Erbium-ytterbium co-dopped ion-exchanged waveguide amplifiers

Introduction to erbium-doped waveguide amplifiers -- The evolution of optical amplifiers -- Integrated optics and rare earth-doped amplifiers -- Fabrication method -- Theoritical study of erbium-ytterbium co-dopted ion-exchange wave guide amplifiers -- Electronic and optical properties of rare earth ions -- Quenching process for er-doped optical amplifiers -- Host materials for erbium -- Ytterbium co-doping of erbium amplifiers -- Fundamental equations -- The rate equation in Edwa -- Gain definition and calculation -- Erbium-doped waveguide fabrication process -- Glass characteristics and parameters -- Ion-Exchange process -- Slab waveguide fabrication on rare earth doped glasses by ion exchange -- Erbium ytterbium co-doped channel waveguide characterization -- Guiding at 4 different wavelengths -- Loss measurement -- Gain measurement

Learned Student Models with Item to Item Knowledge Structures

Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test questions. Their structure is also well grounded in the cognitive theory of knowledge spaces. We study the effectiveness of two Bayesian frameworks to learn item to item structures and to use the induced structures to predict item outcome from a subset of evidence. One approach, POKS, relies on a naive Bayes framework whereas the other is based on the Bayesian network learning and inference framework. Both approaches are assessed over their predictive ability and their computational efficiency in different experimental simulations. The results from simulations over three data sets show that they both can e#ectively perform accurate predictions, but POKS generally displays higher predictive power than the Bayesian network. Moreover, the simplicity of POKS translates to a time e#ciency of one to three orders of magnitude greater than the Bayesian network runs. We furhter explore the use of the item to item approach for handling concepts mastery assessment. The approach investigated consist in augmenting an initial set of observations, based on inferences with the item to item structure, and feed the augmented set to a Bayesian network containing a number of concepts. The results show that augmented set can effectively improve predictive power of a Bayesian network for item outcome, but that improvement does not transfer to the concept assessment in this particular experiment. We discuss di#erent explanations for the results ..

Item-based Bayesian Student Models

Many intelligent educational systems require a component that represents and assesses the knowledge state and the skills of the student. We review how student models can be induced from data and how the skills assessment can be conducted. We show that by relying on graph models with observable nodes, learned student models can be built from small data sets with standard Bayesian Network techniques and Na ve Bayesian models. We also show how to feed a concept assessment model from a learned observable nodes model. Different experiments are reported to evaluate the ability of the models to predict item outcome and concept mastery