{"id":32,"date":"2022-10-14T10:40:46","date_gmt":"2022-10-14T09:40:46","guid":{"rendered":"http:\/\/cde22.education.ed.ac.uk\/jhendry\/?p=32"},"modified":"2022-10-14T10:40:46","modified_gmt":"2022-10-14T09:40:46","slug":"data-collection-and-visualisation","status":"publish","type":"post","link":"http:\/\/cde22.education.ed.ac.uk\/jhendry\/2022\/10\/14\/data-collection-and-visualisation\/","title":{"rendered":"Data collection and visualisation"},"content":{"rendered":"\n

For my data visualisation I wanted to use the theme of personalisation as a follow on from my earlier blog post. While I was considering this I was in the kitchen preparing dinner, not just one dinner but a set of dinners with much personalisation involved, so I started noting down dinner choices and the ingredients and continued to do so for the next 5 days.<\/p>\n\n\n\n

Choosing a food theme for my data collection made me reflect on the exercise we did on the IDEL course where we considered metaphors for learning, for that task I also chose a cooking theme. The idea being that if the output of the cooking (or learning) process is a completed dish then the actors could be the ingredients, the recipe, the equipment, time, and the skill of the cook etc. Varying any one aspect to give it more agency than the other produce quite different results.<\/p>\n\n\n\n

My initial attempts at collecting data were messy as there was variations of how best to record the data. To help organise the information better I transferred my notes into Excel. Having more organised data enabled patterns to be seen in the data which could then be presented in a visual form.<\/p>\n\n\n\n

Data-tables<\/a>Download<\/a><\/div>\n\n\n\n
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When considering some of the observations relating to the data I was aware that there was some relevance to comparing the at home dinner personalisation to the broader area of personalised \/student-centred learning.<\/p>\n\n\n\n

Observation<\/strong><\/td>Edtech\/Personalisation learning comparison<\/strong><\/td><\/tr>
Small tasks which were quick to complete meant timing of the tasks was crucial to avoid everything needing attention at the same time.  <\/td>Individuals completing a series of short tasks may finish at different times. Learners could move through set steps at a pace that suits them. This could cause extra work for the teacher to monitor individual learning platform feedback as everyone may be at a different stage.<\/td><\/tr>
Sometimes the level of ingredients was low, but the time taken for prep, or the volume of equipment was high, so the data obscured the fact that it was a lengthy or complex processes.  <\/td>Individuals have different needs in terms of their learning, accommodating to different requirements can be complex for teachers.   Learning platforms can tailor or provide options for learners to navigate them to an appropriate area\/level of study. This may require a degree of candidate self-direction and self-motivation. Learning platform developers need to have sufficient expertise (or work with experts) in the subject area and pedagogy to create meaningful online access to learning.<\/td><\/tr>
Finding a balance between meeting everyone\u2019s preferences whilst trying to avoid lengthy prep time and large volumes of equipment was not always easy to achieve. The easiest way to do this was to create a main dish (eg pasta) then let everyone add their own extra topping or side dish<\/td>If core curriculum, at an appropriate level of demand, forms the basis of the learning content to be cascaded then additional information\/tasks can be built around this to address the issues of learners who work at a faster\/slower pace or higher\/lower level of difficulty.Large volumes of content can be interpreted as being \u2018value for money\u2019 or \u2018quality\u2019 products. Having wide content areas could be beneficial to learners but only if it is appropriate in terms of level of demand and content relevance.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Undertaking this task, and the earlier task shared on the forum, did help me appreciate how subjective the data collection process can be, which obviously has an impact on the analysis and visual presentation. There are many other areas I could have extended the data collection into, equipment, reasons for choice, outcome, and preference, but I was conscious that some of those areas could potentially be even more subjective as they could involve collecting opinions rather than facts, so the \u2018raw\u2019 data becomes potentially less reliable. Keeping it simple meant more reliable outputs, which is something I would now like to try to practice in the kitchen too<\/p>\n","protected":false},"excerpt":{"rendered":"

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