A Three Question Math Quiz: Who Is Ready to Learn Finance?

2021 ◽  
Author(s):  
Matthew M. Ross ◽  
A. Michelle Wright
Keyword(s):  
Ready To Learn

Impact of Ready to Learn and Play Framework on Occupational Performance for Children With Autism

2021 ◽  
Vol 75 (Supplement_2) ◽  
pp. 7512515366p1-7512515366p1
Author(s):  
Janis Leinfuss ◽  
Erin Ohara
Keyword(s):  
Sensory Processing ◽  
Standard Care ◽  
Intervention Group ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Control Group ◽  
Evidence Based ◽  
Occupational Performance ◽  
Ready To Learn ◽  
Primary Author

Abstract Date Presented Accepted for AOTA INSPIRE 2021 but unable to be presented due to online event limitations. Individuals with autism spectrum disorder (ASD) often have sensory processing difficulties that impact their occupations. Data were analyzed comparing SCOPE scores at pre- and posttest for children receiving standard-care OT or Ready to Learn and Play (RtLP) framework plus standard care. The intervention group showed statistically significant improvement in SCOPE scores over the control group. The RtLP framework shows promise as an evidence-based tool to positively impact the occupational performance of students with ASD. Primary Author and Speaker: Janis Leinfuss Additional Authors and Speakers: Erin Ohara

Metis

2012 ◽  
pp. 24-42 ◽  
Author(s):  
Jody S. Underwood
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Content Area ◽  
Learning Activities ◽  
Digital Learning ◽  
Design Phase ◽  
Learning Contexts ◽  
Good Advice ◽  
E Learning ◽  
Ready To Learn

Recommender systems in e-learning contexts typically try to “intelligently” recommend actions to a learner based on the actions of previous learners. One of the limitations of such systems is that a lot of data is needed in order to recommend meaningful activities. This chapter describes one approach for addressing this limitation in a framework that uses a structured map of mathematics concepts and processes to power a recommender system that will recommend to students digital learning activities for which they are ready. This recommender system is called Metis, for the Greek goddess of good advice, and is currently in the design phase. Metis takes seriously the idea that to build on the knowledge, skills, and abilities (KSAs) that a student has, it is essential to identify those KSAs. Trying to build on KSAs that a student does not have is misguided. Metis recommends activities linked to KSAs that students are ready to learn, and more standard recommender algorithms further refine the list of recommended activities. Taking this approach has the potential to make activities more engaging, which can lead learners to greater interest in the content area.

Sign in / Sign up

Export Citation Format

Share Document