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|title: ||ModelMaker, a Multidisciplinary Web Application to Build Question Generator Models From Basic to Higher Education|
|authors: ||Camejo, Jorge|
|issue date: ||2016|
|abstract: ||PmatE (Mathematics and Education Project) is a Research and Development project started in 1989 at the University of Aveiro, Portugal. For 27 years, PmatE has maintained the mission of applying technologies and developing content and events to foster school success and scientific culture. PmatE provides a large repository of learning objects, with particular emphasis in Question Generator Models (QGM) or simply Models. A QGM is an object for generating questions targeting specific scientific and pedagogical-didactic objectives. Each QGM generates thousands of different questions, thus enabling the exposure of students to the same core problems, but with different instantiations, preventing cheating in exams. The QGM’s are the basis of Portugal National Science Competitions (NSC), a three-day yearly event with about ten thousand participants, and are widely used by schools nationwide, at various levels of education (from basic to higher education), for test and diagnostic exams in several areas (e.g., Mathematics, Physics, Biology, Portuguese, Financial Education, Geosciences and Chemistry).
Until September 2015, each QGM created was written in a LaTeX template, as an intermediate specification of the Model, and later implemented by dedicated programmers from PmatE, thus making the Model development and later corrections a tedious, lengthy and time-consuming task. This work presents PmatE ModelMaker solution, which enables professors with neither a coding background nor latex knowledge, from basic school to higher education from all areas mentioned above, to create and share QGMs through a Web application. ModelMaker, keeps the core concepts of QGM such as “boxes” and “variables”, in order to guarantee the random screens concretization, and incorporates new functionalities and advantages (e.g., autonomy, model versioning, storage of instantiated models, and a management optimization of PmatE Subject Classification).
|publisher version/DOI: ||https://doi.org/10.21125/edulearn.2016.2206|
|source: ||EDULEARN16 Proceedings. 8th International Conference on Education and New Learning Technologies|
|appears in collections||CIDMA - Comunicações|
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