An architecture and algorithm for context-aware resource allocation for digital teaching platforms

PROJECT TITLE :

An architecture and algorithm for context-aware resource allocation for digital teaching platforms

ABSTRACT:

Digital teaching platforms (DTPs) are aimed to support personalization of classroom education to help optimize the learning method. A trend for analysis and development exists relating to methods to investigate multimodal knowledge, reaching to infer how students interact with delivered content and understanding student behavior, educational performance, and also the method lecturers react to student engagement. Existing DTPs will deliver many sorts of insights, a number of that lecturers will use to regulate learning activities in real-time. These technologies need a computing infrastructure capable of collecting and analyzing large volumes of knowledge, and for this, Cloud Computing is a perfect candidate solution. Nonetheless, preliminary field tests with DTPs demonstrate that applying fully remote services is prohibitive in situations with limited bandwidth and a constrained Communication infrastructure. Therefore, we propose an design for DTPs and an algorithm to promote the adjustable balance between native and federated cloud resources. The answer works by deciding where tasks should be executed, based mostly on resource availability and the quality of insights they will offer to lecturers throughout learning sessions. During this work, we tend to detail the system design, describe a proof-of-concept, and discuss the viability of the proposed approach for sensible eventualities.

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