| Computer-aided instruction has been playing a crucial role in supporting learning. Early computer-aided instruction delivered a single style of content to all learners without any consideration of their learning styles. Recently, systems have been developed to adapt content based on the learners' learning styles. These systems use instruments, such as questionnaires and interview, to infer the learning styles. Using such instruments costs learners extra time, and they have to be done explicitly. In addition, these systems do not adapt the learning styles of learners over time. These drawbacks are the problem addressed in this study.; The purpose of this research was to infer the learning styles of students while they are browsing online instruction. This indicates that the inferred process can be done implicitly, in less time, and repeated over time. The focus of this study was on the three cognitive learning styles: holist, serialist, and versatile. In order to achieve this goal, a classification system was developed, which contains three online lessons and uses two mechanisms (Tracking and Questions) to extract useful information about the users' behaviors. The extracted features were used by a collection of classifiers to infer the users' learning styles. These results were compared with those of the Study Preference Questionnaire by calculating the Pearson correlation between them. The major implication of this study is that the classification system developed for this study accurately infered the learning styles. |
