Student-Centered Learning Opportunities For Adolescent English Learners In Flipped Classrooms

This study documents opportunities for diverse adolescent English learners to deeply engage with content and language in flipped learning environments. Through a linked description of teaching practices and student learning experiences in an urban New England high school, the study attempts to understand the potential of flipped instruction in preparing a traditionally underserved population for post-secondary education. Our research partner Patriot High School (PHS) is one of the New England schools implementing flipped learning. PHS represents a typical secondary school context for adolescent English learners: More than half of students speak a language other than English at home and the majority of students are from minority and low-income homes (Massachusetts Department of Elementary and Secondary Education, 2014). PHS is also an urban school committed to implementing student-centered learning strategies to meet the needs of its diverse students

Long-Term Student Experiences in a Hybrid, Open-Ended and Problem Based Adventure Learning Program

In this paper we investigate the experiences of elementary school children over a two-year period during which they engaged with a hybrid Adventure Learning program. In addition to delineating Adventure Learning experiences, we report on educational technology implementations in ecologically valid and complex environments, while drawing inferences on the design of sustainable and successful innovations. Our research indicates that the Adventure Learning experience over the two-year period was dynamic, participatory, engaging, collaborative, and social. Students eagerly became part of the experience both inside and outside of the classroom, and it quickly became apparent that they saw themselves as valued members of the unfolding storyline that mediated their learning. Our recommendations for future research and practice include a call to evaluate "authenticity," focus on the learner experience and narrative, and consider the interplay between pedagogy, technology, and design.Center for Learning and Memor

System upgrade: realising the vision for UK education

A report summarising the findings of the TEL programme in the wider context of technology-enhanced learning and offering recommendations for future strategy in the area was launched on 13th June at the House of Lords to a group of policymakers, technologists and practitioners chaired by Lord Knight. The report – a major outcome of the programme – is written by TEL director Professor Richard Noss and a team of experts in various fields of technology-enhanced learning. The report features the programme’s 12 recommendations for using technology-enhanced learning to upgrade UK education

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging Technologies for Education. SETE 201