Teaching languages for specific and academic purposes in higher education. English, Deutsch, Italiano. International Symposium, Bozen-Bolzano, 29 June 2018

The international symposium “Teaching Languages for Specific and Academic Purposes” aimed to bring together university instructors and researchers of LSP and LAP in order to share and explore approaches, methods, and practices which have emerged within diverse contexts. Topics include needs analyses, TBLT, curriculum development, collaborative reflective writing, correction codes, specific communication skills and competences, institutional challenges, technology and digital literacies, word lists, reading skills, textbooks, L1 transfer, academic writing styles in L1 versus L2, assessment, and motivation. (DIPF/Orig.

Open Educational Resources: Policy, Costs and Transformation

Open Educational Resources (OER) — teaching, learning and research materials that their owners make free for others to use, revise and share — offer a powerful means of expanding the reach and effectiveness of worldwide education. The Commonwealth of Learning (COL) and UNESCO co-organised the World OER Congress in 2012 in Paris. That Congress resulted in the OER Paris Declaration: a statement that urged governments around the world to release, as OER, all teaching, learning and research materials developed with public funds. This book, drawing on 15 case studies contributed by 29 OER researchers and policy-makers from 15 countries across six continents, examines the implementation of the pivotal declaration through the thematic lenses of policy, costs and transformation. The case studies provide a detailed picture of OER policies and initiatives as they are unfolding in different country contexts and adopting a range of approaches, from bottom-up to top-down. The book illuminates the impacts of OER on the costs of producing, distributing and providing access to learning materials, and shows the way that OER can transform the teaching and learning methodology mindset. Recommendations on key actions to be taken by policy-makers, practitioners, OER developers and users are also outlined, particularly within the context of Education 2030. Clearly, progress is being made, although more work must be done if the international community is to realise the full potential of OER. Contents Foreword by the President and CEO, Commonwealth of Learning Foreword by the Assistant Director-General for Education, UNESCO Introduction Open Educational Resources: Policy, Costs and Transformation | Rory McGreal, Fengchun Miao and Sanjaya Mishra Chapter 1 Open Educational Practices in Australia | Carina Bossu Chapter 2 Open Educational Resources Policy for Developing a Knowledge-Based Economy in the Kingdom of Bahrain | Nawal Ebrahim Al Khater, Hala Amer and Fadheela Tallaq Chapter 3 The State of Open Educational Resources in Brazil: Policies and Realities | Carolina Rossini and Oona Castro Chapter 4 Open Educational Resources in Canada | Rory McGreal, Terry Anderson and Dianne Conrad Chapter 5 Caribbean Open Textbooks Initiative | Neil Butcher, Andrew Moore and Sarah Hoosen Chapter 6 Open Educational Resources in Germany | Ulf-Daniel Ehlers Chapter 7 Copyrights in OER Publishing in India: The Case of the National Programme on Technology-Enhanced Learning | Mangala Sunder Krishnan iv Chapter 8 The Promise of Open Educational Resources in Indonesia | Petra Wiyakti Bodrogini and Mohammad Rinaldi Chapter 9 Using Open Educational Resources for Undergraduate Programme Development at Wawasan Open University | Teik Kooi Liew Chapter 10 OERu: Realising Sustainable Education Futures | Wayne Mackintosh Chapter 11 Integrating ICT for Innovative Educational Solutions in Oman: Leveraging OER Policy to Enhance Teaching and Learning | Maimoona Al Abri and Saif Hamed Hilal Al Busaidi Chapter 12 The Polish Open e-Textbooks Project as a Policy Model for Openness of Public Educational Resources | Alek Tarkowski Chapter 13 Open Access to Educational Resources Through Federal Portals and OER in Russia | Svetlana Knyazeva and Aleksei Sigalov Chapter 14 Open Educational Resources for Early Literacy in Africa: The Role of the African Storybook Initiative | Tessa Welch and Jennifer Glennie Chapter 15 Developing an Infrastructure Support for Faculty Use of Open Educational Resources: The Case of the Washington State Community and Technical Colleges System | Boyoung Chae and Mark Jenkins Conclusions | Fengchun Miao, Sanjaya Mishra and Rory McGrea

Have disruptive innovations arrived at the gates of academia?

Disruptive technologies in education and particularly Massive Open Online Courses (MOOCs) continue to be one of the polarising and most controvertible topics in postsecondary education, as they have yet to deliver on their promises. Existing academic literature on MOOCs, the main example of disruptive technology of this thesis, is primarily concerned with student participation, persistence, completion rates and learning in MOOC platforms. There seems however to be very limited scholarly research in the UK investigating the democratising effects and impact of disruptive technologies in Higher Education, particularly the extent to which MOOCs might unlock the gates to accessibility and their impact on universities, teaching and academics, through the lens of critical theory. It is however crucial to evaluate their impact (s) to inform policy decision-making on technology enhanced-learning implementation at tertiary institutions and design of curricula. The Main Research Question (MRQ) and sub-question designed for this study were addressed by conducting eighteen semi-structured interviews (Skype and face-to-face) with participants (academic and senior administrators) from nine countries and nine institutions. The research methods used were primarily qualitative. This thesis contributes to the field of technology-enhanced learning by addressing the current pedagogical limitations of the MOOC format which seem to be the critical impediments that prevent MOOCs, as they are currently designed, from genuinely democratising Higher Education to those who most need it in developed and developing countries. My main original contribution to knowledge is an integrated and adaptive model with critical success factors that would influence the MOOC model’s effectiveness, which, to the best of the author’s knowledge, is unique in the published literature. The findings of this study indicate that MOOCs have democratised access to Higher Education to a certain degree but they are not considered comparable to an on-campus experience and not suitable, in their current form and design, to the needs of the underrepresented in higher education, in developed and developing countries. The findings also indicate that MOOCs are challenging the current economic, business and pedagogical models and delivery mechanisms of traditional Higher Education and these might have an important effect on the academic role and identity. Furthermore, this investigation finds that MOOCs have aroused institutions and academics’ interest in and exploration of technology-enhanced learning, particularly blended learning approaches. Finally, the findings of this study indicate that MOOCs have impelled institutions and academics to rethink the design of more engaging courses and programmes and refocus on student learning to improve online and face-to-face teaching and this added pressure might have created a schism between the educational conservatives and the advocates of reform

Sparse Methods for Robust and Efficient Visual Recognition

Visual recognition has been a subject of extensive research in computer vision. A vast literature exists on feature extraction and learning methods for recognition. However, due to large variations in visual data, robust visual recognition is still an open problem. In recent years, sparse representation-based methods have become popular for visual recognition. By learning a compact dictionary of data and exploiting the notion of sparsity, start-of-the-art results have been obtained on many recognition tasks. However, existing data-driven sparse model techniques may not be optimal for some challenging recognition problems. In this dissertation, we consider some of these recognition tasks and present approaches based on sparse coding for robust and efficient recognition in such cases. First we study the problem of low-resolution face recognition. This is a challenging problem, and methods have been proposed using super-resolution and machine learning based techniques. However, these methods cannot handle variations like illumination changes which can happen at low resolutions, and degrade the performance. We propose a generative approach for classifying low resolution faces, by exploiting 3D face models. Further, we propose a joint sparse coding framework for robust classification at low resolutions. The effectiveness of the method is demonstrated on different face datasets. In the second part, we study a robust feature-level fusion method for multimodal biometric recognition. Although score-level and decision-level fusion methods exist in biometric literature, feature-level fusion is challenging due to different output formats of biometric modalities. In this work, we propose a novel sparse representation-based method for multimodal fusion, and present experimental results for a large multimodal dataset. Robustness to noise and occlusion are demonstrated. In the third part, we consider the problem of domain adaptation, where we want to learn effective classifiers for cases where the test images come from a different distribution than the training data. Typically, due to high cost of human annotation, very few labeled samples are available for images in the test domain. Specifically, we study the problem of adapting sparse dictionary-based classification methods for such cases. We describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both domains in the projected low dimensional space. The proposed method is efficient and performs on par or better than many competing state-of-the-art methods. Lastly, we study an emerging analysis framework of sparse coding for image classification. We show that the analysis sparse coding can give similar performance as the typical synthesis sparse coding methods, while being much faster at sparse encoding. In the end, we conclude the dissertation with discussions and possible future directions

Bowdoin Orient v.135, no.1-25 (2005-2006)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1006/thumbnail.jp