Students Psychometric Assessment in eLearning Paradigm Test phase, srs, design phase and source code final deliverable

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Students Psychometric Assessment in eLearning Paradigm Test phase, srs, design phase and source code final deliverable

Category: AI / Deep Learning

Abstract /

Introduction

With the advent of latest technological trends in assessment modes, there is a need to develop such solution that would help institutions to evaluate students not only on the basis of summative assessment but on the basis of psychometric measurements, specifically in eLearning environment. The main requirement is to collect unique dataset of students studying in eLearning mode having assessed based on psychologist aspect after measuring their responses in active (during the virtual session) and passive mode (recorded session/after the session). The students’ responses will be considered as text base, visual base for mapping those to the threshold you set for declaring optimal learning of any particular student or group. The challenging aspect of this project is to develop an application for evaluation of complete setup. The dataset is unique so it would only be generated using Python libraries or you may take help from (https://www.kaggle.com/). You will use CNN for managing visual database of student’s behavioral responses to be mapped on the scale of their level of attentiveness. As attentiveness drives through to better assessment. This is a challenging project; one should take it if really interested to complete it. You may need to study the in-depth of topics before deep dying into it.

Convolutional Neural Network: https://towardsdatascience.com/convolutionalneural-networks-explained-9cc5188c4939 Deep Learning for Face Recognition: https://machinelearningmastery.com/introduction-to-deep-learning-for-face-

recognition

Functional Requirements

• The designed system must be followed CNN principles for the implementation of stated problem.
• The system will provide interface for student’s assessment mechanism to scale attentive students based on their responses during and after the virtual session (Asking students quizbased questions about the delivered content).
• The system should respond to the variant dataset for students belonging to different demography.
• The graphs show the learning curve of students with more attentiveness in the virtual classroom environment.
• Psychological aspect of students to be measured based on their thinking capability by posting students questions as per bloom’s taxonomy.

Tools:

Python

Tensor Flow

Jupyter Notebook (Open-Source Web Application) NumPy (Library for python)