%0 Journal Article %1 du2018artificial %A du Boulay, Ben %A Poulovasillis, Alexandra %A Holmes, Wayne %A Mavrikis, Manolis %D 2018 %E Luckin, Rose %I UCL Institute of Education Press %K AI AIED artificial education intelligence %P 256–285 %T Artificial Intelligence And Big Data Technologies To Close The Achievement Gap. %U http://oro.open.ac.uk/53020/ %X We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts.

@article{du2018artificial, abstract = {We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts.}, added-at = {2019-05-12T11:48:37.000+0200}, author = {du Boulay, Ben and Poulovasillis, Alexandra and Holmes, Wayne and Mavrikis, Manolis}, biburl = {https://www.bibsonomy.org/bibtex/2e56b9e1d0033e69bcadf34c4af6270d9/yish}, editor = {Luckin, Rose}, interhash = {69f22ce62b5439f7d9cd858d043c331e}, intrahash = {e56b9e1d0033e69bcadf34c4af6270d9}, keywords = {AI AIED artificial education intelligence}, pages = {256–285}, publisher = {UCL Institute of Education Press}, timestamp = {2019-05-12T11:48:37.000+0200}, title = {Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.}, url = {http://oro.open.ac.uk/53020/}, year = 2018 }