Please be more specific!
My refresh button thumb is getting sore!
I don't imagine I have actually failed H817 but I am itching to change my Facebook name to Anna Greathead PG Dip (ODE) (Open).
Please be more specific!
My refresh button thumb is getting sore!
I don't imagine I have actually failed H817 but I am itching to change my Facebook name to Anna Greathead PG Dip (ODE) (Open).
My H818 project is to do with blogging. Blogging seems to have become a separate category but it's significant to remember than it's a technologically enabled version of the traditional diary or journal.
The TED Talk by Amanda Palmer was supposed to get me thinking about open source and open access but it actually go me thinking about how powerful stories are. Amanda could have simply told us that she had chosen to change the financing method for her music but instead she told us a compelling and satisfying story. I daresay I will remember a story long after I have forgotten a lecture.
Diaries are deliciously private which is why (maybe!) we love to read them. The Diary of Anne Frank is a world changing book. Other books have been deliberately styled as diaries (think Adrian Mole, Bridget Jones) because, as a species, it seems that we are interested in the mundane lives of other people. The blog simply takes this model online. And opens it up to a much bigger potential audience. Some stories capture the imagination more than others, but each is valid. Some people are more willing to share their stories than others but every story told is a moment in time captured.
I have started to consider how I can present my Blog Project findings in a story format. It could be quite possible to make the point and give the information within this model whilst keeping the readers / audience engaged and entertained.
As for open access and different methods of monetizing creativity - I have to battle again with the ingrained pragmatic assumptions my 46 years have left me with. Another way should be possible - I just find it hard to envision.
Does anyone else find it almost impossible to login to their OU account in the days immediately after they've submitted a TMA? I have just submitted my ninth OU TMA and must accept that this is my pattern. There seems no avoiding it!
I means I have a busy few days as I try to catch up but, if I am honest, I mostly sketch over the activities in the week immediately after a submission.
Anyway - I'm now five days post submission so I'm back here. Writing a procrastinatory blog post to delay any actual work by a further few minutes!
TMA01 had specific word limits - part 1 was to be no longer than 1500 words and part 2 was to be no longer than 1000 words.
I've taken quite a lot of pride in writing essays of exactly the right length in previous modules - sometimes adding a single adjective or contracting two words to arrive at the satisfyingly even figure!
So - it was 44 hours before submission deadline and I had a part 1 which was 1494 words and a part 2 which was 998 words.... would those crucial eights words be significant? Could they form the sentence which may push the assignment through a grade boundary? Would my tutor roll his eyes at my having handed in an incomplete piece of work?
I ruminated (on social media) and my 22 year old son posted 'Submit it you nerd' - which was nice! You see - he's one of the more relaxed in the human race. He did the last 6,000 words of his dissertation (that is to say all of it) in the last few days before it was due. He didn't set any records but he did pass!
I am sure that somewhere, on the continuum between me and him, is a sensible approach!
The three subjects up for discussion in H818 are inclusion, innovation and implementation. We are to concentrate on one of them. I have chosen implementation because I think it is the most important. Innovation can happen and inclusion can be a key priority but unless the ideas and tools created by the innovators, and the policies and systems developed by the inclusionists are implemented in some way then none of it makes any difference.
Part of the 'problem' of implementation is that some innovation, and some inclusive policy, is hard to work. It may be badly designed albeit based on a good idea. It may be well designed but not as good as an existing tool or system. It may be brilliantly designed but doesn't actually meet a need as perceived by the learners and practitioners.
I think the main problem with implementation is that it has to not only be good enough to ensure the effort which goes into making practical changes or ideological shifts worthwhile but that it has to seem good enough to those having to make the changes and shifts.
It is much easier to install hardware and software than to make a teacher of many decades change their habits, or a student who is already busy and under pressure adopt a new learning tool. It's a linguistic stretch but I would say that changing the coding (the software) in the people involved - aka their culture, habits, experiences - is the most important step in implementing any change of practice.
This TMA is the foundational document for our conference presentation and subsequent EMA. Which means that getting it right will be very beneficial and getting it wrong will create lots of additional work!
I have decided on a few things:
We are all starting to share work in the Open Studio area. The idea is that we share our work, thoughts and so on and get feedback from our tutors and each other.
I have shared a post (similar to the previous blog post) about my ideas for the TMAs, conference presentation and EMA. A few people have fed back to me with encouraging and helpful comments. And I have sought out their work and endeavored to do the same. It just feels right and fair!
Now the logical thing to do would be to look at everyone's work, identify where I had genuine insight and knowledge which may be useful, and contribute mostly in those areas. However - we are not logical creatures. We are relational creatures. I can already see how small communities are likely to form between people who may be working of different kinds of project but who are at the same kind of stage and who have been encouraging and helpful to one another so far.
I have decided to pursue the OU Blog idea for this module. I have found the blog - this blog - to be such a useful tool in my OU journey and I want to see why it's been so great for me and how the tool could be better utilised to ensure other people also benefit from the opportunities for reflection and connection it offers. Not to mention the validation of 30k views and numerous citations in other people's work.
H818 is different to my previous modules. It requires the ongoing development of a single idea / project which is mooted in TMA01, developed in TMA02, presented in the conference and reflected on for the EMA.
The project must have something to do with Openness and also fall under one of the subject areas of inclusion, innovation of implementation.
I have two ideas - neither of which seem ideal but both of which kind of interest me. I am awaiting specific tutor feedback to see which one I should pursue:
1. OU Blogs - who uses them for what
The OU blog has been a revelation for me. I have found it exceptionally useful in a reflective capacity but also for expanding ideas which have piqued my interest and about which I have wanted to explore further. I didn't expect to gain a lot of traction but I have had over 30,000 views of this blog (although far fewer comments and interactions than you might expect). I am really interested to see if other students have found their blogs similarly, or differently, useful and whether the tool is working the way envisaged by the OU initially.
I can imagine getting data from my current and previous OU student groups but also being able to source further OU students through Twitter, Facebook and simply by searching existing blogs for comments and interactions.
I would speak to a small number first to develop good survey questions based on their experiences and comments. Once I had developed a good survey I would place this online and invite as many people as possible to respond. I would include an option to engage in a deeper email conversation for people willing and able to share their experiences more deeply.
Although the OU blogs are not entirely open unless the student allows this they are an example of students producing work which is available to others to reference, discuss and consider. The blog system can promote collaboration and networking as well as promoting reflective practice.
I think that the umbrella here would be implementation and that the presentation of a paper detailing research and findings would be most obvious.
2. Facebook Groups - who uses them for what
My employer administers a number of Facebook groups. A company page, a closed resource page and a private study group page. In total there are over 22,000 members (though obviously many individuals are part of more than one of the groups). The different pages operate slightly differently and garner different levels, and different kinds of engagement. I would be interested to analyse and measure this to see how the groups are being used to distribute educational resources, facilitate collaboration and encourage networking.
It would be difficult to gain consent to use individual's data in the specific report about these groups but as I will be mostly classifying and analysing posts (rather than the people who make those posts) I wonder if this is a less important consideration. I suspect there may be a fine line to tread here and the importance of developing a robust ethical position could not be overstated.
The advantage to this project is that it could enable my employer to better administer and utilise the groups to commercial and educational advantage. This may mean that I am free to use work time to do some of the research!
I think this also most comfortably sits within the 'implementation' area as it is a tool being used to implement many good learning habits and resources. This could be presented as a paper or possibly a workshop on how to best engage people using Facebook.
H818 feels quite different to either H800 or H817.
Both of my previous modules felt solidly academic even though the delivery, assessment and teaching was done differently. I basically knew that I had to do the activities, do some extra reading, write an assignment which answered the question and was internally logical and then I would succeed.
H818 feels, already, like it might present of a challenge. There is a very strong push to produce something to 'publish' rather than a piece of work which will demonstrate to your tutor that you have understood the course and grasped the relevant concepts.
The word 'networked' obviously provides a clue but the extent to which we are to be working as a group n H818 scares me a bit! Group work in H817 was difficult!
Plus I am having to get to grips with yet more OU resources which don't quite match proprietary equivalents in terms of usability, intuitiveness or appearance.
The vision of an open studio within which individuals pursue their creative projects in full view of their peers offers, as John Seely Brown explains, opportunities for critique, feedback, individual progress and group progress. I can immediately see how various individuals with special skills or experiences could help those with different skills and experience (and vice versa) to enable improvement in the outcomes for all.
I cannot think of an example where I have been in this kind of environment but I did immediately picture the Great British Bake Off tent! (The analogy would also work with the Sewing Bee, Throw Down and all similar talent shows!). The Bake Off is a competition so participants should not really help one another but they do! (I know it's generous editing but bear with me!) You periodically hear someone ask a question and, from benches around the tent, people call out their knowledge based on their own experiences. Bakers will sometimes look around the tent and see that their competitors are doing something different from them and it makes them second guess or review their own timetable and process. Imagine if all of those participants were not in competition but working in concert with one another - maybe to cater for a huge posh garden party! If all of that skill were pooled then the sum total of, the quality, of the ensuing product would be so much better than it would be when individuals work alone.
I love the theory. However - I am not very good at receiving criticism however constructive! This aspect of H818 may be a challenge!
I can already see that my personal leap forward in H818 is a renewed grasp of what open scholarship is - not least because of the keynote talk by Martin Weller which opened the H818 conference in 2018.
Martin described how, as the internet began to move into educational settings and learning environments, paradigm shifting predictions were made. When a bleak future is foretold then it is hardly surprising that the steps en route to the predicted outcome are resisted!
As Martin astutely points out - we have not seen the end of the university, nor has the theoretical promise of the MOOC actually altered the landscape of learning forever. We have, however, seen a definite and significant change in the way the learning and teaching is conducted and experienced. We have also seen a similar change in the way the scholarly research and debate.
My studies within MAODE have incorporated quite a lot of thought and discussion about OERs (Open Educational Resources) but I confess that the idea of data being made available for repeated analysis by researchers with different hypotheses had never occurred to me! (I had rather thought that an OER was mostly a sharable and editable lesson plan or learning resource).
The idea of Open Journals seemed to be a non-starter to me as I considered how both authors and journals would be paid for their work but the talk made me realise that many authors may be happy to be 'paid' in citations and reach. (I assume they have income from elsewhere?).
The use of blogs and social media within learning has been a common theme within MAODE but Weller made me consider again that these are not necessarily inferior to journals and conferences in their impact as they may afford a wider reach and greater engagement and connection.
My blog here is close to 30,000 views as of today. I do check the blog counter. I do get some pleasure from the idea that someone, somewhere, has found my ideas and reflections to be valuable. I even like the fact that I know various MAODE colleagues have cited me! Is this blog on a par with an academic journal? Probably not if someone is looking for closely researched and data driven conclusions but maybe if someone is looking for the honest experience and reflections of someone studying, using and providing online education and learning.
"open scholarship has a strong ideological basis rooted in an ethical pursuit for democratization, fundamental human rights, equality, and justice."
Ideological or idealistic? My OU studies have continually challenged the commercial setting from which I operate! As part of the capitalist system we provide learning to people who pay! I can personally get on board with the idea of education and learning being a sacred and privilege which should be considered an end in itself with no thought to the means by which it is achieved.... but that's not something I have experienced.
"open scholarship emphasizes the importance of digital participation for enhanced scholarly outcomes."
I can see how the concept of open education has become conflated with digital participation as the latter enables the former. I think this is serendipity and that the theory of open education need not rely on digital participation.
"open scholarship is treated as an emergent scholarly phenomenon that is co-evolutionary with technological advancements in the larger culture."
See comments above!
"open scholarship is seen as a practical and effective means for achieving scholarly aims that are socially valuable."
Practical and effective means of achieving aims? That sounds more familiar to me that the more idealistic vision spelled out in the first assumption. I wonder if this more pragmatic approach may end up achieving more than the more idealistic one simply by being more palatable.
I completed my EMA about social learning analytics with full weeks to spare. (2 of them to be precise!). It came together gratifyingly nicely and I enjoyed it. Of course it's possible that my tutor will disagree and give me a dreadful mark (not due until December!).
But I had mere weeks off from studies and have now begun H818 - The Networked Practitioner. I want to get MAODE done and dusted by the end of 2020 so can't afford to rest on my laurels!
Watch this space for my H818 adventures!
What will all of this mean for me?
Human nature is essentially self-centred. Any new project, innovation, change or progress will be assessed by individuals by how it will affect them. However successful learning analytics promises to be in terms of creating better environments and activities to foster better learning - the individuals who will need to change their practices to accommodate change will, at least initially, think about what will change for them. Educators may have concerns about increased workload, they may have concerns about their own ability to manage the newly complex world of blended learning and fear that their inability to grasp and engage with it may have consequences for their own careers, they may not really understand what is being asked of them or why change is being implemented which will compromise their engagement.
Why are we doing this?
Changes in LMS or VLE in any institution is likely to be made at a high level but working out the nuts and bolts of the new technology and process falls to the educators at the 'coal face'. The coal face workers may have less understanding of the 'big picture' or long term aims and objectives but will have to make significant, time consuming and difficult changes to their own daily practice, Without a good understanding of the strategic aims it is hard to enthusiastically participate in the strategy.
Our ancient traditions must endure!
Universities have a 'model' which has endured for many centuries in some cases (and even in new universities the 'model' is often older than the institution). The accepted model determines the selection of students, the learning activities, the curriculum, the assessment methods. Any effort to radically change any part of the model meets resistance. University leaders are expected to inspire but not actually make any changes!
This paper attempts to consider 'learning analytics' from a variety of academic perspectives rather than concentrating solely on education.
The aim of the authors was to identify trends and also assess the most influential voices within the field of learning analytics. As well as individual voices the authors also noted that multiple disciplines were writing about learning analytics and that the relative contribution to the overall conversation between different disciplines was not equal in quantity or influence. Their method was to analyse citations and map their use in a structured network. The assumption was the papers most regularly cited, and by the widest range of contributors, could be considered as being more significant and more likely to be moving the discipline forward.
The observation was that the discipline of education – with its easy access to vast quantities of data – was not being as innovative in using that data as one might expect. Education was using simple demographic data alongside easy checkpoints such as student retention and outcomes. The suggestion was made that the data being collected could be used to contribute to better learning and teaching but, at the time of writing, it was not being used that way.
Education may seem the obvious discipline which will both discuss and utilise learning analytics the paper makes clear that other disciplines are also taking the discipline forward including psychology, philosophy, sociology, linguistics, learning sciences, statistics, machine learning/artificial intelligence and computer science.
The authors found that the major disciplines – computer science and education – were diverging and that learning analytics was thus going in more than one direction.
They also found that the most commonly cited papers were not empirical research but more conceptual in nature.
The use of ‘low hanging fruit’ (readily available data) is also discussed with hope that better and more useful learning analytics will develop.
The use of citation networks enables the authors to see where concentrations of papers are being published and how they link to one another. They can assess where ‘crossover’ papers develop which feed into the discussion in more than one academic discipline.
It would be easy to assume that the most regularly cited papers are the most insightful, methodologically consistent and ground-breaking. This would be, I think, an over simplification. Certain journals are more widely read within certain disciplines and the specific place a paper is published will determine, to a great extent, its audience.
I can see the value in this kind of analysis. Where many different researchers from different academic backgrounds are all looking at the same subject – albeit from different angles and with different motives – the potential for a ‘big picture’ (and overarching theory) to emerge is an engaging prospect. I also can see how the varied angles and motives can enable each different discipline to consider new ideas and take their own understanding of, and use of, learning analytics forward.
The opening paragraph of this paper
by Dawson et al. neatly summarises a major weakness with learning
analytics - that the data gathered is gathered incidentally rather than
with pedagogical intent.
The obvious question to ask is 'what data would be more useful?' and then 'how can we collect that data?'
Learning Analytics is based on the premise that the answer to the first
question is 'information about the interactions between learners' based
on the observation that knowledge is increasingly distributed and
learning has become less about learning knowledge from a 'wise sage' and
more about connections and collectively held knowledge.
second question - how can we collect that data? - presents a problem.
It is not difficult to track forum contributions or similar within an
institutions VLE. The interactions can be automatically tracked and the
length, time of and words within those posts can be classified and
codified but the assessing the quality of engagement requires human
input. This is merely the first issue: most interactions between
students don't happen within the VLE. However slick an institutions VLE
is it is unlikely to be as intuitive, familiar and easy as platforms
like Facebook and WhatsApp. Students will opt for easy for them over
helpful for the institution.
The idea of any institution monitoring and analysing my Facebook and WhatsApp conversations is creepy!
I got stuck this weekend.
I grasped the concept that Lockyer et al. were communication. The checkpoint vs process comparison is simple but beautifully so. A major criticism of learning analytics, as we have studied to date, is that it necessarily uses the available data (which has not been collected with pedagogical advances in mind) rather than data being collected specifically with analytics in mind, Checkpoint data is the main data we have and shows us the easy to measure metrics - who clicked on the link? When and where? On what device? How long were they logged in to the VLE? How often did they post in the forum? When did they submit their assignment? How good was their assignment? How does any of this correlate with the socio-demographic data we hold about them?
The process data is more difficult to measure being more nuanced and essentially qualitative. Questions which might generate process data could include:
There have been a number of activities I have got stuck on this week. The material is interesting and accessible but the questions we are supposed to consider as we reflect on it are not!
The activity about the paper by Dyckhoff et al. was really interesting and especially got me ruminating on how learning analytics makes use of data which is incidentally collected - the key word being incidental. The data sets created in learning (and everywhere) are huge and contain a lot of detail about various aspects of life but the data is not collected to be analysed. The analysis happens due to the availability of data, the data is not collected for the purposes of analysis. The prospect is that 'easy' research is done using available data to drive pedagogical change rather than pedagogically useful data being collected in order to drive pedagogy.
This is not to say that learning analytics based on big data are not useful. They might not answer the exact questions which learners, educators and institutions would choose to ask, but they do answer questions. As with any big data set - extracting the useful data from the background noise requires finesse and insight.
This blog about library usage is rich with data driven analysis. Libraries generate data by monitoring access (typically by swipe card, PIN code, login), engagement and activity. Modern libraries - often buildings which could house nothing but internet access to digital books and journals - generate even more specific data. Libraries do still have collections of physical books and journals but as archives are digitalised and new material exclusively published digitally - these will eventually start to shrink. People seem to have an emotional attachment to 'books' (try any conversation comparing a Kindle e-reader to a 'real book' to see!) but researchers are hopefully more pragmatic and appreciate the convenience of not only being able to search for literally millions of publications in seconds but also to search within them for particular chapters, references and sentences. This access to more and more information must impact on the pedagogy of those who teach learners who use libraries. The blog makes the point that data can show correlations but not necessary causation. However - correlation may be enough to provide interventions when a student may be struggling, or redesign when a learning activity fails to inspire engagement.
The final article by Lockyer et al. describes the difference between checkpoint and process analytics. I like these distinctions. There are echoes of summative and formative assessments within it and I feel confident I can grasp their meaning! Within my OU journey the institution can easily assess me using checkpoint analytics - they can see details of my socio-demographic status, they know when, where and for how long I log into the VLE, they know how often I post in the forums (and in my blog), they know what I search for in the library and they know my assignment scores. What they don't know (because the data cannot be automatically mined) is the quality of my forum and blog posts, the level at which I engage with activities and assignments, how many of the library resources which I click on are actually read in any meaningful sense. My tutor may be able to make a valid guess at these factors. The area in which process activities could generate data would be in evidence of inter-student collaboration and communication but as our group work (and study-buddy friendships) operate outside of the VLE, there is not way for the OU to be able to monitor them. (If they did there could be privacy concerns as well).
I know that we use Google Analytics at work but not for Elearning. I have never been involved in the discussions about Google Analytics nor have I seen the data. However - as a data junkie I now carry a strong urge to demand access to all the data sets because I can see how interesting this could be.
With regard to applying
Google Analytics to learning I am initially struck by how this model is
clearly designed with commercial applications in mind. The services
which might be of benefit to a learner or an educator are only useful
insomuch as they may enable better, more targeted and more appropriate,
learning opportunities to be developed.
However - this data *could* be used to help the learner and the educator, The issues with using it that was are not only due to a profit motive of a private company but the way that big data may have to become much more specific to be useful in individual circumstances. I would, for example, be very interested to see how well I am doing in H817 compared to other students on the course, compared to students in previous cohorts. I'd be fascinated to know what expectations the OU may have had for me based on my simple socio-demographic information. I would like to see where I rank in terms of engagement with the material and I would be interested to learn of any aspects of H817 I had simply failed to grasp properly. I would love it if the curriculum I followed in H817 would shift and sway according to my interests, the pace I was most comfortable with and even my personal schedule. If I were to know all of this though it may be at the expense of the privacy of other students, it may be at the expense of the integrity of the course and it may be at the expense of my own work ethic!
This paper from Rebecca Ferguson gives a concise and ordered review of a burgeoning and almost chaotic subject development! It's ironic that something which sounds as definitive as 'learning analytics' can contain so much nuance and so many different opinions. It seems that the term came into use in several contexts simultaneously and was used differently each time.
I feel that the three bullet points on page 9 crystallise the current issues best:
Learning analytics is clearly a term which is used to describe a particular phenomena which can happen on a wide range of scales.
Personally I am interested in how my individual socio-demographic circumstances can predict my success (or otherwise) in any aspect of learning and how interventions can be targeted to assist me. I am not entirely selfish - I am also interested in how this can be applied to other people!
Professionally part of my role is to tutor aspiring medics and dentists for the UCAT exam (formerly UKCAT) which is an aptitude test which must be taken as part of an application to most medical / dental schools. Upon registration the UCAT consortium ask each applicant to complete a series of socio-demographic questions - racial group, gender, level of education reached by parents (the vast majority of applicants are 17 years old) and the job title of their parents (presumably used, alongside education, as a proxy for social class). They already have the applicants post code and know if the applicant lives in a household with low enough income to qualify for a bursary. This information is not supplied to universities but is reportedly used to improve the UCAT for future cohorts. Pilot questions are trialed with a view to ensuring they are as equitable as possible. I imagine that groups of statisticians gather around data sets and assess why various questions performed better with people who had parents with higher educational qualifications, or who shared a similar ethnic group. (Incidentally - I teacher friend of mine got very frustrated one year when the disadvantaged children who attended her inner city primary school were faced with SATS questions about going to the theatre, and using a honey dipper - two activities which were entirely unknown to most of the children she taught. A huge failing in learning analytics or a cynical attempt to maintain existing privilege? Who knows!)
The articles we read in this activity looked at early learning analytics on a national scale. I also find this very interesting. The variation between educational styles and ethos between different countries is driven by many factors including culture, politics, and resources and comparisons can be meaningful and meaningless depending on how well the complexities of the issues are understood.
The first paper compares the USA to other countries - noting not only a lack of progress when compared to nations of similar levels of development and wealth - but also an up and coming threat from developing nations with very large populations.
The second paper introduces the idea of using analytics to shape future US educational policy at the national level with a coherent and unified plan. Affordability and value are key values but a need to match education to likely economic requirements is also significant.
The basic premise of these discussions assumes that there is a direct correlation between graduate level education and national prosperity. There is, at least in these papers in this time, little discussion about what is being studied and to what end - merely that high levels of education are necessary for continued improvement.
As is pointed out - both papers were written before 'learning analytics' was a phrase and though they clearly are referring to a similar process the absence of 'big data' on quite the scale as it is available today means that it's not exactly the same thing being discussed. However - the idea of analytics is clearly here with a vision to use data to improve polcy.
Costa Coffee is my favourite of the high street chains and I buy coffee from there about once a week on average. This article announces the companies intention to use 'big data'. The article is short of specific details but gives a few broad motivations behind the initiative. These are:
As if often the case in Wikipedia, the article on Learning Analytics began as a quick summary and rapidly mushroomed into a far more extensive treatise on the subject. However - the initial definition has had few versions. It changed in the first day, then again a couple of years later, but the sentence written in 2013 is the same as the sentence which opens the article today. The difference is that in today's article this opening sentence is followed by over 4000 words of further information.
Learning analytics is the use of data and models to predict student progress and performance, and the ability to act on that information - 23rd August 2010
Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning. - 24th August 2010
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. - 1st September 2013
I usually like to begin my investigations about an unfamiliar subject with a read of the associated Wikipedia article. I realize that it's not a peer reviewed, 'reliable' source but it is often succinct, accessible (especially to to the non-expert) and well written with good clarity. The learning analytics article is none of these things and it reads as an article written by committee (which is, of course, exactly what it is!).
The impression that the whole article gives me is that the subject of 'Learning Analytics' is as vast, as nebulous, as complex and as multifaceted as the two words would imply. H800 challenged every internal impression and idea I had about the concept of 'learning' so I am keenly aware of how 'simple' ideas can become mosaic when investigated and the word 'analytics' gives us no expectation of a simple and easily defined concept! Put two big concepts together and the creation of a gargantuan concept seems also inevitable!
The simple sentences above describe aspects of learning analytics. My impression is not that those who change the definition claim what is stated is incorrect, but that it's incomplete and inadequate. The extra information, text, ideas and paragraphs don't detract from what has been previously written as much as adding to, augmenting and complementing it. There are a multitude of associated concepts which overlap with Learning Analytics but the edges of each concept is blurry and undefined.
I suspect a concise definition which will satisfy everyone is impossible to develop but by looking at the areas everyone agrees with we can draw some conclusions. Such commonalities include:
TMA03 - the group work assignment - is done. It's been a blast and a trial.
I am truly impressed by what we have devised and created. If it were a real product I would have high hopes for it being successful.
But the last week has felt somewhat anti-climactic. We (sensibly) got everything we needed to do finished by the end of last week and left this week for us to write our individual reflections.
And it's felt oddly chilled!
I didn't feel too stressed. I wasn't rewriting sentences and paragraphs until the early hours of the morning. I submitted it - unsure that it was exactly what was required - without fanfare, relief or panic!
Anyway - onwards to block 4.
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