Porayska-Pomsta, KK; (2015) AI in Education as a methodology for enabling educational evidence-based practice. In: Boticario, J and Muldner, K, (eds.) (Proceedings) 17th International Conference on Artificial Intelligence in Education (AIED 2015). (pp. pp. 52-61). CEUR

Preview

Text (Embargoed pending permissions checks) LesContes-KPP-FINAL.pdf Download (189kB) | Preview

Abstract

Evidence based practice (EBP) is of critical importance in Education where, increasingly, emphasis is placed on the need to equip teachers with an ability to independently generate evidence of their best practices in situ. Such contextualised evidence is seen as the key to in- forming educational practices more generally. One of the key challenges related to EBP lies in the paucity of methods that would allow educa- tional practitioners to generate evidence of their practices at a low-level of detail in a way that is inspectable and reproducible by others. This position paper focuses on the utility and relevance of AI methods of knowledge elicitation and knowledge representation as a means for sup- porting educational evidence-based practices through action research. AI offers methods whose service extends beyond building of ILEs and into real-world teaching practices, whereby teachers can acquire and apply computational design thinking needed to generate the evidence of in- terest. This opens a new dimension for AIEd as a field, i.e. one that demonstrates explicitly the continuing pertinence and a maturing reci- procity of the relationship between AI and Education.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as CSVXMLJSON

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item