64,254 research outputs found
Metadata for describing learning scenarios under European Higher Education Area paradigm
In this paper we identify the requirements for creating formal descriptions of learning scenarios designed under the European Higher
Education Area paradigm, using competences and learning activities as the basic pieces of the learning process, instead of contents and learning resources, pursuing personalization. Classical arrangements of content based courses are no longer enough to describe all the richness of this new learning process, where user profiles, competences and complex hierarchical itineraries need to be properly combined. We study the intersection with the current IMS Learning Design specification and the
additional metadata required for describing such learning scenarios. This new approach involves the use of case based learning and collaborative
learning in order to acquire and develop competences, following adaptive learning paths in two structured levels
Student-Faculty Partnership: The European Framework and the Experience of the Italian Project Employability & Competences.
The article describes the European Framework for Improving Quality of Teaching in Europe and the research carried our at Italian University to explore the student voices in higher education
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Improving School Improvement
PREFACEIn opening this volume, you might be thinking:Is another book on school improvement really needed?Clearly our answer is yes. Our analyses of prevailing school improvement legislation, planning, and literature indicates fundamental deficiencies, especially with respect to enhancing equity of opportunity and closing the achievement gap.Here is what our work uniquely brings to policy and planning tables:(1) An expanded framework for school improvement – We highlight that moving from a two- to a three-component policy and practice framework is essential for closing the opportunity and achievement gaps. (That is, expanding from focusing primarily on instruction and management/government concerns by establishing a third primary component to improve how schools address barriers to learning and teaching.)(2) An emphasis on integrating a deep understanding of motivation – We underscore that concerns about engagement, management of behavior, school climate, equity of opportunity, and student outcomes require an up-to-date grasp of motivation and especially intrinsic motivation.(3) Clarification of the nature and scope of personalized teaching – We define personalization as the process of matching learner motivation and capabilities and stress that it is the learner's perception that determines whether the match is a good one.(4) A reframing of remediation and special education – We formulate these processes as personalized special assistance that is applied in and out of classrooms and practiced in a sequential and hierarchical manner.(5) A prototype for transforming student and learning supports – We provide a framework for a unified, comprehensive, and equitable system designed to address barriers to learning and teaching and re-engage disconnected students and families.(6) A reworking of the leadership structure for whole school improvement --We outline how the operational infrastructure can and must be realigned in keeping with a three component school improvement framework.(7) A systemic approach to enhancing school-community collaboration – We delineate a leadership role for schools in outreaching to communities in order to work on shared concerns through a formal collaborative operational infrastructure that enables weaving together resources to advance the work.(8) An expanded framework for school accountability – We reframe school accountability to ensure a balanced approach that accounts for a shift to a three component school improvement policy.(9) Guidance for substantive, scalable, and sustainable systemic changes –We frame mechanisms and discuss lessons learned related to facilitating fundamental systemic changes and replicating and sustaining them across a district.The frameworks and practices presented are based on our many years of work in schools and from efforts to enhance school-community collaboration. We incorporate insights from various theories and the large body of relevant research and from lessons learned and shared by many school leaders and staff who strive everyday to do their best for children.Our emphasis on new directions in no way is meant to demean current efforts. We know that the demands placed on those working in schools go well beyond what anyone should be asked to do. Given the current working conditions in many schools, our intent is to help make the hard work generate better results. To this end, we highlight new directions and systemic pathways for improving school outcomes.Some of what we propose is difficult to accomplish. Hopefully, the fact that there are schools, districts, and state agencies already trailblazing the way will engender a sense of hope and encouragement to those committed to innovation.It will be obvious that our work owes much to many. We are especially grateful to those who are pioneering major systemic changes across the country. These leaders and so many in the field have generously offered their insights and wisdom. And, of course, we are indebted to hundreds of scholars whose research and writing is a shared treasure. As always, we take this opportunity to thank Perry Nelson and the host of graduate and undergraduate students at UCLA who contribute so much to our work each day, and to the many young people and their families who continue to teach us all.Respectfully submitted for your consideration,Howard Adelman & Linda Taylo
Style Transfer and Extraction for the Handwritten Letters Using Deep Learning
How can we learn, transfer and extract handwriting styles using deep neural
networks? This paper explores these questions using a deep conditioned
autoencoder on the IRON-OFF handwriting data-set. We perform three experiments
that systematically explore the quality of our style extraction procedure.
First, We compare our model to handwriting benchmarks using multidimensional
performance metrics. Second, we explore the quality of style transfer, i.e. how
the model performs on new, unseen writers. In both experiments, we improve the
metrics of state of the art methods by a large margin. Lastly, we analyze the
latent space of our model, and we see that it separates consistently writing
styles.Comment: Accepted in ICAART 201
Handwriting styles: benchmarks and evaluation metrics
Evaluating the style of handwriting generation is a challenging problem,
since it is not well defined. It is a key component in order to develop in
developing systems with more personalized experiences with humans. In this
paper, we propose baseline benchmarks, in order to set anchors to estimate the
relative quality of different handwriting style methods. This will be done
using deep learning techniques, which have shown remarkable results in
different machine learning tasks, learning classification, regression, and most
relevant to our work, generating temporal sequences. We discuss the challenges
associated with evaluating our methods, which is related to evaluation of
generative models in general. We then propose evaluation metrics, which we find
relevant to this problem, and we discuss how we evaluate the evaluation
metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge,
there is no work done before in generating handwriting (either in terms of
methodology or the performance metrics), our in exploring styles using this
dataset.Comment: Submitted to IEEE International Workshop on Deep and Transfer
Learning (DTL 2018
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