基于计划行为理论,运用游戏化和人工神经网络激励学生在学术课程中培养横向能力的方法

Omar Mata, Juana Isabel Méndez, M. Aguilar, P. Ponce, A. Molina
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引用次数: 0

摘要

下一代学生还没有做好应对职场的准备,这不是因为教育有缺陷,而是因为他们学习的新方式和信息的可获得性。教学方法,特别是本科数学的教学方法,需要为新一代的学生更新,在学习过程中发出新的刺激。这些方法和教育工具需要允许学生有定制的学习,这样他们就可以在课堂外学习。教育领域的游戏化为教学、参与和激励学生学习提供了一种新的方式。另一方面,教授在授课时面临的挑战与学生有关,这些学生拥有广泛的不同技能,以前的经验和动机,这些都是学生上课时出现的。因此,蒙特雷理工学院通过Tec-21模式,通过基于挑战的协作工作和学习,发展学生的学科和横向能力,以满足实际的教育需要。本文提出了一种量身定制的方法流程,通过人工神经网络利用游戏化,采用计划行为理论(TPB)支持的基于能力的教学方法,激励学生发展Tec-21模型规定的所需技能
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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
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
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