Shabana K M

Doctoral research scholar at IIT Palakkad

About me

I am a PhD research scholar in the department of Computer Science and Engineering at the Indian Institute of Technology Palakkad. My research focuses on personlizing education by applying AI based techniques for designing optimal activity/content delivery mechanisms tailored to suit individual learner requirements, thereby contributing to better quality of learning for the students.

   

Research interests

Ongoing research work

CurriculumTutor: a novel tutoring algorithm for mastering a curriculum using adaptive activity sequencing

Won the best paper award at AIED 2022 [Paper]

An important problem in an intelligent tutoring system (ITS) is that of adaptive sequencing of learning activities in a personalised manner so as to improve learning gains. In this paper, we consider intelligent tutoring in the learning by doing (LbD) setting, wherein the concepts to be learnt along with their inter-dependencies are available as a curriculum graph, and a given concept is learnt by performing an activity related to that concept (such as solving/answering a problem/question). For this setting, recent works have proposed algorithms based on multi-armed bandits (MAB), where activities are adaptively sequenced using the student response to those activities as a direct feedback. In this paper, we propose CurriculumTutor, a novel technique that combines a MAB algorithm and a change point detection algorithm for the problem of adaptive activity sequencing. Our algorithm improves upon prior MAB algorithms for the LbD setting by (i) providing better learning gains, and (ii) reducing hyper-parameters thereby improving personalisation. We show that our tutoring algorithm significantly outperforms prior approaches in the benchmark domain of two operand addition up to a maximum of four digits.

Unsupervised concept tagging of mathematical questions from student explanations

Presented at AIED 2023 [Paper]

Assigning concept tags to questions enables Intelligent tutoring systems (ITS) to efficiently organize resources, help identify students’ strengths and weaknesses, and recommend suitable learning materials accordingly. Manual tagging is time-consuming, and inefficient for large question banks, and could lead to consistency issues due to differences in the perspectives of individual taggers. Automatic tagging techniques can efficiently generate consistent tags at lower costs. Generating automatic tags for mathematical questions is challenging as the question text is usually short and concise, and the question as well as the answer text contains mathematical symbols and formulas. However, prior works have not studied this problem extensively. In this context, we conducted a study in a graduate-level linear algebra course to understand if student explanations to solving mathematical problems can be employed to generate concept tags associated with those questions. In this work, we propose a method called Unsupervised Skill Tagging (UST) to extract concept tags associated with a given assessment item from explanation text. Using UST on the explanations generated, we show that the explanations indeed contain the expert-specified concept tags.

Content sequencing

Given a set of resources such as videos, worked out examples, etc. related to a given concept, find an optimal sequence of resources that gets a student to master the concept as quickly as possible

Research advisor

Dr. Chandrashekar Lakshminarayanan
Assistant Professor
Department of CSE
IIT Madras
[Homepage]

Publications

  1. Shabana, K. M., Lakshminarayanan, C., & Anil, J. K. (2022). CurriculumTutor: An Adaptive Algorithm for Mastering a Curriculum. In International Conference on Artificial Intelligence in Education (pp. 319-331). Springer, Cham. [Paper] Won the best paper award at AIED 2022: The 23rd International Conference on Artificial Intelligence in Education

  2. Shabana, K.M., Lakshminarayanan, C. (2023). Unsupervised Concept Tagging of Mathematical Questions from Student Explanations. In International Conference on Artificial Intelligence in Education (pp. 627-638), vol 13916. Cham: Springer Nature Switzerland. [Paper]

  3. Shabana, K. M., Nazeer, K. A., Pradhan, M., & Palakal, M. (2015). A computational method for drug repositioning using publicly available gene expression data. BMC bioinformatics, 16(17), S5. [Paper] Won the best paper award at IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2014

  4. Shabana, K. M., & Wilson, J. (2015, May). A novel method for automatic discovery, annotation and interactive visualization of prominent clusters in mobile subscriber datasets. In 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS) (pp. 127-132). IEEE. [Paper]

  5. Shabana, K. M., Wilson, J., & Chaudhury, S. (2016, August). A multi-view non-parametric clustering approach to mobile subscriber segmentation. In 2016 IEEE 18th Conference on Business Informatics (CBI) (Vol. 1, pp. 173-181). IEEE. [Paper]

Work experience

Honors and Awards

Patent

Teaching experience

Teaching Assistantship

Education

Contact

111914007 [at] smail [dot] iitpkd [dot] ac [dot] in