Background Scalp electroencephalography (EEG) remains an invaluable neurophysiological tool in supporting the diagnosis and management of epilepsy and encephalopathy, however, most sub-Saharan countries have very few neurologists per population for EEG analysis and training. Web-based, distance learning programs may provide effective electroencephalogram (EEG) training in resource-poor settings. EEGonline is an interactive, web-based, 6-month multi-modality, learning program designed to teach basic principles and clinical application of EEG. This study aimed to determine the effectiveness of EEGonline in improving EEG analysis and interpretation skills for neurologists, neurology residents and technologists, particularly in resource-limited settings. Methods Between June 2017 and November 2018, 179 learners were registered on the EEGonline course. Of these, 128 learners originating from 20 African countries, Europe, the UK and USA participated in the study. Pre- and post-course multiplechoice question (MCQ) test results and EEGonline user logs were analyzed. Differences in pre- and post-test performance were correlated with quantified exposure to various EEGonline learning modalities. Participants' impressions of EEGonline efficacy and usefulness were assessed through pre- and post-course perception surveys. Results Ninety-one participants attempted both pre- and post-course tests. Mean scores improved from 46.7% ± 17.6% to 64.1% ± 18% respectively (p< 0.001, Cohen's d 0.974). Almost all participants improved regardless of the amount of course material used, however those who used more, tended to have higher scores. The largest percentage-improvement was in the correct identification of normal features (43.2% to 59.1%, p< 0.001, Cohen's d 0.664) and artefacts (43.3% to 61.6%, p< 0.001, Cohen's d 0.836). Improvement in competence was associated with improvement in subjective confidence in EEG analysis. Overall confidence among 72 survey respondents improved significantly from 25.3% to 64.8% (p< 0.001). Lecture notes, end-of-module self-assessment quizzes and discussion forums were the most utilised learning modalities. The majority of survey respondents (97.2%) concluded that EEGonline was a useful learning tool and 93% recommended that similar courses should be included in EEG training curricula. Discussion Almost all participants showed significant improvement in EEG analysis competence (MCQ test scores) and confidence (survey responses) following the educational intervention, regardless of the amount of course material used. Improved identification of normal features and artefacts is particularly useful as it reduces the risk of misdiagnosis which can cause harm. The EEGonline course employed several learning techniques, through its multi-modality format, that may have contributed to the improvement observed, including, self-directed learning, cognitivism, collaborative learning, contextual learning and reflective learning. Subjective confidence likely correlates with competence and may be useful to gauge learners' needs and levels of understanding about a subject. Learning preferences vary among adult learners, it is unclear if one learning modality (that is, video, audio, lecture notes, epoch activities, discussion forums) is superior to others, but it seems as though a multi-modal approach may be the most sensible. Conclusions This study demonstrated that a multi-modal, online EEG teaching tool was effective in improving EEG analysis and interpretation skills and may be a useful supplement for EEG teaching especially in resource-poor settings. Given the optimistic findings of this study, we encourage the development and evaluation of further online neurology teaching tools.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/33630 |
| Date | 15 July 2021 |
| Creators | Asukile, Melody Tunsubilege |
| Contributors | Tucker, Lawrence, Viljoen, Charle |
| Publisher | Faculty of Health Sciences, Department of Medicine |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MMed |
| Format | application/pdf |
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