Critical questions for discussion:
1. Adaptive learning systems tend to focus on individual outcomes (Lin/Kuo, Adaptive Networked Learning Environments Using Learning Objects, Learner Profiles and Inhabited Virtual Learning Worlds, Fifth IEEE International Conference on Advanced Learning Technologies, 2005, 3). How can they be designed to enable and support collaborative learning, as well? Should they be? Knewton’s dashboard includes an affordance that allows teachers to group students who are working on the same material together, and it also enables peer review opportunities. Can this design feature overcome the individualized nature of the system and truly enable collaborative work? If students are grouped with other students working at the same level, is there a danger of creating performance-driven silos in a classroom?
2. Given Dreambox’s suggestion that their intelligent adaptive learning tools can effectively replace an instructor, the direction presented by the Rocketship case study either suggests that this claim is not supported in practice, or that replacing teachers with IAL systems is not a desirable model for education. Instructional designers and software engineers who work on IAL platforms may want to consider digital affordances that empower a teacher to insert their feedback and guidance within the IAL trajectory developed for each student. Further, in order for IAL systems to be seen as complementary to, and supportive of, the work and role of the teacher, instructional designers and software engineers may want to strongly consider developing dashboards that enable teachers to shape their instruction or facilitation based on easily interpreted data of student performance. Might some teachers need some tutoring in terms of reading visual representations of data and making decisions based on their interpretations of the data?
2. Will Knewton’s model and approach at a grade 10-12 level generate the same levels of performance improvement as seen with the Arizona State University example? Can a teacher effectively manage the classroom experience when all students are learning on their own personalized path? Does the enhanced data and insight into their student’s strengths and weaknesses make up for these challenges?
4. Adaptive learning systems have the capability to collect and store a students’ performance over the course of the education. Rather than collecting solely the student’s overall mark or best mark, adaptive learning systems can capture a student’s performance journey over time. So, for example, students who initially perform poorly in a course or in their educational career have a permanent digital record of their earlier performance. Is it ethical to collect this data? With whom should it be shared and under what conditions?
5. Can adaptive learning systems create a false expectation among students that the workforce will “customize” their job content to their needs?
6. Differentiated instruction critics suggest that students need to be challenged in thinking in ways that don’t come naturally to them. For example, creating tactile lessons for students who have difficulty with visual/auditory lessons may result in a dependance on a particular method of communication within that students life. Is it clear where adaptive learning models fall into this debate? When tailoring learning paths, do they identify instructional methods that appear to benefit a particular learner and then emphasize that type of instruction in order to generate results? Or do they identify weaknesses in the students learning and focus on practice and instruction in that realm, effectively working to mitigate a student’s areas of weakness?