Work-in-Progress: TextMatch - A Semantically Enhanced Textbook Recommendation System

Raiyan Ishmam; Salma Emara; Hamid S Timorabadi

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: Innovative Learning Tools and Visualizations in ECE Curriculum
Tagged Division: Electrical and Computer Engineering Division (ECE)
Page Count: 15
Permanent URL: https://peer.asee.org/57551
Download Count: 2

Salma Emara is an Assistant Professor, Teaching Stream in the Department of Electrical and Computer Engineering at the University of Toronto. She received her B.Sc. in Electronics and Communications Engineering from the American University in Cairo in 2018, and her Ph.D. in Computer Engineering from the University of Toronto under the supervision of Professor Baochun Li in 2022. Her Ph.D. research focuses on improving reinforcement learning algorithms to solve problems in computer networking algorithms. Currently, her research focuses on developing pedagogical practices to enhance debugging skills for beginner programmers and utilizing natural language processing in engineering education. She believes that engineers learn by doing, which makes her committed to engaging students through in-class activities and problem-solving assignments and projects. She strives to create inclusive learning environments for all students from different backgrounds.

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Hamid S Timorabadi P.Eng. University of Toronto

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Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the application of digital signal processing in power systems.

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Abstract

Instructors and students alike face challenges when it comes to acquiring targeted, high-quality educational materials for their courses. Both instructors and students often benefit from well-structured, customized notes or textbook material that aligns closely with the teaching and learning objectives. Nevertheless, textbook focus may not align with specific course objectives and finding relevant material within them can be cumbersome. Instructors may need to tailor their courses on short notice to address student feedback or emerging needs, while students often seek supplementary reading to deepen their understanding of specific topics. Both scenarios require quick and precise access to relevant educational resources, but manually searching through large volumes of textbook material is time-consuming and inefficient.

Online methods can provide a faster searching experience by recommending materials based on the user’s specific queries. Existing online platforms, such as library websites, recommend books using keyword search. While keyword search can be effective, it is limited to direct matches or predefined synonyms, often resulting in irrelevant results due to typos or misworded queries. Furthermore, users may not always know the precise terms they are looking for but still seek related concepts. To address this limitation, semantic search is a potential solution.

TextMatch is an implementation of semantic search on a large existing database of books. Through contextual interpretation of user queries, TextMatch reranks results from an initial broad keyword search and returns a refined list of recommendations. This refinement allows for more accurate and meaningful results compared to what would be returned by a simple keyword search mechanism.

TextMatch was evaluated on a variety of metrics, which were compared to those from a search algorithm that only performed keyword search. Evaluation of TextMatch demonstrated improved Precision@10 and Mean Reciprocal Rank (MRR) scores compared to basic keyword search, while maintaining low search times. This highlights the significance of incorporating semantic search capabilities to provide students and instructors with contextually relevant educational materials that enhance the learning experience.

Citation
Format

Ishmam, R., & Emara, S., & Timorabadi, H. S. (2025, June), Work-in-Progress: TextMatch - A Semantically Enhanced Textbook Recommendation System Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/57551

TY - CPAPER
AB - Instructors and students alike face challenges when it comes to acquiring targeted, high-quality educational materials for their courses. Both instructors and students often benefit from well-structured, customized notes or textbook material that aligns closely with the teaching and learning objectives. Nevertheless, textbook focus may not align with specific course objectives and finding relevant material within them can be cumbersome. Instructors may need to tailor their courses on short notice to address student feedback or emerging needs, while students often seek supplementary reading to deepen their understanding of specific topics. Both scenarios require quick and precise access to relevant educational resources, but manually searching through large volumes of textbook material is time-consuming and inefficient.

Online methods can provide a faster searching experience by recommending materials based on the user’s specific queries. Existing online platforms, such as library websites, recommend books using keyword search. While keyword search can be effective, it is limited to direct matches or predefined synonyms, often resulting in irrelevant results due to typos or misworded queries. Furthermore, users may not always know the precise terms they are looking for but still seek related concepts. To address this limitation, semantic search is a potential solution.

TextMatch is an implementation of semantic search on a large existing database of books. Through contextual interpretation of user queries, TextMatch reranks results from an initial broad keyword search and returns a refined list of recommendations. This refinement allows for more accurate and meaningful results compared to what would be returned by a simple keyword search mechanism.

TextMatch was evaluated on a variety of metrics, which were compared to those from a search algorithm that only performed keyword search. Evaluation of TextMatch demonstrated improved Precision@10 and Mean Reciprocal Rank (MRR) scores compared to basic keyword search, while maintaining low search times. This highlights the significance of incorporating semantic search capabilities to provide students and instructors with contextually relevant educational materials that enhance the learning experience.
AU - Raiyan Ishmam
AU - Salma Emara
AU - Hamid S Timorabadi P.Eng.
CY - Montreal, Quebec, Canada
DA - 2025/06/22
PB - ASEE Conferences
TI - Work-in-Progress: TextMatch - A Semantically Enhanced Textbook Recommendation System
UR - https://peer.asee.org/57551
ER -