Task analysis

About: Task analysis is a research topic. Over the lifetime, 10432 publications have been published within this topic receiving 283481 citations.

Incorporating Global Englishes into the ELT classroom

Abstract: Increasing students’ awareness of the globalization of English is a daunting task for teachers, especially considering the lack of globally oriented ELT materials available. This study builds on previous research in response to recent calls for more classroom-level research, and reports on the use of a student presentation task to introduce and raise awareness of Global Englishes in a Japanese English language classroom. An analysis of student reflections showed that the presentation task allowed students to select and explore Englishes salient to their experiences and interests. In researching and imparting knowledge of their chosen variety, and by listening to their classmates’ presentations, the task raised students’ awareness of variation in English, and challenged attitudes towards Englishes that differed from standard models presented in typical ELT materials in Japan. Tasks such as the one presented here provide practitioners with avenues to incorporate Global Englishes into classroom practice.

Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling.

Abstract: Complex thought and behaviour arise through dynamic recruitment of large-scale brain networks The signatures of this process may be observable in electrophysiological data; yet robust modelling of rapidly changing functional network structure on rapid cognitive timescales remains a considerable challenge Here, we present one potential solution using Hidden Markov Models (HMMs), which are able to identify brain states characterised by engaging distinct functional networks that reoccur over time We show how the HMM can be inferred on continuous, parcellated source-space Magnetoencephalography (MEG) task data in an unsupervised manner, without any knowledge of the task timings We apply this to a freely available MEG dataset in which participants completed a face perception task, and reveal task-dependent HMM states that represent whole-brain dynamic networks transiently bursting at millisecond time scales as cognition unfolds The analysis pipeline demonstrates a general way in which the HMM can be used to do a statistically valid whole-brain, group-level task analysis on MEG task data, which could be readily adapted to a wide range of task-based studies

The Effect of L1 Orthography on Non-Native Vowel Perception.

Abstract: Previous research has shown that orthography influences the learning and processing of spoken non-native words. In this paper, we examine the effect of L1 orthography on non-native sound perception. In Experiment 1, 204 Spanish learners of Dutch and a control group of 20 native speakers of Dutch were asked to classify Dutch vowel tokens by choosing from auditorily presented options, in one task, and from the orthographic representations of Dutch vowels, in a second task. The results show that vowel categorization varied across tasks: the most difficult vowels in the purely auditory task were the easiest in the orthographic task and, conversely, vowels with a relatively high success rate in the purely auditory task were poorly classified in the orthographic task. The results of Experiment 2 with 22 monolingual Peruvian Spanish listeners replicated the main results of Experiment 1 and confirmed the existence of orthographic effects. Together, the two experiments show that when listening to auditory stimuli only, native speakers of Spanish have great difficulty classifying certain Dutch vowels, regardless of the amount of experience they may have with the Dutch language. Importantly, the pairing of auditory stimuli with orthographic labels can help or hinder Spanish listeners' sound categorization, depending on the specific sound contrast.