- The context: Informal learning at the Powerhouse museum of science, technology and design.
- The learning experience: give students the opportunity to experience the design process in 1 of 4 different design disciplines: engineering, architecture, digital media and fashion design.
- The goal: To focus on writing the script for the digital characters and experience in a way that specifically embodies/communicates in the languages of particular fields - thus giving students an opportunity to experience design from various perspectives.
The disciplines were divided into categories using Legitimation Code Theory (based on interviews with professional engineers, architects, digital and fashion designers). The interview results placed Engineering in a realm where objective knowledge and methods are prioritized above subjective characteristics of the individual engineer. Fashion design was placed opposite where subjective experience, intuition, personal creativity and experience were privileged over technique and procedure, while architecture and digital media fell in between the two employing the values and language of both the objective and subjective fairly equally.
These various perspectives were then used to develop different styles of computer-based advisors for students. For example, one advisor would guide a student through a design learning experience providing methodical processes, templates and detailed instructions (employing the engineer's language and approach to design). Another advisor would guide a student through the learning experience by prompting more open-ended experience based reflection. A third would compress and balance the two. The students could choose their advisor for the experience (which involved exploration in the museum with a laptop which housed the advisor and elearning environment.) Their selection was based on short bios of how different advisors like to work, how they define design, what they like and dislike. Students could also choose how much guidance they preferred to receive, what kind of object they wanted to design, and which stage of the design lifecycle they wanted to participate in.
In her research, Caravalho makes the point that eLearning environments are often created without thought given to incorporating the language of the field from which the content comes. This language includes what kinds of knowledge is valued in the field (eg. subjective v. objective) and how meaning is made and used to evaluate the success of outcomes.
Although as Learning Interface Designers we won't generally be writing the script for an expereince, Caravalho's point can certainly be extended to the visual language of an environment as well. At the very least, being aware of the values and language of a field should inform a learning interface -- at the most basic level to promote engagement in learners with an interest in that field, but also to enrich what they are learning as a side-effect.
An LID moment...
There was an interesting LID issue that arose during the seminar in which an interface design choice effected the learner's experience. When students are asked to chose their advisor in the program (a decision which is meant to be based on the advisor's values and approach to design), the learners first have to click on a photo of a person, then listen to them describe themselves. They can then click back to try another one or continue with this one.
The problem highlighted is that your initial choice as a user is necessarily based purely on visual appearance. Making a more informed decision requires more work (clicking back, listening to another and another). This could surely skew decision results. Carvalho revealed that, indeed, most students reported having selected advisors based on appearance.
Possible solutions presented involved not showing any pictures, including summary blurbs of their bios next to the photos to provide more non-appearance based information, or perhaps having the bios appear more dynamically on the same page. Just another example of how interface design decisions (sometimes apparently small ones) can actively effect learning results.
