Examining the data to identify essential questions - guilty before innocent
by Khalid Khan; Jon Mason
International Journal of Smart Technology and Learning (IJSMARTTL), Vol. 1, No. 3, 2019

Abstract: We use the democratic legal position of presuming innocence until proven guilty as a metaphor to be considered in reverse when examining data: guilty before innocent. As the giant Internet corporations take greater control of the entire data production and consumption lifecycle there is much at stake. Clichéd phrases that generalise '21st century skills' seem no longer adequate for describing the skills and competencies needed by next generation 'smart' learners. As educators, shifting focus from digital to data literacy moves our attention from the competencies necessary in interacting with devices to the data that is produced. Post-truth and big data describe new realities in which any mix of data, information and knowledge demands scrutiny and validation. As educators, we seek to identify the kinds of questions that require deep investigation as we develop and refine informed inquiry necessary in an age enabled and disrupted by digital innovation and ubiquitous data. Identifying questions invokes critical thinking and is further informed by mathematical thinking. We propose essential learning skills as a construct that could guide further research into the issues raised.

Online publication date: Tue, 07-May-2019

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