On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

This paper describes the student modeling component of ANDES, an Intelligent Tutoring System for Newtonian physics. ANDES' student model uses a Bayesian network to do long-term knowledge assessment, plan recognition and prediction of students' actions during problem solving. The network is updated in real time, using an approximate anytime algorithm based on stochastic sampling, as a student solves problems with ANDES.The information in the student model is used by ANDES' Help system to tailor its support when the student reaches impasses in the problem solving process. In this paper, we describe the knowledge structures represented in the student model and discuss the implementation of the Bayesian network assessor. We also present a preliminary evaluation of the time performance of stochastic sampling algorithms to update the network

Cognitive grounding

Pursuit of efficient probability-based inference in complex networks of interdependent variables is an active topic in current statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the potential role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts and tools of the approach are reviewed, but emphasis is on special considerations that arise in the ITS context. We explore how this approach can support generalized claims about aspects of student proficiency through the combination of detailed epistemic analysis of particular actions within a system with probability-based inference. The psychology of learning in the domain and the instructional approach are seen to play crucial roles. Ideas are illustrated with HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics system. Key words: Bayesian inference networks, cognitive diagnosis, HYDRIVE

Calciphylaxis, early identification and management: a report of 2 cases

Patients with end-stage renal disease often suffer from dermatological disorders secondary to uraemic complications, ranging from uncomfortable to life-threatening.\ud \ud Calcific uraemic arteriolopathy (CUA), or calciphylaxis, is a life-threatening calcification of arterioles leading to necrotic infarcts of the skin and subcutaneous tissue (panniculus adiposus) with a high potential to progress to bacterial sepsis and death. The incidence of CUA is approximately 4.1% in patients on dialysis with the reported incidence increasing over the past 10 years.\ud \ud CUA is associated with significant morbidity and high mortality. Data is limited, but studies suggest an 8-fold increase in the risk of death compared to controls and a one year cause-specific survival rate of 45.8%.\ud \ud Early signs: Sudden onset of erythema and livedo reticularis pattern commonly on the abdomen, hips and thighs, followed in several days by palpable, painful, pre-ulcerative, subcutaneous plaques with surrounding pruritic areas. Subsequently, these areas ulcerate revealing regions of necrotic subcutaneous adipose tissue covered by eschars.\ud \ud Risk Factors: Female, Caucasian, obesity, diabetes mellitus, time on dialysis, systemic corticosteroid use, laboratory findings: low serum albumin, elevated serum phosphate, elevated serum alkaline phosphatase.\ud \ud Management: Aggressive wound care consisting of frequent debridement, vacuum dressings and systemic antibiotics; pain management and maintenance of good nutrition; oral and intravenous bisphosphonates and hyperbaric oxygen therapy.\ud \ud Optimally, CUA is prevented; early diagnosis significantly improves the prognosis, therefore the diagnostician must maintain a high degree of suspicion with patients showing early dermatological, pre-ulcerative signs of CUA. Diagnosis is initially clinical with later histological confirmation of vascular calcification and fibrosis

Layered target burnthrough experiments using 50 nsec KrF laser pulses

Experiments have been performed on two types of planar layered targets using the Sprite KrF laser. The targets lused were: (1) 0.25 to 3.0 microns of Al deposited on an SiO/sub 2/ substrate and (2) 0.25 to 3.0 microns of CH (parylene-N) deposited on 0.20 microns of Al on an SiO/sub 2/ substrate. The laser was characterized by a pulse length of 50 nsec FWHM, an intensity of 2 x 10/sup 10/ watts/cm/sup 2/ and a wavelength of 248.5 nm. A filtered photoiodide and a streak camera, each operating in the visible, viewed the rear of the target. We measured the time from the beginning of the laser pulse to the onset of the visible light signal as seen by the photoiodide at the rear of the initially opaque target. This time is referred to as the burnthrough time. We obtain an estimate of the mass ablation by plotting the mass ablation depth (mass density times target thickness in ..mu..gm/cm/sup 2/) versus the burnthrough time. These results are consistent with earlier mass loss measurements and with analytic and hydro-code calculations (LASNEX). The streak camera data shows emission at target positions larger than the laser focal spot, and thus are consistent with 1-D and 2-D calculations which show target surface ablation to be primarily driven by reradiated photons from the hot laser produced plasma