The significance of pauses in EFL listening comprehension tests

Many EFL (English as a Foreign Language) listening comprehension tests use multiple-choice formats. How well such tests are devised is a crucial issue in EFL assessment and instruction. An important aspect of such tests is the time interval between items. Pauses between items are highly significant because they affect the processing of oral linguistic data and EFL learners require time to focus on form, as suggested by Krashen's Monitor Model. The present study examines the effects of variation in time interval between test items on the performance of a group of EFL learners studying English for a BA degree at an Iranian institution of higher education. Twenty-nine undergraduate students in a listening comprehension class took part in the study. Data were collected on their beginning proficiency and listening ability. As part of their course, the learners also took three parallel listening comprehension tests developed by the National Test Center of the institution (the central branch of the University Of Payame-Noor). These three listening tests were modified and the tapes were rerecorded to include 10- 30-, and 60-second intervals between items. The analysis of variance between their performances on the tests indicated that the length of time interval between items was a very significant factor. Students performed significantly better on the test with 30-second intervals between items. The findings of the study sensitize EFL teachers to plan for the assessment of listening performance. They also contribute to arguments about the depth of linguistic processing and the issue of time in EFL listening comprehension

Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation

The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and incorporates spatial information through the use of superpixel-based 'documents.' In our application of PM-LDA, we employ the Normal Compositional Model in which endmembers are represented as Normal distributions to account for spectral variability and proportion vectors are modeled as random variables governed by a Dirichlet distribution. The use of the Dirichlet distribution enforces positivity and sum-to-one constraints on the proportion values. Algorithm results on real hyperspectral data indicate that PM-LDA produces endmember distributions that represent the ground truth classes and their associated variability