How can exploratory learning with games and simulations within the curriculum be most effectively evaluated?

There have been few attempts to introduce frameworks that can help support tutors evaluate educational games and simulations that can be most effective in their particular learning context and subject area. The lack of a dedicated framework has produced a significant impediment for uptake of games and simulations particularly in formal learning contexts. This paper aims to address this shortcoming by introducing a four-dimensional framework for helping tutors to evaluate the potential of using games- and simulation- based learning in their practice, and to support more critical approaches to this form of games and simulations. The four-dimensional framework is applied to two examples from practice to test its efficacy and structure critical reflection upon practice

Light Stop Searches at the LHC in Events with One Hard Photon or Jet and Missing Energy

Low energy supersymmetric models provide a solution to the hierarchy problem and also have the necessary ingredients to solve two of the most outstanding issues in cosmology: the origin of the baryon asymmetry and the source of dark matter. In the MSSM, weak scale generation of the baryon asymmetry may be achieved in the presence of light stops, with masses lower than about 130 GeV. Moreover, the proper dark matter density may be obtained in the stop-neutralino co-annihilation region, where the stop-neutralino mass difference is smaller than a few tens of GeV. Searches for scalar top quarks (stops) in pair production processes at the Tevatron and at the Large Hadron Collider (LHC) become very challenging in this region of parameters. At the LHC, however, light stops proceeding from the decay of gluino pairs may be identified, provided the gluino mass is smaller than about 900 GeV. In this article we propose an alternative method for stop searches in the co-annihilation region, based on the search for these particles in events with missing energy plus one hard photon or jet. We show that this method is quite efficient and, when complemented with ongoing Tevatron searches, allows to probe stop masses up to about 160 GeV, fully probing the region of parameters consistent with electroweak baryogenesis in the MSSM.Comment: 17 pages, 6 figure

Bosonic corrections to the effective leptonic weak mixing angle at the two-loop level

Details of the recent calculation of the two-loop bosonic corrections to the effective leptonic weak mixing angle are presented. In particular, the expansion in the difference of the W and Z boson masses is studied and some of the master integrals needed are given in analytic form.Comment: 5 pages, 4 figures, to appear in the proceedings of the 7th International Symposium on Radiative Corrections (RADCOR05), Shonan Village, Japan, 200

Two Loop Electroweak Bosonic Corrections to the Muon Decay Lifetime

A review of the calculation of the two loop bosonic corrections to $\Delta r$ is presented. Factorization and matching onto the Fermi model are discussed. An approximate formula, describing the quantity over the interesting range of Higgs boson mass values from 100 GeV to 1 TeV is given.Comment: 5 pages, 3 figures, minor corrections, to appear in the proceedings of the RADCOR 2002/Loops and Legs in Quantum Field Theory workshop, Kloster Banz, Germany, 8-13 Sep 200

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences