scholarly journals Quantifying “deep learning” in geomatics engineering by means of classroom observations

2018 ◽  
Author(s):  
Elena Rangelova ◽  
Ivan Detchev ◽  
Scott Packer
Keyword(s):  
Deep Learning ◽  
Teaching And Learning ◽  
Early Stage ◽  
Classroom Observations ◽  
Threshold Concepts ◽  
Main Method ◽  
Teaching And Learning Research ◽  
Behavioural Engagement ◽  
Upper Level ◽  
Troublesome Knowledge

On the spectra of soft-hard and pure-applied disciplines, geomatics engineering can be categorized as hard and applied, similarly to other engineering disciplines. One can expect that geomatics engineering would score lower in deep learning as such patterns have been observed for other engineering disciplines compared to soft and pure ones. Some students in upper level courses in geomatics engineering may still struggle with fundamental knowledge from lower level courses. This makes it hard for instructors to create an environment for deep learning. They may have to spend a significant amount of class time reviewing basic concepts, and not as much time is left for building up more complex concepts and problem solving. In order to be more successful in tackling higher level learning outcomes, it would be useful to identify areas of troublesome knowledge and specific threshold concepts in key geomatics engineering courses. By addressing these concepts, instructors can eliminate, or at least minimize, the bottlenecks in the learning process. This is the aim of the teaching and learning research study presented in this paper.The main method for collecting data for this study is classroom observations complemented by minute papers at the end of each course unit. Even though the study is in its early stage, some correlations between the type of lessons delivered and the student cognitive and behavioural engagement can be seen, and some concepts can already be identified as probable threshold concepts. As far as the authors are aware, this is the first study on threshold concepts in geomatics engineering

scholarly journals ROLES OF TECHNOLOGY IN STUDENT LEARNING OF UNIVERSITY LEVEL BIOSTATISTICS

2014 ◽  
Vol 13 (1) ◽  
pp. 66-76
Author(s):  
WEILI XU ◽  
YUCHEN ZHANG ◽  
CHENG SU ◽  
ZHUANG CUI ◽  
XIUYING QI
Keyword(s):  
Student Learning ◽  
Undergraduate Students ◽  
Teaching And Learning ◽  
Statistics Education ◽  
Threshold Concepts ◽  
Research Journal ◽  
Technology In Teaching ◽  
University Level ◽  
Troublesome Knowledge ◽  
Improve Student Learning

This study explored threshold concepts and areas of troublesome knowledge among students enrolled in a basic biostatistics course at the university level. The main area of troublesome knowledge among students was targeted by using technology to improve student learning. A total of 102 undergraduate students who responded to structured questionnaires were included in this study. The results suggest that threshold concepts regarding “statistics” and “random sample” need to be better understood. “Confidence interval” and “hypothesis testing” were the two most frequent troublesome areas among the participants.The pedagogical role of technology in teaching and learning statistics, and the mechanisms whereby technology may improve student learning were discussed. First published May 2014 at Statistics Education Research Journal Archives

Tailoring Design Development and Ideation in Catering To Industry Demands

2017 ◽  
Vol 14 (1) ◽  
pp. 67
Author(s):  
Fadila Mohd Yusof ◽  
Azmir Mamat Nawi ◽  
Azhari Md Hashim ◽  
Ahmad Fazlan Ahmad Zamri ◽  
Abu Hanifa Ab Hamid ◽  
...  
Keyword(s):  
Teaching And Learning ◽  
Industrial Design ◽  
Protocol Analysis ◽  
Early Stage ◽  
Idea Generation ◽  
Learning Processes ◽  
Design Stage ◽  
Design Development ◽  
Conceptual Design Phase ◽  
Industry Practice

Design development is one of the processes in the teaching and learning of industrial design. This process is important during the early stage of ideas before continuing to the next design stage. This study was conducted to investigate the comparison between  academic  syllabus  and  industry  practices  whether  these  processes  are  highly dependent on the idea generation and interaction related to the designer or to the student itself. The data were gathered through an observation of industry practice during conceptual design phase, teaching and learning process in academic through Video Protocol Analysis (VPA) method and interviews with industry practitioners via structured and unstructured questionnaires. The data were analysed by using NVivo software in order to formulate the results. The findings may possibly contribute to the teaching and learning processes especially in the improvement of industrial design syllabus in order to meet the industry demands. Keywords: design development, industrial design, industry demands

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

scholarly journals Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaodong Wang ◽  
Ying Chen ◽  
Yunshu Gao ◽  
Huiqing Zhang ◽  
Zehui Guan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Main Method ◽  
Learning Framework ◽  
Prognostic Assessment ◽  
N Stage ◽  
Cancer Outcome ◽  
Whole Slide Images

AbstractN-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.

Diagnosing breast cancer tumors using stacked ensemble model

2021 ◽  
pp. 1-9
Author(s):  
Ahmet Haşim Yurttakal ◽  
Hasan Erbay ◽  
Türkan İkizceli ◽  
Seyhan Karaçavuş ◽  
Cenker Biçer
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Early Stage ◽  
False Negative ◽  
Gradient Boosting ◽  
Physical Sign ◽  
Ensemble Model ◽  
Learning Methods ◽  
Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

scholarly journals Technology-Powered Strategies to Rethink the Pedagogy of History and Cultural Heritage through Symmetries and Narratives

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 367 ◽  
Author(s):  
Martín López-Nores ◽  
Omar Bravo-Quezada ◽  
Maddalena Bassani ◽  
Angeliki Antoniou ◽  
Ioanna Lykourentzou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Web ◽  
Cultural Heritage ◽  
Digital Humanities ◽  
Teaching And Learning ◽  
Computational Analysis ◽  
Learning Technologies ◽  
Cross Border ◽  
New Information ◽  
New Strategies

Recent advances in semantic web and deep learning technologies enable new means for the computational analysis of vast amounts of information from the field of digital humanities. We discuss how some of the techniques can be used to identify historical and cultural symmetries between different characters, locations, events or venues, and how these can be harnessed to develop new strategies to promote intercultural and cross-border aspects that support the teaching and learning of history and heritage. The strategies have been put to the test in the context of the European project CrossCult, revealing enormous potential to encourage curiosity to discover new information and increase retention of learned information.

Classroom observations and supervision – essential dimensions of pedagogical leadership

2014 ◽  
Vol 28 (7) ◽  
pp. 856-868 ◽  
Author(s):  
Helene Ärlestig ◽  
Monika Törnsen
Keyword(s):  
Student Learning ◽  
Teaching And Learning ◽  
First Century ◽  
Classroom Observations ◽  
Main Task ◽  
Teacher Capacity ◽  
Content Type ◽  
Democratic Leadership ◽  
Core Mission ◽  
Learning Issues

Purpose – The main task of every school is to contribute to student learning and achievement. In the twenty-first century, national and international evaluations and comparisons have focussed on measurable student and school results. Not only teachers but also principals are held accountable for school results, which increase expectations of principals to work to enhance student learning and improve outcomes. In Sweden, a principal's work with a given school's core mission is labeled as pedagogical leadership, a concept that includes diverse activities related to national goals and school results. Aspects of pedagogical leadership include principals’ classroom observations and communication about teaching and learning issues. The purpose of this paper is to describe a model of pedagogical leadership as a base for principals’ experience with the aim to develop their understanding of pedagogical leadership. Design/methodology/approach – The paper builds on data from three groups of principals who participated in a course to learn more about pedagogical leadership. Findings – The participating principals performed their pedagogical leadership in different manners and with varying quality. During the course, there was a shift in what activities and duties the principals prioritized. The findings highlight the importance of democratic leadership and the improvement of teacher capacity and student outcomes. Practical implications – The paper gives practical examples on how principals can improve their understanding of pedagogical leadership. Originality/value – There are few articles on how pedagogical leadership is understood and practiced. The paper provides a model for pedagogical leadership and empirical data that shows that the concept deserves to be viewed as a qualitative concept that need interpretation.

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