Teacher Educators’ Perceptions of Schoolteacher Feedback Literacy: Implications for Feedback Training in Teacher Education Programmes

Few studies have empirically explored the specific elements of schoolteachers’ feedback literacy in spite of its crucial role in supporting student learning in classrooms. To address this research gap, individual interviews were conducted with 20 teacher educators in Hong Kong. The interviewees were asked to explicate the mind maps of schoolteacher feedback literacy that they had previously drawn. Data analysis revealed that the participants perceived schoolteacher feedback literacy as a three-dimensional concept, comprising knowledge, competence and disposition with specifications. In addition, the participants believed that schoolteacher feedback literacy was gradually evolving from a qualified level to a fully professional level over time. In their views, understanding subject content knowledge and developing positive feedback dispositions were prerequisites for developing feedback competencies. The findings of this study enhance the understanding of schoolteacher feedback literacy from the perspective of teacher educators and offer guidance for providing effective feedback training in teacher education programmes

Triplet-based Deep Similarity Learning for Person Re-Identification

In recent years, person re-identification (re-id) catches great attention in both computer vision community and industry. In this paper, we propose a new framework for person re-identification with a triplet-based deep similarity learning using convolutional neural networks (CNNs). The network is trained with triplet input: two of them have the same class labels and the other one is different. It aims to learn the deep feature representation, with which the distance within the same class is decreased, while the distance between the different classes is increased as much as possible. Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one. However, the enormous number of possible triplet data among the large number of training samples makes the training impossible. To address this challenge, a double-sampling scheme is proposed to generate triplets of images as effective as possible. The proposed framework is evaluated on several benchmark datasets. The experimental results show that, our method is effective for the task of person re-identification and it is comparable or even outperforms the state-of-the-art methods.Comment: ICCV Workshops 201