A matter of trust: : Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students

Surface induced selective delamination of amphiphilic ABA block copolymer thin films

This is the result of an ongoing collaboration with Dr. N. Sommerdijk’s Biomaterials group at the University of Eindhoven (the Netherlands) and illustrates the close collaboration that exists in pursuing the design and application of novel polymeric materials between the two groups. This details work on a physical phenomenon (selective delamination) and key materials (amphiphilic block copolymers) that have subsequently been applied in the design of novel biomaterials. These results have appeared in a larger body of work including Advanced Materials, Angewandtie Chemie International Edition and the Journal of Materials Chemistry

Performance of sinusoidally deformed hydrophone line arrays

It is well known that array deformations can distort beam patterns and introduce bearing errors if the beamformer assumes linearity. It is also known that deformed arrays can resolve left–right ambiguities, provided the shape is known. In this work, these two effects are studied for undamped and damped sinusoidally deformed arrays with small deformation amplitudes in the horizontal (x,y) plane only. By use of fixed arc-length separations along the array, the hydrophone (x,y) coordinates are determined numerically and the error in assuming equal x spacing is summarized for a sample array. Array-response patterns are analyzed for two conditions: (1) when the deformed array shape is assumed linear and (2) when the deformed array shape is known exactly. Degradations resulting from assuming linearity and the ability to resolve left–right ambiguities are discussed in terms of reduced gain, degraded angular resolution, and bearing errors. Shape-unknown signal-gain degradation ranges to 7 dB at broadside, but is less than 1 dB near endfire. For the shape-known case, signal gain for the true peak is greater than signal gain for the ambiguous peak by up to 9 dB for sources at broadside and to just over 2.5 dB for arrivals near endfire. © 1999 Acoustical Society of America

Reading Comprehension, Figurative Language Instruction, and the Turkish English Language Learner

According to the 2000 U.S. Census, the Turkish-speaking population in the United States increased significantly in the 1990s and has risen steadily over time. Today, the highest concentration is located in the states of New York, California, New Jersey, and Florida. Kaya (2003) reported a geographical dispersion across the U.S., from New York to Alaska, with the wealthiest living in Florida. Turkish students make up the ninth largest student population in the U.S. and the largest percentage of students compared to their homeland population. This article identifies and explores many of these challenges by observing the transition of Hakan, a Turkish-speaking fifth grade student, as he encounters a new culture and learns a new language. In particular, we focus on the acquisition of figurative language in a Turkish-speaking English Language Learner (ELL). Some issues and questions addressed in the article include effective methodologies for the assessment of figurative language acquisition in the Turkish and English languages, effective instructional strategies to scaffold Turkish-speaking English Language Learners’ (ELLs) acquisition of figurative language, and linguistic factors that might affect Turkish-speaking students’ transition to English. The article sets forth theoretical underpinnings for the chosen assessment and instructional strategies, as well as a summary of supporting research in the area of Turkish-speaking ELLs

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students

Norine, the knowledgebase dedicated to nonribosomal peptides, is now open to crowdsourcing

International audienceSince its creation in 2006, Norine remains the unique knowledgebase dedicated to non-ribosomal pep-tides (NRPs). These secondary metabolites, produced by bacteria and fungi, harbor diverse interesting biological activities (such as antibiotic, anti-tumor, siderophore or surfactant) directly related to the diversity of their structures. The Norine team goal is to collect the NRPs and provide tools to analyze them efficiently. We have developed a user-friendly interface and dedicated tools to provide a complete bioinformatics platform. The knowledgebase gathers abundant and valuable annotations on more than 1100 NRPs. To increase the quantity of described NRPs and improve the quality of associated annotations , we are now opening Norine to crowdsourc-ing. We believe that contributors from the scientific community are the best experts to annotate the NRPs they work on. We have developed MyNorine to facilitate the submission of new NRPs or modifications of stored ones. This article presents MyNorine and other novelties of Norine interface released since the first publication. Norine is freely accessible from the following URL: http://bioinfo.lifl.fr/NRP

Increasing crop production in Russia and Ukraine—regional and global impacts from intensification and recultivation

Russia and Ukraine are countries with relatively large untapped agricultural potentials, both in terms of abandoned agricultural land and substantial yield gaps. Here we present a comprehensive assessment of Russian and Ukrainian crop production potentials and we analyze possible impacts of their future utilization, on a regional as well as global scale. To this end, the total amount of available abandoned land and potential yields in Russia and Ukraine are estimated and explicitly implemented in an economic agricultural sector model. We find that cereal (barley, corn, and wheat) production in Russia and Ukraine could increase by up to 64% in 2030 to 267 million tons, compared to a baseline scenario. Oilseeds (rapeseed, soybean, and sunflower) production could increase by 84% to 50 million tons, respectively. In comparison to the baseline, common net exports of Ukraine and Russia could increase by up to 86.3 million tons of cereals and 18.9 million tons of oilseeds in 2030, representing 4% and 3.6% of the global production of these crops, respectively. Furthermore, we find that production potentials due to intensification are ten times larger than potentials due to recultivation of abandoned land. Consequently, we also find stronger impacts from intensification at the global scale. A utilization of crop production potentials in Russia and Ukraine could globally save up to 21 million hectares of cropland and reduce average global crop prices by more than 3%

Addressing climate change adaptation with a stochastic integrated assessment model: Analysis of common agricultural policy measures

Stochastic agro-economic model GLOBIOM is used to demonstrate how best to design and evaluate the CAP’s financial and structural measures, both individually and jointly, in the face of inherent uncertainty and risk. The model accounts for plausible shocks simultaneously and derives measures that are robust against all shock scenarios; it can thus help avoid the irreversibility and sunk costs that occur in unexpected scenarios.To allow adequate agricultural production, we show that the distribution of CAP funds needs to account for exposure to risks, security targets, and the synergies between policy measures, including production, trade, storage, and irrigation technologies