Using a Modified Dictogloss to Improve English as a Second Language Learners’ use of Genre-appropriate Conventions and Style

This article explores the potential for using a modified dictogloss task to improve ESL learners' use of genre-appropriate conventions and genre-appropriate style in the context of genre-based instruction for writing. Dictogloss has been traditionally used to enhance learners' focus on lexical and grammatical features through discussions during the joint reconstruction of a text. The innovation of the current practice lies in its application to teach generic aspects specific to a particular text type, such as formatting, register, and organization. This practice is potentially important as it is a meaningful, productive task that raises learners' awareness of genre-related features of writing – an often-neglected but important aspect of learning to write. This practice was carried out in a secondary ESL classroom in Hong Kong, and the evaluation was based on an analysis of improvements in learners' genre-specific conventions and genre-specific style. Implications of this practice are discussed in terms of the possibilities for using model texts in dictogloss tasks within a genre-based approach to writing.

Simultaneous Multislice Brain MRI T1 Mapping with Improved Low-Rank Modeling

To accelerate data acquisition speed in magnetic resonance imaging (MRI), multiple slices are simultaneously acquired using multiband pulses. Simultaneous multislice (SMS) imaging typically unfolds slice aliasing from the acquired collapsed slices. In this study, we extended the SMS framework to accelerated MR parameter quantification such as T1 mapping. Assuming that the slice-specific null space and signal subspace are invariant along the parameter dimension, we formulated the SMS framework as a constrained optimization problem under a joint reconstruction framework such that the noise and signal subspaces are used for slice separation and recovery, respectively. The proposed method was validated on 3T MR human brain scans. We successfully demonstrated that the proposed method outperforms competing methods in suppressing aliasing artifacts and noise at high SMS accelerations, thus leading to accurate T1 maps.