large areas of nature reserves, very good mobile connectivity even off the roads, and environmental awareness (even if actions are contradictory). It is also a critical place for migrating bird, and there is a small visitors and research park near Eilat. At least from the point of view of LTSER, there’s a potential for a range of activities that can cater for local and for tourists.

Fourth, it was interesting to have discussions about citizen science that moved well beyond concerns over data quality (although I did have some of those too – as expected!). Amongst ecologists, the term citizen science is familiar, though not the full range of possibilities and issues. There were many questions about potential cross sites projects, recruitment and maintaining work with participants, creating new projects, and even using the results from citizen science in policy processes and gaining legitimacy.

Fifth, and something that I think worth exploring further – I couldn’t escape the thought that it will be very interesting to compare the kibbutz social and cultural organisation over time with open source and open knowledge projects. A concern that we heard through the visit is about the need for demographic growth but with very specific and testing conditions for anyone who wants to join – beyond the challenging environmental conditions. There is a fascinating mix of strong ideological motivations (settling the desert, leaving in communal settings, doing agriculture in the desert) with actions that are about comfort and quality of life, and as a result, concern about the ageing of the core population many of them from the founding generation. I can see parallels with open knowledge projects such as  OpenStreetMap, or citizen science projects, where you hear two contradictory statements at one – a wish to bring more people on, combined with a strong demand for commitment, and practical barriers to entry, which as a result create a stable core community which slowly age…

The workshop was summarised graphically by Aya Auerbach, in the following way.



From environmental management to organisational strategy development: Using Drivers-Pressure-State-Impact-Response with ECSA

This week, together with Margaret Gold, I facilitated a strategy meeting of the European Citizen Science Association.31520287784_20489a734e At the moment, because a recent lecture in the Introduction to Citizen Science and Scientific Crowdsourcing course that was dedicated to environmental citizen science, the “Driving forces-Pressures-State-Impacts -Responses” (DPSIR) is in the front of my mind. In addition, next week I’ll participate in a workshop about Long-Term Socio-Ecological Research (LTSER) where I would discuss citizen science in another context where DPSIR is a common framework.

However, if you are not familiar with large-scale environmental management, where it is widely used since the mid-1990s,  you’re not expected to know about it. It got its critics, but continue to be considered as an important policy tool. DPSIR start by thinking about driving forces – trends or mega-trends that are influencing the ecosystem that you’re looking at. The drivers lead to specific pressures, for example, pollution or habitat fragmentation. To understand the pressures, we need to monitor and understand the state of the system – this is lots of time where citizen science and sensing data are used. Next, we can understand the potential impacts and then think of policy responses. So far, hopefully clear? You can read more about DPSIR here.

I haven’t come across the use of DPSIR outside the environmental area (but maybe there is?). However, as I was thinking about it, as we prepared for the meeting, I suggested that we give it a go as a way to consider strategic actions and work for ECSA. It turns out that DPSIR is a very good tool for organisational development! It allowed us to have a 20 minutes session in which we could think about external trends, and then translate them into a concrete action. Here is an example (made up, of course, I can’t disclose details from a facilitated meeting…). I’m marking positive things, from the point of view of the organisation, as (+) and negative as (-).

Let’s think of a citizen science coordination society (CitScCoSo). in terms of drivers, an example will be “increase recognition of citizen science”, as Google Trends chart shows. Next, there are the pressures which include (-) the growth in other organisations that are dedicated to citizen science and compete with CitScCoSo, which mean that it will need to work harder to maintain its position, (+) increase in requests to participate in activities, projects, meetings, talks etc which will create opportunity to raise profile and recognition. CitScCoSo current state can be that the organisation is funded for 5 more years and have a little spare capacity for other activities. The impacts can be (+) more opportunities for research funding and collaborations or, (-) demand for more office space for CitScCoSo (-) lack of IT infrastructure for internal organisational processes. Finally, all this analysis can help CitScCoSo in response – securing funding for more employees or a plan for growth.

When you do that on a flipchart with 5 columns for the DPSIR element, it becomes a rapid and creative process for people to work through.

As I pointed, a short exercise with ECSA board showed that this can work, and I hope that the outcomes are helpful to the organisation. I will be interested to hear if anyone else know of alternative applications of DPSIR…


Participatory soundscape sensing – joint paper with Dr Chunming Li

One of the lovely aspects of scientific research is its international dimension – the opportunity to collaborate with people from different places, cultures, and necessarily practices and points of view.

PSSonline-CMLiDuring 2017, Dr Chnming Li, of the Institute of Urban Environment of the Chinese Academy of Science, was a visiting researcher in ExCiteS. Dr Li research is on participatory sensing and the development of sensors and applications for the urban environment. We collaborated on a paper that described the Participatory Soundscape Sensing project that he is developing, with an app on Android mobile phones, called SPL Meter, that is used to carry out the participatory sensing.

One demonstration that culture matter is in the app request for classification of sound as “harmonious” – a qualification of the sound in the right place, such as traffic noise on the road, or birds in the park. This is a quality that I haven’t encountered in studies in Europe or USA.

The paper is: “Li, C., Liu, Y., and Haklay, M., 2018, Participatory soundscape sensing, Landscape and Urban Planning 173: 64-69

Here is the abstract of the paper, and a link to the paper itself:

“Soundscape research offers new ways to explore the acoustic environment and potentially address challenges. A comprehensive understanding of soundscape characteristics and quality requires efficient data collection and analysis methods. This paper describes Participatory Soundscape Sensing (PSS), a worldwide soundscape investigation and evaluation project. We describe the calibration method for sound pressure levels (SPL) measured by mobile phone, analyze the PSS’s data temporal-spatial distribution characteristics, and discuss the impact of the participants’ age and gender on the data quality. Furthermore, we analyze the sound comfort level relationships
with each class of land use, sound sources, subjective evaluation, sound level, sound harmoniousness, gender, and age using over a year of shared data. The results suggest that PSS has distinct advantages in enhancing the amount and coverage of soundscape data. The PSS data distribution is closely related to the temporal pattern of the human work-rest schedule, population density, and the level of cyber-infrastructure. Adults (19–40 years old) are higher-quality data providers, and women exhibit better performance with respect to data integrity than men. Increasing the proportion of natural source sounds and reducing the proportion of humanmade sources of sound is expected to enhance the sound comfort level. A higher proportion of sound harmoniousness
leads to higher sound comfort, and the higher proportion of subjective evaluation sound level does not lead to decreased sound comfort. We suggest that the crowdsourcing data with participatory sensing will provide a new perspective in soundscape investigation, evaluation, and planning.”

The paper is available on ScienceDirect or also here

DITOs, Doing It TOgether Science – introductory video

The Doing It Together Science (DITOs) project is now in its 20th Month. It is a 3-year project, funded by the EU Horizon 2020 programme, that is aimed to increase awareness of and participation in citizen science across Europe and beyond. As such, it is focused on communication, coordination, and support of citizen science activities. Therefore, the project promotes the sharing of best practices among existing networks for a greater public and policy engagement with citizen science through a wide range of events and activities. Some of these activities include doing citizen science, as ‘engaging by doing’ is central to the effort of the project. Other activities, both online and offline, are focused on communicating different facets of citizen science, from in-depth engagement with small and organised groups to large-scale engagement via social media.
DITOs supports existing and new projects across the landscape of citizen science: top-down projects, in which people join an activity that is designed and coordinated by scientists; bottom-up science activities, in which people, scientifically trained or not, organise a research project around a problem of direct concern (this is sometimes known as DIY (Do It Yourself) science); as well as collaborative projects that are created jointly by scientists and participants.

In collaboration across the consortium, the Waag Society produced a short video of less than 3 minutes about the project. It was made from material from our events and it is good to such a short introduction to explain what the project is about…

Citizen Science & Scientific Crowdsourcing – week 5 – Data quality

This week, in the “Introduction to Citizen Science & Scientific Crowdsourcing“, our focus was on data management, to complete the first part of the course (the second part starts in a week’s time since we have a mid-term “Reading Week” at UCL).

The part that I’ve enjoyed most in developing was the segment that addresses the data quality concerns that are frequently raised about citizen science and geographic crowdsourcing. Here are the slides from this segment, and below them a rationale for the content and detailed notes

I’ve written a lot on this blog about data quality and in many talks that I gave about citizen science and crowdsourced geographic information, the question about data quality is the first one to come up. It is a valid question, and it had led to useful research – for example on OpenStreetMap and I recall the early conversations, 10 years ago, during a journey to the Association for Geographic Information (AGI) conference about the quality and the longevity potential of OSM.

However, when you are being asked the same question again, and again, and again, at some point, you start considering “why am I being asked this question?”. Especially when you know that it’s been over 10 years since it was demonstrated that the quality is beyond “good enough”, and that there are over 50 papers on citizen science quality. So why is the problem so persistent?

Therefore, the purpose of the segment was to explain the concerns about citizen science data quality and their origin, then to explain a core misunderstanding (that the same quality assessment methods that are used in “scarcity” conditions work in “abundance” conditions), and then cover the main approaches to ensure quality (based on my article for the international encyclopedia of geography). The aim is to equip the students with a suitable explanation on why you need to approach citizen science projects differently, and then to inform them of the available methods. Quite a lot for 10 minutes!

So here are the notes from the slides:

[Slide 1] When it comes to citizen science, it is very common to hear suggestions that the data is not good enough and that volunteers cannot collect data at a good quality, because unlike trained researchers, they don’t understand who they are – a perception that we know little about the people that are involved and therefore we don’t know about their ability. There are also perceptions that like Wikipedia, it is all a very loosely coordinate and therefore there are no strict data quality procedures. However, we know that even in the Wikipedia case that when the scientific journal Nature shown over a decade ago (2005) that Wikipedia is resulting with similar quality to Encyclopaedia Britannica, and we will see that OpenStreetMap is producing data of a similar quality to professional services.
In citizen science where sensing and data collection from instruments is included, there are also concerns over the quality of the instruments and their calibration – the ability to compare the results with high-end instruments.
The opening of the Hunter et al. paper (which offers some solutions), summarises the concerned that are raised over data

[Slide 2] Based on conversations with scientists and concerned that are appearing in the literature, there is also a cultural aspect at play which is expressed in many ways – with data quality being used as an outlet to express them. This can be similar to the concerns that were raised in the cult of the amateur (which we’ve seen in week 2 regarding the critique of crowdsourcing) to protect the position of professional scientists and to avoid the need to change practices. There are also special concerns when citizen science is connected to activism, as this seems to “politicise” science or make the data suspicious – we will see next lecture that the story is more complex. Finally, and more kindly, we can also notice that because scientists are used to top-down mechanisms, they find alternative ways of doing data collection and ensuring quality unfamiliar and untested.

[Slide 3] Against this background, it is not surprising to see that checking data quality in citizen science is a popular research topic. Caren Cooper have identified over 50 papers that compare citizen science data with those that were collected by professional – as she points: “To satisfy those who want some nitty gritty about how citizen science projects actually address data quality, here is my medium-length answer, a brief review of the technical aspects of designing and implementing citizen science to ensure the data are fit for intended uses. When it comes to crowd-driven citizen science, it makes sense to assess how those data are handled and used appropriately. Rather than question whether citizen science data quality is low or high, ask whether it is fit or unfit for a given purpose. For example, in studies of species distributions, data on presence-only will fit fewer purposes (like invasive species monitoring) than data on presence and absence, which are more powerful. Designing protocols so that citizen scientists report what they do not see can be challenging which is why some projects place special emphasize on the importance of “zero data.”
It is a misnomer that the quality of each individual data point can be assessed without context. Yet one of the most common way to examine citizen science data quality has been to compare volunteer data to those collected by trained technicians and scientists. Even a few years ago I’d noticed over 50 papers making these types of comparisons and the overwhelming evidence suggested that volunteer data are fine. And in those few instances when volunteer observations did not match those of professionals, that was evidence of poor project design. While these studies can be reassuring, they are not always necessary nor would they ever be sufficient.” (http://blogs.plos.org/citizensci/2016/12/21/quality-and-quantity-with-citizen-science/)

[Slide 4] One way to examine the issue with data quality is to think of the clash between two concepts and systems of thinking on how to address quality issue – we can consider the condition of standard scientific research conditions as ones of scarcity: limited funding, limited number of people with the necessary skills, a limited laboratory space, expensive instruments that need to be used in a very specific way – sometimes unique instruments.
The conditions of citizen science, on the other hand, are of abundance – we have a large number of participants, with multiple skills, but the cost per participant is low, they bring their own instruments, use their own time, and are also distributed in places that we usually don’t get to (backyards, across the country – we talked about it in week 2). Conditions of abundance are different and require different thinking for quality assurance.

[Slide 5] Here some of the differences. Under conditions of scarcity, it is worth investing in long training to ensure that the data collection is as good as possible the first time it is attempted since time is scarce. Also, we would try to maximise the output from each activity that our researcher carried out, and we will put procedures and standards to ensure “once & good” or even “once & best” optimisation. We can also force all the people in the study to use the same equipment and software, as this streamlines the process.
On the other hand, in abundance conditions we need to assume that people are coming with a whole range of skills and that training can be variable – some people will get trained on the activity over a long time, while to start the process we would want people to have light training and join it. We also thinking of activities differently – e.g. conceiving the data collection as micro-tasks. We might also have multiple procedures and even different ways to record information to cater for a different audience. We will also need to expect a whole range of instrumentation, with sometimes limited information about the characteristics of the instruments.
Once we understand the new condition, we can come up with appropriate data collection procedures that ensure data quality that is suitable for this context.

[Slide 6] There are multiple ways of ensuring data quality in citizen science data. Let’s briefly look at each one of these. The first 3 methods were suggested by Mike Goodchild and Lina Li in a paper from 2012.

[Slide 7] The first method for quality assurance is crowdsourcing – the use of multiple people who are carrying out the same work, in fact, doing peer review or replication of the analysis which is desirable across the sciences. As Watson and Floridi argued, using the examine of Zooniverse, the approaches that are being used in crowdsourcing give these methods a stronger claim on accuracy and scientific correct identification because they are comparing multiple observers who work independently.

[Slide 8] The social form of quality assurance is using more and less experienced participants as a way to check the information and ensure that the data is correct. This is fairly common in many areas of biodiversity observations and integrated into iSpot, but also exist in other areas, such as mapping, where some information get moderated (we’ve seen that in Google Local Guides, when a place is deleted).

[Slide 9] The geographical rules are especially relevant to information about mapping and locations. Because we know things about the nature of geography – the most obvious is land and sea in this example – we can use this knowledge to check that the information that is provided makes sense, such as this sample of two bumble bees that are recorded in OPAL in the middle of the sea. While it might be the case that someone seen them while sailing or on some other vessel, we can integrate a rule into our data management system and ask for more details when we get observations in such a location. There are many other such rules – about streams, lakes, slopes and more.

[Slide 10] The ‘domain’ approach is an extension of the geographic one, and in addition to geographical knowledge uses a specific knowledge that is relevant to the domain in which information is collected. For example, in many citizen science projects that involved collecting biological observations, there will be some body of information about species distribution both spatially and temporally. Therefore, a new observation can be tested against this knowledge, again algorithmically, and help in ensuring that new observations are accurate. If we see a monarch butterfly within the marked area, we can assume that it will not harm the dataset even if it was a mistaken identity, while an outlier (temporally, geographically, or in other characteristics) should stand out.

[Slide 11] The ‘instrumental observation’ approach removes some of the subjective aspects of data collection by a human that might make an error, and rely instead on the availability of equipment that the person is using. Because of the increase in availability of accurate-enough equipment, such as the various sensors that are integrated in smartphones, many people keep in their pockets mobile computers with the ability to collect location, direction, imagery and sound. For example, images files that are captured in smartphones include in the file the GPS coordinates and time-stamp, which for a vast majority of people are beyond their ability to manipulate. Thus, the automatic instrumental recording of information provides evidence for the quality and accuracy of the information. This is where the metadata of the information becomes very valuable as it provides the necessary evidence.

[Slide 12] Finally, the ‘process oriented’ approach bring citizen science closer to traditional industrial processes. Under this approach, the participants go through some training before collecting information, and the process of data collection or analysis is highly structured to ensure that the resulting information is of suitable quality. This can include the provision of standardised equipment, online training or instruction sheets and a structured data recording process. For example, volunteers who participate in the US Community Collaborative Rain, Hail & Snow network (CoCoRaHS) receive standardised rain gauge, instructions on how to install it and online resources to learn about data collection and reporting.

[Slide 13]  What is important to be aware of is that methods are not being used alone but in combination. The analysis by Wiggins et al. in 2011 includes a framework that includes 17 different mechanisms for ensuring data quality. It is therefore not surprising that with appropriate design, citizen science projects can provide high-quality data.



Citizen Science for Observing and Understanding the Earth

Since the end of 2015, I’ve been using the following mapping of citizen science activities in a range of talks:

Range of citizen science activities
Explaining citizen science

The purpose of this way of presentation is to provide a way to guide my audience through the landscape of citizen science (see examples on SlideShare). The reason that I came up with it, is that since 2011 I give talks about citizen science. It started with the understanding that I can’t explain extreme citizen science when my audience doesn’t understand what citizen science is, and that turned into general talks on citizen science.

Similarly to Caren Cooper, I have an inclusive approach to citizen science activities, so in talks, I covered everything – from bird watching to DIY science. I felt that it’s too much information, so this “hierarchy” provides a map to go through the overview (you can look at our online course to see why it’s not a great typology). It is a very useful way to go through the different aspects of citizen science, while also being flexible enough to adapt it – I can switch the “long-running citizen science” fields according to the audience (e.g. marine projects for marine students).

An invitation for Pierre-Philippe Mathieu (European Space Agency) in 2015 was an opportunity to turn this mapping and presentation into a book chapter. The book is dedicated to “Earth Observation Open Science and Innovation and was edited by Pierre-Philippe and Christoph Aubrecht.

When I got to writing the chapter, I contacted two researchers with further knowledge of citizen science and Earth Observation – Suvodeep Mazumdar and Jessica Wardlaw. I was pleased that they were happy to join me in the effort.

Personally, I’m very pleased that we could include in the chapter the story of the International Geophysical Year, (thank Alice Bell for this gem), with Moonwatch and Sputnik monitoring.

The book is finally out, it is open access, and you can read our chapter, “Citizen Science for Observing and Understanding the Earth” for free (as well as all the other chapters). The abstract of the paper is provided below:

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges.


Citizen Science & Scientific Crowdsourcing – week 3 – Participation inequality

One of the aspects that fascinates me about citizen science and crowdsourcing is the nature of participation and in particular participation inequality. As I’ve noted last week, when you look at large scale systems, you expected to see it in them (so Google Local Guides is exhibiting 95:5:0.005 ratio).

I knew that this phenomenon has been observed many times in Massive Online Open Courses (MOOCs) so I expected it to happen in the course. I’m particularly interested in the question of the dynamic aspect of participation inequality: for example, at the point of the beginning of the “introduction to citizen science and scientific crowdsourcing” course, every single person is at exactly the same level of participation – 0. However, within three weeks, we are starting to see the pattern emerges. Here are some of the numbers:

At this point in time, there are 497 people that went through the trouble of accessing UCLeXtend and creating a profile. They are a small group of the people that seen the blog post (about 1,100) or the tweet about it (about 600 likes, retweets or clicking on the link). There are further 400 people that filled in the online form that I set before the course was open and stated their interest in it.

The course is structured as a set of lectures, each of them broken into segments of 10 minutes each, and although the annotated slides are available and it is likely that many people prefer them over listening to a PowerPoint video (it’s better in class!), the rate of viewing of the videos gives an indication of engagement.

Here are our viewing statistics for now:


We can start seeing how the sub-tasks (viewing a series of videos) is already creating the inequality – lots of people watch part of the first video, and either give up (maybe switching to the notes) or leaving it to another time. By part 4 of the first lecture, we are already at very few views (the “Lecture 3 Part 2” video is the one that I’ve integrated in the previous blog post).

What is interesting to see is how fast participation inequality emerges within the online course, and notice that there is now a core of about 5-10 people (about 1% to 2%) that are following the course at the same rate as the 9 students who are in the face to face class. I expect people to also follow the course over a longer period of time, so I wouldn’t read too much into the pattern and wait until the end of the course and a bit after it to do a full analysis.

When I was considering setting up the course as a hybrid online/offline, I was expecting this, since the amount of time that is required to follow up the course is nearly 4-5 hours a week – something reasonable for an MSc student during a course, but tough for a distance learner (I have a huge appreciation to these 10 people that are following!).