Active learning in a real-world bioengineering problem: A pilot-study on ophthalmologic data processing

Abstract

Active learning is a format alternative to the conventional lecture/recitation/laboratory; research results have reported that it is suitable to encourage student inquiry and foster peer mentoring. Although the availability of computer-based learning materials in biomedical sciences is increasing, there are relatively few studies aimed to integrate traditional methods of teaching with inquiry-based approaches utilizing these Information and Communication Technologies (ICT) tools. This paper describes a pilot-study on a comprehensive active laboratory course about digital ophthalmologic signal classification, experienced by a group of undergraduates in Bio-Electronic Engineering. During the activity, the students became able to discriminate healthy subjects from patients affected by two retinal pathologies: Achromatopsia or Congenital Stationary Night Blindness. The study was based on the analysis and classification of the electroretinograms, that record the retinal response to a light flash. To process electroretinographic data, a software based on the Empirical Mode Decomposition and an Artificial Neural Network was used. Our findings indicate that this laboratory experience can be considered effective in improving student's reasoning skills and that students acting as investigators achieve a better outcome, presumably because this activity satisfies their psychological needs for autonomy, competence, and relatedness.