Exploring the relationship between reading strategy use and multiple intelligences among successful L2 readers

Over the years, the multiple intelligences theory (MIT) proposed by Howard Gardner has renewed interest in learners’ use of effective learning strategies and produced interesting results. This MIT-oriented study investigated the role of successful L2 readers’ multiple intelligences in their effective use of reading strategies. To this end, a TOEFL reading comprehension test was administered to a cohort of 135 English as a foreign language students at several universities in the southwest and centre of Iran, and 80 students were identified as successful L2 readers based on the ETS rating scale and their TOEFL scores. Then, they answered an MI questionnaire originally developed by Armstrong and a reading strategies inventory adapted by Singhal. The data were quantitatively analysed using correlations and multiple regressions. The results revealed that linguistic, logical–mathematical and intrapersonal intelligences were the good L2 readers’ most dominant intelligences, while bodily intelligence was the least common type. In addition, they mostly employed metacognitive and cognitive strategies but rarely drew upon affective and compensation strategies while reading. Further, there was a significant positive relationship between linguistic, logical–mathematical, spatial, interpersonal, and intrapersonal intelligences and the use of metacognitive and cognitive reading strategies. Similar relationships were also found between linguistic intelligence and the participants’ use of memory strategy, on one hand, and between interpersonal intelligence and compensation and social strategy use, on the other. Importantly, linguistic and intrapersonal intelligences as well as metacognitive and cognitive strategy use were shown to be the best predictors of reading comprehension. Finally, the theoretical or pedagogical implications of the findings are discussed.     

An improved particle filtering-based approach for health prediction and prognosis of nonlinear systems

Health monitoring of nonlinear systems is broadly concerned with the system health tracking and its prediction to future time horizons. Estimation and prediction schemes constitute as principle components of any health monitoring technique. Particle filter (PF) represents a powerful tool for performing state and parameter estimation as well as prediction of nonlinear dynamical systems. Estimation of the system parameters along with the states can yield an up-to-date and reliable model that can be used for long-term prediction problems through utilization of particle filters. This feature enables one to deal with uncertainty issues in the resulting prediction step as the time horizon is extended. Towards this end, this paper presents an improved method to achieve uncertainty management for long-term prediction of nonlinear systems by using particle filters. In our proposed approach, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizon. Particles are then propagated to future time instants according to a resampling algorithm instead of considering constant weights for the particles propagation in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models for development of an observation forecasting scheme. This task is addressed through an outer adjustment loop for adaptively changing the sliding observation injection window based on the Mahalanobis distance criterion. Our proposed approach is then applied to predicting the health condition as well as the remaining useful life (RUL) of a gas turbine engine that is affected by degradations in the system health parameters. Extensive simulation and case studies are conducted to demonstrate and illustrate the capabilities and performance characteristics of our proposed and developed schemes.