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

Metric Selection and Metric Learning for Matching Tasks

A quarter of a century after the world-wide web was born, we have grown accustomed to having easy access to a wealth of data sets and open-source software. The value of these resources is restricted if they are not properly integrated and maintained. A lot of this work boils down to matching; finding existing records about entities and enriching them with information from a new data source. In the realm of code this means integrating new code snippets into a code base while avoiding duplication. In this thesis, we address two different such matching problems. First, we leverage the diverse and mature set of string similarity measures in an iterative semisupervised learning approach to string matching. It is designed to query a user to make a sequence of decisions on specific cases of string matching. We show that we can find almost optimal solutions after only a small amount of such input. The low labelling complexity of our algorithm is due to addressing the cold start problem that is inherent to Active Learning; by ranking queries by variance before the arrival of enough supervision information, and by a self-regulating mechanism that counteracts initial biases. Second, we address the matching of code fragments for deduplication. Programming code is not only a tool, but also a resource that itself demands maintenance. Code duplication is a frequent problem arising especially from modern development practice. There are many reasons to detect and address code duplicates, for example to keep a clean and maintainable codebase. In such more complex data structures, string similarity measures are inadequate. In their stead, we study a modern supervised Metric Learning approach to model code similarity with Neural Networks. We find that in such a model representing the elementary tokens with a pretrained word embedding is the most important ingredient. Our results show both qualitatively (by visualization) that relatedness is modelled well by the embeddings and quantitatively (by ablation) that the encoded information is useful for the downstream matching task. As a non-technical contribution, we unify the common challenges arising in supervised learning approaches to Record Matching, Code Clone Detection and generic Metric Learning tasks. We give a novel account to string similarity measures from a psychological standpoint and point out and document one longstanding naming conflict in string similarity measures. Finally, we point out the overlap of latest research in Code Clone Detection with the field of Natural Language Processing

Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning

This paper proposes a two-level hierarchical matching framework for Integrated Hybrid Resources (IHRs) with grid constraints. An IHR is a collection of Renewable Energy Sources (RES) and flexible customers within a certain power system zone, endowed with an agent to match. The key idea is to pick the IHR zones so that the power loss effects within the IHRs can be neglected. This simplifies the overall matching problem into independent IHR-level matching problems and an upper-level optimal power flow problem to meet the IHR-level upstream flow requirements while respecting the grid constraints. Within each IHR, the agent employs a scalable Deep Reinforcement Learning algorithm to identify matching solutions such that the customer's service constraints are met. The central agent then solves an optimal power flow problem with the IHRs as the nodes, with their active power flow and reactive power {capacities}, and grid constraints to scalably determine the final flows such that matched power can be delivered to the extent the grid constraints are satisfied. The proposed framework is implemented on a test power distribution system, and multiple case studies are presented to substantiate the welfare efficiency of the proposed solution and the satisfaction of the grid and customers' servicing constraints