Investigating task difficulty: learners’ and teachers’ perceptions

This paper presents an investigation into learners’ and teachers’ perceptions of and criteria for task difficulty. Ten second language learners performed four oral narrative tasks and were retrospectively interviewed about which tasks they perceived as difficult, what factors affected this difficulty and how they identified and defined this task difficulty. Ten EFL/ESOL teachers were given the same tasks and asked to consider the difficulty of the tasks for their learners, and were invited to discuss the factors they believed contributed to this difficulty. Qualitative analysis of the data revealed that, although there were some differences between the two groups’ perceptions of task difficulty, there was substantial similarity between them in terms of the criteria they considered in identifying and defining task difficulty. The findings of this study lend support to the tenets of a cognitive approach to task-based language learning, and demonstrate which aspects of two models of task difficulty reflect the teachers’ and learners’ perceptions and perspectives.

Native speakers and task performance: comparing effects on complexity, fluency, and lexical diversity

This article argues that a native-speaker baseline is a neglected dimension of studies into second language (L2) performance. If we investigate how learners perform language tasks, we should distinguish what performance features are due to their processing an L2 and which are due to their performing a particular task. Having defined what we mean by “native speaker,” we present the background to a research study into task features on nonnative task performance, designed to include native-speaker data as a baseline for interpreting nonnative-speaker performance. The nonnative results, published in this journal (Tavakoli & Foster, 2008) are recapitulated and then the native-speaker results are presented and discussed in the light of them. The study is guided by the assumption that limited attentional resources impact on L2 performance and explores how narrative design features—namely complexity of storyline and tightness of narrative structure— affect complexity, fluency, accuracy, and lexical diversity in language. The results show that both native and nonnative speakers are prompted by storyline complexity to use more subordinated language, but narrative structure had different effects on native and nonnative fluency. The learners, who were based in either London or Tehran, did not differ in their performance when compared to each other, except in lexical diversity, where the learners in London were close to native-speaker levels. The implications of the results for the applicability of Levelt’s model of speaking to an L2 are discussed, as is the potential for further L2 research using native speakers as a baseline.

Assessing L2 task performance: understanding effects of task design

The overarching aim of the research reported here was to investigate the effects of task structure and storyline complexity of oral narrative tasks on second language task performance. Participants were 60 Iranian language learners of English who performed six narrative tasks of varying degree of structure and storyline complexity in an assessment setting. A number of analytic detailed measures were employed to examine whether there were any differences in the participants’ performances elicited by the different tasks in terms of their accuracy, fluency, syntactic complexity and lexical diversity. Results of the data analysis showed that performance in the more structured tasks was more accurate and to a great extent more fluent than that in the less structured tasks. The results further revealed that syntactic complexity of L2 performance was related to the storyline complexity, i.e. more syntactic complexity was associated with narratives that had both foreground and background storylines. These findings strongly suggest that there is some unsystematic variance in the participants’ performance triggered by the different aspects of task design.

Task design and second language performance: the effect of narrative type on learner output

This article reports on a detailed empirical study of the way narrative task design influences the oral performance of second-language (L2) learners. Building on previous research findings, two dimensions of narrative design were chosen for investigation: narrative complexity and inherent narrative structure. Narrative complexity refers to the presence of simultaneous storylines; in this case, we compared single-story narratives with dual-story narratives. Inherent narrative structure refers to the order of events in a narrative; we compared narratives where this was fixed to others where the events could be reordered without loss of coherence. Additionally, we explored the influence of learning context on performance by gathering data from two comparable groups of participants: 60 learners in a foreign language context in Teheran and 40 in an L2 context in London. All participants recounted two of four narratives from cartoon pictures prompts, giving a between-subjects design for narrative complexity and a within-subjects design for inherent narrative structure. The results show clearly that for both groups, L2 performance was affected by the design of the task: Syntactic complexity was supported by narrative storyline complexity and grammatical accuracy was supported by an inherently fixed narrative structure. We reason that the task of recounting simultaneous events leads learners into attempting more hypotactic language, such as subordinate clauses that follow, for example, while, although, at the same time as, etc. We reason also that a tight narrative structure allows learners to achieve greater accuracy in the L2 (within minutes of performing less accurately on a loosely structured narrative) because the tight ordering of events releases attentional resources that would otherwise be spent on finding connections between the pictures. The learning context was shown to have no effect on either accuracy or fluency but an unexpectedly clear effect on syntactic complexity and lexical diversity. The learners in London seem to have benefited from being in the target language environment by developing not more accurate grammar but a more diverse resource of English words and syntactic choices. In a companion article (Foster & Tavakoli, 2009) we compared their performance with native-speaker baseline data and see that, in terms of nativelike selection of vocabulary and phrasing, the learners in London are closing in on native-speaker norms. The study provides empirical evidence that L2 performance is affected by task design in predictable ways. It also shows that living within the target language environment, and presumably using the L2 in a host of everyday tasks outside the classroom, confers a distinct lexical advantage, not a grammatical one.

Researching task difficulty: towards understanding L2 proficiency

The prime purpose of this article is to put teachers’, learners’ and research perspectives of task difficulty (TD) together and to investigate whether teachers’ and learners’ perceptions of and criteria for TD are in line with the available research on TD. A summary of three interrelated empirical studies on learner and teacher perceptions of TD is presented before the findings are discussed in light of the current models of TD. The chapter concludes by arguing that cognitive demands of a task are a signifcant factor that contributes to TD and should be considered more critically by L2 educators and SLA researchers.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.