Identification of learning styles online by observing learners’ browsing behaviour through a neural network

Abstract

Abstract Identification of individual learning style is important when developing adaptive educational hypermedia systems. Current systems ask learners to complete questionnaires to identify their learning styles, which might not be appropriate in some contexts. The goal of this research is to identify the learner's learning style by simply observing his/her browsing behaviour without asking the learner to answer any questions or filling out any form. It is implemented through a multi‐layer feed forward neural network (MLFF). Browsing behaviour, in this research, includes three factors, the use of embedded support devices (ESDs), the selection of link types, and the navigation between visited/unvisited nodes. The experiment results showed the proposed model performed well in identifying learning styles. Link type is the dominant factor and Time shift may not be a major factor in the identification of learning styles. Because of the fast execution property of neural networks and identification of learning styles online, it is possible to incorporate learning styles into online adaptive educational web‐based systems.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 46 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.