Realisation of University-Industry Collaboration through Industry-Based Learning at Indonesian Higher Education

The collaboration between universities and industries has become increasingly important for the development of Science and Technology. This is particularly more prominent in the Science Technology Engineering and Mathematics (STEM) disciplines. Literature suggest that the key element of University-Industry Partnership (UIP) is the exchange of knowledge that is mutually beneficial for both parties. One real example of the collaborations is Industry-Based Learning (IBL) in which university students are coming into industries to experience and learn how the skills and knowledge acquired in the classroom are implemented in work places. This paper investigate how the University-Industry Collaboration program is implemented though Industry-Based Learning (IBL) at Indonesian Universities. The research findings offer useful insights and create a new knowledge in the field of STEM education and collaborative learning. The research will contribute to existing knowledge by providing empirical understanding of this topic. The outcomes can be used to improve the quality of University-Industry Partnership programs at Indonesian Universities and inform Indonesian higher education authorities and their industrial partners of an alternative approach to enhance their IBL programs.

Learning in Immersive worlds: A review of game-based learning

The report has been produced to inform practitioners who are considering using games and simulations in their practice. Towards this end, the work includes a review of the literature and a series of case studies from practice to illustrate the range of uses of games and to synthesise key issues and themes arising from learning in immersive worlds.

Questions and answers - copyright for work based learning

This Q and A provides an overview of copyright law and how it applies in a variety of scenarios relevant to work based learning (WBL) providers.

Motores térmicos de vapor, gas y de combustión interna a través de PBL (Project Based Learning)

Nivel educativo: Grado. Duración (en horas): De 31 a 40 horas

Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.