Building a progression culture: exploring learning organisations’ use of the Progression Matrix

This research paper explores the implementation of The Progression Matrix in schools, colleges and other learning organisations such as training providers. The project builds on existing research on The Progression Matrix and finds evidence which suggests that the approach provides a useful conceptual model around which learning organisations can re-orientate their practice and deliver enhanced progression for learners.Aimhighe

Leveraging Disease Progression Learning for Medical Image Recognition

Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of a disease may continuously follow a certain progression pattern. In this paper, we propose a novel method that leverages disease progression learning for medical image recognition. In our method, sequences of images ordered by disease stages are learned by a neural network that consists of a shared vision model for feature extraction and a long short-term memory network for the learning of stage sequences. Auxiliary vision outputs are also included to capture stage features that tend to be discrete along the disease progression. Our proposed method is evaluated on a public diabetic retinopathy dataset, and achieves about 3.3% improvement in disease staging accuracy, compared to the baseline method that does not use disease progression learning

Progression skills module 2: Getting ahead in learning

Progression skills modules are designed to support schools in delivering practical pupil workshops to help focus gifted and talented (G&T) or potential G&T pupils to aim high and achieve their best. This module explores the link between higher-order thinking and top examination grades. The module considers aspects of critical thinking and academic language and links this to examination skills. Pupils are enabled to begin to plan for success. Comprises: teacher notes, slide presentation, & pupil handouts

e2e : passport for E2E learners : version 2

"Guidance for the completion of the E2E Passport. The documentation to support the processes of Referral, Initial Assessment, Planning, Reviewing and Recording Learning and Progression in E2E" -- front cover

Learning Face Age Progression: A Pyramid Architecture of GANs

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.Comment: CVPR 2018. V4 and V2 are the same, i.e. the conference version; V3 is a related but different work, which is mistakenly submitted and will be submitted as a new arXiv pape

A Construct-Modeling Approach to Develop a Learning Progression of how Students Understand the Structure of Matter

This paper builds on the current literature base about learning progressions in science to address the question, “What is the nature of the learning progression in the content domain of the structure of matter?” We introduce a learning progression in response to that question and illustrate a methodology, the Construct Modeling (Wilson, 2005) approach, for investigating the progression through a developmentally based iterative process. This study puts forth a progression of how students understand the structure of matter by empirically inter-relating constructs of different levels of sophistication using a sample of 1,087 middle grade students from a large diverse public school district in the western part of the United States. The study also shows that student thinking can be more complex than hypothesized as in the case of our discovery of a substructure of understanding in a single construct within the larger progression. Data were analyzed using a multidimensional Rasch model. Implications for teaching and learning are discussed—we suggest that the teacher’s choice of instructional approach needs to be fashioned in terms of a model, grounded in evidence, of the paths through which learning might best proceed, working toward the desired targets by a pedagogy which also cultivates students’ development as effective learners. This research sheds light on the need for assessment methods to be used as guides for formative work and as tools to ensure the learning goals have been achieved at the end of the learning period. The development and investigation of a learning progression of how students understand the structure of matter using the Construct Modeling approach makes an important contribution to the research on learning progressions and serves as a guide to the planning and implementation in the teaching of this topic. # 2017 Wiley Periodicals, Inc. J Res Sci Teach 54: 1024–1048, 201