Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Computer Science and Engineering (JCSE)
Bhatia, Ashima Bhatnagar
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Measuring Computational Psychometrics Analysis Motivational Level in Learner’s using Different Parameters through Deep Learning Algorithm Bhatia, Ashima Bhatnagar; Mittal, Kavita
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Learning is an ongoing process irrespective of age, gender and geographical location of acquiring new understanding, knowledge, behaviours, skills, values, attitudes, and preferences. Formative assessment methods have emerged and evolved to integrate learning, evaluation and education models. Not only is it critical to understand a learner's skills and how to improve and enhance them, but we also need to consider what the learner is doing; we need to consider navigational patterns. The extended learning and assessment system, a paradigm for doing research, captures this entire view of learning and evaluation systems. The function of computational psychometrics is to facilitating the translation from raw data to meaningful concepts. In this research study, several factors are considered for psychometric analysis of different kinds of learners, and based on a motivational level, many interesting conclusions have been drawn and presented in the result section at the end of the paper. Deep learning model Ludwig Classifier used to calculate, motivational Level is obtained for 100 number of epochs and it is found that the loss is decreasing and in other words, the accuracy of the machine goes on increasing. Each of the categories discussed here has new capabilities, or at the very least expansions of current ones.
Co-Authors Mittal, Kavita