Applied linguistics and mathematics education: More than words and numbers

The preceding set of papers has explored various aspects of the role of language in mathematics education. The papers reflect the work of individual contributors. An important part of our collaboration, however, has been the conversation between us. This paper reflects aspects of that conversation, as we draw together some of the themes that have emerged during our work. In particular, we discuss some of the implications of our analyses for theory, policy, practice and inter-disciplinarity in mathematics education and applied linguistics

Approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data

Knowledge discovery and data mining generally discovers implicit, previously unknown, and useful knowledge from data. As one of the popular knowledge discovery and data mining tasks, frequent itemset mining, in particular, discovers knowledge in the form of sets of frequently co-occurring items, events, or objects. On the one hand, in many real-life applications, users mine frequent patterns from traditional databases of precise data, in which users know certainly the presence of items in transactions. On the other hand, in many other real-life applications, users mine frequent itemsets from probabilistic sets of uncertain data, in which users are uncertain about the likelihood of the presence of items in transactions. Each item in these probabilistic sets of uncertain data is often associated with an existential probability expressing the likelihood of its presence in that transaction. To mine frequent itemsets from these probabilistic datasets, many existing algorithms capture lots of information to compute expected support. To reduce the amount of space required, algorithms capture some but not all information in computing or approximating expected support. The tradeoff is that the upper bounds to expected support may not be tight. In this paper, we examine several upper bounds and recommend to the user which ones consume less space while providing good approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data

Characterizing university students’ self-regulated learning behavior using dispositional learning analytics

[EN] Learning analytics can be used in conjunction with learner dispositions to identify at-risk students and provide personalized guidance on how to improve. Participants in the current study were students (n=192) studying a first year anatomy and physiology course. A two-step cluster analysis was performed using learning analytics data from the learning management system and self-regulated learning behavior from meta-learning assessment tasks. Three clusters of students were identified – high, medium and low self-regulated learners. High self-regulated learners were engaged with the meta-learning tasks, reported the most self-regulated learning strategies and used new strategies during semester. They also had the highest academic achievement. Compared to low self-regulated leaners, medium self-regulated learners were more engaged in the meta-learning tasks and used more learning strategies during semester, including new strategies; however, both medium and low self-regulated learners had similar levels of academic achievement. It is possible that the medium self-regulated learners represent students who were attempting to improve their learning, but had not yet found strategies that were right for them. Future evaluation of academic performance may determine whether the attempts to improve learning by medium self-regulated learners distinguishes them from low self-regulated learners in the later years of their study.Ainscough, L.; Leung, R.; Colthorpe, K.; Langfield, T. (2019). Characterizing university students’ self-regulated learning behavior using dispositional learning analytics. En HEAD'19. 5th International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 233-241. https://doi.org/10.4995/HEAD19.2019.9153OCS23324

Characterizing genetic diversity and creating novel gene pools in rice for trait dissection and gene function discovery

Rice diversity is the foundation for rice improvement programs. At IRRI, over 100,000 rice accessions are deposited, and intelligent use of this diversity can not only help solve current production problems but also create future production opportunities and tackle climate change challenges. To fully explore and utilize rice diversity, two ingredients are needed: 1 - the genetic blueprints of diverse rice accessions in use, 2 - plant populations with recombined genotypes allowing expression of phenotypic variation and discovery of new genes/QTLs for use in breeding programs. Sequencing of the genomes & obtaining SNP genotypes of many rice accessions is feasible due to decreasing cost of advanced DNA sequencing technologies. Coupled with the creation of populations suitable for trait dissection / phenotyping, discovery of gene functions and allelic variations causal to important agronomic traits becomes possible. This in turn will provide rich biological evidences to the rice/cereal crop genome annotation community

Dense and accurate motion and strain estimation in high resolution speckle images using an image-adaptive approach

Digital image processing methods represent a viable and well acknowledged alternative to strain gauges and interferometric techniques for determining full-field displacements and strains in materials under stress. This paper presents an image adaptive technique for dense motion and strain estimation using high-resolution speckle images that show the analyzed material in its original and deformed states. The algorithm starts by dividing the speckle image showing the original state into irregular cells taking into consideration both spatial and gradient image information present. Subsequently the Newton-Raphson digital image correlation technique is applied to calculate the corresponding motion for each cell. Adaptive spatial regularization in the form of the Geman-McClure robust spatial estimator is employed to increase the spatial consistency of the motion components of a cell with respect to the components of neighbouring cells. To obtain the final strain information, local least-squares fitting using a linear displacement model is performed on the horizontal and vertical displacement fields. To evaluate the presented image partitioning and strain estimation techniques two numerical and two real experiments are employed. The numerical experiments simulate the deformation of a specimen with constant strain across the surface as well as small rigid-body rotations present while real experiments consist specimens that undergo uniaxial stress. The results indicate very good accuracy of the recovered strains as well as better rotation insensitivity compared to classical techniques