In Vocational Education and Training (VET) institutions, teachers face important difficulties in the teaching process due to a wide variety of student’s special educational needs as well as student’s lack of: the adequate level of basic competence, motivation, concentration, attention, confidence and background knowledge, among other aspects. Regarding the attention to these aspects, many studies have reported positive impact of Augmented Reality (AR) applications in primary, secondary and higher education in terms of student’s motivation, learning gains, collaboration, interaction, learning attitudes and enjoyment, among others. However, very little has been done in terms of AR applications in VET as well as their impact on wide variety of student’s special educational needs such as learning difficulties. This paper introduces a marker-based mobile AR application named Paint-cAR for supporting the learning process of repairing paint on a car in the context of a vocational education programme of car’s maintenance. The application was developed using a methodology for developing mobile AR applications for educational purposes from a collaborative creation process (Co-Creation) and based on the Universal Design for Learning (UDL). A cross-sectional evaluation study was conducted to validate the Paint-cAR application in a real scenarioThis work is supported in part by the Spanish Science and Education Ministry in the Open Co-Creation Project (TIN2014-53082-R). Jorge Bacca, Silvia Baldiris and Ramon Fabregat belong to the BCDS group (ref. GRCT40) which is part of the DURSI consolidated research group COMUNICACIONS I SISTEMES INTELLIGENTS (CSI) (ref. SGR-1469). Jorge Bacca would like to thank the financial support for the Grant (FPI-MICCIN) provided by the Spanish Ministry of Economy and Competitiveness (BES-2012-059846). Authors would like to thank to teachers Joan Clopés (Institut Montilivi) and Narcis Vidal (Institut Narcis Monturiol) that participated in the design of PaintcAR application. The authors also acknowledge the support of NSER

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A coupled deep learning approach for coded aperture design and single-pixel measurements classification is proposed. A whole neural network is trained to simultaneously optimize the binary sensing matrix of a single-pixel camera (SPC) and the parameters of a classification network, considering the constraints imposed by the compressive architecture. Then, new single-pixel measurements can be acquired and classified with the learned parameters. This method avoids the reconstruction process while maintaining classification reliability. In particular, two network architectures were proposed, one learns re-projected measurements to the image size, and the other extracts small features directly from the compressive measurements. They were simulated using two image data sets and a test-bed implementation. The first network beats in around 10% the accuracy reached by the state-of-the-art methods. A 2x increase in computing time is achieved with the second proposed net.

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Research on Augmented Reality (AR) in education has demonstrated that AR applications designed with diverse components boost student motivation in educational settings. However, most of the research conducted to date, does not define exactly what those components are and how these components positively affect student motivation. This study, therefore, attempts to identify some of the components that positively affect student motivation in mobile AR learning experiences to contribute to the design and development of motivational AR learning experiences for the Vocational Education and Training (VET) level of education. To identify these components, a research model constructed from the literature was empirically validated with data obtained from two sources: 35 students from four VET institutes interacting with an AR application for learning for a period of 20 days, and a self-report measure obtained from the Instructional Materials Motivation Survey (IMMS). We found that the following variables: use of scaffolding, real-time feedback, degree of success, time on-task and learning outcomes are positively correlated with the four dimensions of the ARCS model of motivation: Attention, Relevance, Confidence, and Satisfaction. Implications of these results are also described.