CET Research Groups
Artificial Intelligence in Education

The domain of e-learning is evolving into ubiquitous and pervasive. This rapid evolution demands automation of different teaching-learning processes. The focus of the group is to employ core artificial intelligence, language processing and semantic web techniques in solving real world e-learning problems. Six research scholars are working on thriving research problems in e-learning domain.

Group Members

Faculty-in-charge

  • Dr. Plaban Kumar Bhowmick

Research Scholars

  • Archana Sahu
  • Krishnendu Ghosh
  • Pankaj Singh
  • Subhayan Roy
  • Poonam Anthony
  • Susmita Sandhu
Research Areas
Automated Answer Grading

Objective of this work is to explore textual features and machine learning models to automatically grade the student provided free text answers. This work makes use of semantic similarity, contrast/negation detection and argumentative text analysis to predict grade with help of regression models. Future scope of this work includes deploying deep learning techniques towards answer grading.

Automated Question Retrieval

Questions are essential for learner assessment. Given a learning material in textual or other form, the task is to retrieve relevant questions from Community Question Answering Forums like Stackexchange, Yahoo Answers etc. The current work not only focuses on query-based question retrieval but also focus on retrieving questions based on educational metadata like educational objectives (Blooms Taxonomy), pedagogic strategy etc.

Augmenting Learner Experience

Web 2.0 has brought about a sea change in educational content contribution and delivery. Anyone can contribute educational contents from anywhere. This provided opportunity to the teachers or experts to create and contribute quality online learning materials. This work aims at augmenting learner experience by augmenting learning materials with additional information. Categories of augmentations include

  • Automated assessment item generation for text and video based learning materials
  • Video lecture navigation through automated topic map generation
  • Linking important parts of learning materials with external resources (e.g., Wikipedia, animation, games etc.)
Learning Analytics

We focus on capturing learner interaction data with an e-learning system and perform analytics over captured data to predict different events or traits in teaching-learning process.

Semantic Web and E-Learning