This project is about implementation and validation of Gooru algorithms in the deep-end table. Deep end engineering is implemented in the form of a collection of “big tables” that contain materialized data relevant to several semantic operations. There are two big tables that come under deep-end big tables.
1) Activity Big Table: This table characterizes different elements of learning resources. These elements or columns include: Transcript, Summarization, Phrase Cloud, Competency, Relevance, Engagement, Efficacy, Instructional Practice and Rigor. This is part of curation of learning resources by using big data analytics.
It includes implementation of classification algorithm, relevance, engagement and efficacy scores. After computation of the scores for a subset of resources in datascope db, these scores will be computed for all the classified resources (3.4 million) from nucleaus db and will be populated for all the classified resources in the ABT of deep-end. Each of these columns are computed for a pair of learning resource and a competency.
2) Learning Big Table: This table characterizes a learner’s learning profile with respect to a subject. These elements or columns include performance, progress, proficiency, citizenship, authority, markers and goals.This is part of location of learner by using big data analytics.
It includes implementation of algorithms for computation of performance, progress, proficiency, authority and citizenship for learners and populate it into the LBT of deep-end.
Columns such as competency, transcripts, summarization, relevance, engagement, performance, progress, proficiency, authority and citizenship are implemented.
Implementation for computation of remaining columns is in progress.
1) Dr Aparna Lalingkar
2) Chaitali Diwan
3) Praseeda K
4) Udit Jindal
5) Ankit Gahalawat
6) Prasun Joshi
7) Palash Gupta
8) Urja Kothari
9) Rachna Shriwas
10) Shreyans Vora
11) Arjun Singh Kanwal