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octubre 2020
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Smartly Learning through step decomposition, automation and Gamification

Smartly Learning through step decomposition, automation and Gamification
Francisco J. Gallego-Durán, Carlos J. Villagrá Arnedo, Rafael Molina-Carmona, Faraón Llorens-Largo
Grupo de investigación Smart Learning
Universidad de Alicante

Track 5. Smart Learning
International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’20)
https://2020.teemconference.eu
Online Conference, October 21-23, 2020

Abstract:
On previous years teaching Logic and Algebra many student conceptual issues were identified by analysing their solution attempts to exercises. Present work proposes a new design of exercises and student workflow to target these issues.
Classical algebraic exercises integrate many concepts. Most issues identified were related to low- level concepts. Moreover, students proved unable to identify these issues and solve them by practicing. They tended to get frustrated not knowing the causes of their failures.
This design proposal starts with minimal exercises requiring a single step to be solved. Classical exercises are decomposed into these single steps. Simplicity of these exercises helps generating many instances automatically. Design focus is placed on previously identified issues. Designed exercises are composed in a pyramidal model of knowledge.
To motivate students to carry out many of the proposed exercises, Gamification techniques are used. Designed exercises are automated using Moodle questionnaires. These questionnaires are contextualized as adventure activities in a role play story line, including Quests, Dungeons, Weapons and Bosses. Rules are designed according to this context. Detailed design is included for replication.


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