Computational literacies

If there are multiple views on what counts as literacy, there seems to be a range of views on what counts as computational literacies in the fields of science, media and computing. While not specifically focused on the notion of computational literacies, Peppler & Kafai (2007) recognize that youths must be able to participate in new media cultures by "learning the [necessary] visual, semiotic, aural and technological literacies" (pg. 5). Scratch, a programming tool, presents opportunities for youths to participate by creating digital productions. One can argue that the representational form underlying the literacies being supported by Scratch is concrete in nature, as opposed to the abstractions underlying diSessa's (2000).

Wing (2008) characterizes the ability to operationalize abstractions and understand the relationships across multiple layers of abstraction as computational thinking, or arguably, as computational literacy. He contends that this form of computational thinking is useful across multiple disciplines such as economics and biology. Such a claim however, needs to take into account the basis of the representational forms that underlie specific genres and their social niches (diSessa, 2000). But what exactly is the representational form that underlie computational thinking or Boxer for that matter (Sandoval, 2001)? Could the box model, even if abstract, serve as a basic notation system that can affect other genres and social niches? How does abstraction work as a principle, and is there an underlying model that we can use to understand different forms of media? How does abstraction help us understand the fundamental representational form of Scratch, which is quite concrete?

To address these questions, it is perhaps useful to take into account what abstract and concrete representations do for learners. It may very well be that abstract forms of representations may be instrumental for experts, whereas novices learn better with concrete forms of representations, but a combination of both concrete and abstract is arguably present in both Scratch and Boxer. The degree of abstraction and concrete however varies tremendously, largely because there is no single model that informs the creation of notation systems. I'm unsure as to whether this interpretation is valid, and more thinking is definitely required to make sense of literacy in general.

In terms of the model, diSessa's pillars of literacy can be embedded as part of the lifeworld; the material, social and cognitive aspects of literacy is relevant to how individuals think about being literate and how objects are imbued with these affordances. Note that the 3 aspects of literacy maps on to Bourdieu's (1986) forms of capital; economic, social, cultural (note that individual disposition or habitus is a culmination of these 3 factors according to Bourdieu). I have not changed the basic design of the model, but have instead used the model to illustrate how one may think about literacy by using the model.

Mobile technologies

Mobile technologies are powerful tools that allow individuals to immerse themselves into authentic scientific inquiries that will potentially lead to learning (Colella, 2000; Klopfer, Yoon & Rivas, 2004; Squire & Klopfer, 2007). While virtual learning environments may allow learners to explore these spaces through the use of an avatar, by interacting with simulations or with instructing teachable agents, learners are confined in a physical space, usually the computer laboratory. Colella (2000) notes that participatory simulations, or "life-sized, computer-supported simulations" (pg. 471) are microworlds that have underlying rules that constrain a user's actions. Participatory simulations moreover allow students to construct an understanding of the world by using their intuitions (Klopfer, Yoon & Rivas, 2004) and create a safe space where students can potentially learn from their failures (Squire & Klopfer, 2007). While the potential of mobile technologies are important, the lack of feedback and scaffolding may impact students' learning, as demonstrated by Squire and Klopfer (2007). While the idea of a sandbox may be interesting, free play in participatory simulations is arguably similar to the concept of pure discovery learning, where students receive no guidance from the instructor (cf. Mayer, 2004). In contrast, Colella's (2000) analysis of the disease simulation underscores the importance of framing these activities in a way that students are able to make sense findings although there was no analysis of the impact of researchers and facilitators in guiding student understanding. Regardless of these findings, mobile technologies can indeed be powerful learning tools, if designed carefully.

Model 3.1

With this in mind, I have not updated the old model, because I believe that it is still able to explain the interactions within this particular system/ecology. However, I have explicated certain aspects of the model and created two separate Participatory Simulation models which illustrate the interactions between students. In particular, I used Bourdieu's (1986) concept of capital to explain how aspects of our lifeworld or our interpretations of our experiences are influenced by not only by our individual characteristics but also by other structures that exist independent of us (e.g., economy, educational qualifications, class, race, gender, etc.). This is a vital component to explicate because our interactions with others and objects are influenced by our lifeworlds, but the lifeworld is also changed through our interactions with others and objects. Objects themselves are not value-free; what we see of things in the world dictate how we interact with them. The designed mobile technologies for instance are value-laden objects that have to be unpacked by students. At the same time, the role of the instructor cannot be taken out of any learning situation, if one wants to achieve better learning (see model 3.2).

Model 3.2

The elaboration of lifeworlds moreover, is useful when one tries to understand how a female student would approach mobile technologies, in contrast to a male student. Note that the outcome of such interactions affect our ways of thinking and being, which can be thought of outcomes or goals for a given activity. Using Klopfer, Yoon and Rivas' (2004), findings about increased students learning, the model illustrates that despite an apparent difference in attitudes towards games, females experienced learning gains, comparable to their  their male counterpart, as illustrated in model 3.3. Thus, we are able to focus on aspects of the object or activity design that may have resulted in this gain. Currently, the model is unable to pinpoint the various aspects within the system/design that has to be changed, but given that its focus is on the interactions between individuals and objects, perhaps another model or revision may be in order. Suggestions and feedback regarding this is welcomed!

Model 3.3