Group Formation-Finding-Your-Matching-Card in a Collaborative Learning Classroom

[EN] This paper presents a non-traditional strategy of group formation that engages students in utilizing prior learned knowledge to solve problems at a collaborative learning classroom. Through the grouping process students communicate mathematical thinking with their peers and physically moving around to find their matching cards and group parteners. The grouping process warms up students to launch an active learning mode. Although the grouping method was implemented in the mathematics content course for preservice elementary teachers and the capstone course for preservice secondary mathematics teachers, it could perfectly fit different types of classrooms including grades K-12 or college level.Liang, S. (2021). Group Formation-Finding-Your-Matching-Card in a Collaborative Learning Classroom. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 545-553. https://doi.org/10.4995/HEAd21.2021.12786OCS54555

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378

Distributed ART Networks for Learning, Recognition, and Prediction

Adaptive resonance theory (ART) models have been used for learning and prediction in a wide variety of applications. Winner-take-all coding allows these networks to maintain stable memories, but this type of code representation can cause problems such as category proliferation with fast learning and a noisy training set. A new class of ART models with an arbitrarily distributed code representation is outlined here. With winner-take-all coding, the unsupervised distributed ART model (dART) reduces to fuzzy ART and the supervised distributed ARTMAP model (dARTMAP) reduces to fuzzy ARTMAP. dART automatically apportions learned changes according to the degree of activation of each node, which permits fast as well as slow learning with compressed or distributed codes. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Dynamic weights that project to coding nodes obey a distributed instar leaning law and those that originate from coding nodes obey a distributed outstar learning law. Inputs activate distributed codes through phasic and tonic signal components with dual computational properties, and a parallel distributed match-reset-search process helps stabilize memory.National Science Foundation (IRI 94-0 1659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657