Diagnosing Student Node Mastery: Impact of Varying Item Response Modeling Approaches

An important feature of learning maps, such as Dynamic Learning Maps and Enhanced Learning Maps, is their ability to accommodate nation-wide specifications of standards, such as the Common Core State Standards, within the map nodes along with relevant instruction. These features are especially useful for remedial instruction, given that accurate diagnosis is available. The year-end achievement tests are potentially useful in this regard. Unfortunately, the current use of total score or area sub-scores are neither sufficiently precise nor sufficiently reliable to diagnose mastery at the node level especially when students vary in their patterns of mastery. The current study examines varying approaches to using the year-end test for diagnosis. Prediction at the item level was obtained using parameters from varying item response theory (IRT) models. The results support using mixture class IRT models predicting mastery in which either items or node scores vary in difficulty for students in different latent classes. Not only did the mixture models fit better but trait score reliability was also maintained for the predictions of node mastery.

A Policy Improvement Algorithm for Solving a Mixture Class of Perfect Information and AR-AT Semi-Markov Games

Zero-sum two-person discounted semi-Markov games with finite state and action spaces are studied where a collection of states having Perfect Information (PI) property is mixed with another collection of states having Additive Reward–Additive Transition and Action Independent Transition Time (AR-AT-AITT) property. For such a PI/AR-AT-AITT mixture class of games, we prove the existence of an optimal pure stationary strategy for each player. We develop a policy improvement algorithm for solving discounted semi-Markov decision processes (one player version of semi-Markov games) and using it we obtain a policy-improvement type algorithm for computing an optimal strategy pair of a PI/AR-AT-AITT mixture semi-Markov game. Finally, we extend our results when the states having PI property are replaced by a subclass of Switching Control (SC) states.

THE ANALYSIS OF CRACKS IN BEAMS OF QUAY NO. 80A AT KLAIPEDA PASSENGER AND CARGO TERMINAL

The cracks can be technological, appear during the usage time of construction, they can also be regular, and appear under the design load or they can also be unexpected and appear due to accidental impacts. Our research has shown that the cracks have appeared in reinforced concrete beams of the superstructure in quays No. 80 and 81 after the construction of Klaipeda Passenger and cargo terminal are caused by shrinkage strain, when the reinforced concrete beams has hardened. Normal hardening concrete shrinkage deformation is inevitable, but it depends on a number of factors, in particular, that in the present structures occurred because: - W/C ratio of concrete mixture exceeds the allowable values; - coarseness of selected aggregates (D = 16 mm) are too small for the construction of a massive wall; - concrete stratification and possible lack of care for hardening concrete (ambient temperature, etc.), this segregation could be affected by to big spread of concrete mixture (class of slump S3). The crack width at reinforced concrete beams in the superstructure of quay No.80 varies from 0.05 to 0.5 mm. This are a non-structural cracks, so they do not reduce significantly the mechanical strength and stability of the structure. Also, research has shown that the relationship between the crack width and the distance between the cracks is weak. Proposals for elimination of potential causes of cracking are presented according to the results.

Class Definition and Mixture Class Definition by Means of Construction of Convex Hull Boundaries: Application to Analysis for Animal Fat Adulteration

A visual classification technique based on the construction of convex hull boundaries in combination with a principal component analysis is described. This combined technique was evaluated in the situation in which a distinction has to be made between 2 pure animal fat classes and the corresponding mixture class. In the first instance, a principal component analysis is carried out to ensure the 2-dimensional and thus visual aspect of the technique. Convex hulls are then constructed in the 2-dimensional principal component plane to delimit the boundaries of the different classes to be defined. The effectiveness of the constructed hull boundaries in the definition of class-membership was investigated by means of the classification of different simulated test samples. The results show that, at least for the tested applications, the technique is valid, although some false positive classifications occur. The detection of outliers especially seemed to pose problems. Therefore, some propositions are made of how to refine the developed hull technique to enhance the classification results.