DAY 4 Sunday 14th April 2019

Sunday morning we saw all five baby rabbits snuggling.

Then we checked the tummies of all five babies for milk because it would mean that they’ve been fed.

Eventually we came to the runt of the litter, and we calmly put Freya on her back, and put the baby on one of her teets.

When we put the baby on her tummy Freya went into some sort of trance, and stayed rock still, it was amazing.

By Otto

DAY 3 Saturday 13th April 2019

Today we sadly found a dead baby but to our surprise the five are still alive! It was a sixth baby, probably dead since birth. The reason for it’s death was it was two times the size of the other babies and probably had heart problems. By Dex

This is a picture of Freya munching on the grass. She is always extremely hungry since having 5 cheeky little monkeys – screeching for milk!
I put her out on the grass today because of her love of the green stuff! And it is a lot easier for us – no more picking grass for her to eat. By Dex

DAY 2 Friday 12th April 2019

Freya feeds her babies twice a day – early in the morning and late in the evening. We have noticed the skin colour developing: darker, less pink.

You can see in this video that they’re starting to make noises!:

By Dex

Today, we also found out how important it is to check the babies day by day. My mum was very worried that if Freya smelt a human scent, she would abandon the babies….But I knew that domestic rabbits are used to human contact – especially Freya!.

The reason I needed to hold the babies was to check their bellies for size – rabbit babies should have nice, round bellies since if they aren’t full they will starve. This is one of the babies that stayed still long enough for us to get a good pic! By Dex

DAY 1 Thursday 11th April 2019

Hello! This is day one of the Freya and Reggie baby bunny blog. But let’s rewind to the day before…..Initially we weren’t sure that our FreyFrey was pregnant but then we saw a major clue….Freya was pulling out fur from her ruff on her chest – frantically. So, we knew something was up…..

Dexter first found the babies. They were all tucked up in their nest, squirming about. We got absolutely overwhlemed at these FIVE BABY RABBITS!!!!

By Otto

P.S In fact, we need to back up further our first signs that Freya was pregnant came two weeks ago when we woke up one morning to find her furiously gathering large amounts of hay in her mouth to make a nest….See this:


A quick note here about the proud father! Reggie the wonderful little bunny has pretty much made a full recovery from his ‘paresis’ – partial paralysis, of the hind legs – apparently very common in rabbits.

Miraculously, with the worming medicine he can now run and hop a little more each day.

Mum started to get worried that Freya wasn’t spending much time with them as she knew cats, dogs and human mums don’t leave their newborns…Freya seemed to be sitting away from the nest, resting or eating….. She was worried that they wouldn’t get enough milk. So I researched how rabbits look after their babies and it turns out since they are ‘prey’ animals, they try to attract as little attention as possible to their babies. This was why Freya was ‘making out’ there were no babies. By Dex

pPlan: a Personal Development Planning & Mentoring tool

I don’t want to repeat what can currently read on the tools site  (offline now, or seen in the planning essentials sheet or  user guide page .  Also problems/questions this prototype is trying to answer can be seen in the survey.  So the four screenshots below are intented to give a quick insight into how the tool can be used from a mentors point of view, although the metees view is not hugely different as the user guide illustrates.

Figrure 1 below is the dashboard of a user who has a mentor themself, but is also a mentor to three individuals. The dashboard displays links to the boards of these three mentees as well as links to request other mentors/mentees and withdraw informed consent.  Note: click images to enlarge

pPlan Dashboard
figure 1: pPlan Dashboard of user who is a mentor to 3 individuals

Figure 2 below is a “mentors” view on their “mentee’s” Board (Grid-mapped view).  The idea is users can develop their plans by creating different ideal situations/goals to which they can add one or many SMART plans… All of whcih can receive inline feedback from authorised mentors:

Mentors view of mentees board
figure 2: Mentors view of mentees board (Grid-mapped view) – where inline feedback on artefacts can be given by mentors

Although the prototype attempts to capture the minimum “background” information a mentor might need from a student by including a “Current Situation” description section and a Values inventory, from feedback on the tool already received it is clear the chief selling point is the structure provided to the student and mentor to model the thinking and planning process for future events – and receive inline feedback. For example, this overarching student planning board encompasses achieving course success and career planning:

This board is more focused only supporting the internship placement:

(NOTE: these are “view only” links – you need to setup a pPlan account to use tool)

Figure 3 below is an administrator view displaying some of the tools backend administration.  The tool has been built using the python Django webframework and is hosted securely in the cloud.

pPlan Administration
Figure 3: pPlan Administrator portal

Figure 4 below is a site map showing most of the pages the inter relationships

Figure4: Site map of the pPlan prototype

To be clear this is a prototype tool being beta tested online now (August & September 2018).  So feedback is welcome and will be useful in helping to decide future development.

Rudimentary system for importing Canvas grades into the Student Information System

SIS integration is a topic discussed at the CanvasUKHE Group & the need for cases studies which illustrate what is possible and how it is achieved in practice. These slides and code could contribute to case studies regarding the integration of assessment & grades data with SIS or MIS systems

Content first posted on here:

Areas of Poor Air Quality in London – What can we do about it?

I’ve not studied geography since year nine,  but am interested in our environment and visualising data, so opted to study a Geographic Information Systems module for my Masters degree.  Bellow is the report* I submitted, where I have had a go at estimating the size of the population affected by poor air quality in the London borough of Camden’s five designated Air Quality Focus Areas. In the conclusion and appendices at the end I make some of my own suggestions about what we can do to improve this air quality.  I didn’t know about until after submitting, but it is very relevant.  If I had time, given there is data on the quantity of traffic on certain roads, and we have the number of children on roll at school/nursery, I would try and estimate the number children who come near to the high use roads on their way:

Note because the module might set a similar assignment in future, I have removed content at the colleges request.  It now displays ” # Removed content was here…” .

1. An introduction with background information for this report and the aims of the report


Air Quality Focus Areas in London

“There are 187 Air Quality Focus Areas in the Capital (2013). These are locations that not only exceed the EU annual mean limit value for NO2 but are also locations with high human exposure. The Focus Areas were defined to address concerns raised by boroughs within the LAQM review process and forecasted air pollution trends.” ((, 2013)
The brief for this report is: “Camden Council would like a preliminary analysis for estimating the approximate number of people who are affected by poor air quality, as defined by the London Air Quality Focus Areas. In particular, they are interested in if and how the locational relationship between the Focus Areas and other features such as the road network, underground stations and the green spaces in Camden can be used to explain the size of the affected population.” Camden has five focus areas which are:

1. Kilburn High Road from Willesden Lane to Grevill Place (ID: 30 | Old ID: 108 )
2. Marylebone Road from Edgware Road to Euston/King’s Cross Junction (ID: 31 | Old ID: 127)
3. Holborn High Street and Southampton Row Junction (ID: 29 | Old ID: 134)
4. Swiss Cottage from South Hamstead to Finchley Road Station (ID: 32 | Old ID: 136)
5. Camden High Street from Mornington Cresent to Chalk Farm (ID: 28 | Old ID: 137)


Emissions and their impact in Camden

Figure 1: Camden Focus Areas - with their new London number within the 187 areas (with NO2 (μg/m3)
Figure 1: Camden Focus Areas – with their new London number within the 187 areas (with NO2 (μg/m3)

Journalists at the Telegraph, a national newspaper looked at the government published statistics, and present some of these in an article designed to alert and catch their readers attention. The article is called “Mapped: Where is air pollution killing the most people?” For Camden, the map on website states “In Camden there were 87 deaths due to air pollution in 2010. A total of 1157 years were lost. The mean anthropogenic PM2.5 (µg m−3) was 13.8, which is a measure of the air pollution in the area.” (Telegraph Media Group Limited: Sarmad Jawad, 2015)

The main pollutants of concern, with London emissions recorded in 2013 are: Nitrogen Oxides (NOx), particulate matter (PM10 & PM2.5), Carbon dioxide (CO2) . The sources of these omissions and how they are recorded spatially in GIS data systems can be seen from this slide of the London Atmospheric Emissions Inventory (LAEI) – workshop:

Figure 2: Emission Sources - Data Type
Figure 2: Emission Sources – Data Type

(Greater London Authority (GLA): LONDON ATMOSPHERIC EMISSIONS INVENTORY (LAEI) 2013, 2016)


Aims of report, sourcing & exploring data and making decisions


The aim of this report is to estimate the number of people affected by poor air quality in the five focus areas that fall mostly in the London borough of Camden. To do this, the different sources of population entering, exiting and living in the focus areas would have to be established. To get started, I searched for the best quality and up-to-date data from a variety of sources (see this reports list of data used) and used ArcGIS to aggregate these datasets to get a high-level overview and compile descriptive statistics:

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Decision 1 (Location of resident populations)

Notable was that the five Camden focus areas intersected with four other London Boroughs: Brent, City of London, Islington, Westminster. See figure 3, “LSOA base population”, row two. For this reason, the decision was made to source and merge population data for all these boroughs with spatial data, into one GIS layer.

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Decision 2 (Apportionment method of resident population to use)

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Decision 3 (100m high risk zone and applying a 50m buffer to Marylebone road 50m)

In his website article Bill Adam’s cites research and states: “On average, PM concentration is significantly higher within 330 feet (100 meters) of major highways than it is further away. (Zhu, et al., 2002)” (Adams, 2015).  For this reason, it was decided to add a 50m buffer to Marylebone road  focus area edge, as is in some places the distance between the road an this edge was considerably less than 100m.  See section 3 for more information

Decision 4 (Population sources not included in estimations)

Other populations exposed, but where further research needed to determine the number of people affected, and where there is no readily available data are:

  1. Bus and Coach users entering & exiting the focus area
  2. Cyclists – NO data available and Cycle roots in a ITN Urban Paths data outside all FA
  3. Pedestrians walking into FA e.g. walking to school/work within catchment area but starting beyond FA boundaries

For possible cyclist’s exposure, some effort went into converting ITN Urban Paths data (GML format) to an ArcGIS compatible format using Feature Manipulation Engine (FME).  However, it was only to find to my surprise that there are no cycle paths intersecting with focus areas.

Figure 7: Searching for cyclist data – using Feature Manipulation Engine (FME) to convert data types

I would have liked to include data quantifying pedestrians walking from homes outside the focus areas, into the focus areas and returning, particularly children of school age, as they are at the highest risk to health due to their height and stage of development.  Instead “Important Buildings” data (see list) and symbology were used to clearly present proximity of schools and hospitals:

Figure 8: Schools & high risk populations

Decision 5 (Time: minimum daily exposure of affected population)

One general assumptions made, is that to be counted as affected population, a person should have a minimum of two minutes’ exposure to pollutants emitted in one Focus Areas, in a minimum of 1 day, in a year.  My hypothesis is that two minutes is enough time for someone to exit a station in a focus area and then leave the FA by the shortest path. Because all estimations are the number of people effected daily, children could be affected 190 times – the number of days in a school year (Long, 2016).

Decision 6 (sources of population entering, exiting and living in the focus areas)

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My hypothesis and ideas about the relationship of geographic features to emissions and populations affected


I have some local knowledge of the five focus areas, as at some point in my life I have either, lived, worked or studied within approximately 100-500 meters of them, and they all to some extent form a street canyon (preventing dispersion of emissions).  Because I am only counting affected populations if they are 100m or less from the emissions source (roads) and all significant green spaces are further away than 100m, they will not factor in estimations.  I say more about proximity to green spaces in the conclusion.

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3. Maps


Note: All maps also show features which can reduce vehicle emissions such as cycle paths and car recharging points.

Overview (reference) map: showing population input features

This map is a combination of a reference map, with Choropleth mapping to show AADT daily traffic flows on the roads. Traffic flows are classified using standard deviation

Air quality in Camden Focus Areas

Detail of (reference) map: showing population input features for Kilburn High road and Swiss Cottage


Detail of map above

Overview (thematic) map: populations and emissions


Overview (thematic) map: populations and emissions

Detailed (thematic) map: populations and emissions


Detailed (thematic) map: populations and emissions

4. Description on the process of analysis

Much of the process of analysis has already be described in the introduction, so the following descriptions build on top of these.


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5. Conclusion – discussion on the analysis output and possible limitations arising from the methodology used and the data provided.


I think as a range, “848,814 – 1,840,279” as an estimation of people affected, is probably reasonably accurate.  However more data (collection) and research is needed to accurately measure and map the populations affected.  This could provide:

  • More information on the long-term health impact of air quality
  • More up-to-date data (i.e. data used old: census was 2011 and LAEI data is 2013)
  • Cyclist, bus, coach and pedestrian exposure geo-data (e.g. how many cycle for more than 10minues a day in a focus area?)
  • Construct more accurate, and less arbitrary (as I have used) methods of weighting data

The figure below shows the more detailed breakdown of methodology and calculations, than presented above:

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A Serious limitation to the methodology due to data available

The range, “848,814 – 1,840,279”, does not distinguish between a person who is exposed for two minutes in a year[3], and a child who walks for 30 minutes to and from school 190 days a year  on Finchley road, receiving 5,700 minutes exposure.  For this reason, future estimations should use a methodology where population affected is ranked according to exposure time and maybe concentrations. Using school enrolment and catchment area data could be explored with a view to advising parents on routes to school.

Note on locational relationship of Focus Areas to Green spaces

It can be argued that the proximity of the focus areas to the greenspaces is not close enough to mitigate the effects of air quality pollutants on populations because many live, work, wait for buses in the focus areas, or walk through the middle of them on a daily basis.  You could argue that there is a deficiency of green spaces, but this would not in itself improve the air quality unless it reduced the source and quantity of the emitted pollutants.   This could be achieved by pedestrianizing (routes in) the focus areas, greening the focus areas themselves, and there by partially increasing green space.  (Interesting article )

For thoughts on how to reduce human exposure to pollutants and the concentration of pollutants please see appendix item 3.



(, G. L. A., 2013. Air Quality Focus Areas. [Online]
Available at:
[Accessed 27 January 2016].

Adams, B., 2015. What is a safe distance to live or work near high auto emission roads?. [Online]
Available at:
[Accessed 3 1 2017].

Greater London Authority (GLA): LONDON ATMOSPHERIC EMISSIONS INVENTORY (LAEI) 2013, 2016. London Atmospheric Emissions Inventory 2013 : Workshop_DataStructure – Description of the data structure in the LAEI2013. [Online]
Available at:
[Accessed 27 December 2016].

Long, R., 2016. The School Day and Year – Commons Briefing papers SN07148. [Online]
Available at:
[Accessed 6 1 2017].

Telegraph Media Group Limited: Sarmad Jawad, a. A. K., 2015. Mapped: Where is air pollution killing the most people?. [Online]
Available at:
[Accessed 27 December 2016].


Table of Figures

Figure 1: Camden Focus Areas – with their new London number within the 187 areas (with NO2 (μg/m3) 2

Figure 2: Emission Sources – Data Type. 3

Figure 3: Descriptive statistics & information. 3

Figure 4:Incuding population data from other Boroughs (Kilburn left, Marylebone road right) 4

Figure 5:Early high-level exploration of sourced data column charts representing population. 4

Figure 6: Stacked bar chart estimating populations exposed. 5

Figure 7: Searching for cyclist data – using Feature Manipulation Engine (FME) to convert data types. 6

Figure 8: Schools & high risk populations. 6

Figure 9: Estimated range of affected population in Camden Focus areas. 7

Figure 10:Apportioning population to LSOA areas of Focus area. 13

Figure 11: Sum of apportioned LSOA populations in a focus area. 13

Figure 12: Summing Camden only LSOA populations. 14

Figure 13:LSOA areas clipped to Focus Area. 14

Figure 14: ArcGIS buffer tool in use. 15

Figure 15:Justification of 50m Buffer on Marylebone road. 15

Figure 16: Select by location – stations within distance of 200m.. 16

Figure 17: LAEI Road Traffic Data workshop slide. 16

Figure 18: Method of calculating number of traffic point in each Focus area. 17

Figure 19: Changing special data type to make comparative analysis in maps. 17

Figure 20: Detailed breakdown of methodology calculation. 18

Figure 7: Searching for cyclist data – using Feature Manipulation Engine (FME) to convert data types. 20

Figure 8: Schools & high risk populations. 21





1) Cycle lanes data towards estimating cyclist population affected by poor air quality

Some effort went into converting ITN Urban Paths data (GML format) to an ArcGIS compatible format.  Eventually after looking at various conversion tools, this data, including cycle paths, was converted using Safe Software’s: Feature Manipulation Engine (FME).  However, it was only to find to my surprise that there are no cycle paths intersecting with focus areas.

Figure 7: Searching for cyclist data – using Feature Manipulation Engine (FME) to convert data types

2) Pedestrian exposure to poor air quality and presenting proximity of schools to emissions carriageways

I would have liked to include data quantifying pedestrians walking from homes outside the focus areas, into the focus areas and back, particularly children of school age, as they are at the highest risk to health due to their height and stage of development etc.  What I have done is use “Important Buildings” data (see list) and symbology to clearly demarcate the proximity of schools and hospitals etc. to the focus zones.  See figure below:

Figure 8: Schools & high risk populations

3) Ending thoughts on how to reduce human exposure to pollutants and concentration of pollutants

  • Have real-time information in road signs, like on motorways, but showing concentrations of pollutants in the air and for the benefit of pedestrians, cyclists and motorists.
  • Greater political and voter effort required to speed up Public road transport (Buses) and Taxis/Cabs becoming totally low emission (electric) fleets of vehicles
  • More Electric recharge points outside congestion zone. i.e. the density of charging points north of Marylebone road lowers dramatically
  • More support, regulation and pressure on industry (vehicle & electric battery manufactures) to produce vehicles of viable cost
  • Reduce public transport congestion by promoting flexitime e.g. later & earlier starts to working day
  • Continue enhancing the cycle lane network. To this end, re-engineering existing canal paths, railway and underground paths to include covered cycle paths.  For example, the physical space used to bring intercity trains into the heart of London, could be more efficiently used by re-engineering to including cycle paths or tunnels (in both directions) to run alongside the train tracks and enable cyclists from the suburbs to commute into town on bicycle. Perhaps straight into Euston or Kings Cross for example.  This would reduce the load on Tube and bus networks




End notes

[1] Where, if a full chemical analysis of the body before and after exposure were performed, they might for example, find an increase of of PM10 deposited in the respiratory tract or NO2…
[2] Annual average daily traffic:  “Traditionally, it is the total volume of vehicle traffic of a highway or road for a year divided by 365 days. AADT is a useful and simple measurement of how busy the road is. Newer advances from traffic data providers are now providing AADT by side of the road, by day of week and by time of day.”
[3] For example, walking from Euston to University College hospital through focus area 31.


Notes from the Learning Analytics and Knowledge (#LAK16) Conference

My colleague at LSBM Karen Stepanyan, and I spent the week (April 25-29) at LAK 16 in Edinburgh.  Also taking in the ending Key note for Learning@Scale.  Here I record a few notes under three headers, and re-present slides that resonated with me.  As part of the week I participated in these workshops:


Note 1:
Analytics on a live performance (presenting slides in this case)

Somebody at a music college gave me some feedback on their institutions progress with the Canvas LMS. I asked them what might be of interest at LAK16, and they responded: the analytics of performance. Being a musician of sorts myself this was interesting. This paper from Cross-LAK was the closest I encountered to the analytics of a performance: Towards a distributed framework to analyze multimodal data
Vanessa Echeverria, Federico Domínguez and Katherine Chiluiza

Abstract: Data synchronization gathered from multiple sensors and its corresponding reliable data analysis has become a difficult challenge for scalable multimodal learning systems. To tackle this particular issue, we developed a distributed framework to decouple the capture task from the analysis task through nodes across a publish/subscription server…

What was most of interest to me: To see a system with automatic analysis and reporting on the quality of a persons “presentation”  or “performance” – based on analysis of audio and video streams and textural analysis of presentation slides.

Application Example: Multimodal Learning System
Figure 3. Setup of the Multimodal Learning System

Multimodal Learning System- Hardware and Software
Multimodal Learning System- Hardware and Software


Note 2:
Metacognition in learning and Experience Sampling Apps @Scale

These notes are not about one paper, but about what a number of presentations/papers made me wonder.  In short: why am I not aware of an Apps/projects working at scale (~thousands of schools), to help learners, and gather data related to meta-cognition (and maybe the learners emotions as well) – perhaps using an experience sampling method (ESM)?  After all, governments around the world spend a considerable amount of money on educating young people, and it is often less effective than hoped.  This seems like a great albeit difficult project to increase the usefulness of Learning Analytics.

This was a short conversation on twitter related to the thought above. Prompted in part by discussions at the LAL workshop, this  tweet and the App described by Carrie Demmans in her short paper “English Language Learner Experiences of Formal and Informal Learning Environments” … Keywords: ubiquitous learning, experience sampling methodology, informal learning, formal learning, language learning, English language learner, communication, affect, analytics.  Youtube video of presentation

Experience Sampling Methodology - Mobile App
Experience Sampling Methodology – Mobile App. From Carrie Demmans Epp’s LAK short paper: English language learner experiences of formal and informal learning environments

Also I have always thought one of the values of being introduced to the concept of ‘Preferred Learning styles’ was it gets us thinking about how we learn, and the most efficient ways of learning for our selves – meta-cognition.  However “There presently is no empirical justification for tailoring instruction to student’s supposedly different learning styles”.  This is a slide from Prof. dr. Paul A. Kirschner’s keynote lecture

Looking at wrong or invalid variables
Dystopia 3: Looking at wrong or invalid variables

…So this is what got me thinking about Metacognition in learning, and developing Experience Sampling Apps @Scale


Note 3:
New Forms of Assessment

A Dispatch from the Psychometric Front by Professor Robert J. Mislevy.  This was another interesting lecture…Examples applications using new forms of assessment discussed are shown in this slide below: SimCityEDU; Packet Tracer – Cisco Networking Academy….

A Dispatch from the Psychometric Front by Professor Robert J. Mislevy
A Dispatch from the Psychometric Front by Professor Robert J. Mislevy


Useful resources

Doug Clow’s Live blogs:

Google Apps for Education with Hapara & Moodle

With this screen cast and accompanying materials in a Moodle course , I am trying give an overview of what in all likelihood, will in time become the standard way in which schools, colleges & other types of organisations manage large parts of their Information, Communication and Technology.  However the focus will be on the anytime formative assessment of students work (files).   For a good number of years I have been demonstrating & encouraging the adoption of what I think is effective use of technology, and thought I would share  this.   This was in part motivated by the needs of my own teaching practice

Youtube screen cast:

Diagram giving overview of Google Apps, Hapara & Moodle:


Link to Moodle course with accompanying materials: