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Experiments with Word Embeddings for Sequential Questioning
- Author / Creator
- McDonald, Emma
As a student learns to program, there will be gaps in the student's knowledge that must be addressed for the student to gain a full understanding of the material. A student's answer to a single question may provide some insight into the student's level of understanding. However, a well-chosen sequence of questions might more accurately identify any misunderstandings. For example, the popular 20 Questions game relies on a sequence of well-chosen questions for one player to guess what another player is thinking.
Inspired by the 20 Questions game, we suggest a method to select the next question in a sequence to identify gaps in a student's understanding. We model introductory computing science terms with word embeddings trained from a collection of Python course notes and textbooks. We also introduce a test suite of computing science concepts. Each of the 17 tests is an algebraic equation and each term in the equation is represented by one of our word embeddings. Thus, a test can be evaluated to produce a result that corresponds to another word embedding in the model.
The test suite represents a collection of concepts and skills that an introductory computing science student must learn. We demonstrate that we can represent a computing science concept by adding relevant substituent concepts and removing irrelevant concepts. We then posit that this ability can be used to diagnose the gap in understanding and recommend a relevant next question based on a student's answers so far.
- Graduation date
- Spring 2022
- Type of Item
- Master of Science
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.