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A Methodology to Motivate Students to Develop Transversal Competencies in Academic Courses Based on the Theory of Planned Behavior by using Gamification and ANNs

机译:基于游戏化和人工神经网络的计划行为理论激励学生发展学术课程横向能力的方法论

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The next generation of students are not getting well prepared to deal with the world of work, not because of a defective education but rather because of the new ways they learn and the availability of the information. Teaching methodologies, specific in mathematics for undergrads, requires to be updated for the new generations to send new stimulus in the learning process. These methods and educational tools need to allow the students to have customized learning so they can study outside the classroom. Gamification in education field offers a new way to teach, engage and motivate students to study. On the other hand, the challenges that a professor's faces up when they teach are related to students who possess a wide range of different skills, previous experiences and motivations that are present when the students take the class. Thus, Tecnologico de Monterrey, through the Tec-21 Model, is developing disciplinaries and transversals competences in students through collaborative work and learning based on challenges to attend the actual educative needs. This paper proposes a tailored methodological process that takes advantage of the gamification through an Artificial Neural Network using a competence-based teaching method supported by the theory of planned behavior (TPB) to motivate students to develop the required skills specified on the Tec-21 model.
机译:下一代学生并没有为应付工作世界做好充分的准备,这不是因为教育水平低下,而是因为他们学习的新方法和信息的可用性。面向本科生的数学教学方法需要针对新一代进行更新,以在学习过程中激发新的动力。这些方法和教育工具需要允许学生进行自定义学习,以便他们可以在教室外学习。教育领域的游戏化提供了一种新的教学,参与和激励学生学习的方式。另一方面,教授在教授时面临的挑战与学生具有各种各样的技能,以前的经验和上课时表现出的动机有关。因此,蒙特雷技术学院(Tecnologico de Monterrey)通过Tec-21模式,通过协作工作和基于挑战的学习来发展学生的学科和横向能力,以应对实际的教育需求。本文提出了一种量身定制的方法学过程,该过程利用了基于能力的教学方法(通过计划行为理论(TPB)支持)通过人工神经网络进行的游戏化,以激发学生发展Tec-21模型上指定的所需技能。

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