Developing reasoning within a geometric learning progression: Implications for curriculum development and classroom practices

2021 ◽  
pp. 000494412110365
Author(s):  
Rebecca Seah ◽  
Marj Horne
Keyword(s):  
Mixed Method ◽  
Classroom Practice ◽  
Rasch Analysis ◽  
Large Scale ◽  
Classroom Practices ◽  
Mathematical Reasoning ◽  
Learning Progression ◽  
Fine Grained ◽  
Reasoning Abilities ◽  
Geometric Thinking

Promoting reasoning is the goal of mathematics education. While reasoning behaviours can be observed, how to characterise them and nurture their growth remains ambiguous. In this article, we report our effort in drafting a learning progression and geometric thinking model and using them to investigate Australian students’ geometric reasoning abilities. The data were taken from a large-scale study into the development of mathematical reasoning. Rasch analysis resulted in eight thinking zones being charted. Using a mixed method, we analysed 446 Year 7 to 10 students’ responses on a task that requires them to enlarge a logo, state its coordinates and calculate the enlarged area. In-depth, fine-grained analysis of students’ explanations revealed the range of skills and techniques students used to reason about the situation. The findings suggest that higher level reasoning was characterised by evidence of increased visualisation skills and proficient use of mixed mediums to communicate intent. The implications of the findings for curriculum and classroom practice are discussed.

Teaching to and Beyond the Test: The Influence of Mandated Accountability Testing in One Social Studies Teacher's Classroom

2013 ◽  
Vol 115 (6) ◽  
pp. 1-32
Author(s):  
Jacob W. Neumann
Keyword(s):  
Social Studies ◽  
Classroom Practice ◽  
Classroom Practices ◽  
Instructional Time ◽  
Fine Grained ◽  
Teachers Beliefs ◽  
Accountability Testing ◽  
Mandated Testing ◽  
The Impact

Background/Context The nature of the impact of state-mandated accountability testing on teachers’ classroom practices remains contested. While many researchers argue that teachers change their teaching in response to mandated testing, others contend that the nature and degree of the impact of testing on teaching remains unclear. The research on the relationship between testing and teaching in social studies follows this pattern. For example, some researchers argue that mandated testing fosters a “just the facts, ma'am” approach to teaching social studies. Others, however, contend that factors such as teachers’ personal beliefs about social studies and about what learners need to know are equally, if not more, determinative influences on teaching as are testing pressures. Focus of Study This article presents an extended and fine-grained analysis of the influence of state-mandated accountability testing on one social studies teacher's classroom practice. Research Design Grounded in the narrative inquiry tradition, this case study spans approximately two and a half years of fieldwork, including approximately 110 days of observations of one eighth-grade U.S. history classroom. Conclusions The findings from this study shed light on the problems and frustrations that one teacher faces when confronted with a testing apparatus that limits her instructional time with students and an accountability exam that emphasizes a “bare bones” approach to content. While no generalizable conclusions are intended to be drawn from this study, the data presented in this article nonetheless add support to the viewpoint that while state-mandated accountability testing does influence classroom teaching, teachers’ beliefs about subject matter and their goals for students play an equal, if not larger, role in shaping their classroom practices.

scholarly journals STUDENTS’ PERFORMANCE SKILLS IN CREATIVE MATHEMATICAL REASONING

2018 ◽  
Vol 7 (2) ◽  
pp. 83 ◽  
Author(s):  
Heris Hendriana ◽  
Rully Charitas Indra Prahmana ◽  
Wahyu Hidayat
Keyword(s):  
Mathematics Education ◽  
Mixed Method ◽  
Mathematical Reasoning ◽  
The Other ◽  
Reasoning Ability ◽  
Master Program ◽  
Performance Skills ◽  
Creative Reasoning ◽  
Reasoning Abilities ◽  
Quantitative And Qualitative Methods

This study aims to examine mathematics teacher-candidate students’ mathematical creative reasoning ability based on the level of Adversity Quotient (AQ). This study uses a mixed method of sequential type by combining quantitative and qualitative methods in order. Population in this study is all students attending the course of Calculus in Mathematics Education of Master Program at STKIP Siliwangi that consist of 270 students divided into six classes. The results are AQ gives effect to the achievement of students’ mathematical creative reasoning abilities based on the whole and the type of AQ climber, champer, and quitter. The achievement of students’ mathematical creative reasoning abilities and based on AQ, the champer and climber fall into the medium category, while on the quitter type, it falls into the category of low. On the other hands, the achievement of students’ mathematical creative reasoning abilities is yet to be achieved well at the indicator of novelty.

scholarly journals Kemampuan Penalaran Matematis Peserta Didik Melalui Model Pembelajaran Reciprocal Teaching Dengan Berbantuan Alat Peraga “Kartu Pintar” Pada Materi Prisma Dan Limas Kelas VIII SMP Negeri 1 Pandaan Tahun Ajaran 2017/2018

2019 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Putri Amalia Cahyani
Keyword(s):  
Smart Card ◽  
Mixed Method ◽  
Mathematical Reasoning ◽  
Learning Model ◽  
Reciprocal Teaching ◽  
Reasoning Abilities ◽  
Teaching Aids ◽  
Public Middle School ◽  
Class Viii ◽  
Teaching Learning

The purpose of this study was to determine whether there is an increase in mathematical reasoning abilities and to describe the mathematical reasoning abilities of students through the Reciprocal Teaching learning model assisted with "smart card" teaching aids on prism and pyramid material class VIII Public Middle School 1 Pandaan 2017/2018 Academic Year. The research method used in this study, which is a mixed method with an experimental mixed method design design, was to wrap a basic mixed method design into a further strategy, namely by adding processes into the experimental procedure. The results showed that based on the results of quantitative data analysis, it was concluded that there was an increase between mathematical reasoning abilities before and after using the Reciprocal Teaching learning model with the help of "smart card" teaching aids on class VIII prism and pyramid material in Pandaan 1 Public Middle School. From some of the data analysis shows that mathematical reasoning ability increases after the application of the Reciprocal Teaching learning model with the help of "smart card" teaching aids.

scholarly journals Strategic resources and smallholder performance at the bottom of the pyramid

2019 ◽  
Vol 22 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Matthias Olthaar ◽  
Wilfred Dolfsma ◽  
Clemens Lutz ◽  
Florian Noseleit
Keyword(s):  
Large Scale ◽  
Business Environment ◽  
Agricultural Economics ◽  
Local Market ◽  
Bottom Of The Pyramid ◽  
Fine Grained ◽  
Strategic Resources ◽  
Relative Contribution ◽  
The Relationship ◽  
Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

scholarly journals Inverse Infiltration Modeling of Dike Covers Made of Dredged Material Using PEST and AMALGAM

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 41
Author(s):  
Tim Jurisch ◽  
Stefan Cantré ◽  
Fokke Saathoff
Keyword(s):  
Large Scale ◽  
Measurement Data ◽  
Fine Grained ◽  
Dredged Materials ◽  
Measurement Results ◽  
Large Scale Field ◽  
Eigenvalue Ratio ◽  
Ill Posed ◽  
Materials Used ◽  
Installation Technology

A variety of studies recently proved the applicability of different dried, fine-grained dredged materials as replacement material for erosion-resistant sea dike covers. In Rostock, Germany, a large-scale field experiment was conducted, in which different dredged materials were tested with regard to installation technology, stability, turf development, infiltration, and erosion resistance. The infiltration experiments to study the development of a seepage line in the dike body showed unexpected measurement results. Due to the high complexity of the problem, standard geo-hydraulic models proved to be unable to analyze these results. Therefore, different methods of inverse infiltration modeling were applied, such as the parameter estimation tool (PEST) and the AMALGAM algorithm. In the paper, the two approaches are compared and discussed. A sensitivity analysis proved the presumption of a non-linear model behavior for the infiltration problem and the Eigenvalue ratio indicates that the dike infiltration is an ill-posed problem. Although this complicates the inverse modeling (e.g., termination in local minima), parameter sets close to an optimum were found with both the PEST and the AMALGAM algorithms. Together with the field measurement data, this information supports the rating of the effective material properties of the applied dredged materials used as dike cover material.

CLIL as a pathway for cross-curricular and translingual classroom practices: A comparative quantitative study on Scottish and Spanish teachers’ views

2021 ◽  
pp. 136216882110324
Author(s):  
Xabier San Isidro
Keyword(s):  
Conceptual Framework ◽  
Quantitative Study ◽  
Classroom Practice ◽  
Classroom Practices ◽  
Research Tool ◽  
Second Phase ◽  
Integrated Learning ◽  
Two Phase ◽  
Pilot Testing ◽  
Final Questionnaire

Despite the numerous attempts to characterize Content and Language Integrated Learning (CLIL), the specialized literature has shown a dearth of cross-contextual studies on how stakeholders conceptualize classroom practice. This article presents the results of a two-phase comparative quantitative study on teachers’ views on CLIL design, implementation and results in two different contexts, Scotland ( n = 127) and Spain ( n = 186). The first phase focused on the creation, pilot-testing and validation of the research tool. The second phase consisted in administering the final questionnaire and analysing the results. The primary goals were (1) to ascertain whether practitioners’ perceptions on CLIL effects and classroom practices match the topics addressed by research; and (2) to analyse and compare teachers’ views in the two contexts. The study offers interesting insights into the main challenges in integrating language and content. Besides providing a conceptual framework for identifiable classroom practice, findings revealed that both cohorts shared broadly similar perceptions, although the Spanish respondents showed more positive views and significantly higher support for this approach.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

CSMC

2021 ◽  
Vol 5 (4) ◽  
pp. 1-22
Author(s):  
Hai Wang ◽  
Baoshen Guo ◽  
Shuai Wang ◽  
Tian He ◽  
Desheng Zhang
Keyword(s):  
Large Scale ◽  
Mobile Network ◽  
Signal Propagation ◽  
Spectrum Management ◽  
External Information ◽  
Temporal Uncertainty ◽  
Large Area ◽  
Fine Grained ◽  
Map Construction ◽  
Spatio Temporal

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

A Large-scale Analysis of Hundreds of In-memory Key-value Cache Clusters at Twitter

2021 ◽  
Vol 17 (3) ◽  
pp. 1-35
Author(s):  
Juncheng Yang ◽  
Yao Yue ◽  
K. V. Rashmi
Keyword(s):  
Large Scale ◽  
Production Systems ◽  
Wide Spectrum ◽  
Use Cases ◽  
Scale Analysis ◽  
Business Logic ◽  
Traffic Pattern ◽  
Fine Grained ◽  
Memory Cache ◽  
Large Scale Analysis

Modern web services use in-memory caching extensively to increase throughput and reduce latency. There have been several workload analyses of production systems that have fueled research in improving the effectiveness of in-memory caching systems. However, the coverage is still sparse considering the wide spectrum of industrial cache use cases. In this work, we significantly further the understanding of real-world cache workloads by collecting production traces from 153 in-memory cache clusters at Twitter, sifting through over 80 TB of data, and sometimes interpreting the workloads in the context of the business logic behind them. We perform a comprehensive analysis to characterize cache workloads based on traffic pattern, time-to-live (TTL), popularity distribution, and size distribution. A fine-grained view of different workloads uncover the diversity of use cases: many are far more write-heavy or more skewed than previously shown and some display unique temporal patterns. We also observe that TTL is an important and sometimes defining parameter of cache working sets. Our simulations show that ideal replacement strategy in production caches can be surprising, for example, FIFO works the best for a large number of workloads.

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