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In-Detail Analysis on Custom Teaching and Learning Framework
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 33 |
| Year of Publication: 2020 |
| Authors: Subhabrata Sengupta, Anish Banerjee, Satyajit Chakrabarti |
10.5120/ijca2020920390
|
Subhabrata Sengupta, Anish Banerjee, Satyajit Chakrabarti . In-Detail Analysis on Custom Teaching and Learning Framework. International Journal of Computer Applications. 176, 33 ( Jun 2020), 10-15. DOI=10.5120/ijca2020920390
Abstract
In the field of Information recovery, the primary goal is to discover importance just as the most significant data as for certain inquiries. In any case, the primary issue with respect to recovery has consistently been, that the pursuit region is tremendous to such an extent that it has gotten hard to recover relevant data productively. It has been seen that the conventional ontological authoritative data causes superfluous additional CPU cost, while the client inquiries, for the most part, focus on a particular space. What's more, another difficult issue in such manner is the key expression extraction from the question which has likewise a significant job for pinpointing looking to a particular recovery space. By centering, these restrictions and difficulties, we have focused on our data recovery framework, especially towards assessment question recovery so as to take into account the interest of different assessment-related inquiries. The inquiry data has been composed according to the ontological relationship among different classes and a characteristic language parser will be utilized during key-phrase extraction for proficient recovery of inquiries most ideally requested as for the level of importance to the questions. Open learning analytics (OLA) is a moderately new part of learning analytics (LA) which rose because of the developing interest for self-sorted out, organized, and long-lasting learning opportunities. In this paper, we present the goal - question - indicator (GQI) approach for PLA and give the applied, structure, usage and assessment subtleties of the pointer motor segment of the open learning analytics platform (OpenLAP) that draws in end-clients in the pointer age process by supporting them in defining objectives, offering conversation starters, and self-characterizing pointers..
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