GCRD 6353: Seminar 2
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GCRD 6353: Seminar 2

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This addresses material covered in our second f2f session (Truro/Halifax).

This addresses material covered in our second f2f session (Truro/Halifax).

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  • 1. Literacy & New Technologies GCRD 6353.81/82 Research Seminar in Curriculum Studies Colin Lankshear & Michele Knobel
  • 2. Slideshow available here: www.slideshare.net/netgrrrl
  • 3. Academic report Purpose of this report is to examine your experiences in producing a digital media artifact in relation to 1-2 concepts associated with learning to participate in a new literacy practice You will be reflecting upon the process of learning a new literacy in ways designed to inform our work as teachers, learners, and cultural producers.
  • 4. Analysing your group’s data There are three broad ways to approach analysing your data. 1. Theory-guided; that is, using pre-determined categories (based on key theoretical concepts). 2. Emergent; that is, grounded analysis of data that generates categories out of the data itself 3. A mix of both. The main thing to begin with is to go through your group’s data and look at what patterns start to “fall out” or emerge (with an eye to outlier events as well, as well as to key concepts). Play good, close attention to all your data (written, spoken and observed).
  • 5. Theory-guided data analysis: Using pre-determined categories
    • What pre-determined categories pertaining to learning a new literacy can you identify from close reading of the entire data set? (e.g., collaboration of a certain kind, participatory culture, affinity spaces, performance before competence, distributed expertise, just-in-time learning)
    • To what extent are these concepts or categories embodied across the different kinds of data we’ve collected?
    • To what extent does the literature fully explain what you observed? Or does your data extend/challenge the concept as currently written about?
    • See the “Krista Group” paper on the course website for an example of theory-guided data analysis
  • 6. Emergent data analysis: Categories arising in a ‘grounded’ way
    • What patterns, regularities, categories (or outliers) emerge from close reading of the entire data set?
    • To what extent are these patterns/categories evinced across the different kinds of data we’ve collected?
    • What—if anything—do you notice happening over time with respect to these patterns/categories (or outliers)?
    • See the “DaraPGroup” paper on the course website for an example of emergent data analysis
  • 7. Data analysis: General comments
    • It’s a recursive process—you may (1) start with a concept and examine your data, or (2) examine your data and revisit relevant concepts, adjust things (your understanding of the concept, your definition of a concept, etc.). For example, your data might challenge the concept of “affinity” in Gee’s concept of “affinity space”—such as what constitutes an affinity/shared interest, can it be temporary etc.
    • Think about your data—what things are starting to stand out as being pivotal moments, or key “a-ha” moments that afford you real insights into your learning, in your “coming to be” something, in your understanding of what it means to use theoretical concepts to explain stuff, etc.
  • 8. Data analysis: Coding data
    • How you generate codes
    • When you code data, you code either using pre-determined categories and/or, or you use open coding techniques for grounded, emergent data analysis
    • Refer to your textbook for more on this
  • 9. Return to the theory to discuss your findings
    • To what extent and in what ways do the patterns you’ve found resonate with the concept of collaborative learning and associated ideas (e.g., affinity spaces, learning to be, social practice, D iscourse and literacy, participatory culture, new ethos stuff, appreciative systems, deep learning, collaboration, distributed expertise, participation, just-in-time-learning, and so on)?
    • Make sure that the authors of the additional texts you find and read “fit” with a sociocultural orientation. Don’t be random in your write-up.
  • 10. Writing your report
    • This is more like a research report rather than an essay
    • Keep in mind assessment criteria in your syllabus
    • Focus on “being an academic”
    • Remember that there are different types of academic literature. These include: research literature, commentaries, analytic papers (that analyse and discuss concepts and ideas), and research methodology literature (how-to-do research).
  • 11. Possible structure for your report
    • Introduction—summary of the categories at the heart of your paper; what you hoped to learn; context and rationale for media artifact; rationale for making the artifact
    • Overview of what you did—qualitative study of X (including how you collected your data etc., time, location, resources available)
    • Review of the literature pertaining to key concepts in the literature to frame your report
    • An overview of the artifact you produced (e.g., what is stop motion animation etc.)
    • Summary of how you collected your data, and how you analysed it (this will include citations to research methodology literature)
    • Identify briefly all the main categories you found, and identify which ones you will focus on
    • Discussing each category (including a definition of the concept/category based on your readings; this may be tweaked based on your data)—this includes reference to theory stuff ( this is likely to be the largest section of your paper )
    • Implications for your own teaching (don’t over-generalise)
    • Conclusion—summary of what you did and found and of your learning
  • 12. Writing tips
    • Don’t just slot quotes in—weave them into your discussion so that they support your analysis and don’t stand in for your own discussion
    • Direct quotes should not start or end a paragraph
    • Pay attention to dates of publication
    • Make sure that direct quotes are relevant (just because someone uses the term “collaboration” doesn’t necessarily mean they mean it in the same way)
    • Remember to use cohesive ties (e.g., moreover, in contrast, in addition, furthermore, on the one hand + on the other hand, however, therefore, otherwise, considering x)
    • If you get stuck, just write down what you want to say in everyday language as a starting point, then work from there
    • Keep your bibliography going as you work
    • Don’t use a dictionary to define concepts
    • APA referencing conventions
  • 13. “ Bad” use of quotes “ There is a real need for reflection on teachers’ conceptions of textuality and literacy as they exist “ for specific social purposes inside and outside schooling and in the intermediary spaces and places between them ” (Nixon, 2003, p. 409). As Kelly (2000) wrote, “ to move beyond romantic notions of English is, often, to retreat from and to reconfigure once familiar and highly invested desires embedded in our personal and social histories ” (p. 86). It is no wonder then that, as Merchant (2008) writes, “ it is hard for us [ELA educators] to know which dispositions, values and practices will remain important and which new ones may be required ” (p. 751).”
  • 14. Writing tips (cont.)
    • Aim at sounding plausible by using a bunch of academic discourse moves:
    •  “ The weight of spoken data suggests that over the course of five days our ways of speaking about photoshopping changed in a subtle but interesting manner that signalled at least some shift from being novices towards being more proficient users of photoshopping tools and techniques. These changes included growing use of key technical terms associated with photoshopping: “hue”—which means ….; “saturation”, which means …; “blur”, which means….”
  • 15. Writing tips (cont.) What was perhaps most significant in this study, however, was the degree to which being immersed in creating a series of photoshopped images and documenting this immersion now makes it possible to discern elements of the “new ethos” dimension of new literacies as described by Lankshear and Knobel (2006, p. x). To recapitulate, the “new ethos” dimension of new literacies is concerned with …… In our data, identifiable elements of this dimension of new literacies elements include x, y, and z. In our data, X is …. For example, A glanced over at B’s screen and said, “Oh that looks marvellous! Do you think he needs some shadow under his feet to ‘ground’ him a little, though?” (C’s fieldnotes, 13/07/10, p. 14) In this example it is possible to see how A is demonstrating some of the shared values of what constitutes effective photoshopping and its goal of realism and authenticity. A talks about “grounding” the figure in the image; this, it can be argued, demonstrates that A understands the importance of……
  • 16. Writing tips (cont.) It is also possible to argue that A also understands the culture of constructive criticism that is valued within the online DIY photoshopping community (cf., Merchant, 2010; Potter, 2010). A begins with a supportive evaluation, then couches her suggestion as a question--leaving any subsequent changes to the discretion of B. …. This pattern—supportive evaluation, then constructive suggestion—re-appeared throughout our records of spoken data (out of x documented utterances, 1002 utterances took this form). Interestingly, this pattern resonates with reviewer feedback patterns found in Rebecca Black’s research into fan fiction writing (Black, 2009). Black found …. For us, this pattern in our own data suggests… (and then link to, say, Jenkins’ concept of participatory culture, of Gee and Black’s take on participating effectively in an affinity space etc.).
  • 17. http://sites.google.com/site/ourmsvupages/halifax-and-truro-2011 Three different examples are archived here Examples of final reports…
  • 18. Thinking ahead: Final class
    • Final class: 25 or 28 May.
      • The first half of this final workshop will be devoted to getting the written papers in good shape for the final burst.
      • Second half will be a show case of your media artifacts.
    • Academic essay due: midnight (your time), 25 or 28 May. Email your formal papers to us at:
      • colin.lankshear@msvu.ca and colin.lankshear@yahoo.com and knobelm@mail.montclair.edu
      • Make sure you receive a confirmation email from us within 48 hrs of sending.