To add on to the factors that Pintrich et al talk about, above and beyond the nature of interactions between students and the teacher, it is important to consider the definition of the term authentic, especially when the authors claim that classrooms don’t offer students the change to engage in authentic tasks. My guess is that even within a classroom there are different conceptions of the term for different individuals which means that within a sociocultural context, there are different students who are differently motivated for the same task. This suggests we need to look at sharing of epistemic responsibility within specific tasks to ensure that student motivation is maintained. It is important to consider interest and value beliefs are a ‘self-generated context’ (p.184) which means that the differences in the context for different individuals must be taken into account.
Brown et al’s views of schools being centres where students ‘learn how to learn’ is a valid criticism of the cognitive apprenticeship model. The criticism is sharpened by the fact that different students are motivated by different activities and the cognitive apprenticeship model may not be suitable for all students. The notion of “appropriation” takes into account the importance of the ZPD without relying on a particular apprenticeship, thereby leaving education as an exercise in learning how to learn. This would potentially pave the way for students to be part of a community of practice post school as the notions of what constitute such communities and how to appropriate from them is taught in schools, by teaching them how to adopt the ‘discourse structures, goals, values and belief systems’ (p.194) of a classroom community.In this manner, the authentic activity is defined explicitly here as a “thinking internship” (p.223). Synthesising this approach with Brown et al to see the effect of the learner community on individual affective factors would be an interesting line of study,
Brown et al’s model would facilitate learning science as viewed by Driver et al as being introduced to the ‘concepts, symbols and conventions of the scientific community’ (Driver et al, 1994, p.8). However, in contrast to Driver et al, Brown et al don’t support the idea of introducing the norms and practices of scientific communities directly in the classroom. To me, this a wise move for two reasons. Firstly, classrooms don’t support the epistemic beliefs and practices of a scientific community. Secondly, students don’t need to ‘think like scientists’. I think that there is a crucial difference between science proficiency and science literacy that forms an important subtext between the two articles. Brown et al are looking to establish an effective community in the classroom based on distributed expertise with a well designed curriculum that elaborated on the role of thinking, technology that functions as the tools for the community and a dynamic assessment strategy that is situated on the edge of the ZPD to ensure that learning is a continuing process. Driver et al would recommend that students engage in ‘mastering some of the norms and practices that are… characteristic of the scientific community’ (p.9)
Pea’s (p.93) argument that intelligence is distributed in the tools and the environment seems to me an extension of Vygotsky’s arguments about the capabilities of tools to aid it activity. The only difference here is that Pea seems to be attributing intelligence to the tool whereas Vyogtsky’s emphaises a genetic analysis of the history of the tool. These two seem to mean the same thing. Pea explicitly brings it up in technology, but that doesn’t make it a novel argument. That being said, Pea raises some interesting considerations, especially for tool use in the classroom. His argument that we move from ‘creation of representations’ to ‘different representations for selection’ (p.68) suggests that students need to be aware of the role of representations and the need for analysing their metarepresentational beliefs.Tools also change what “one needs to know” emphasising that epistemological importance of different aspects of the curricula need to be reconsidered. Tools and technology affect all forms of learning. Yet, I find myself wondering if there is a certain degree of risk to adoption in the classroom. As Pea argues, there are trade-offs in terms of what he terms ‘low-level understanding’ that are automatised by tools. But, there is a certain degree of cognitive utility in learning these processes that Pea assumes leads to ‘low-level understanding’. The problem with technological tool use is that it reduces the ability to build the basic foundations of topics when such foundations are rooted in repetitive low-level tasks such as graphing. Learning to graph functions plays a huge role in helping students understand how functions and variations in functions work. Of course, one can’t deny that graphing calculators are extremely useful, I only argue that a certain amount of caution must be noted while ‘teaching for the design of distributed intelligence’
Ashwin – While I do think interest and value beliefs are different for every child, I wonder if they are truly “self-generated contexts.” In my eyes, these affective qualities influencing learning can be generated much like learning is; they are a function of the community(ies) one belongs to. In the example from Brown et al., each student had a different motivation for doing what they did, however, as a class, they all had a common motivation. While I am not saying the class’ motivation overruled other motivations, I am saying that these motivations are not generated entirely in the mind by individuals; they are the consequence of various cultures a person is involved in. The work that is then done by the individual is negotiating the tensions that may arise between these different motivations. This is where motivation gets foggy for me however because either different communities have power they exert on the individual that determine which motivation “wins out” or people have another realm of motivation that is untouched by communities. The only place I can think to talk about this is a theoretical framework for personhood created by Dr. Jeanine Staples that positions people as spirits (Staples, 2016). Where I am struggling currently is deciding what and when work is done in the mind versus in interactions because the two are inseparable for me at the moment.
Reference:
Staples, J. (2016). The Revelations of Asher Toward Supreme Love in Self. (S. Jackson, R. Brock, R. Johnson III, & C. Dillard, Eds.). New York: Peter Lang.
In regards to the use of technology and the trade off with “low-level understanding”, I think this is something that has already happened with some concepts. For example, most people don’t really know what logarithmic and exponential functions are for, but can use them on a calculator. As someone who more or less writes math programs for chemistry applications, I couldn’t say what logs were created for either, and yet that doesn’t impede my work. So, while I would naturally also feel some concern to the use of technology for graphing without learning to graph by hand, for example, I think we could argue that not needing to teach graphing by hand will allow us to address other topics in greater depth.
I like the idea of synthesizing Brown et al’s and Pintrich et al’s work. I think affective factors like motivation may be improved in an environment where learning to learn is the goal, as opposed to “learning” to memorize a certain amount of content. I agree that this may be an important point to determining the difference between scientific proficiency and literacy – the scientifically literate population don’t need to be enculturated into a scientific community, but to the community of learners.