Javascript must be enabled for the correct page display

Student modelling using a genetic algorithm

Plantinga, E. (2003) Student modelling using a genetic algorithm. Doctoral, Artificial Intelligence.

KI_DR_2003_Edo-Plantinga.CV.pdf - Published Version

Download (3MB) | Preview


The focus of the research described in this thesis is on modelling a student's knowledge and skills in an Intelligent Tutoring System (ITS) using a genetic algorithm (GA). ITSs are computer-based teaching systems that adapt the material that is taught to the level of the student. This means that the system will present the student hints, exercises, questions, etc. that are expected to be most beneficial for this particular student. In order to do this it is necessary to keep track of a student model: a set of beliefs about the knowledge and skills of a student. The key observation that inspired this research was that students often give answers that can be interpreted in several ways. It is not always possible to translate an observation of the student to a direct update of the student model because of this ambiguity. Human tutors do not seem to have a lot of difficulties with this problem: apparently their beliefs about a student can be updated flexibly enough to avoid serious misconceptions. The technique that we propose in this thesis is a novel way of handling this problem of ambiguity. Rather than choosing one interpretation of an observation and discarding the other possible interpretations (or making them less likely), we chose to implement a system in which all possible interpretations are considered simultaneously. The student models that have interpreted the observations correctly will generally be able to make better predictions about the next answer of the student. Every student model has a fitness parameter to signify how good the predictions of this student model have been in the past. The fitter student models are more likely to be retained as acceptable hypotheses of the student's knowledge and skills, whereas the less fit student models are more likely to be discarded. In this way student models that can interpret the observations well will evolve. Because of the similarities with the biological process of natural selection this approach is referred to as a genetic algorithm approach. We have implemented a simplified version of the proposed algorithm to gain an insight into the principles of how the algorithm functions. We have tested this simplified version using artificial students. These are simulations of human students whose knowledge changes on the basis of the material that they practise. As a consequence, the way that they solve problems also changes. We have tested to what extent our algoritlun was able to track these changes in knowledge and therefore in observable behaviour. We observed that the algorithm could only find an accurate model of our artificial students in the more simplified test cases. The algorithm in its current form is not robust enough for more complex (and therefore more realistic) test cases.

Item Type: Thesis (Doctoral)
Degree programme: Artificial Intelligence
Thesis type: Doctoral
Language: English
Date Deposited: 15 Feb 2018 07:28
Last Modified: 15 Feb 2018 07:28

Actions (login required)

View Item View Item