Learning begins in delight and ends in wisdom – Gardner Campbell
With its simplicity and panache, the purpose of delight is offered in a similar spirit to those so far: hope, independence, curiosity, magical experience, connection, confidence, enthusiasm, optimism, preparation … and to the seemingly over-arching theme of helping people become what they are capable of becoming.
However this made me wonder how education actually supports a journey from delight to wisdom; a seemingly different journey than that from the classroom to the exam hall.
An education provider, as social institution, implies a certain structure of time and space and creates its own social system of relationships – perhaps most commonly being one of categorisation:
- Categorisation of learning providers through league tables;
- Categorisation of learning through curriculum;
- Categorisation of learners through standardised testing.
For Foucault such classifications operate a disciplinary function that constitutes the individual as effect and object of power (pouvoir) and knowledge (connaissance). This produces the individual ‘case’ – learner as UCAS points, bachelor, master, drop-out, failure – the examination fixing individual differences and the commonality of potential.
Such systems may well suggest that the end of education is the dawn of learning.
An alternative to categorisation is sense-making, a process based on exploration rather than exploitation. In other words education must shift from instruction to discovery; from boring to building. Taking this further, McLuhan suggests that anyone who makes a distinction between education and entertainment doesn’t know the first thing about either. This should shift interest to the territory of knowledge (saviour) to be explored rather than domains of knowledge (connaissance) being imposed.
Following Deleuze & Guattari this introduces an alternative concept of power (puissance) as a range of potential or ‘capacity for existence’. This power resides with the learner – the power to be, so beautifully presented already as burning brightly, building minds, or the magical key to unlocking potential.
For me delight is the interest that can spark a connection with the world. Interest-driven learning or rhizomatic learning provide examples where there aren’t ‘things people should know’ but rather ‘new connections to be made’. The purpose of education and importance of teachers becomes to help learners follow their delights and make new connections; the community becomes the curriculum
As a technologist my interest has been in understanding how personalised learning might see systems adapt to the learner rather than learners to systems. One need only look at the impact of the long tail or the social network as ways of piquing personal interest and connecting people with shared interests.
While possibilities exist for improving learning, technology itself cannot act as an isolated catalyst for these changes which is why debates like this are so important. The classification model of education will resist such changes: rather than allow learners to explore through technology, a computer curriculum was developed … before the computer could change School, School changed the computer.
To reverse this, the purpose of delight seeks to help learners perceive education as something they’re participants in rather than recipients of.
I have spent a lot of time practising writing and even doing presentations, but far less time exploring colour. This became apparent while attempting to design a document that needs to make an impression and be pleasing to the viewer. I decided to try an adapted activity from a Colour Theory lesson, which I’ve shared below (took about 2 hours). This is a fun exercise in creativity and like free writing it may help engage otherwise dormant thinking processes.
(1) Draw a colour wheel – I usually use an online colour scheme designer, however this doesn’t really encourage me to think about the choices.
(2) Find a location on Google Street View (I didn’t intend or have time to draw on street for this)
(3) Draw the negative space – this has 2 purposes: getting your brain to think differently and creating colour spaces for next step;
(4) If available copy to coloured card – I didn’t have any but might pick some up for next time
(5) Choose complementary colours for spaces (I had pastels so went with bold colours and hue change whereas coloured pencil may have been preferable)
I have to wonder if this was an exercise in procrastination or creativity – the negative spaces took a while to get used to. While I could have adapted a Word template in the same time as choosing a colour for my new format (using Scribus), this would have been far less enjoyable. I won’t claim to have radically altered my thinking, however it does draw attention to how the eyes perceive both colour and text in the same and yet different ways. While I can definitely improve my drawing, it also seems sensible to add colour to any set of thinking tools.
[Learning] begins in delight and ends in wisdom.
Gardner Campbell gave an interesting talk about the danger of reductive models of learning that reduce the scope of education by limiting views of adjacent possibilities. Gardner argues that learning is one of the most complex processes one could study where no single theory is a good representation of all observations.
A potential issue with the traditional VLE/LMS model is that it has nothing to do with the self, identity or complexity that form learning. As such any analytics that relate to individual performance (i.e. behaviourial) are uninteresting for learning. Identity is not about just the self, but also about the other sets of selves that we interact with. An interesting analytics would help understand and encourage connections with the world rather than attempt to control them (e.g. the filter bubble effect that reduces exposure to challenging viewpoints).
The issue is that measuring what you get leads to getting what you measure. Measuring 1-dimensional models of student performance (as success) and at-risk (as point of intervention) reduces learning to behaviourism approaches. The structures being measured are inscrutable methods to get from one point to another and Taylorist models can be applied to test the effectiveness of each station. However complexity is the new reality – learning is non-linear and unpredictable.
Instead models of analytics that involve measuring student contributions and how they link to the world, are more interesting and should encourage further contributions and connections. Here connections as engagement can provide dynamic indications of success. Also rather than looking for the moment a learner is ‘about to fail’ one might look for moments of ‘beginning to learn’ as the point of intervention in a system that is able to learn.
Similar to my previous post, measuring the ‘beginning to learn’ moment hints towards Vygotsky’s Zone of Proximal Development. and further prompts investigations utlising Engeström’s Expansive Cycles. Learning analytics should drive new models of understanding for personalised learning for which engagement may be the new metric.
I have been pondering the last couple of weeks what type of data or patterns might one look for from learning analytics. There are lots of different projects I have seen or been involved in that are emerging:
- Retention focused – identifying learners at-risk of drop-out from the course;
- Performance focused – predicting final exam success;
- Activity focused – quantitative views of activity;
- Course focused – usually linked to a bench-marking of staff performance;
- Engagement focused – what types of things are people doing;
A further idea emerged for me when reading the NY Times article: ‘How Companies Learn Your Secrets‘. The article details how supermarkets might gather information about you. The main goal for shopper analytics is to identify approaching periods when consumer’s patterns are subject to change. For example the number one period for this is when a new baby is born, or in marketing terms when parents are ‘exhausted, overwhelmed and their shopping patterns and brand loyalties are up for grabs’. The approaching aspect is crucial here as the earlier an intervention occurs the more likely it is to beat other interventions, or in other words they more likely they are to switch to your store.
A similar model emerges in Vygotksy’s (1978) concept of the Zone of Proximal Development. Vygotsky proposes that for effective pedagogical interventions one must calculate at least two development levels: actual development – that which the learner can achieve unaided (e.g. tests) and potential development – that which the learner can achieve with support. The zone of proximal development is the difference between the two. Vygotsky argues that ‘by using this method we can take account of not only the cycles and maturation processes that have already been completed but also those processes that are currently in a state of formation, that are just beginning to mature and develop‘ (pg. 87).
The zone of proximal development refers to the maturing functions that are relevant for the next development period, and Chaiklin (2003) further clarifies that social interaction does not create these functions but provides conditions for identifying their existence and the extent they are developed. Interaction and social network analysis alongside behavioural psychology may offer insights while prompting some further thought on the design of interactions.
Vygotsky further suggests this requires a re-examination of formal subject disciplines and their relation to overall development. For Vygotsky this cannot be solved in a single formula and diverse research around the zone of proximal development is required. Fortunately learner analytics isn’t about a single formula either (despite its philosopher stone appeal) – semantically linked data allows diverse sets to be explored and may be directed at revealing the ‘ripeness’ of maturing functions. It seems such an approach to learning analytics (one focused on development) may require new approaches to designing learning environments, in which analytics are an integral tool rather than a retrospective analysis of existing data.
Chaiklin, S. (2003). The Zone of Proximal Development in Vygotsky’s Analysis of Learning and Instruction. In A. Kozulin, B. Gindis, V. S. Ageyev, & S. M. Miller (Eds.), Vygotsky’s Educational Theory in Cultural Context (pp. 39-64). Cambridge: Cambridge University Press.
Vygotsky, L. (1978). Mind in Society. Cambridge, MA and London: Harvard University Press.
At a recent meeting I was prompted of the need to map the informal communications of everyday collaboration with the more formal expectations of project management – as the adage goes: ‘two monologues do not make a dialogue’. On the other hand two equally weighted discourses directed at the same referential object (or theme) must intersect and enter into a semantic bond (Bakhtin, 1984). While ideologically I might hope of encouraging innovative collaboration through renouncement of monologic habits and primitive definitiveness, in a practical sense I needed to integrate the formal plan with dynamic feature delivery. For me AGILE approaches better capture everyday collaboration while PRINCE2 better handles the formal project staging (taking the best bits of both).
The project, as most of mine do, involves the implementation of an integrated Moodle / Mahara platform enhanced with a range of customisations. So some features are delivered out of the box by the software and some require bespoke development. Rather than sharing spreadsheets and (un)versioned documents, we have implemented Pivotal Tracker (@pivotallabs) which has proved effective in its simplicity. In order to transform a 20+ page document of requirements into a deliverable items requires a quick review and mapping of how we label and score stories. Using a macro to get the original document table into a workable spreadsheet, one can then apply a workflow mapping between the methodologies before importing into pivotal.
|Icebox||New ideas to be scoped for future iterations||Requirements for later stages|
|Backlog||Scheduled items for next iteration (outside current velocity)||Requirements in the next work package|
|Current||To be delivered in current iteration||Requirements in the current work package|
|Done||Features accepted as delivered||Signed-off requirements|
The other area I needed a mapping was between the notions of implementation (what the software does) and development (what we need to change). We also extended our point scale for development to map implementation items onto this (it remains to be seen if the value mapping is equivalent as velocity starts to be recorded).
|0||No action||No action|
|1||Language string change||Default feature / configuration|
|2||Minor interface change||3rd party plug-in|
|3||Exact requirements understood||Module combinations / learning design|
|5||Good idea of requirements – refine through iteration||Multiple options / possible training need|
|8||“Epic” – further investigation required||Further investigation required|
With a sensible labelling system to cross-map the system aspects (e.g. core, 3rd-party, development) to the requirements sections (e.g. content, assessment, communication) one can filter on the key aspects in groups and check their status. The significance of tagging over categorisation for linked data approaches should not be underestimated here, as it allows the information to be presented within different hierarchies. A weekly review of the current and next iteration now simplifies the communication process.
I don’t claim to have done anything radical here, other than reinforce in my mind the importance of keeping the project focus on communication. Tools, methodologies and most importantly documents are only as good as the dialogues they mediate. While creative (or productive) ideas will originate in the informal everyday collaborations, if they cannot be scoped into the project then they may disappear – worse yet, this may then discourage new ideas and limit overall project innovation.
Once upon a time the most agile prince was a frog:
‘Its very funny to be a frog
You can dive into the water and cross the rivers
And the oceans
And you can jump all the time and everywhere’
– M83, Raconte-Moi Histoire
I hope that the project can instil this light-hearted approach to its management.
Bakhtin, M. M. (1984). Problems of Dostoevsky’s Poetics. Minneapolis: University of Minnesota Press.
When discussing the impact for learners at Purdue, John Campbell mentioned that the traffic lights led students to perceive that their tutors were there to support them more. This is similar to a conversation I had at University of Greenwich about the experience of a self service tutorial registration tool – students perceived that the LMS (Moodle) was offering them more choice because of it. I like this subtle impact of technology – I have no doubt the tutors at Purdue have always been supportive in the same way that tutorial selection was always permitted, however the introduction of technology makes the process visible and to some extent more tangible. To me this is how good technologies make for what John described as ‘actionable intelligence’.
What appeals to me about learning analytics in this sense is captured in the following quote you’ll see me use at conferences and have lost the source:
There’s probably a long way to go with learners generally to get them to perceive education as something they’re participants in rather than recipients of … I think it’s simply that we haven’t got far enough down the line yet with the whole situation.
This leads me into two examples of from projects that I have been involved in and cross into domains of leanring analytics, although I realise there are so much more to still be done:
This development is in its infancy and shares a starting point with the UMBC project looking at quantitative use of the LMS initially for comaprison with grades, satisfactions surveys etc as we extract the data. This looks at overall activity in the past 4 weeks on the LMS for students in a course group to identify users who may be showing signs of disengagement. It colour codes based on inter-quartile ranges for the group.
Personal Development Plans
This is part of a wider development for learning plans, but includes student status elements, attendance, and other indicators on a single dashboard available to tutor and student. Similar Purdue this approach embedded into student processes saw retention increase. In the evaluated courses retention rose from 62% to 92% and achievement from 69% to 73% in the first year of this transformation project.
The use of “academic” or “learning” analytics seems interchangeable in some articles. The below table gives an idea of their differences (Siemens and Long)
|Type of Analytics||Level or Object of Analysis||Who Benefits|
|Course-level: social networks, conceptual development, discourse analysis, “intelligent curriculum”||Learners, faculty|
|Departmental: predictive modelling, patterns of success/failure||Learners, faculty|
|Institutional: learner profiles, performance of academics, knowledge flow||Administrators, funders, marketing|
|Regional (state/provincial): comparisons between systems||Funders, administrators|
|National and International||National governments, education authorities|
In a survey of the use of academic analytics Goldstein (2005) finds that the level of satisfaction with academic analytics increases as the complexity of the technology platform increases, which seems to suggest this offsets the 50% higher investment such technologies require to implement. The survey also reveals that central finance, central admissions and institutional research are the most likely users with department head and staff, deans and their staff and central human resources least likely to engage.
More recently the need to measure learning outcomes and increase student retention is changing the nature of analytics. Financial and strategic imperatives mean analytics that can prevent drop-outs are gaining the attention of senior figures and technology providers. At a conference of colleges in the US Gliniecki expressed this by suggesting “we used to focus on just bringing them in, but now it’s about what happens to them”. And Bill Truehart, president of Achieving the Dream, summarises that “there is a different expectation now, and it is about completion.”
I always wonder how someone ‘completes’ learning, especially in the context of the lifelong agenda (and the infinite rooms philosophy) – to what extent is modelling programme completion a useful tool for learning? ‘Teaching to the test’ is a common reported problem across English schools that draws from a form of analytics that represents itself in the school league table. Here examinations, the typcial measure of completion, combine hierarchical survelliance and normalise judgement (Foucault, 1977). In this sense analytics may become a tool for exercising power – possibly why Goldstein finds such popularity for their use within central adminsitration. Modelling social relationships as an “index of power” seems to be a commonly understood goal in mobile phone analytics for example.
Goldstein concludes that our ability to achieve advances in academic analytics is not likely to be limited by technology. It will be understanding the meaning of data and not using tools is likely to present the biggest challenge. One must hope however that the field does not succumb to information as an implosion of meaning. Baudrillard suggests that where we think information produces meaning, the opposite occurs – information devours its own content. In the hyperreal society the real becomes confused with the model. This seems a very pertinent warning when trying to model student behaviour in the context of completion. I am aware of many occassions when “completion” was not a motivation for me undertaking some study and bore no relation to my experience of learning. It is crucial that the student, and not their data, remains the focus of learning.
Baudrillard, J. (1994). Simulacra and Simulation. Ann Arbor: The University of Michigan Press.
Goldstein, P. J. (2005). Academic Analytics : The Uses of Management Information and Technology in Higher Education. Retrieved from http://net.educause.edu/ir/library/pdf/ecar_so/ers/ers0508/EKF0508.pdf
Foucault, M. (1977). Discipline and Punish: The Birth of the Prison (1991st ed.). London: Penguin.
Kolowich, S. (2010). Technology and the Completion Agenda. Inside Higher Ed. Retrieved from http://www.insidehighered.com/news/2010/11/09/completion
Kolowich, S. (2010). The Completion Agenda. Inside Higher Ed. Retrieved from http://www.insidehighered.com/news/2010/04/19/completion
Learning Analytics – Week 1
For Baker and Yacef (2009) educational data is recognised to be different from other data sets due to multi-level hierarchy and non-independence. In their brief introduction to the state of Educational Data Mining (EDM), this rapidly growing research field originally emerged from exploration of student-computer interactions but have diversified into a broad spectrum of activities. The following taxonomy is presented as a summary of key activities in the field.
- Density estimation
- Relationship mining
- Association rule mining
- Correlation mining
- Sequential pattern mining
- Causal data mining
- Distillation of data for human judgment
- Discovery with models
While items 1-4 are common to classical data mining the last item is unusal and has gained significant prominence within EDM. Item Response Theory, Bayes Nets, and Markov Decision Processes enter the field increasingly as psychometrics and student models merge into EDM.
- Improvement of student models, such as the student’s current knowledge, motivation, meta-cognition, and attitudes;
- Improving models of a domain’s knowledge structure;
- Discovering which types of pedagogical support are most effective, either overall or for different groups of students or in different situations;
- Looking for empirical evidence to refine and extend educational theories and well-known educational phenomena.
Baker, R. S. J. D., & Yacef, K. (2009). The State of Educational Data Mining in 2009 : A Review and Future Visions. JEDM – Journal of Educational Data Mining, 1(1). Retrieved from http://www.educationaldatamining.org/JEDM/index.php?option=com_content&view=category&layout=blog&id=36&Itemid=55
Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit. Retrieved from http://communication.ucsd.edu/MCA/Paper/Engestrom/expanding/toc.htm