scholarly journals In search of the margin spaces

2021 ◽  
Vol 3 (1) ◽  
pp. 34-36
Author(s):  
Cristian Rodriguez
Keyword(s):  
Deep Learning ◽  
Secondary School ◽  
Knowledge Society ◽  
Extended Mind ◽  
Actor Network Theory ◽  
Mobile Technologies ◽  
Adoption Of Technology ◽  
Learning Ecosystem ◽  
Standardised Testing ◽  
The Impact

Virtual and Distance Learning are certainly not disruptive technologies, but rather a trend in contemporary education. However, the potential of new mobile technologies, and the openness to new pedagogies associated with them, is enabling teaching professionals to design learning instances that could truly disrupt traditional schooling (Agarwal, 2013). Societal changes such as the rise of the knowledge society and disintermediation of education (Prince, 2014) demand a learning ecosystem where learners can create their own opportunities to develop life-skills and problem solve (Hannon, Patton & Temperley, 2011). This means that learners should be given the opportunity to be their own agents of learning and build collaborative networks with both instructors and peers. However, secondary school education is still organised around students’ attainment and standardised testing, where curriculum normalisation demands supersede student-centeredness (Leadbeater, 2005). This paradigm-clash together with some limitations on teachers’ digital capabilities or/and the organizational constraints on the adoption of technology challenges the full implementation of ICT as a neural network that could enable full-personalisation and, therefore, a deep learning ecosystem. Digital technologies can no longer be considered a simple tool to access, organise and communicate information. At its lowest denomination, technology becomes a constitutive structure “which partly constitutes the things to which is applied” (Van der Hoven, 2006, p68), and acts as a medium that both impacts on the way we explore reality and mediates its understanding (Carr, 2011; Cardinali et. al; 2009; Doidge, 2007; Kurtweil, 2005, quoted by White, 2011). At its highest denomination, technology can be understood from the perspective of sociomateriality, where material means are “constitutive of both activities and identities” (Orlikowski & Scott, 2008, p 455). From the perspective of the Extended Mind Theory (Clark & Chalmers, 1998) humans and tools can work together as a functional organ, blurring the boundaries between human and technology (Hannon, 2018).  Technologies are not the only medium capable of enabling deep learning, since “learning is not only the development of the fundamental competencies [but] also developing the personal, interpersonal and cognitive capabilities that allow one to diagnose what is going on in the complex, constantly shifting human and technical context of real-world practice and then match an appropriate response.” (Fullan & Scott, 2014, p4.). However, it would be naive to disregard the impact that emerging technologies are having not only on pedagogy, but also, on the economic and political pressures being transferred into secondary schools (Bolstad et al.,2012. p1).  The aim of my research is to inquire into the tools that high-achieving senior secondary school learners are using to interact with digital texts in order to (a) structure their learning [organising]; (b) articulating the "external mind" [signposting] and (c) representing mental processes [synthesising] and its intersection with Biesta’s (2010) notion of the dimensions of Education from the perspective of Complexity and ANT (actor-network) theory.

MHealth Applications for People with Cognitive Impairment and Their Caregivers: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Qing Zhao ◽  
Pei Chen ◽  
Yu Zhang ◽  
Haining Liu ◽  
Xianwen Li
Keyword(s):  
Cognitive Impairment ◽  
Sample Size ◽  
Research Design ◽  
Scoping Review ◽  
Life Support ◽  
Small Sample ◽  
Mobile Technologies ◽  
Control Group ◽  
Caregiver Support ◽  
The Impact

BACKGROUND Mobile health application has become an important tool for healthcare systems. One such tool is the delivery of assisting in people with cognitive impairment and their caregivers. OBJECTIVE This scoping review aims to explore and evaluate the existing evidence and challenges on the use of mHealth applications that assisting in people with cognitive impairment and their caregivers. METHODS Nine databases, including PubMed, EMBASE, Cochrane, PsycARTICLES, CINAHL, Web of Science, Applied Science & Technology Source, IEEE Xplore and the ACM Digital Library were searched from inception through June 2020 for the studies of mHealth applications on people with cognitive impairment and their caregivers. Two reviewers independently extracted, checked synthesized data independently. RESULTS Of the 6101 studies retrieved, 64 studies met the inclusion criteria. Three categories emerged from this scoping review. These categories are ‘application functionality’, ‘evaluation strategies’, ‘barriers and challenges’. All the included studies were categorized into 7 groups based on functionality: (1) cognitive assessment; (2) cognitive training; (3) life support; (4) caregiver support; (5) symptom management; (6) reminiscence therapy; (7) exercise intervention. The included studies were broadly categorized into four types: (1) Usability testing; (2) Pilot and feasibility studies; (3) Validation studies; and (4) Efficacy or Effectiveness design. These studies had many defects in research design such as: (1) small sample size; (2) deficiency in active control group; (3) deficiency in analyzing the effectiveness of intervention components; (4) lack of adverse reactions and economic evaluation; (5) lack of consideration about the education level, electronic health literacy and smartphone proficiency of the participants; (6) deficiency in assessment tool; (7) lack of rating the quality of mHealth application. Some progress should be improved in the design of smartphone application functionality, such as: (1) the design of cognitive measurements and training game need to be differentiated; (2) reduce the impact of the learning effect. Besides this, few studies used health behavior theory and performed with standardized reporting. CONCLUSIONS Preliminary results show that mobile technologies facilitate the assistance in people with cognitive impairment and their caregivers. The majority of mHealth application interventions incorporated usability outcome and health outcomes. However, these studies have many defects in research design that limit the extrapolation of research. The content of mHealth application is urgently improved to adapt to demonstrate the real effect. In addition, further research with strong methodological rigor and adequate sample size are needed to examine the feasibility, effectiveness, and cost-effectiveness of mHealth applications for people with cognitive impairment and their caregivers.

scholarly journals The impact of deep learning on document classification using semantically rich representations

2019 ◽  
Vol 56 (5) ◽  
pp. 1618-1632 ◽  
Author(s):  
Zenun Kastrati ◽  
Ali Shariq Imran ◽  
Sule Yildirim Yayilgan
Keyword(s):  
Deep Learning ◽  
Document Classification ◽  
The Impact

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era

2020 ◽  
pp. 1-10
Author(s):  
Colin J. McMahon ◽  
Justin T. Tretter ◽  
Theresa Faulkner ◽  
R. Krishna Kumar ◽  
Andrew N. Redington ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carbon Footprint ◽  
Quantitative Research ◽  
Survey Design ◽  
Hybrid Approach ◽  
Disruptive Innovation ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
E Learning ◽  
The Impact

Abstract Objective: This study investigated the impact of the Webinar on deep human learning of CHD. Materials and methods: This cross-sectional survey design study used an open and closed-ended questionnaire to assess the impact of the Webinar on deep learning of topical areas within the management of the post-operative tetralogy of Fallot patients. This was a quantitative research methodology using descriptive statistical analyses with a sequential explanatory design. Results: One thousand-three-hundred and seventy-four participants from 100 countries on 6 continents joined the Webinar, 557 (40%) of whom completed the questionnaire. Over 70% of participants reported that they “agreed” or “strongly agreed” that the Webinar format promoted deep learning for each of the topics compared to other standard learning methods (textbook and journal learning). Two-thirds expressed a preference for attending a Webinar rather than an international conference. Over 80% of participants highlighted significant barriers to attending conferences including cost (79%), distance to travel (49%), time commitment (51%), and family commitments (35%). Strengths of the Webinar included expertise, concise high-quality presentations often discussing contentious issues, and the platform quality. The main weakness was a limited time for questions. Just over 53% expressed a concern for the carbon footprint involved in attending conferences and preferred to attend a Webinar. Conclusion: E-learning Webinars represent a disruptive innovation, which promotes deep learning, greater multidisciplinary participation, and greater attendee satisfaction with fewer barriers to participation. Although Webinars will never fully replace conferences, a hybrid approach may reduce the need for conferencing, reduce carbon footprint. and promote a “sustainable academia”.

scholarly journals The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

2021 ◽  
Vol 184 ◽  
pp. 148-155
Author(s):  
Abdul Munem Nerabie ◽  
Manar AlKhatib ◽  
Sujith Samuel Mathew ◽  
May El Barachi ◽  
Farhad Oroumchian
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
The Impact ◽  
Speech Tagging
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