DOI: 10.5281/zenodo.8349468
title: Eye Tracking based Learning Style Identification for Learning Management Systems

description: Abstract: 

In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.

Research: 

As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: 



Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, In 2024 Symposium on Eye Tracking Research and Applications. ETRA.

Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence.  Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany

Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In ICERI2023 Proceedings (pp. 5153-5162). IATED.

Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 138-147). ECSEE.


The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. 

Experimental Setup:

In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. In order to reduce the external influence on the experiment, variables such as:



order, number, and presentation of the stimuli,

instruction to the participant prior to the experiment,

position of the participant in respect to the Eye Tracking equipment,

environment such as illuminance and ambient noise for the participant,

Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration


were attempted to keep constant and consistent throughout the experiment. 

Equipment: 

In this study, the Tobii Pro Fusion (https://go.tobii.com/tobii-pro-fusion-user-manual) eye tracker is utilized without a chin rest along with the Tobii IVT filter for fixation detection and Tobii Pro Lab software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.

Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design. 

Procedure: 

Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment. 

Stimuli: 

The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. illustrations or key words, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. In detail, the AOI grouping regarding:



table of contents and summary contain only a single AOI each,

illustrations, key words, theory, exercise, example and additional material contain three AOIs each,

supporting text and multiple choice question contain two AOIs each.


Research data management: 

To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire. 
description: The descriptions and the abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. 
description: We thank the funding project FH-Invest (FKZ: 13FH101IN6) run by Prof. Dr. Jürgen Mottok for providing equipment for the eye tracking laboratory and Prof. Dr. Christian Wolff from the University of Regensburg for arranging the laboratory areas. The present paper is supported by the 'German Federal Ministry of Education and Research' (BMBF) through the granting of the funding project HASKI (FKZ: 16DHBKI035).