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Scoop.it!
AbstractAssessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected an 
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Scoop.it!
Ana Cristina Pratas's insight:
Utopia or dystopia"Prof Paul Prinsloo, Research Professor: Department of Business Management at the University of South Africa (UNISA), led delegates through a rapid-fire presentation that was as optimistic about the future as it was disturbing. It can be summed up in his opening comments: "I am interested in the duality that has sprung up about robots being smart, how they can speed up repetitive tasks and do away with mundane ones and then the dystopian version of a robotic future". As an academic, Prof Prinsloo questioned the notion of progress which, he argued, was unthinkingly understood as something 'good'. But he went further to ask questions like "good for who and why". Similarly, the question of growth was also under interrogation because we cannot continue growing, it's unsustainable. "We need to degrow," he quipped. Progress, he argued, especially through technology, could be a curse as much as it could be a blessing; it could be used to alleviate pain at the same time as it was being used to wage war.
He quoted Neil Selwyn, a professor from Monash as stating that educational technology "needs to be understood as a knot of social, political, economic and cultural agendas that is riddled with complications, contradictions and conflicts." For Prof Prinsloo, that was problematic when added to a space like a university. "Technology realigns existing power relations and structures creating a more complex, knotty, inter-generational asymmetry. You add artificial intelligence (AI) to a country that is as unequal as South Africa, what happens?""
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Scoop.it!
AbstractStudent data, whether in the form of engagement data, assignments or examinations, form the foundation for assessment and evaluation in higher education. As higher education institution
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Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments about the quality of learning. Learning analytics fall in the nexus between assessment of and for learning. As such it has the potential to deliver value in the form of (1) understanding student learning, (2) analysing learning behaviour (looking to identify not only factors that may indicate risk of failing, but for opportunities to deepen learning), (3) predicting students-at-risk (or identifying where students have specific learning needs), and (4) prescribing elements to be included to ensure not only the effectiveness of teaching, but also of learning. Learning analytics have underlying default positions that may not only skew their impact but also impact negatively on students in realising their potential. We examine a selection of default positions and point to how these positions/assumptions may adversely affect students’ chances of success, deepening the understanding of learning.