Is Personalized Learning Meeting Its Productivity Promise? Early Lessons From Pioneering Schools

Blending computer-based and teacher-led instruction promises to help schools meet students' individual needs by organizing and prioritizing staff and technology in more productive ways. However, this fiscal analysis of eight new charter schools that implemented personalized learning this year finds that early difficulty in forecasting enrollment and revenue can undermine implementation of the model.As a result of missed enrollment and revenue projections:The schools spent less on technology and more on personnel than planned: instead of a combined $1.7 million on technology in the early stages, they spent just$650,000Student-to-computer ratios were higher and schools spent less than planned on instructional and performance reporting software.Projected budget deficits in five of the schools threaten their ability to sustain on public funding.Among the brief's recommendations for those hoping to implement personalized-learning models in the future:Invest in student recruitment efforts up front to ensure enrollment targets are met.Develop a 'worst-case scenario' budget where fundraising and enrollment estimates fall 20 -- 25 percent below target.Manage contracts proactively: be explicit about needs, establish performance requirements, and negotiate trial periods to make sure products meet the school's needs.The eight personalized-learning schools included in this analysis were chosen to receive Next Generation Learning Challenges (NGLC) competitive start-up grants. CRPE is midway through a study of twenty personalized-learning schools that received NGLC grants. The study examines how the schools allocate their resources, how they manage the new costs of technology, and whether they can become financially sustainable on public revenues. CRPE will continue to track spending in all twenty schools this year and publish its findings next spring.This study is funded by the Bill & Melinda Gates Foundation

Editorial

The harbour porpoise is a key predator in Norwegian coastal communities, therefore studying its feeding ecology is important to understand its ecological role and may shed light on the dynamics of Norwegian coastal ecosystems. The diet of 134 harbour porpoises bycaught in Autumn 2016 (n = 61) and Spring 2017 (n = 73) in Norwegian coastal waters and fjords was investigated using both stable isotopes (δ15N and δ13C) and stomach contents. A total of 23 prey groups were identified in the stomachs, though most porpoises had consumed between 1 and 4 prey groups. Harbour porpoises mainly fed on gadoid fishes, and saithe (juvenile) was by far the most important prey species. Pelagic, lipid-rich prey species such as capelin and herring contributed much less to the diet. While lipid-rich prey species are thought to be essential for harbour porpoises, due to their high metabolic demands, this study highlights the importance of lean but more available prey in the diet. Harbour porpoises mainly fed on small prey species or on the juveniles of large-sized gadoids (e.g. saithe, cod). Both the stable isotope and stomach content analyses showed a significant ontogenetic shift, with differences in the isotopic and diet composition of calves compared to the more similar juveniles and adults. The stable isotopes may suggest a greater use of benthic or coastal resources, or a decreasing reliance on dietary lipids to synthesize muscle tissues with increasing body size. There was no significant difference in the isotopic and diet composition between male and female porpoises, suggesting both use similar habitats and prey resources. Although saithe was dominant in all sampling periods and areas, spatiotemporal variations in diet were observed and are likely related to seasonal and geographical changes in prey availability (i.e., prey spawning, seasonal migrations, species distribution). However, spatiotemporal variations in stable isotope composition cannot conclusively be linked to the diet, as knowledge on the isotopic baseline in time and space is lacking. The long-term differences in diet composition between the late 1980’s and now suggest that prey availability has changed. This study confirms harbour porpoises are generalist predators that consume a wide variety of prey species and display a flexible foraging behaviour, feeding opportunistically on locally abundant and accessible prey

Nerve-targeted probes for fluorescence-guided intraoperative imaging.

A fundamental goal of many surgeries is nerve preservation, as inadvertent injury can lead to patient morbidity including numbness, pain, localized paralysis and incontinence. Nerve identification during surgery relies on multiple parameters including anatomy, texture, color and relationship to surrounding structures using white light illumination. We propose that fluorescent labeling of nerves can enhance the contrast between nerves and adjacent tissue during surgery which may lead to improved outcomes. Methods: Nerve binding peptide sequences including HNP401 were identified by phage display using selective binding to dissected nerve tissue. Peptide dye conjugates including FAM-HNP401 and structural variants were synthesized and screened for nerve binding after topical application on fresh rodent and human tissue and in-vivo after systemic IV administration into both mice and rats. Nerve to muscle contrast was quantified by measuring fluorescent intensity after topical or systemic administration of peptide dye conjugate. Results: Peptide dye conjugate FAM-HNP401 showed selective binding to human sural nerve with 10.9x fluorescence signal intensity (1374.44 ± 425.96) compared to a previously identified peptide FAM-NP41 (126.17 ± 61.03). FAM-HNP401 showed nerve-to-muscle contrast of 3.03 ± 0.57. FAM-HNP401 binds and highlight multiple human peripheral nerves including lower leg sural, upper arm medial antebrachial as well as autonomic nerves isolated from human prostate. Conclusion: Phage display has identified a novel peptide that selectively binds to ex-vivo human nerves and in-vivo using rodent models. FAM-HNP401 or an optimized variant could be translated for use in a clinical setting for intraoperative identification of human nerves to improve visualization and potentially decrease the incidence of intra-surgical nerve injury

Detection of invasive species in Wetlands: Practical dl with heavily imbalanced data

Deep Learning (DL) has become popular due to its ease of use and accuracy, with Transfer Learning (TL) effectively reducing the number of images needed to solve environmental problems. However, this approach has some limitations which we set out to explore: Our goal is to detect the presence of an invasive blueberry species in aerial images of wetlands. This is a key problem in ecosystem protection which is also challenging in terms of DL due to the severe imbalance present in the data. Results for the ResNet50 network show a high classification accuracy while largely ignoring the blueberry class, rendering these results of limited practical interest to detect that specific class. Moreover, by using loss function weighting and data augmentation results more akin to our practical application, our goals can be obtained. Our experiments regarding TL show that ImageNet weights do not produce satisfactory results when only the final layer of the network is trained. Furthermore, only minor gains are obtained compared with random weights when the whole network is retrained. Finally, in a study of state-of-the-art DL architectures best results were obtained by the ResNeXt architecture with 93.75 True Positive Rate and 98.11 accuracy for the Blueberry class with ResNet50, Densenet, and wideResNet obtaining close results. © 2020 by the authors. Licensee MDPI, Basel, Switzerland