Experience-based learning: Food solution projects

2022 ◽  
pp. 421-430
Author(s):  
Myriam Loeffler ◽  
Maarten van der Kamp
2014 ◽  
Vol 2 (5) ◽  
pp. 316-324
Author(s):  
Marlene Fagundes Carvalho Gonçalves ◽  
RonildoAlves dos Santos ◽  
Marta AngélicaIossi Silva ◽  
Cinira Magali Fortuna ◽  
LucianeSá de Andrade

2019 ◽  
Vol 1 (2) ◽  
pp. 186-193
Author(s):  
Minh Tan Tang ◽  
Tuan Van Phan

The paper generally presents about integrating soft skills into teaching by using experience-based teaching method. This method is the process in which the teacher plays the roles of organizing, guiding, orienting and implementing activities with learners, helping learners to find new knowledge, values and capabilities. That new knowledge and capacity continue to be verified in the process of experiencing reality, solving tasks posed by teacher, and then sharing the knowledge that has just been acquired with their friends and lecturer. Therefore, learners will be more receptive. Through the article, the authors would like to share teaching methods via practical experience in teaching specialized subjects of Mechanical Engineering to help students have more opportunities to experience, to apply the knowledge into reality, thence, forming skills and practical capacity as well as promoting the creative potential of the learners themselves.


2021 ◽  
Author(s):  
Maria Costello ◽  
Peter Cantillon ◽  
Rosemary Geoghegan ◽  
Dara Byrne ◽  
Aoife Lowery ◽  
...  

Author(s):  
Qi Han ◽  
Theo Arentze ◽  
Harry Timmermans ◽  
Davy Janssens ◽  
Geert Wets

Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling approach focusing on the dynamic formation of (location) choice sets. Individual travelers are represented as agents, each with their cognition of the environment, habits, and activity-travel patterns. Agents learn through their experiences with the transport systems, changes in the environments and from their social network. Conceptually, agents are assumed to have an aspiration level associated with choice sets that in combination with evaluation results determine whether the agent will start exploring or persist in habitual behavior; an activation level of each (location) alternative that determines whether or not the alternative is included in the choice set in the next time step, and an expected (utility) function to evaluate each (location) alternative given current beliefs. Each of these elements is dynamic. Based on principles of reinforcement learning, Bayesian learning, and social comparison theories, the framework specifies functions for experience-based learning, extended and integrated with social learning.


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