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In this paper, a multi-mode digital teaching approach is proposed based on the use of the VARK (Visual, Aural, Read/Write, Kinaesthetic) model where students have different styles (one or more) that improve their learning (face-to-face and online). Our research question is on the effectiveness of this approach in terms of learning efficacy and students' satisfaction. An experiment with 41 students has been carried out for five months to answer the research question and to provide a first validation of using VARK for multi-mode digital HCI teaching. During the experiment, the theoretical sessions were given through videoconference using Microsoft Teams and with the support of Moodle. In the practical sessions, students had to create a software prototype following a User-Centred Design with a real client. For this, they used Discord to collaborate in their groups, Teams to ask questions to teachers and PowerPoint and Genially to present their work online to the class through a Teams videoconference. A regression model has been provided to predict the VARK indicated by the questionnaire to each student with a prediction success of nearly 77%. Using the VARK multi-mode digital teaching approach has proved valid, and effective and beneficial in the teaching of HCI with a significant improvement in the learning scores and satisfaction levels of the students even with respect to pre-COVID-19 where the teaching was face-to-face.

An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of use. Additional elements are lecture contents and the scheduling of classes and lab sessions. Learning gain in the topic of greedy algorithms was measured in the short term for two groups of students: an experimental group taught with the new didactic method, and a control group taught with a traditional approach. The results show a significant learning gain improvement in the experimental group, while the students taught with a traditional method had little learning gain. In addition, the levels of Bloom's taxonomy at which improvements occurred were identified. In the control group, improvements were found at the knowledge level. In the experimental group, however, improvements were found at the knowledge and the comprehension levels, although not at the analysis level. These results are encouraging and indicate directions for future research, as analysis skills are important in algorithm courses.