Long-Term Memory Retention of Educational Content

Long-Term Memory Retention of Educational Content: How Machine Learning concepts can be remembered for the rest of our careers with the right practice questions

Mušić, Ismail (TU Delft Electrical Engineering, Mathematics and Computer Science)

Migut, M.A. (mentor)
Tielman, M.L. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2024-02-01

To aid the teachings of machine learning (ML), the usage of elaborative interrogative practice questions (EIPQ) is proposed to increase the long-term memory retention of said teaching. Firstly, the existing expectations of students in the current educational landscape are analyzed by taking a look at the undergraduate course present in Delft University of Technology's own Computer Science and Engineering program (CSE2510). Then, relevant theories and techniques for long-term memory retention through practice questions are introduced and applied to CSE2510 content. Finally, an experiment was carried out where roughly half of the participants made use of these newly created EIPQ, while the other half mostly used existing questions, serving as a control group (CQ). The results showed that, compared to the existing practice questions, the use of the newly created EIPQ had a profound impact on the long-term knowledge retention of the learning content. The participants who made use of EIPQ had an average retention ratio of 0.82, compared to the participants who made use of CQ, who had an average retention ratio of 0.57. Therefore, it is suggested that including EIPQ in our current educational model has favorable benefits to the students' knowledge retention of the learned content. A recommendation is made on how to carry out these methods in practice, keeping compatibility with existing learning objectives in mind.

Elaborative Interrogation
Machine Learning (ML)
knowledge retention
teaching method
Practice Question
Real-world problems
Long-term memory

http://resolver.tudelft.nl/uuid:4b1ba358-509f-44e4-96cb-f768c82e21fe

51.999069,4.378320

Student theses

bachelor thesis

© 2024 Ismail Mušić