OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
The Reading for Understanding initiative (2010-2017) was $120 million research investment by the US DOE across six research teams to improve reading comprehension across preK to 12th grade. Our assessment team project, “Assessing Reading for Understanding: A Theory-based, Developmental Approach (R305F100005)”, was charged with creating theoretical and developmentally appropriate learning/assessments. Building on our prior R&D, we developed scenario-based assessments (SBAs), as well as explored/developed complementary constructs or measures (foundational reading and linguistic skills, background knowledge, motivation, metacognition). Since that time, I and colleagues have continued to evolve the conceptual and practical research around SBAs and related constructs, with applications of SBAs now being used in large scale assessments (PISA, PIAAC, the US NAEP), with adult education and post-secondary learners, and commercially in the US. In this talk, I will provide a retrospective review of our research to date, as well as, time permitting, a glimpse into our current projects incorporation large-language models and other AI into the design and delivery in assessment/learning paradigms – hopefully, stimulating some critical discussion with audience.
Please note that this seminar is hybrid, taking place in Seminar Room D, 15 Norham Gardens, and also available on Teams
teams.microsoft.com/l/meetup-join/19%3aH7IoYwBLlY_nR8d0DFzqC4yXRigyhbzyOceuytRk4g01%40thread.tacv2/1740401121174?context=%7b%22Tid%22%3a%22cc95de1b-97f5-4f93-b4ba-fe68b852cf91%22%2c%22Oid%22%3a%22be558437-bc8f-4f3b-801c-af85d95b70ea%22%7d