Concept Inventories (CIs) have been developed to assess students' understanding of the main concepts in a study area. They typically consist of a set of Multiple Choice Questions (MCQs). What is unique about these CIs that, in addition to the correct answer, they are designed, through a very thorough and collaborative process, to include a number of distractors that represent commonly observed conceptual misunderstandings.
The ease of marking MCQs has made them an attractive method for use in concept inventories. However, this may pose a limitation on how the test scores can be used to provide better insights into students' misconceptions, and this hinders efforts to attempt to correct them.
In order to address this problem, our recent research proposedextendingmultiple choice questions inconcept inventories to include a text field for students to provide short answers, allowing students to express the reasoning behind their choice of a specific answer.
Conducting literature reviews, developing and implementing algorithms, collecting data and interpreting and analysing results.
This project aims to use the combination of prior information (containing both textual and multiple-choice data) to predict a confidence that students have guessed their responses given. The project will have a maths focus, most likely looking at statistics and algorithms such as Bayes Theorem. As a part of the research, we hope to be able to use past information and data to predict future confidences.
Skills and experience
- Fluent in Matlab and Python programming
- Excellent math and calculus skills.
You may be able to apply for a research scholarship in our annual scholarship round.
Contact the supervisor for more information.