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

Enactivism and ethnomethodological conversation analysis as tools for expanding Universal Design for Learning: the case of visually impaired mathematics students

Blind and visually impaired mathematics students must rely on accessible materials such as tactile diagrams to learn mathematics. However, these compensatory materials are frequently found to offer students inferior opportunities for engaging in mathematical practice and do not allow sensorily heterogenous students to collaborate. Such prevailing problems of access and interaction are central concerns of Universal Design for Learning (UDL), an engineering paradigm for inclusive participation in cultural praxis like mathematics. Rather than directly adapt existing artifacts for broader usage, UDL process begins by interrogating the praxis these artifacts serve and then radically re-imagining tools and ecologies to optimize usability for all learners. We argue for the utility of two additional frameworks to enhance UDL efforts: (a) enactivism, a cognitive-sciences view of learning, knowing, and reasoning as modal activity; and (b) ethnomethodological conversation analysis (EMCA), which investigates participants’ multimodal methods for coordinating action and meaning. Combined, these approaches help frame the design and evaluation of opportunities for heterogeneous students to learn mathematics collaboratively in inclusive classrooms by coordinating perceptuo-motor solutions to joint manipulation problems. We contextualize the thesis with a proposal for a pluralist design for proportions, in which a pair of students jointly operate an interactive technological device

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Pedagogies of Design and Multiliterate Learner Identities

In an era of multiliteracies, teaching and learning have become knowledge performances at multiple levels. Instead of a singular, linear focus upon print technologies, the techno-oriented philosophy of teaching aims at providing a rhizomatic network of texts where there is a close link between, and often an overlap of, different designs—linguistic, visual, spatial, and gestural—to construct the multiliterate learner. In this paper, I discuss the role of multimodal literacies in a primary classroom, affirming the role of multiliteracies and decentring the pre-dominance of linguistic at the cost of other designs. While the print media are acknowledged as significant to literacy, the multimodality of print is enhanced through visual and spatial design (Kenner, 2004). Through graphic examples of ICT applications of designs in a primary classroom, I demonstrate that students are operating through multitextual and digitextual (Everett, 2003) practices. What follows is the complex positioning and re-situating of teacher and learner identities engaged in learning through the knowledge processes of experiencing, identifying, applying and critiquing concepts (Kalantzis & Cope, 2004). In particular, I argue that within the diversity of present day classrooms, the digital oriented, multiliterate learner is implicated in constant identity construction by drawing upon macro and micro social practices. I conclude by reiterating the significance of new technologies and new literacy practices as essential to the construction of new learner identities

Challenges in Transcribing Multimodal Data: A Case Study

open2siComputer-mediated communication (CMC) once meant principally text-based communication mediated by computers, but rapid technological advances in recent years have heralded an era of multimodal communication with a growing emphasis on audio and video synchronous interaction. As CMC, in all its variants (text chats, video chats, forums, blogs, SMS, etc.), has become normalized practice in personal and professional lives, educational initiatives, particularly language teaching and learning, are following suit. For researchers interested in exploring learner interactions in complex technology-supported learning environments, new challenges inevitably emerge. This article looks at the challenges of transcribing and representing multimodal data (visual, oral, and textual) when engaging in computer-assisted language learning research. When transcribing and representing such data, the choices made depend very much on the specific research questions addressed, hence in this paper we explore these challenges through discussion of a specific case study where the researchers were seeking to explore the emergence of identity through interaction in an online, multimodal situated space. Given the limited amount of literature addressing the transcription of online multimodal communication, it is felt that this article is a timely contribution to researchers interested in exploring interaction in CMC language and intercultural learning environments.Cited 10 times as of November 2020 including the prestigious Language Learning Sans Frontiers: A Translanguaging View L Wei, WYJ Ho - Annual Review of Applied Linguistics, 2018 - cambridge.org In this article, we present an analytical approach that focuses on how transnational and translingual learners mobilize their multilingual, multimodal, and multisemiotic repertoires, as well as their learning and work experiences, as resources in language learning. The … Cited by 23 Related articles All 11 versionsopenFrancesca, Helm; Melinda DoolyHelm, Francesca; Melinda, Dool

How Do I Address You? Modelling addressing behavior based on an analysis of a multi-modal corpora of conversational discourse

Addressing is a special kind of referring and thus principles of multi-modal referring expression generation will also be basic for generation of address terms and addressing gestures for conversational agents. Addressing is a special kind of referring because of the different (second person instead of object) role that the referent has in the interaction. Based on an analysis of addressing behaviour in multi-party face-to-face conversations (meetings, TV discussions as well as theater plays), we present outlines of a model for generating multi-modal verbal and non-verbal addressing behaviour for agents in multi-party interactions

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc