Designing for Reflection: A Case Study with Digital Tabletops and Digital Mysteries

Designing for Reflection: A Case Study with Digital Tabletops and Digital Mysteries

Ahmed Kharrufa (Newcastle University, UK), David Leat (Newcastle University, UK) and Patrick Olivier (Newcastle University, UK)
Copyright: © 2013 |Pages: 25
DOI: 10.4018/978-1-4666-1933-3.ch013

Abstract

In this case study, the authors revisit the benefits of reflection for learning and classify three different types of reflection support as evident in the pedagogy literature: post-activity, inter-activity and part-of-activity. They present their design of a collaborative learning application (Digital Mysteries) as implemented on the emerging digital tabletop technology. The design of Digital Mysteries aims at demonstrating the potential of technology for providing support for all the identified types of reflection. The application was evaluated through 12 trials with 6 groups of students 11-14 years old in a school environment. Two of the six groups carried out repeated trials with the goal of evaluating benefits from repeated use and to overcome effects resulting from the novelty of the technology. The trials showed clear evidence of reflective interactions, caused by the application’s design, which positively affected subsequent trials. The authors conclude with a number of generalized recommendations for designers of collaborative learning environments.
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Case Description

Reflection and Learning

The higher level thinking skills targeted in this case study include metacognition and reflection. Metacognition refers to “the knowledge about and regulation of one’s cognitive activities in the learning process” (Veenman, Van Hout-Wolters, & Afflerbach, 2006, p. 3). It is considered to be the higher order agent that monitors and controls the cognitive system and be part of that system at the same time. As reported by Veenman et al. (2006), metacognitive skills account for 17% of variance in learning while intellectual ability accounts for only 10%, and both share 20% of variance in learning for students of different ages and backgrounds, and for different tasks. This finding lead to the conclusion that an adequate level of metacognitive skills can compensate for students’ cognitive limitations. Such skills can be developed by (1) embedding metacognitive instruction into the learning task design, (2) making students aware of the usefulness of these skills, and (3) prolonged training on these skills to guarantee their application. Moreover, Lipman, Sharp, and Oscanyan (1980) suggested exposure to philosophical discussion as a method to encourage a disposition towards thoughtfulness.

Proper reflection support helps in making students aware of their thinking processes (metacognition) and problem solving strategies (Boyle, 1997). Thus, reflection can be considered as the tool that, with prolonged training, can help to achieve the points described in the previous paragraph to instruct metacognitive skills. The benefits of reflection for learning are well established (Boyle, 1997; Collins & Brown, 1988; Baker & Lund, 1997); Boyle (1997) went as far as considering reflection to be “the ultimate expression of education.” Therefore, targeting reflection has been the main design goal in our investigation of the support of technology for higher level thinking skills.

Reflection can improve the learning experience of the students by making them think back on how they solved a problem, consider their mistakes and alternative problem solving strategies, and derive abstractions about their thinking process and compare it with their earlier performances or to the performances of others. This enables the students to identify weaknesses, strengths, and areas for improvement, which consequently increases the benefits, gained from any learning experience. If mistakes and weaknesses in a problem solving process are not highlighted and discussed, students are likely to repeat the same mistakes, and follow the same incorrect procedures. If they do not recognize strengths or successes, they may fail to apply them in future planning stages of problem solving. By understanding the different types of reflection, learning application designers can leverage the affordances of technology to fully support reflection.

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