Equating Minimum-Competency Tests: Comparisons of Methods

The 1986 scores from Florida's Statewide Student Assessment Test, Part II (SSAT-II), a minimum-competency test required for high school graduation in Florida, were placed on the scale of the 1984 scores from that test using five different equating procedures. For the highest scoring 84 % of the students, four of the five methods yielded results within 1.5 raw-score points of each other. They would be essentially equally satisfactory in this situation, in which the tests were made parallel item by item in difficulty and content and the groups of examinees were population cohorts separated by only 2 years. Also, the results from six different lengths of anchor items were compared. Anchors of 25, 20, 15, or 10 randomly selected items provided equatings as effective as 30 items using the concurrent IRT equating method, but an anchor of 5 randomly selected items did not     

Scoring a Performance-Based Assessment by Modeling the Judgments of Experts

Performance assessments typically require expert judges to individually rate each performance. This results in a limitation in the use of such assessments because the rating process may be extremely time consuming. This article describes a scoring algorithm that is based on expert judgments but requires the rating of only a sample of performances. A regression-based policy capturing procedure was implemented to model the judgment policies of experts. The data set was a seven-case performance assessment of physician patient management skills. The assessment used a computer-based simulation of the patient care environment. The results showed a substantial improvement in correspondence between scores produced using the algorithm and actual ratings, when compared to raw scores. Scores based on the algorithm were also shown to be superior to raw scores and equal to expert ratings for making pass/fail decisions which agreed with those made by an independent committee of experts     

Dissipativity of discrete-time BAM stochastic neural networks with Markovian switching and impulses

This paper addresses the problem of global exponential dissipativity for a class of uncertain discrete-time BAM stochastic neural networks with time-varying delays, Markovian jumping and impulses. By constructing a proper Lyapunov–Krasovskii functional and combining with linear matrix inequality (LMI) technique, several sufficient conditions are derived for verifying the global exponential dissipativity in the mean square of such stochastic discrete-time BAM neural networks. The derived conditions are established in terms of linear matrix inequalities, which can be easily solved by some available software packages. One important feature presented in our paper is that without employing model transformation and free-weighting matrices our obtained result leads to less conservatism. Additionally, three numerical examples with simulation results are provided to show the effectiveness and usefulness of the obtained result.     

Special care needs and risk for child maltreatment reports among babies that graduated from the Neonatal Intensive Care

Newborns discharged from intensive care are at elevated risk for child welfare reports, especially for child neglect. This study investigates the role of caregiving burden as a risk predictor among the NICU graduate population. Discharge data were captured for 2,463 infants graduating from a Neonatal Intensive Care Unit (NICU) during 2005–2008, then linked to child welfare reports at a median 3.2 year follow-up. Survival analyses were used to examine child welfare report outcomes conditional on caregiving burden and its moderating relationships with other family risk factors. Caregiving burden was associated primarily with an increased risk of child welfare reporting during the first few months to first year of life, after which risk was similar to NICU graduates without caregiving burden. Caregiving burden effects were potentiated by having three or more siblings in the family. A history of prior child welfare reports predicted very high risk, regardless of caregiving burden. Young maternal age increased risk. The findings suggest that the immediate months after NICU discharge may be an important window of child neglect prevention opportunity among newborns with special caregiving needs. This may be a key time to provide caregiver support and monitoring, particularly when caregivers have multiple children.     

Novel global robust exponential stability criterion for uncertain inertial-type BAM neural networks with discrete and distributed time-varying delays via Lagrange sense

In this paper, the global robust exponential stability problem for a class of uncertain inertial-type BAM neural networks with both time-varying delays is focused through Lagrange sense. The existence of time-varying delays in discrete and distributed terms is explored with the availability of lower and upper bounds of time-varying delays. Firstly, we transform the proposed inertial BAM neural networks to usual one. Secondly, by the aid of LKF, stability theory, integral inequality, some novel sufficient conditions for the global robust exponential stability of the addressed neural networks are obtained in terms of linear matrix inequalities, which can be easily tested in practice by utilizing LMI control toolbox in MATLAB software. Furthermore, many comparisons of proposed work are listed with some existing literatures to get less conservatism. Finally, two numerical examples are provided to demonstrate the advantages and superiority of our theoretical outcomes.     

Personalized recommender system for e-Learning environment

Traditional e-Learning environments are based on static contents considering that all learners are similar, so they are not able to respond to each learner’s needs. These systems are less adaptive and once a system that supports a particular strategy has been designed and implemented, it is less likely to change according to student’s interactions and preferences. New educational systems should appear to ensure the personalization of learning contents. This work aims to develop a new personalization approach that provides to students the best learning materials according to their preferences, interests, background knowledge, and their memory capacity to store information. A new recommendation approach based on collaborative and content-based filtering is presented: NPR_eL (New multi-Personalized Recommender for e Learning). This approach was integrated in a learning environment in order to deliver personalized learning material. We demonstrate the effectiveness of our approach through the design, implementation, analysis and evaluation of a personal learning environment.     

Insert geometry effects on surface roughness in turning process of AISI D2 steel

Surface roughness is an important parameter for ensuring that the dimension of geometry is within the permitted tolerance.The ideal surface roughness is determined by the feed rate and the geometry of the tool.However,several uncontrollable factors including work material factors,tool angle,and machine tool vibration,may also influence surface roughness.The objective of this study was to compare the measured surface roughness (from experiment) to the theoretical surface roughness (from theoretical calculation) and to investigate the surface roughness resulting from two types of insert,‘C’ type and ‘T’ type.The experiment was focused on the turning process,using a lathe machine Colchester 6000.The feed rate was varied within the recommended feed rate range.We found that there were large deviations between the measured and theoretical surface roughness at a low feed rate (0.05 mm/r) from the application of both inserts.A work material factor of AISI D2 steel that affects the chip character is presumably responsible for this phenomenon.Interestingly,at a high feed rate (0.4 mm/r),the ‘C’ type insert resulted in 40% lower roughness compared to the ‘T’ type due to the difference in insert geometry.This study shows that the geometry of an insert may result in a different surface quality at a particular level of feed rate.     

The scholarship of teaching and learning: challenges for Malaysian academics

This paper explores the adoption of the scholarship of teaching and learning (SoTL) by 10 Malaysian university academics. SoTL was part of a pioneering sector-wide initiative for improving teaching and learning. The qualitative study showed that there had been no true learning phase for SoTL because academics had high expectations of rapid success in their new research area, including journal publication. Most of their peers did not understand or value SoTL and so SoTL researchers established minority self-sustaining groups across disciplines and universities. SoTL brought new ways of thinking about teaching that were transformational for the teacher but not necessarily welcomed by the students. Students resisted radical change, and teachers were drawn back to educational practices they no longer valued. SoTL was experienced in a hierarchical educational culture where questioning of self and others was difficult and this hindered teacher development. We conclude with recommendations about realistic expectations and the importance of social learning for those considering SoTL for academic development.