ABSTRACT Arguably, the classroom teacher is an important factor, if not the most important factor, in helping students with dyslexia to read and write well, yet there is little known about whether teachers perceive that they have the knowledge and confidence to teach these students. The present study was a national online survey of schools in Aotearoa New Zealand to explore this issue through the eyes of teachers. The sample consisted of 594 school leaders and teaching staff (elementary and secondary). The design of the study and analysis of data was descriptive, combining numbers with teacher comments. They provide initial data on dyslexia in schools. On the one hand, the data indicated that teachers and leaders were generally very positive about teaching students with dyslexia, for example, many rated their classrooms as “dyslexia friendly”. Teachers rated themselves as confident to help students with dyslexia and make a difference. On the other hand, the data indicated that teachers and leaders were not satisfied with the level of training and resources available to them. Some said they were working in the dark, and largely left to their own devices. Another less positive result was that many schools did not screen for dyslexia, or have a dyslexia policy, or share information with parents. Taken together, these findings at the teacher and school level have important practical implications. Teachers and school leaders want to help. We identify teacher training as a necessary mechanism for improving outcomes for students with dyslexia.

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.