This longitudinal study compared the development of oral language and more specifically narrative skills (storytelling and story retelling) in children with specific language impairment (SLI) with and without literacy delay. Therefore, 18 children with SLI and 18 matched controls with normal literacy were followed from the last year of kindergarten (mean age = 5 years 5 months) until the beginning of grade 3 (mean age = 8 years 1 month). Oral language tests measuring vocabulary, morphology, sentence and text comprehension and narrative skills were administered yearly. Based on first and third grade reading and spelling achievement, both groups were divided into a group with and a group without literacy problems. Results showed that the children with SLI and literacy delay had persistent oral language problems across all assessed language domains. The children with SLI and normal literacy skills scored also persistently low on vocabulary, morphology and story retelling skills. Only on listening comprehension and storytelling, they evolved towards the level of the control group. In conclusion, oral language skills in children with SLI and normal literacy skills remained in general poor, despite their intact literacy development during the first years of literacy instruction. Only for listening comprehension and storytelling, they improved, probably as a result of more print exposure.
This paper identifies some common and specific pitfalls in the development of sign language technologies targeted at deaf communities, with a specific focus on signing avatars. It makes the call to urgently interrogate some of the ideologies behind those technologies, including issues of ethical and responsible development. The paper addresses four separate and interlinked issues: ideologies about deaf people and mediated communication, bias in data sets and learning, user feedback, and applications of the technologies. The paper ends with several take away points for both technology developers and deaf NGOs. Technology developers should give more consideration to diversifying their team and working interdisciplinary, and be mindful of the biases that inevitably creep into data sets. There should also be a consideration of the technologies’ end users. Sign language interpreters are not the end users nor should they be seen as the benchmark for language use. Technology developers and deaf NGOs can engage in a dialogue about how to prioritize application domains and prioritize within application domains. Finally, deaf NGOs policy statements will need to take a longer view, and use avatars to think of a significantly better system compared to what sign language interpreting services can provide.
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Extended Reality (XR) technologies—including virtual reality (VR), augmented reality (AR), and mixed reality (MR)—offer transformative opportunities for education by enabling immersive and interactive learning experiences. In this study, we employed a mixed-methods approach that combined systematic desk research with an expert member check to evaluate existing pedagogical frameworks for XR integration. We analyzed several established models (e.g., TPACK, TIM, SAMR, CAMIL, and DigCompEdu) to assess their strengths and limitations in addressing the unique competencies required for XRsupported teaching. Our results indicate that, while these models offer valuable insights into technology integration, they often fall short in specifying XR-specific competencies. Consequently, we extended the DigCompEdu framework by identifying and refining concrete building blocks for teacher professionalization in XR. The conclusions drawn from this research underscore the necessity for targeted professional development that equips educators with the practical skills needed to effectively implement XR in diverse educational settings, thereby providing actionable strategies for fostering digital innovation in teaching and learning.
MULTIFILE
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
