When we start exploring Experience API, xAPI, one of our interest is self-reporting. We talked about the reason in this post “Tell Your Own Learning Story through #xAPI“. Of course this topic is very controversy. But the basic idea is xAPI data can form a great feedback to learners and help learners improve their learning power – the ability to learn!
Authority of xAPI statements
First of all, in xAPI spec. the authority represents how that statement ended up in the LRS and correspondingly suggest the level of trust of that statement. A statement where the “actor” and “authority” match has the lowest level of trust as it was self generated. A statement where the “authority” is a single Agent but different from the “actor” will often have a higher level of trust, assuming that the authorizing Agent is trusted. The level of trust of a particular Agent can vary based on how hard it is to assume control of that Agent.
To solve this issue, self-reporting can be proved / validated by an instructor or an expert later with another statement.
The most effective learners are metacognitive
Jackie Gerstein discussed about this topic in a whole post here.
“The most effective learners are metacognitive; that is, they are mindful of how they learn, set personal learning goals, regularly self-assess and adjust their performance, and use strategies to support their learning”. (http://sites.cdnis.edu.hk/school/ls/2011/05/12/teachers-as-lead-learners/).
“Significant changes are taking place in our society and cultures, largely driven by the participative and collaborative technologies of the Web. New technologies are re-framing expectations for teaching and learning as well as the importance of helping students “learn how to learn” and become self-directed. Web 2.0 and social media are also providing new opportunities for teachers to not only help shape new learning practices, but to become re-energized learners themselves–and to model that learning in significant ways to students. ” (Steve Hargedon: http://www.edjewcon.org/blog/steve-hargadons-closing-keynote-school-2-0-becoming-the-lead-learner/)
Learning dispositions and meta-competencies
From Simon Buckingham Shum(The Open University, UK) and Ruth Deakin Crick(University of Bristol, UK): “Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics“, the research introduces the concept of meta-competencies as one of several approaches to characterising the demands on learners made by today’s society, and it escalates the problem of school disengagement.
If learners are, for whatever reason, fundamentally not disposed to learn, then extrinsic drivers around exam performance are unlikely to succeed. As Dewey (1933) observed: (Dewey, J. How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process. Heath and Co, Boston, 1933.)
Knowledge of methods alone will not suffice: there must be the desire, the will, to employ them. This desire is an affair of personal disposition. (p.30)
From “Hoskins, B. and Deakin Crick, R. Competencies for Learning to Learn and Active Citizenship: different currencies or the same coin? Joint Reserach Centre European Commission, Ispra, 2008”:
Competence is a combination of knowledge, skills, understanding, values, attitudes and desires, which lead to effective, embodied human action in the world, in a particular domain. Skillful accomplishment in authentic settings requires not only mastery of knowledge, but the skills, values, attitudes, desires and motivation to apply it in a particular socio-historical context, requiring a sense of agency, action and value.
Where formal learning is highly specialised and discipline bound, very often graduates, including those with traditional degrees in ‘vocational’ subjects like engineering or law, find themselves with jobs in which they cannot make much use of whatever specialist knowledge they possess. The acquisition of subject matter knowledge is no longer enough for survival and success in a society characterized by massive data flows, an environment in constant flux, and unprecedented levels of uncertainty (e.g. around how socio-technical complex systems will behave, and around what can or should be believed a true, or ethically sound). What is needed in addition is the ability to identify and nurture a personal portfolio of competencies that enable personal and collective responses to complex challenges.
In this paper “Haste, H. Ambiguity, autonomy and agency. OECD, Hogreffe and Huber. 2001”, Haste summarises competencies required for 21st century survival. She identifies one overarching ‘meta-competence’ which is the ability to manage the tension between innovation and continuity, and argues that this is constituted in five sub-competences: the ability to (i) adaptively assimilate changing technologies (ii) deal with ambiguity and diversity (iii) find and sustain community links (iv) manage motivation and emotion and (v) enact moral responsibility and citizenship. To be competent in this richer, more expansive sense, the ‘possession’ of knowledge is necessary but not sufficient. Also required are personal qualities and dispositions, a secure-enough sense of identity and purpose, and a range of new skills that enable links to be made across domains and processes.
Simon and Ruth propose that ‘disposition analytics’ could spark intrinsic motivation by giving learners insight into how they approach learning in general, and how they can become more skillfully equipped for many other aspects of their lives beyond school.
Learning analytics cannot operate without data. For some approaches, this data is a by-product of learner activity, ‘data exhaust’ left in the online platform as learners engage with each other and learning resources. Other approaches depend on users self-disclosing ‘metadata’ about themselves intentionally, knowing that it will be sensed and possibly acted on by people or machines, known and unknown to them. Such ‘intentional metadata’ typically discloses higher order information about one’s state or intentions, which are harder to infer from low-level system event logs. Examples of higher order metadata would include emotional mood during one’s studies, the decision to ‘play’ with an idea or perspective, or setting out to build one’s reputation in a group. These might be disclosed in twitter-style updates, blog posts, comments in a meeting, written work and responses to quizzes/questionnaires. In looking to future research at the end, we signal new work on inferring dispositions from the ‘exhaust’ traces that learners leave in online environments, but the focus of this paper is on self-reported data gathered via a self diagnostic
Self-report is a standard means of gathering data in the social sciences. An extensive literature review informed the development of a self-report questionnaire called ELLI (Effective Lifelong Learning Inventory) whose internal structure was factor analysed, and validated through loading against seven dimensions. The inventory is a self-report web questionnaire comprising 72 items in the schools version and 75 in the adult version. It measures what learners say about themselves in a particular domain, at a particular point in time.
“Learning Power” is a multi-dimensional construct that has come to used widely in educational contexts in the last ten years. It is derived from literature analysis, and interviews with educational researchers and practitioners about the factors, which in their experience, make good learners. The seven dimensions which have been identified harness what is hypothesised to be “the power to learn”. A brief description of the seven dimensions:
- Effective learners know that learning itself is learnable.
- Effective learners have energy and a desire to find things out.
- Effective learners are on the lookout for links between what they are learning and what they already know.
- Dependent and fragile learners more easily go to pieces when they get stuck or make mistakes.
- Effective learners are able to look at things in different ways and to imagine new possibilities.
- Effective learners are good at managing the balance between being sociable and being private in their learning.
- More effective learners know more about their own learning. They are more reflective and better at self-evaluation.
The research proves that self-reporting pedagogy is meaningful with good designs and learning analytics. The progress in the design and implementation of learning analytics based on a research validated multidimensional construct termed “learning power” was reported.
Simon Buckingham Shum is still working on quantifying deeper learning dispositions, check out his blog “Learning Emergence“. He proposes three approaches to mindset assessment, which have different qualities, and tradeoffs.
There is a free, nine-week online course that will allow K-16 educators to learn about how deeper learning can be put into practice – Deeper Learning MOOC.