Part of the meta-learning activity consists in building a mental model of the learning context and of oneself inside this context, so that thoughts and actions can be tuned to it. The purpose of this chapter has therefore been to review and categorise a selection of instruments fostering students’ reflection about task- related and self-related aspects of their learning activity. RAs materialise a “re- flective learning” orientation. The last part of this chapter elaborates on three challenges bound to the investigation of this orientation.
Challenge 1 – Acceptance of the idea
An obvious condition to the acceptance of reflective practice is a better under- standing of its core ideas (Leat, Thomas, & Reid, 2012). Despite growing evi- dence that investing efforts in developing students’ ability to reflect on how they are learning has a positive impact on what they learn (Watkins, 2001), sys- tematic articulation between learning and meta-learning is sparingly deployed in courses. A broader acceptance of reflection in learning claims for a demonstra- tion to teachers and learners of the pay-offs and benefits of this articulation. This can be obtained only through the empirical validation of sensible patterns for simultaneous or sequential combination of RAs in courses.
Challenge 2 – Exploration of the value of tracked data for instruction
Some RAs found in the literature are based on the mirroring of personal tracked data. It is plausible that self-analytic behaviours could be trained by exploiting the unique tracking facilities of electronic environments. Indeed mining learn- ers’ interactions is a common concern of adaptive system improvement. Yet, it is usually undertaken as a back-office task and not in view to mirror their ac- tions to students.
Some authors have expressed interest for the exploitation of different kinds of interaction “footprints”. However, the targeted stakeholders have seldom been the students themselves but rather researchers (Leclercq, Fernandez, & Prendez, 1992; Perry & Winne, 2006) or instructors (Diagne, 2009; Mazza & Dimitrova, 2004; Nagi & Suesawaluk, 2008; Scheuer & Zinn, 2007; Zhang, Almeroth, Knight, Bulger, & Mayer, 2007). These works are based on information visuali- sation techniques. They take the data collected by learning management systems and generate graphical representations that can be used by tutors to gain under- standing of what is happening in distance learning classes and to better regulate their courses.
A few researchers tried to place learning traces in learners’ hands in attempts to prompt them to become agents and researchers in their own learning processes (Kostons, Van Gog, & Paas, 2009; Narciss, Proske, & Koerndle, 2007; Specht, Kravcik, Pesin, & Klemke, 2001; Van Gog, Jarodzka, Scheiter, Gerjets, & Paas, 2009). A systematic investigation of the RAs based on the feedback to learners of their personal tracked data deserves further attention.
In straight line with the mirroring of personal tracked data is the creation of “Learning Dashboards” (see Chapter 9), conceived as information and commu- nication spaces condensing, combining and explaining situation-related (tar- geted learning goals, available learning resources, mandatory/optional tasks, needed/trained skills, time allocation, marks, etc.), self-related (tasks completed, achieved learning goals, resources consulted, etc.), and social-related (yard- sticks) learning cues. Learning dashboards would simultaneously be a place for answers and for questions regarding personal learner information and fixed/imposed learning situation components.
Such a research agenda dedicated to mirroring issues could be grounded, among others, on Azevedo’s work (2005). The author suggests a new way of thinking about computers as meta-cognitive tools designed to detect, trace, monitor, and foster learners’ self-regulated learning of conceptually challenging topics. Mak- ing learning traces (beyond the marks at the tests) available has a potential to steer learner’s attention towards meta-learning levels, which is an essential con- dition to the efficient and meaningful execution of professional learning. (This dissertation sometimes refers to “meta-learning” preferably to “meta-cognition”, because the term encompasses more than cognition, embracing aspects of the learning experience like semantic intensity, affective dimensions, social rela- tions or context appraisal (Jackson, 2004; Watkins, 2006). Also, the term “meta- learning” might be more easily understood by teachers and students).
Challenge 3 – Inquiry into the links between reflection and personalisation
There is very few research available (Waldeck, 2006, 2007) about what makes a teacher (Verpoorten, Renson, Westera, & Specht, 2010) or a student feel that a unit of learning is personalised, and about the impact of this feeling. What makes learning personal? What fosters its ownership? It should be investigated whether the promotion of meta-learning, through the use of RAs, might influ- ence this inner perception of personalised learning. The relationship between reflective practice and sense of personalisation merits additional surveys.
Conclusion
With a classification framework and the mapping of 35 RAs onto it, this chapter provides a synthetic and synoptic view of techniques to stimulate reflection. The kind of interaction implied and the object targeted by the reflection can profita-
Reflection amplifiers: a classification framework | 47 bly be used as descriptors of these techniques. Even when hesitations occur, the framework and its controlled vocabulary help to engage discussion over the roles and significance of the RAs. As a descriptive aid, the model can be used to analyse an existing opportunity for reflection. As a prescriptive aid, it can help choosing the most appropriate technique for new training sequences or for the enhancement of existing ones. To educators and instructional designers who ponder over possibilities to infuse reflective practice in a course, this offers a means to evaluate and compare different RAs within the same category and across categories.
In a context of investigation into the conditions and effects of learning with ex- plicit reflective thinking affordances, this chapter outlined a systematised way of looking at and talking about reflection amplifiers. It is considered as an entry point for tackling the challenges raised by the funnelling of online courses into a reflective approach to learning.