ABSTRACT
Studies tackling handwriting recognition and its applications using deep learning have been promoted by developing advanced machine learning techniques. Yet, a shortage in research that serves the Arabic language and helps develop teaching and learning processes still exists. Moreover, COVID-19 pandemic affected the education system considerably in many countries and yielded an immediate shift to distance learning and extensive use of e-learning tools. An intelligent system was proposed and used in this paper to recognize isolated Arabic handwritten characters. Particularly, pre-trained CNN models were exploited and fine-tuned to meet the requirements of the considered application. Specifically, the designed system automatically supports teaching Arabic letters and evaluating children's writing skills. The Arabic Handwritten Character Dataset (AHCD) was used to train the models built upon ResNet-18 and assess the overall system performance. Furthermore, several models were investigated using various hyper-parameter settings in order to determine the most accurate one. The best model with the highest accuracy rate of 99% was used and integrated into the proposed system to recognize the Arabic alphabets. © 2023 IEEE.
ABSTRACT
Online teaching has come to play an increasingly important role in higher education, especially during the Covid-19 pandemic. Being unable to be on campus with face-to-face teaching can be challenging. This article provides a possible option of how first-year engineering students at NTNU (Norwegian University of Science and Technology) adapt to mastering higher mathematics. The purpose of the research is to see whether a more ‘face to face' experienced online teaching technological method will have a positive impact on student engagement, as well as exam performance. We refer to this new method as ‘TeachUs' in this paper for the sake of convenience. This method gives eye contact experience for the students, which has been shown to improve engagement. The surveys of the student groups confirmed the preference of this teaching method as compared to conventional screen sharing methods.
ABSTRACT
The events of the past 2 years related to the pandemic have shown that it is increasingly important to find new tools to help mental health experts in diagnosing mood disorders. Leaving aside the long-covid cognitive (e.g., difficulty in concentration) and bodily (e.g., loss of smell) effects, the short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the "online” handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients. To this purpose, patients with clinical depression (n = 14), individuals with high sub-clinical (diagnosed by a test rather than a doctor) depressive traits (n = 15) and healthy individuals (n = 20) were recruited and asked to perform four online drawing/handwriting tasks using a digitizing tablet and a special writing device. From the raw collected online data, seventeen drawing/writing features (categorized into five categories) were extracted, and compared among the three groups of the involved participants, through ANOVA repeated measures analyses. The main results of this study show that Time features are more effective in discriminating between healthy and participants with sub-clinical depressive characteristics. On the other hand, Ductus and Pressure features are more effective in discriminating between clinical depressed and healthy participants. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This visual-essay is based in the domestic space during Covid-19 lockdown in NSW, Australia, from 24 March to 25 May 2020. It is an auto-ethnographic documentation of how one family managed living with ADHD under lockdown. Assumptions and stereotypes about ADHD are broken down and challenged in the supporting narrative. These explain the complexity of living with ADHD and illustrate how very small, seemingly insignificant acts can have a large impact on managing the compounding effect an unprecedented situation like lockdown, on issues prevalent in those with ADHD such as anxiety, organisation, and lack of control over in personal circumstances. These are framed within the larger context of preceding catastrophic 2019/2020 summer bushfires that occurred in Australia, to provide the reader with insights into the emotional states in which many people in Australia entered lockdown. The author invites the reader to pay attention to the details of each of these images, to observe the visual communication cues that appear on the whiteboards, such handwriting by different people, doodles, illustrations, colour coding, and the layout that evolved over time, as personal agency within the family members developed.
ABSTRACT
Reading and writing rely on related foundational literacy skills (e.g., phonological processing, phonological memory, phonemic awareness). Therefore, students struggling with reading often have literacy difficulties that continue throughout their school years. However, lack of time and resources may make it difficult for schools to implement interventions for both reading and writing. Interventions that combine instruction for both skills may help to mitigate time and resource constraints. This paper reports the results of two pilot studies designed to examine the effectiveness of the Write Sounds integrated handwriting intervention for students with word-level deficits in reading and writing. Study 1 included 33 students struggling with handwriting and word-level reading or spelling difficulties in second and third grade. We randomly assigned participants to receive the Write Sounds intervention or a business-as-usual control. At posttest, students who received the Write Sounds intervention (n = 17) significantly outperformed the control group (n = 16) on researcher-created measures of handwriting quality and overall legibility. In study 2, three first-grade students who showed difficulty with reading, spelling, and phonemic awareness received instruction with Write Sounds. We implemented a multiple-baseline design. Results showed that Write Sounds increased participants' word reading abilities. Results of both studies suggest that Write Sounds showed promise of effectiveness. Limitations such as small sample sizes and the COVID-19 pandemic may have impacted the findings. © by LDW 2022.
ABSTRACT
The COVID-19 pandemic disrupted education around the world, resulting in the implementation of different forms of remote instruction. The present study provided a description of one interesting and unique approach to providing such instruction by analyzing 144 language arts lessons designed and implemented by 61 distinguished and experienced teachers in Xiangzhou, China. The lessons were used to teach first and second grade students the pronunciation, meaning, recognition, and writing of simplified Chinese characters. These lessons provide a possible model for teaching Chinese characters in the future. The 144 lessons were delivered synchronously through live video interactions with two to four students, while other students were able to access them simultaneously at home via an internet device or on TV (the lessons were accessed 2.1 million times). Lessons were taught four to seven times a week, and teachers devoted 58% of lesson time to teaching characters: 69% and 46% of lesson time was spent teaching characters in grades one and two, respectively. A large number of recommended behaviors for teaching characters (77 out of 80 behaviors assessed) were applied across the 144 lessons, but a relatively small number of teaching behaviors (14) were used in each lesson. This typically included two behaviors for teaching character recognition and four behaviors each for teaching pronunciation, meaning, and writing of characters. Congruently, 6.32, 5.83, 5.49, and 3.78 min per lessons were used to teach character pronunciation, writing, meaning, and recognition, respectively. Character instruction in these lessons was coherently and logically designed, but all live interactions between teachers and students were teacher directed. Directions for future research are presented and implications for practice discussed.
ABSTRACT
Handwriting can help children to improve their learning of a language and fine-tune their motor skills. Every child needs to develop her/his handwriting skill to grasp new concepts appropriately and learn the language vocabulary. Therefore, in-hand manipulation of the traditional pen is highly important to develop pre-writing and transform the scribbled writings to legible ones at a later stage. In this paper, we evaluate the effectiveness of a customized haptic device in improving the children motor skills and their handwriting quality of Arabic letters. We use the Touch™ device from the 3D-Systems company with a controllable stylus that can be adapted to children needs. Fifteen pupils from the Deutsch International School in Doha, have participated in this experience after obtaining all necessary ethical approvals from concerned stakeholders. We conducted the experiments for a period of two weeks with the assistance of the school instructors and staff. Results show that there is an important increase of children motivation, and a good improvement of their motor skills and handwriting experience. The device can be used at home to learn independently during COVID-19 pandemic that continues to hit severely the whole world and enforces schools to adapt online teaching approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Online teaching has come to play an increasingly important role in higher education, especially during the Covid-19 pandemic. Being unable to be on campus with face-to-face teaching can be challenging. This article provides a possible option of how first-year engineering students at NTNU (Norwegian University of Science and Technology) adapt to mastering higher mathematics. The purpose of the research is to see whether a more 'face to face' experienced online teaching technological method will have a positive impact on student engagement, as well as exam performance. We refer to this new method as 'TeachUs' in this paper for the sake of convenience. This method gives eye contact experience for the students, which has been shown to improve engagement. The surveys of the student groups confirmed the preference of this teaching method as compared to conventional screen sharing methods.
ABSTRACT
Traditional paper-based children's spelling assessments were hampered due to Covid-19 because existing technologies did not provide strategic signals to teachers, such as the child's handwriting direction and how they read what they write. Our project emerged as a novel method to assess children's spelling by touchscreens in this context. Hence, this paper aims to extend community knowledge concerning children's experience and perception of handwriting spelling on tablet devices. The experiment consisted in presenting three handwriting methods (paper and pencil, finger and pen writing) and was conducted with eight Brazilian children between 4.5 and 7 years old. In addition to observation, in our experimental protocol we adopted the Fun Sorter, Again-Again Table, and the Smileyometer as evaluation tools. Our results show children were excited about handwriting using a touch pen on the tablet. Most of them even revealed they prefer the pen tablet mode to the traditional paper and pencil mode. However, the majority of children did not feel comfortable writing by finger, and it required more time than other methods. Furthermore, we observed child's handwriting using finger looks different when compared to paper and pencil, while the tracing using a touch pen is similar to the registration produced on paper. © 2022 Owner/Author.
ABSTRACT
This paper presents AcousticPAD, a contactless and robust handwriting recognition system that extends the input and interactions beyond the touchscreen using acoustic signals, thus very useful under the impact of the COVID-19 epidemic. To achieve this, we carefully exploit acoustic pulse signals with high accuracy of time of fight (ToF) measurements. Then we employ trilateration localization method to capture the trajectory of handwriting in air. After that, we incorporate a data augmentation module to enhance the handwriting recognition performance. Finally, we customize a back propagation neural network that leverages augmented image dataset to train a model and recognize the acoustic system generated handwriting characters. We implement AcousticPAD prototype using cheap commodity acoustic sensors, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of AcousticPAD, and show that it supports 10 digits and 26 English letters recognition at high accuracies. © 2022, The Author(s).
ABSTRACT
The current study examined how Chinese characters were taught by primary grade teachers in Macao during online instruction resulting from the COVID-19 pandemic (i.e., emergency remote instruction). A random sample of 313 first to third grade teachers in public and private schools were surveyed about their instructional practices. Most teachers surveyed (72%) reported they taught a lesson about Chinese characters once every 3-4 weeks during emergency remote instruction, and 83% and 81% of teachers indicated they assigned homework for writing and reading characters, respectively, at the same rate. On average, they reportedly spent 97 min per week teaching students to write, read, and understand the meaning of new characters, devoting equal time to each of these skills. They also indicated students practiced writing and reading characters in class for 40 min per week. They further noted students were expected to spend 35 min a day practicing writing and reading characters for homework. While teachers reportedly used a variety of instructional practices for teaching characters (M = 30.38), the typical teacher applied less than one-half (N = 64) of practices assessed. Teachers reported use of asynchronous (online learning activities which can be completed at other times) and synchronous (real-time videos and audio/text) teaching methods and perceptions of adequacy of technical support predicted reported teaching practices. The findings from this study raise questions about the teaching of Chinese characters in Macao during emergency remote instruction.
ABSTRACT
The move from student to qualified practitioner is a defining moment for many healthcare professionals as they embark on their career. This study invited Radiographers to recount their experiences of the above, with particular emphasis on those who qualified during the Covid-19 pandemic. Individual interviews were conducted over Zoom. The researchers utilised trigger questions intended to facilitate discussion and reflection of the participants first working day post qualification. The research results were analysed using a qualitative data analysis method - including MAXQDA software and the Braun and Clarke thematic analysis. The shorthand notes taken during the interviews were transcribed, and then manually coded to over 180 individual coding points. This code system allowed the extracts to be collated, and themes to be identified. The primary themes extracted from the data were;Student to Practitioner transition, impact of colleague interaction, placement location, adapting to hospital life, and unique effects of the pandemic. There were a number of findings of particular note. Most participants felt that they had maximisedwhat they could learn as a student, and needed to take the next step without the 'student safety net' of having someone double check their work. Many were happy to gain employment where they had been on placement previously. Participants spoke about how the actual radiography was the easy part for themon Day 1. They weren't worried or surprised by it. Itwas adapting to hospital life as a whole, that proved challenging. Wearing a bleeper for the first time, sorting out payroll,managing the patient flow, and above all - talking with other hospital teams and justifying why something was or was not done.
ABSTRACT
Background: Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders. Materials and Methods: One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm. Results: Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83). Discussion: Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.
ABSTRACT
The study reports pedagogical adaptations that Chinese language instructors made to support students' character learning during emergency remote teaching in 2020. Data from an online questionnaire and follow-up interviews show that the handwriting requirement in the language curriculum was ified to way to technology-based instruction, making the conventionally isolated and solitary task of character learning more integrative and interactive. Beginning-level instructors' use of technology in character instruction was correlated with their self-confidence, perceived time sufficiency, technology access, and support received. Meanwhile, intermediate- and upper-level instructors' self-confidence and perceived values of online teaching were factors associated with their technology use. The crucial role of teacher communities in offering language-specific training and peer support is emphasized, and implications to the broader field of foreign language teaching are discussed.
ABSTRACT
Aim: To assess the effect of COVID 19 on ward notes documentation standards Method: 100 ward notes entries (before COVID, during COVID Peak, and after COVID Peak, 100 each) were evaluated against set ward notes documentation standards derived from the GMC Good Medical Practice document. The results were analysed, and the three data sets were compared to assess any effect of the COVID 19 pandemic on ward notes documentation standards. Results: Individually, clear handwriting and documenting signatures showed a slight decline, and a slight increase was seen in the use of unknown abbreviations during the COVID-19 Peak. However, documentations standards were maintained across other categories and even showed improvement in some standards. Overall compliance showed a small improvement rather than a decline during the COVID 19 Peak. Conclusions: COVID 19 Pandemic has certainly had an effect on every aspect of life globally. The health sector came under significant pressure and saw unprecedented stressful working conditions during the peak of the pandemic. However, even under the unprecedented pressures of COVID 19, we were able to maintain remarkable documentation standards and even showed improvements across various standards. It should be kept in mind that there are limitations to this Audit and further studies are needed to confirm these findings.
ABSTRACT
Purpose>This study examines the management rostering systems that inform the ways medical scientists are allocated their work in the public healthcare sector in Australia. Promoting the contributions of medical scientists should be a priority given the important roles they are performing in relation to COVID-19 and the demand for medical testing doubling their workloads (COVID-19 National Incident Room Surveillance Team, 2020). This study examines the impact of work on medical scientists and rostering in a context of uncertain work conditions, budget restraints and technological change that ultimately affect the quality of patient care. This study utilises the Job-Demands-Resources theoretical framework (JD-R) to examine the various job demands on medical scientists and the resources available to them.Design/methodology/approach>Using a qualitative methodological approach, this study conducted 23 semi-structured interviews with managers and trade union officials and 9 focus groups with 53 medical scientists, making a total 76 participants from four large public hospitals.Findings>Due to increasing demands for pathology services, this study demonstrates that a lack of job resources, staff shortages, poor rostering practices such as increased workloads that lead to absenteeism, often illegible handwritten changes to rosters and ineffectual management lead to detrimental consequences for medical scientists’ job stress and well-being. Moreover, medical science work is hidden and not fully understood and often not respected by other clinicians, hospital management or the public. These factors have contributed to medical scientists’ lack of control over their work and causes job stress and burnout. Despite this, medical scientists use their personal resources to buffer the effects of excessive workloads and deliver high quality of patient care.Originality/value>Findings suggest that developing mechanisms to promote sustainable employment practices for medical scientists are critical for the escalating demands in pathology.
ABSTRACT
Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data. It was found that deep learning approaches can be used for big data analysis successfully. Applications include virtual assistants such as Alexa and Siri, facial recognition, personalization, natural language processing, autonomous cars, automatic handwriting generation, news aggregation, the colorization of black and white images, the addition of sound to silent films, pixel restoration, and deep dreaming. As a review, this paper aims to categorically cover several widely used deep learning algorithms along with their architectures and their practical applications: backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing. In addition, challenges of deep learning are also presented in this paper, such as AutoML-Zero, neural architecture search, evolutionary deep learning, and others. The pros and cons of these algorithms and their applications in healthcare are explored, alongside the future direction of this domain. This paper presents a review and a checkpoint to systemize the popular algorithms and to encourage further innovation regarding their applications. For new researchers in the field of deep learning, this review can help them to obtain many details about the advantages, disadvantages, applications, and working mechanisms of a number of deep learning algorithms. In addition, we introduce detailed information on how to apply several deep learning algorithms in healthcare, such as in relation to the COVID-19 pandemic. By presenting many challenges of deep learning in one section, we hope to increase awareness of these challenges, and how they can be dealt with. This could also motivate researchers to find solutions for these challenges.
ABSTRACT
With the popularization of mobile information tools, handwriting recognition technology has also entered the era of large-scale applications. Online handwriting recognition enables users to input text most naturally and conveniently, easy to learn and use. Any handwritten text recognition system requires a large dataset to be trained and tested with the handwriting text data. However, collecting handwritten text data from different people is a huge challenge especially during COVID19 pandemic time. Hence, this study aims to design and develop a dynamic website for collecting handwriting text. This can help the developer of a handwriting text recognition system to solve the data collection problem. Through the online collect data method, it makes the process of collecting handwriting data more efficient and convenient. The sequence of coordinate points (x, y) of the handwriting text movements will be the collecting data and store into a database. Besides that, the project evaluated the prototype of the website to ensure the usability of the website. Based on the result of evaluation, the website is a user friendly and useful system. The study helps to understand the requirements and user interface of the website for collecting handwriting text. It can be used as a reference model for developers and researchers in the field of data collection and preprocessing stage of online handwriting recognition.