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Lecturers’ attitude towards the use of e-learning tools in higher education: a case of Portugal

Fonseca José Manuel, Garcia Jorge Esparteiro, Vieira Bruno, Teixeira Andreia Sofia

Engineering Management in Production and Services

2023

Vol. 15, no. 2

23--34

This study aims to assess the lecturers’ opinions about the use of e-learning tools to support distance and blended learning in higher education in Portugal, evidently reinforced by the COVID-19 pandemic. This research was based on a qualitative methodology, specifically, a focus group with professors from five higher education institutions from different geographical areas in Portugal. The obtained results were analysed along four main dimensions: (1) the level of knowledge of e-learning tools, (2) the reasons for using or (3) not using them, and, finally, (4) the opinion of lecturers on the student assessment process using these tools. The results showed that in addition to the concerns with smooth running classes and the appropriate delivery of the syllabus, the lecturers considered the transition to the e-learning context to have been easy. They noted a high level of literacy in the used tools, believed in the continued use of e-learning in the post-pandemic context, indicated several advantages for those involved in the e-learning context and a majority of limitations related to the time required for the adoption of more tools; and, finally, underlined the student assessment issue, which was pointed out as the most sensitive topic in the whole e-learning context. The study informed on the lecturers’ perspective on e-learning and the used tools and provided insight into their perceived usefulness and benefits for lecturers and students. An especially strong concern was verified on the part of lecturers to optimise e-learning tools to provide better knowledge delivery to students.

Terrain classification using static and dynamic texture features by uav downwash effect

Carvalho João Pedro, Fonseca José Manuel, Mora André Damas

Journal of Automation Mobile Robotics and Intelligent Systems

2019

Vol. 13, No. 1

84--93

Knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and emergency landings areas. For example, an unmanned aerial vehicle (UAV) can use it to find a suitable landing position or to cooperate with other robots to navigate across an unknown region. Previous works on terrain classification from RGB images taken onboard of UAVs shown that only static pixel-based features were tested with a considerable classification error. This paper presents a computer vision algorithm capable of identifying the terrain from RGB images with improved accuracy. The algorithm complement the static image features and dynamic texture patterns produced by UAVs rotors downwash effect (visible at lower altitudes) and machine learning methods to classify the underlying terrain. The system is validated using videos acquired onboard of a UAV with a RGB camera.