DEC Scan Journal : Volume 36 Issue 1
2017 Volume 36, Issue 1 40 Contents Editorial Learning & teaching Research Share this Resource reviews The task involves designing a simulation of a closed fish tank system consisting of fish, duckweed, Nitrosomonas bacteria and Nitrobacter bacteria. It involves designing a model of the entities and processes involved in the phenomenon using an agent-based, visual programming platform. A possible sequence of tasks could be: 1. Students begin with programming the behaviour of single agents in the ecosystem [Unistructural] 2. Students gradually develop more complex programs for modelling the behaviour of multiple species within the ecosystem [Multistructural] 3. Students gradually develop more complex programs for modelling the interactions between multiple species within the ecosystem [Relational] (Sengupta et al., 2013, p. 363) 4. Students compare their model to an expert model of the phenomenon and adjust their model accordingly using data [Extended Abstract] 5. Students program new agents – for example, water snails – and re-test models [Extended Abstract] 6. Students may apply the developed model and the learned science concepts in a new context [Extended Abstract]. In terms of learning programming, according to Sengupta (2013) these modelling activities introduce students to fundamental programming constructs: • conditionals (needs-based interactions between agents) • loops (for repetitions of an action) • code re-use and encapsulation (Sengupta et al., 2013, p. 369) [Reusing and modularising in Brennan and Resnick’s terminology]. In addition to these, the modelling activities would introduce students to concepts discussed by Brennan and Resnick, namely: events; parallelism; operators; data. The activity would clearly provide opportunities for students to enact the four practices identified by Brennan and Resnick, namely, • being incremental and iterative • testing and debugging, (particularly in step 4) • reusing and remixing, (particularly in steps 4-6) and • abstracting and modularizing (particularly in steps 4-6). The task requires each of the general capabilities constitutive of computational thinking: • systematic thinking • holistic thinking – particularly in steps 3,4,5 and 6 • critical thinking • creative thinking (in both senses we identified). Seeding success in these tasks demands pedagogy that satisfies the three dimensions of the NSW Quality Teaching Model • intellectual quality – pedagogy focused on producing deep understanding of important, substantive concepts, skills and ideas • quality learning environment – pedagogy that creates classrooms where students and teachers work productively in an environment focused on learning • significance – pedagogy that helps make learning meaningful and important to students. (NSW Department of Education and Training, 2003). The guided design processes in these tasks is analogous to a guided inquiry process (Kuhlthau, 2010). Both are planned, targeted, supervised interventions, grounded in the constructivist approach. Both can be designed and assessed using the SOLO framework. Conclusions In this paper we have examined the question of what computational thinking is and how it might be assessed. We suggested that the SOLO taxonomy could be used as a framework for evaluating the quality of performances in tasks involving computational thinking, and at the same time, the requisite computational concepts, practices, perspectives and capabilities. The SOLO framework can be used to indicate how students might build on their performance (for example, how they might relate and extend their ideas). Teachers can use the framework to ensure that assessment and learning tasks have a low floor but a high ceiling.
Volume 35 Issue 4
Volume 36 Issue 2