Consciousness-raising tasks versus deductive approach : two form-focused instruction types in teaching grammar to Iranian high school EFL learners

Consciousness-raising (CR) task is a new way of teaching grammar developed in communicative contexts although little has been written on the effectiveness of CR tasks in EFL setting. The present study is an attempt to investigate the impact of CR tasks in Iranian EFL setting by comparing them with deductive, grammar lessons common in the Iranian schools. The subjects of this study were 80 EFL pre-university male students who were randomly assigned to an experimental group and a control one. The control group received three ordinary teacher-fronted, deductive lessons, a common way of teaching methodology in Iran, on three grammatical structures (adverb placement, indirect object placement and the use of relative clause). The experimental group, however, was treated with three ‘consciousness-raising’ (CR) tasks dealing with the same target structures. The results showed that in the short-run, CR tasks were as effective as deductive approach in promoting the learners’ grammatical knowledge while in the long-run, the CR group maintained their gains more effectively than the deductive group. The conclusion is that CR tasks can function more effectively than deductive approach if the following conditions are met: (a) performing the consciousness-raising tasks in learners’ L1; (b) providing the learners with feedback whenever they encounter a problem in solving the tasks; (c) grouping the learners in such a way that at least one learner in each group would be more proficient than the other members to help the less proficient ones understand and discover the rules more effectively.

An experimental study of original and ordered fuzzy ARTMAP neural networks in pattern classification tasks

This paper investigates the effectiveness of an ordering algorithm applied to the supervised Fuzzy ARTMAP (FAM) neural network in pattern classification tasks. Before presenting the input patterns to the FAM network (known as ordered FAM), a fixed order of input patterns is first identified using the ordering algorithm. An experimental study is conducted to compare the results from ordered FAM with the average and voting results from original FAM. In the study, a pool of the original FAM networks is trained using different sequences of input patterns, and the results are averaged. Outputs from various original FAM networks can also be combined using a majority voting strategy to reach a final result. A database comprising various symptoms and measurements of patients suffering from heart attack is used to evaluate the various schemes of the FAM network in medical pattern classification tasks. The results are compared, analyzed, and discussed.

Boosting the performance of the fuzzy min-max neural network in pattern classification tasks

In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed.

The task of designing tasks for teacher education and development

Posing, adapting, and creating worthwhile tasks that support student learning are significant pedagogical practices of teachers. However in mathematics education, task design and curriculum development are not often the responsibility of practicing teachers. Textbooks can become the main source of mathematics problems and teachers often follow the text and its sequence of problems. Although mathematics teachers can benefit from access to research-based curriculum resources, limited opportunities to adapt and design tasks can make it challenging to meet the diverse needs and interests of their students. More opportunities for learning to create problems are needed for both practicing and beginning mathematics teachers. What then is involved in learning to pose, adapt and create worthwhile mathematics tasks? More specifically, how can teacher educators design tasks that support teacher candidates in learning to create mathematical tasks? As teacher educators we have, over the past few years, used our own courses as sites in Australia and Canada for investigating various contexts to support preservice teachers in their learning to adapt and create mathematical tasks. Our research includes both large scale and small qualitative studies to explore the perspectives of teacher candidates on learning to create and pose mathematical problems, the kinds of problems they pose, and the opportunities and challenges this offers us as teacher educators. Our work is inspired by Variation Theory that focuses on learning as the act of awareness and discernment of variation. We are exploring both how variation theory can be useful for preservice teachers in their designing, posing and adapting of mathematical tasks and for teacher educators in their design of such pedagogical tasks. The results of our work support the argument that tasks to design, adapt and pose mathematical problems enhance their pedagogical understanding and should be a feature of teacher education courses. This is a new area for research and practice; further exploration of the suitability of particular tasks in elementary mathematics teacher education is recommended.

A novel defect detection and identification method in optical inspection

Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers' training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.

Validity of an upper-body-mounted accelerometer to measure peak vertical and resultant force during running and change-of-direction tasks

This study assessed the validity of a tri-axial accelerometer worn on the upper body to estimate peak forces during running and change-of-direction tasks. Seventeen participants completed four different running and change-of-direction tasks (0°, 45°, 90°, and 180°; five trials per condition). Peak crania-caudal and resultant acceleration was converted to force and compared against peak force plate ground reaction force (GRF) in two formats (raw and smoothed). The resultant smoothed (10 Hz) and crania-caudal raw (except 180°) accelerometer values were not significantly different to resultant and vertical GRF for all running and change-of-direction tasks, respectively. Resultant accelerometer measures showed no to strong significant correlations (r = 0.00–0.76) and moderate to large measurement errors (coefficient of variation [CV] = 11.7–23.9%). Crania-caudal accelerometer measures showed small to moderate correlations (r = − 0.26 to 0.39) and moderate to large measurement errors (CV = 15.0–20.6%). Accelerometers, within integrated micro-technology tracking devices and worn on the upper body, can provide a relative measure of peak impact force experienced during running and two change-of-direction tasks (45° and 90°) provided that resultant smoothed values are used.

Trunk postures and upper-body muscle activation during physically demanding wildfire suppression tasks

This study examined the trunk postures and upper-body muscle activations during four physically demanding wildfire suppression tasks. Bilateral, wireless surface electromyography was recorded from the trapezius and erector spinae muscles of nine experienced, wildfire fighters. Synchronised video captured two retroreflective markers to allow for quantification of two-dimensional sagittal trunk flexion. In all tasks, significantly longer time was spent in the mild and severe trunk flexion (p ≤ 0.002) compared to the time spent in a neutral posture. Mean and peak muscle activation in all tasks exceeded previously established safe limits. These activation levels also significantly increased through the performance of each task (p < 0.001). The results suggest that the wildfire suppression tasks analysed impose significant musculoskeletal demand on firefighters. Fire agencies should consider developing interventions to reduce the exposure of their personnel to these potentially injurious musculoskeletal demands.