Journal Article2024Cited by 7

Dataset of computer science course queries from students: Categorized and scored according to Bloom’s taxonomy

K. A. U. Zaman, A. Islam, M. A. Sayed

Data in Brief, 2024

Elsevier BV, Vol. 53, pp. 110109, ISBN: 2352-3409

  • Quality Education — Relevance: 71%
DOI: 10.1016/j.dib.2024.110109Open AccessURL

Abstract

"Why don't students learn?" is a common question that educators try to address. To encourage students to become more engaged in the learning process, we believe in fostering their natural curiosity by encouraging them to ask high-level questions. To support this approach, we have compiled a dataset of questions that we hope will aid in the training of artificial intelligence (AI) models and ultimately improve the learning experience for students. To develop our dataset, we collected anonymous student questioning data in the Summer 2023 semester, utilizing our online application named "Palta Question", resulting in a dataset of 8,811 unique questions. The dataset consists of students' inquiries which underwent basic question validation using a sophisticated keyword-based approach, manual categorization by topic and course content, as well as complexity assessment using Bloom's taxonomy keywords which have also been included in the dataset. To ensure question uniqueness, we implemented the Levenshtein distance algorithm to exclude questions with a high similarity rate. This dataset provides targeted insights into student inquiry patterns and knowledge gaps within the domain of 'Introduction to Computers and Research' and 'Data Structure' courses, originating from the students at Independent University, Bangladesh (IUB). While its scope is confined to a specific student group and academic context, limiting broader applicability, it remains valuable for detailed studies in these subjects and serves as a useful foundation for AI-based educational research tools. To demonstrate the effectiveness of the dataset, we also tested it to train the AI to perform basic tasks like sorting questions according to their courses and topics. However, we envision researchers utilizing it to enhance education and aid in students' learning.

Keywords

  • knowledge gaps
  • student questions
  • Levenshtein distance
  • question classification
  • computer science education
  • Bloom's taxonomy
  • AI-based educational tools
  • data structures course
  • introductory computer science

References

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  6. 6.Khandoker Ashik Uz Zaman, Ashraful Islam, Yusuf Mahbubul Islam, Md Abu Sayed. (2024). Dataset of computer science course queries from students: Categorized and scored according to Bloom's taxonomy. Data in Brief, 53, 110109[10.1016/j.dib.2024.110109]

Cited By

  1. 1.Nurassyl Kerimbayev, Karlygash Adamova, Rustam Shadiev, Zehra Altinay. (2025). Intelligent educational technologies in individual learning: a systematic literature review. Smart Learning Environments, 12(1)[10.1186/s40561-024-00360-3]
  2. 2.Anitia Lubbe, Elma Marais, Donnavan Kruger. (2025). Cultivating independent thinkers: The triad of artificial intelligence, Bloom’s taxonomy and critical thinking in assessment pedagogy. Education and Information Technologies, 30(12), 17589–17622[10.1007/s10639-025-13476-x]
  3. 3.Khandoker Ashik Uz Zaman, Ashraful Islam, Yusuf Mahbubul Islam, Md Abu Sayed. (2024). Dataset of computer science course queries from students: Categorized and scored according to Bloom's taxonomy. Data in Brief, 53, 110109[10.1016/j.dib.2024.110109]
  4. 4.Zhanhao Cui, Ye Wang, Xinya Huang, Wen Wu, Wenxin Hu. (2025). Book2QA: A Framework for Integrating LLMs to Generate High-quality QA Data from Textbooks. , 1–8[10.1109/ijcnn64981.2025.11227625]
  5. 5.Aray M. Kassenkhan, Aigerim M. Abshukirova, Zhuldyz B. Kalpeyeva, Arman T. Sholpanbayev. (2026). Design and Implementation of an AI Tool for Fostering Critical Thinking in Secondary Schools. Procedia Computer Science, 280, 1013–1017[10.1016/j.procs.2026.04.129]
  6. 6.Anitia Lubbe. (2025). Universities can turn AI from a threat to an opportunity by teaching critical thinking. [10.64628/aaj.6tveerfcw]
  7. 7.Sucipto, Didik Dwi Prasetya, Triyanna Widiyaningtyas. (2024). An Evaluation of the Impact of Dataset Size on Classification Performance in the Cognitive Bloom's Taxonomy. , 131–136[10.1109/bts-i2c63534.2024.10941910]
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