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One of the greatest advantages of augmented reality (AR) in education is that AR increases student motivation. Nevertheless, there is a gap between the research on student motivation in AR and the definition of frameworks to inform and guide the design and development of AR applications that effectively support student motivation. In this paper, we attempt to bridge that gap as we introduce and evaluate a framework for designing motivational AR applications. Our framework has been built upon three theoretical foundations: motivational design, universal design for learning and co-creation. The evaluation study was conducted with 58 chemistry students enrolled in the vocational education and training (VET) program for Laboratory Operations, and we found that the framework not only effectively supports the four dimensions of Keller's (2010) ARCS (attention, relevance, confidence and satisfaction) model of motivation, but also demonstrates exceptional results in the Attention and Confidence dimensions of motivation.

Este artículo presenta resultados preliminares de un estudio que buscó establecer las oportunidades de preparación para enseñar matemática de los futuros profesores de Educación General Básica. Se analizaron mallas curriculares de 36 carreras, programas de Matemática y de Didáctica de la Matemática de 12 carreras, y se tomaron pruebas y encuestas a alumnos a dos niveles de formación de cuatro carreras (analizadas con mayor profundidad), lo que incluyó entrevistas a sus profesores. El estudio mostró un número insuficiente de cursos de Matemática y de Didáctica de la Matemática, ausencia de temas importantes del currículo escolar (cuya enseñanza se sabe débil y confusa), bajo rendimiento de estos estudiantes en preguntas de matemática elemental y una amplísima mayoría de estudiantes de nivel avanzado que estiman insuficiente la preparación que reciben en Matemática y en Didáctica de la Matemática.

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Automatic scoring and feedback tools have become critical components of online learning proliferation. These tools range from multiple-choice questions to grading essays using machine learning (ML). Learning environments such as massive open online courses (MOOCs) would not be possible without them. The usage of this mechanism has brought many exciting areas of study, from the design of questions to the ML grading tools' precision and accuracy. This paper analyzes the findings of 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool. This analysis gives an overview of the trends, challenges, and open questions in this research area. The results indicate that automatic scoring and feedback have many advantages. The most important benefits include enabling scaling the number of students without adding a proportional number of instructors, improving the student experience by reducing the time between submission grading and feedback, and removing bias in scoring.On the other hand, these technologies have some drawbacks. The main problem is creating a disincentive to develop innovative answers that do not match the expected one or have not been considered when preparing the problem. Another drawback is potentially training the student to answer the question instead of learning the concepts. With this, given the exitance of a correct answer, such an answer could be leaked to the internet, making it easier for students to avoid solving the problem. Overall, each of these drawbacks presents an opportunity to look at ways to improve technologies to use these tools to provide a better learning experience to students.