I must admit to quite a remarkable change of heart after this week’s videos and readings. At the start of the week, I read “The Next Battle for Openness: Data, Algorithms, and Competency Mapping” and I had a moment’s panic! What do I know about any of those things? I may have a fairly good grasp of the science of learning, but I am only a novice instructional designer, and I know nothing whatsoever about being a data scientist or behavioural economist! However, I now understand so much more about learning analytics, and the collection and usage of learner data. I was introduced to the work of Candace Thielle and was blown away by her use of adaptive learning “long before it was cool”!
|Stephen Downes, George Siemens, & David Wiley|
I have followed the work of George Siemens and Stephen Downes for a number of years, and, since the start of this course, have a profound respect for David Wiley. I currently provide staff development. That is the work of the Tertiary Teaching Unit. My area of responsibility is that of elearning. I can imagine how incredibly valuable it would be to have a system that bundled up analytic information and translated the work of the underlying algorithms into real language that our lecturers could use and understand. I have worked with lecturers who have a profound knowledge of learning pedagogies and practices in some faculties, but I have also worked with lecturers holding multiple PhDs and/or years of work-place experience, in areas such as civil engineering and maritime logistics, who really have no understanding of learning pedagogy or practice. Many of these lecturers are faced with considerable barriers, including English as a second, third, or even fourth language.
|My institution, Manukau Institute of Technology|
Yes, the process of collecting learner information leading to efficient and facilitative learning environments, needs to be open. As Norman Bier (2017) indicated, “I think we are already in a world where data-driven approaches and materials are already being developed, adopted and embraced – by vendors, by schools, by foundations and by government. The future is already here”. Bier (2017) indicated that research has already shown lower costs, improved results, and the expedited understanding that leads to new pedagogy and innovative practice. Mention was made of ethical concerns. I appreciate these exist, but found so much that was impelling and inspiring, that I would rather leave the negative arguments for another discussion entirely.
Bier (2017) warned against the learning analysis systems operating as business models offering subscription services. He saw this as counter-intuitive to the whole open movement. He cautioned about the urgency of openness in learning analytics as “proprietary solutions have made enormous inroads in claiming the data-driven space for their own”. He suggested full transparency to inform decision-making. He further suggested that transparency would facilitate the identification and remediation of biases introduced to the digital sphere by individual coders and developers. “And the transparency that’s inherent in the open approach is the best way that I know to ensure this work can happen” (Bier, 2017).
I was very grateful for watching the video recording of Candace Thiell and not just relying on the written article. Thiell was interviewed by EdSurge (2017) for the Thought Leader Interview Series when she was in attendance at the Arizona State University, plus Global Silicon Valley (ASU+GSV) Summit. GSV are a group of companies and entrepreneurs who are developing technology to transform the world of work and education. At 16 minutes, 30 seconds, the video includes a large segment that resonated with me (not in the article). She spoke about mindset and working with algorithms that used principles of mindset. As part of my PhD research, I have incorporated mindset analysis.
At Stanford, Thiell has worked with mindset guru, Carol Dweck. For an explanation of fixed versus growth mindsets, see the two, brief videos below.
Dweck’s growth mindset suggests that the brain is strengthened through struggle. Mindset interventions have been introduced into courseware. Before the introduction of complex problems, the courseware introduces “booster interventions” that educate the students on how the brain works. Data collected reveals that mindset intervention has led to students persisting for longer periods and achieving a greater amount of learning.
Another colleague of Thiell’s, Ryan Baker, has introduced affect detectors, picking up on the learner’s affectual condition. The introduction of timed mindset interventions when material is cognitively complex together with information on the affective state of the learner, seems to be a most effective, positive inclusion in to courseware. This is a most remarkable example of using the power of the algorithm to make a teaching decision.
|Ryan Baker * affect detectors|
It seems appropriate to end with some thoughts from Stephen Downes (2017) from The Next Battle for Openness. Downes always thinks outside of the box. He suggested that the challenges for openness may not be limited to the types of data but to the way that data will be used. He referred to George Orwell’s “thought crime” and whether we could be open with the way we think. He discussed the possibility of mind-to-mind direct communication. That is not such an outrageous suggestion. Back in 1982, when I was engaged in research into mutual hypnosis, I had evidence of telepathic communication under mutual hypnosis. So, how open will mind-to-mind communication be?
|The future of communication? Open or not?|
Downes speculated further about combining genetic and algorithmic data to end up with a hybrid human-machine language. Would this be open? Would it be ethical? Downes (2017) stated, “A lot of the issues of ethics and what it means to be a person and what it means to be a society are going to be challenged by the new possibilities of creating, manipulating, and sharing new kinds of information. And I think openness is going to be challenged by these things”. We really cannot conceive all the possibilities that we may face in the future. Whatever happens, progress in learning and education needs to be open, collegial, and shared, so that we can find solutions to problems that may yet arise, together.
|Forward Woman Artifical Intelligence Robot|