Search results for: second language vocabulary learning

Authors: Tarek Abdelkader, Suresh Selvaraj, Prasad Iyer, Yong Mun Hin, Hajmath Begum, P. Gopalakrishnakone

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Nowadays, more and more educational institutes, as well as students, rely on 3D anatomy programs as an important tool that helps students correlate the actual locations of anatomical structures in a 3D dimension. Lately, virtual reality (VR) is gaining more favor from the younger generations due to its higher interactive mode. As a result, using virtual reality as a gamified learning platform for anatomy became the current goal. We present a model where a Virtual Human Anatomy Program (VHAP) was developed to assist with the anatomy learning experience of students. The anatomy module has been built, mostly, from real patient CT scans. Segmentation and surface rendering were used to create the 3D model by direct segmentation of CT scans for each organ individually and exporting that model as a 3D file. After acquiring the 3D files for all needed organs, all the files were introduced into a Virtual Reality environment as a complete body anatomy model. In this ongoing experiment, students from different Allied Health orientations are testing the VHAP. Specifically, the cardiovascular system has been selected as the focus system of study since all of our students finished learning about it in the 1st trimester. The initial results suggest that the VHAP system is adding value to the learning process of our students, encouraging them to get more involved and to ask more questions. Involved students comments show that they are excited about the VHAP system with comments about its interactivity as well as the ability to use it solo as a self-learning aid in combination with the lectures. Some students also experienced minor side effects like dizziness.

Keywords: 3D construction, health sciences, teaching pedagogy, virtual reality

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Authors: Lucélia Alcântara

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Reading has been seen as a passive skill by many people for a long time. However, when one comes to study it deeply and in a such a way that the act of reading equals acquiring knowledge through living an experience that belongs to him/her, passive definitely becomes active. Material development with a focus on reading has to consider much more than reading strategies. The following questions are asked: Is the material appropriate to the students’ reality? Does it make students think and state their points of view? With that in mind a lesson has been developed to illustrate theory becoming practice. Knowledge, criticality, intercultural experience and social interaction. That is what reading is for.

Keywords: reading, culture, material development, learning

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Authors: Emma Nemishalyan

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It is believed that learners’ mother tongue (L1 hereafter) has a huge impact on their second language acquisition (L2 hereafter). This hypothesis has been exposed to both positive and negative criticism. Based on research results of a wide range of learners’ corpora (Chinese, Japanese, Spanish among others) the hypothesis has either been proved or disproved. However, no such study has been conducted on the Armenian learners. The aim of this paper is to understand the implication of the hypothesis on the Armenian learners’ corpus in terms of the use of the passive voice. To this end, the method of Contrastive Interlanguage Analysis (hereafter CIA) has been used on native speakers’ corpus (Louvain Corpus of Native English Essays (LOCNESS)) and Armenian learners’ corpus which has been compiled by me in compliance with International Corpus of Learner English (ICLE) guidelines. CIA compares the interlanguage (the language produced by learners) with the one produced by native speakers. With the help of this method, it is possible not only to highlight the mistakes that learners make, but also to underline the under or overuses. The choice of the grammar issue (passive voice) is conditioned by the fact that typologically Armenian and English are drastically different as they belong to different branches. Moreover, the passive voice is considered to be one of the most problematic grammar topics to be acquired by learners of the English language. Based on this difference, we hypothesized that Armenian learners would either overuse or underuse some types of the passive voice. With the help of Lancsbox software, we have identified the frequency rates of passive voice usage in LOCNESS and Armenian learners’ corpus to understand whether the latter have the same usage pattern of the passive voice as the native speakers. Secondly, we have identified the types of the passive voice used by the Armenian leaners trying to track down the reasons in their mother tongue. The results of the study showed that Armenian learners underused the passive voices in contrast to native speakers. Furthermore, the hypothesis that learners’ L1 has an impact on learners’ L2 acquisition and production was proved.

Keywords: corpus linguistics, applied linguistics, second language acquisition, corpus compilation

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: J. S. M. Yang, G. E. T. Chua

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Diploma in Optometry (OPT) course is a three-year program offered by Ngee Ann Polytechnic (NP) to train students to provide primary eye care. Students are equipped with foundational conceptual knowledge and practical skills in the first three semesters before clinical modules in fourth to six semesters. In the clinical modules, students typically have difficulties in integrating the acquired knowledge and skills from the past semesters to perform general eye examinations on public patients at NP Optometry Centre (NPOC). To help the students overcome the challenge, a web-based game Virtual Optometrist (VO) was developed to help students apply their skills and knowledge through scenario-based learning. It consisted of two interfaces, Optical Practice Counter (OPC) and Optometric Consultation Room (OCR), to provide two simulated settings for authentic learning experiences. In OPC, students would recommend and provide appropriate frame and lens selection based on virtual patient’s case history. In OCR, students would diagnose and manage virtual patients with common ocular conditions. Simulated scenarios provided real-world clinical situations that required contextual application of integrated knowledge from relevant modules. The stages in OPC and OCR are of increasing complexity to align to expected students’ clinical competency as they progress to more senior semesters. This prevented gameplay fatigue as VO was used over the semesters to achieve different learning outcomes. Numerous feedback opportunities were provided to students based on their decisions to allow individualized learning to take place. The game-based learning element in VO was achieved through the scoreboard and leader board to enhance students' motivation to perform. Scores were based on the speed and accuracy of students’ responses to the questions posed in the simulated scenarios, preparing the students to perform accurately and effectively under time pressure in a realistic optometric environment. Learning analytics was generated in VO’s backend office based on students’ responses, offering real-time data on distinctive and observable learners’ behavior to monitor students’ engagement and learning progress. The backend office allowed versatility to add, edit, and delete scenarios for different intended learning outcomes. Likert Scale was used to measure students’ learning experience with VO for OPT Year 2 and 3 students. The survey results highlighted the learning benefits of implementing VO in the different modules, such as enhancing recall and reinforcement of clinical knowledge for contextual application to develop higher-order thinking skills, increasing efficiency in clinical decision-making, facilitating learning through immediate feedback and second attempts, providing exposure to common and significant ocular conditions, and training effective communication skills. The results showed that VO has been useful in reinforcing optometry students’ learning and supporting the development of higher-order thinking, increasing efficiency in clinical decision-making, and allowing students to learn from their mistakes with immediate feedback and second attempts. VO also exposed the students to diverse ocular conditions through simulated real-world clinical scenarios, which may otherwise not be encountered in NPOC, and promoted effective communication skills.

Keywords: authentic learning, game-based learning, scenario-based learning, simulated clinical scenarios

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Authors: M. Omer, D. Wilson

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Creating a database scheme is essentially a manual process. From a requirement specification, the information contained within has to be analyzed and reduced into a set of tables, attributes and relationships. This is a time-consuming process that has to go through several stages before an acceptable database schema is achieved. The purpose of this paper is to implement a Natural Language Processing (NLP) based tool to produce a from a requirement specification. The Stanford CoreNLP version 3.3.1 and the Java programming were used to implement the proposed model. The outcome of this study indicates that the first draft of a relational database schema can be extracted from a requirement specification by using NLP tools and techniques with minimum user intervention. Therefore, this method is a step forward in finding a solution that requires little or no user intervention.

Keywords: information extraction, natural language processing, relation extraction

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Authors: Shaik Jilani Basha, Inavolu Avinash, Alla Venu Sai Reddy, Bitragunta Taraka Ramu

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We can use the neural style transfer technique to build a picture with the same "content" as the beginning image but the "style" of the picture we've chosen. Neural style transfer is a technique for merging the style of one image into another while retaining its original information. The only change is how the image is formatted to give it an additional artistic sense. The content image depicts the plan or drawing, as well as the colors of the drawing or paintings used to portray the style. It is a computer vision programme that learns and processes images through deep convolutional neural networks. To implement software, we used to train deep learning models with the train data, and whenever a user takes an image and a styled image, the output will be as the style gets transferred to the original image, and it will be shown as the output.

Keywords: neural networks, computer vision, deep learning, convolutional neural networks

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Authors: Darius Singh

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This study adopts a research, develop, and deploy methodology to create a state-of-the-art forest preschool environment using technology and the Gaia (Earth) Education Philosophy as design support. The new philosophy adopts an ancient Greek terminology, “Gaia,” meaning “Mother Earth”, and it take its principle to model everything with the oldest living and breathing entity that it know – Earth. This includes using nature and biomimicry-based principles in building design, environments, curricula, teaching, learning, values and outcomes for children. The study highlights the potential effectiveness of the Gaia (Earth) Education Philosophy as a means of designing Earth-inspired environments for children’s learning. The discuss the strengths of biomimicry-based design principles and propose a curriculum that emphasizes natural outcomes for early childhood learning. Theoretical implications of the study are that the Gaia (Earth) Education Philosophy could serve as a strong foundation for educating young learners.it present a unique approach that promotes connections with Earth-principles and lessons that can contribute to the development of social and environmental consciousness among children and help educate generations to come into a stable and balanced future.

Keywords: earth science, nature education, sustainability, gaia, forest school, nature, inspirational teaching and learning

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Loreta Axhami

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Media literacy in the digital age emerges not only as a set of skills to generate true knowledge and information but also as a pedagogy methodology, as a kind of educational philosophy. In addition to such innovations as information integration and communication technologies, media infrastructures, and web usage in the educational system, media literacy enables the change in the learning methods, pedagogy, teaching programs, and school curriculum itself. In this framework, this study focuses on ICT's impact on teaching and learning methods and the degree they are reflected in the Albanian education system. The study is based on a combination of quantitative and qualitative methods of scientific research. Referring to the study findings, it results that student’s limited access to the internet in school, focus on the hardcopy textbooks and the role of the teacher as the only or main source of knowledge and information are some of the main factors contributing to the implementation of authoritarian pedagogical methods in the Albanian education system. In these circumstances, the implementation of media literacy is recommended as an apt educational process for the 21st century, which requires a reconceptualization of textbooks as well as the application of modern teaching and learning methods by integrating information and communication technologies.

Keywords: authoritarian pedagogic model, education system, ICT, media literacy

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Authors: Thifhuriwi Emmanuel Madzunye, Patricia Harpur, Ephias Ruhode

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A review of the pertinent literature identified a gap concerning the hindrances and opportunities accompanying the implementation of real-time online learning systems (RTOLs) in rural areas. Whilst RTOLs present a possible solution to teaching and learning issues in rural areas, little is known about the implementation of digital strategies among schools in isolated communities. This study explores associated guidelines that have the potential to inform decision-making where Internet-based education could improve educational opportunities. A systematic literature review has the potential to consolidate and focus on disparate literature served to collect interlinked data from specific sources in a structured manner. During qualitative data analysis (QDA) of selected publications via the application of a QDA tool - ATLAS.ti, the following overarching themes emerged: digital divide, educational strategy, human factors, and support. Furthermore, findings from data collection and literature review suggest that signiant factors include a lack of digital knowledge, infrastructure shortcomings such as a lack of computers, poor internet connectivity, and handicapped real-time online may limit students’ progress. The study recommends that timeous consideration should be given to the influence of the digital divide. Additionally, the evolution of educational strategy that adopts digital approaches, a focus on training of role-players and stakeholders concerning human factors, and the seeking of governmental funding and support are essential to the implementation and success of RTOLs.

Keywords: communication, digital divide, digital skills, distance, educational strategy, government, ICT, infrastructures, learners, limpopo, lukalo, network, online learning systems, political-unrest, real-time, real-time online learning, real-time online learning system, pass-rate, resources, rural area, school, support, teachers, teaching and learning and training

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Authors: Rui Mao, Heming Ji, Xiaoyu Wang

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Keystroke dynamics are widely used in identity recognition. It has the advantage that the individual typing rhythm is difficult to imitate. It also supports continuous authentication through the keyboard without extra devices. The existing keystroke dynamics authentication methods based on machine learning have a drawback in supporting relatively complex scenarios with massive data. There are drawbacks to both feature extraction and model optimization in these methods. To overcome the above weakness, an authentication model of keystroke dynamics based on deep learning is proposed. The model uses feature vectors formed by keystroke content and keystroke time. It ensures efficient continuous authentication by cooperating attention mechanisms with the combination of CNN and Bi-LSTM. The model has been tested with Open Data Buffalo dataset, and the result shows that the FRR is 3.09%, FAR is 3.03%, and EER is 4.23%. This proves that the model is efficient and accurate on continuous authentication.

Keywords: keystroke dynamics, identity authentication, deep learning, CNN, LSTM

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Authors: Gizem Barreto Martins

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This paper explores the subversive potential inherent in the theatrical language employed within Arthur Miller's The Crucible. The research argues that this play intricately weaves ideological connections with its audience and the historical epoch it represents, effectively serving as a channel for ideological and cultural interaction potentially exerting subversive influences on social and political realms. Using a historical-materialist methodology that situates the play within its historical and political context, all while examining its connections with theater and literary theories, the paper raises a fundamental query: How does this dramatic work embody subversion, presenting a style unburdened by the performative conventions of daily life and prevailing codes and systems of representation? In response to this inquiry, the study asserts that theatrical language has the capacity to function as a subversive catalyst against prevailing ideologies, actively contributing to the process of social transformation. To substantiate this claim, the research conducts a detailed analysis of the selected play, employing the semiotic framework pioneered by Gilles Deleuze and Felix Guattari.

Keywords: arthur miller, The crucible, gilles deleuze, felix guattari, theater and literary theories

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Authors: Sima Modirkhameneh, Mohammad Mohammadpanah

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In the process of EFL teachers’ development, self-led reflection (SLR) is thought to have an imperative role because it may help teachers analyze, evaluate, and contemplate what is happening in their classes. Such contemplations can not only enhance the quality of their instruction and provide better learning environments for learners but also improve the quality of their classroom management (CM). Accordingly, understanding the effect of teachers’ SLR practices may help us gain valuable insights into what possible modifications SLR may bring about in all aspects of EFL teachers' practitioners, especially their CM. The main purpose of this case study was, thus, to investigate the impact of SLR practices of 12 Iranian EFL teachers on their CM based on the universal classroom management checklist (UCMC). In addition, another objective of the current study was to have a clear image of EFL teachers’ perceptions of their own SLR practices and their possible outcomes. By conducting repeated reflective interviews, observations, and feedback of the participants over five teaching sessions, the researcher analyzed the outcomes qualitatively through the process of meaning categorization and data interpretation based on the principles of Grounded Theory. The results demonstrated that EFL teachers utilized SLR practices to improve different aspects of their language teaching skills and CM in different contexts. Almost all participants had positive comments and reactions about the effect of SLR on their CM procedures in different aspects (expectations and routines, behavior-specific praise, error corrections, prompts and precorrections, opportunity to respond, strengths and weaknesses of CM, teachers’ perception, CM ability, and learning process). Otherwise stated, results implied that familiarity with the UCMC criteria and reflective practices contributes to modifying teacher participants’ perceptions about their CM procedure and utilizing the reflective practices in their teaching styles. The results are thought to be valuably beneficial for teachers, teacher educators, and policymakers, who are recommended to pay special attention to the contributions as well as the complexity of reflective teaching. The study concludes with more detailed results and implications and useful directions for future research.

Keywords: classroom management, EFL teachers, reflective practices, self-led reflection

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

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Authors: Natalia M. Nepomniashchikh

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The research focuses on Russian and English linguoculturemes Caucasian mountaineer and Scottish Highlander, the effort of comparative-contrastive analysis was made. In order to reach the aim, the analysis of the vocabulary definitions of the concepts under consideration was taken, which made it possible to build the lexical-semantic fields of both lexical items in Russian and English. This stage of research helped to turn to the linguistic-cultural fields construction. To build these fields, literary pieces containing the concepts under consideration and the items directly related to them were taken from the works about the Caucasus mountains and mountaineers living there by M. Yu. Lermontov and the ones by W. Scott devoted to the Scottish Highlands and their inhabitants. All collected data was systematized in schemes and tables reflecting the differences and intercrossing areas.

Keywords: lexemes, lexical items, lexical-semantic field, linguistic-cultural field, linguoculturemes

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Chiara De Amicis, Sonia Falconieri, Mesut Tastan

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In this study, we analyze the language employed by Chief Executive Officers (CEOs) and Chief Financial Officers (CFOs) during earnings conference calls from a gender perspective. We find evidences that conference calls held by female CEOs and/or CFOs exhibit a higher level of optimism compared to conference calls held by male CEOs and/or CFOs. Moreover, female managers tend to present and discuss firm performances with less vagueness as compared to their male colleagues. We then observe the market reaction around each earnings conference call: while manager optimism is perceived as a good signal by investors, manager vagueness significantly dampens the market reaction around the call. Whether the gender of the CEO and/or the CFO delivering the conference call affects investors’ perceptions about the firm performance is still an open question. Some evidences show that the language employed by female managers conveys more valuable information for market participants as compared to the language employed by their male counterparts. This study contributes to a growing literature in finance and accounting that uses textual analysis to assess the informativeness of corporate disclosure. To our knowledge, this is the first paper that aims at answering the question whether the gender of firm’s top managers does matter when it comes to assess the informativeness of corporate spoken communication. We believe that our results will be of relevance for future research in the field. Moreover, our evidence may be used in support of the debate if a larger participation by women in the management of companies should be encouraged or not.

Keywords: conference calls, even study, gender, market reaction, textual analysis

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Authors: Ahmet Erdoğan, Şahin Kesici, Mustafa Baloğlu

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Information technologies have lead to changes in the areas of communication, learning, and teaching. Besides offering many opportunities to the learners, these technologies have changed the teaching methods and beliefs of teachers. What the Technological Pedagogical Content Knowledge (TPACK) means to the teachers is considerably important to integrate technology successfully into teaching processes. It is necessary to understand how to plan and apply teacher training programs in order to balance students’ pedagogical and technological knowledge. Because of many inefficient teacher training programs, teachers have difficulties in relating technology, pedagogy and content knowledge each other. While providing an efficient training supported with technology, understanding the three main components (technology, pedagogy and content knowledge) and their relationship are very crucial. The purpose of this study is to determine whether motivational beliefs and self-regulated learning strategies are significant predictors of mathematics teacher candidates' TPACK perceptions. A hundred seventy five Turkish mathematics teachers candidates responded to the Motivated Strategies for Learning Questionnaire (MSLQ) and the Technological Pedagogical And Content Knowledge (TPACK) Scale. Of the group, 129 (73.7%) were women and 46 (26.3%) were men. Participants' ages ranged from 20 to 31 years with a mean of 23.04 years (SD = 2.001). In this study, a multiple linear regression analysis was used. In multiple linear regression analysis, the relationship between the predictor variables, mathematics teacher candidates' motivational beliefs, and self-regulated learning strategies, and the dependent variable, TPACK perceptions, were tested. It was determined that self-efficacy for learning and performance and intrinsic goal orientation are significant predictors of mathematics teacher candidates' TPACK perceptions. Additionally, mathematics teacher candidates' critical thinking, metacognitive self-regulation, organisation, time and study environment management, and help-seeking were found to be significant predictors for their TPACK perceptions.

Keywords: candidate mathematics teachers, motivational beliefs, self-regulated learning strategies, technological and pedagogical knowledge, content knowledge

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Authors: M. Greeshma, V. Ashvini, P. Jayarekha

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Project based learning (PBL) is a student-driven educational framework and offers the student an opportunity for in-depth investigations of courses. This paper presents the need of PBL in engineering education for the student to graduate with a capacity to design and implement complex problems. The implementation strategy of PBL and its related challenges are presented. The case study that energizes the engineering curriculum with a relevance to the real-world of technology along with its benefits to the students is also included.

Keywords: PBL, engineering education, curriculum, implement complex

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Authors: Yesuselvi Manickam, Tan Soon Chin

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Internship is a supervised and structured learning experience related to one’s field of study or career goal. Internship allows students to obtain work experience and the opportunity to apply skills learned during university. Internship is a valuable learning experience for students; however, literature on employer assessment is scarce on Malaysian student’s internship experience. This study focuses on employer’s perspective on student’s performances during their three months of internship. The results are based on the descriptive analysis of 45 sets of question gathered from the on-site supervisors of the interns. The survey of 45 on-site supervisor’s feedback was collected through postal mail. It was found that, interns have not met their on-site supervisor’s expectations in many areas. The significance of this study is employer’s assessment on the internship shall be used as feedback to improve on ways how to prepare students for their internship and employments in future.

Keywords: employers perspective, internship, structured learning, student’s performances

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Authors: Somayeh Sadat Hashemi Kamangar, Fatemeh Bakouie, Shahriar Gharibzadeh

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In this study, we target to exploit Maximum Spanning Trees (MST) of children's semantic networks to investigate their language development. To do so, we examine the graph-theoretic properties of word-embedding networks. The networks are made of words children learn prior to the age of 30 months as the nodes and the links which are built from the cosine vector similarity of words normatively acquired by children prior to two and a half years of age. These networks are weighted graphs and the strength of each link is determined by the numerical similarities of the two words (nodes) on the sides of the link. To avoid changing the weighted networks to the binaries by setting a threshold, constructing MSTs might present a solution. MST is a unique sub-graph that connects all the nodes in such a way that the sum of all the link weights is maximized without forming cycles. MSTs as the backbone of the semantic networks are suitable to examine developmental changes in semantic network topology in children. From these trees, several parameters were calculated to characterize the developmental change in network organization. We showed that MSTs provides an elegant method sensitive to capture subtle developmental changes in semantic network organization.

Keywords: maximum spanning trees, word-embedding, semantic networks, language development

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Authors: Jane Killough

Abstract:

A podcast series was developed to support learning amongst first-year undergraduate mental health nursing students. Many first-year students do not have any clinical experience and find it difficult to engage with theory, which can present as cumbersome. Further, it can be challenging to relate abstract concepts to everyday mental health practice. Mental health professionals and service users from practice were interviewed on a range of core topics that are key to year one learning. The podcasts were made available, and students could access these recordings at their convenience to fit in with busy daily routines. The aim was to enable meaningful learning by providing access to those who have lived experience and who can, in effect, bring to life the theory being taught in university and essentially bridge the theory and practice gap while fostering working relationships between practice and academics. The student experience will be evaluated using a logic model.

Keywords: education, mental health nursing students, podcast, practice, undergraduate

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Authors: Porntipa Taksin, Jutamas Wongchan, Amornrat Karamee

Abstract:

The researchers’ experience of student affairs in 2011-2013, we found that few undergraduate nursing students become student association members who participated in co-curricular activities, they have limited skill of self-directed-learning and leadership. We developed “A Leadership Promoting Program” using Self-Directed Learning concept. The program included six activities: 1) Breaking the ice, Decoding time, Creative SMO, Know me-Understand you, Positive thinking, and Creative dialogue, which include four aspects of these activities: decision-making, implementation, benefits, and evaluation. The one-group, pretest-posttest quasi-experimental research was designed to examine the effects of the program on participation in co-curricular activities. Thirty five students participated in the program. All were members of the board of undergraduate nursing student association of Boromarajonani College of Nursing, Chonburi. All subjects completed the questionnaire about participation in the activities at beginning and at the end of the program. Data were analyzed using descriptive statistics and dependent t-test. The results showed that the posttest scores of all four aspects mean were significantly higher than the pretest scores (t=3.30, p<.01). Three aspects had high mean scores, Benefits (Mean = 3.24, S.D. = 0.83), Decision-making (Mean = 3.21, S.D. = 0.59), and Implementation (Mean=3.06, S.D.=0.52). However, scores on evaluation falls in moderate scale (Mean = 2.68, S.D. = 1.13). Therefore, the Leadership Promoting Program based on Self-Directed Learning Approach could be a method to improve students’ participation in co-curricular activities and leadership.

Keywords: participation in co-curricular activities, undergraduate nursing students, leadership promoting program, self-directed learning

Procedia PDF Downloads 339

Authors: Fiasal Assiri, Joanna Wincenciak, David Morrison-Love

Abstract:

With the rapid spread of the COVID-19 pandemic, the Saudi government suspended students from going to school to combat the outbreak. As e-learning was not applied at all in schools, online teaching and learning have been revived in Saudi Arabia by providing a new platform called ‘Madrasati.’ Several studies have used the Decomposed Theory of Planned Behaviour (DTPB)to examineindividuals’ intention behavior in many fields. However, there is a lack of studies investigating the determinants of teachers’ continued intention touseMadrasati platform. The purpose of this paper is to present a conceptual model in light of DTPB. To enhance the predictability of the model, the study incorporates other variables, including learning content quality and interactivity as sub-factors under the perceived usefulness, students and government influences under the subjective norms, and technical support and prior e-learning experience under the perceived behavioral control. The model will be further validated using a mixed methods approach. Such findings would help administrators and stakeholders to understand teachers’ needs and develop new methods that might encourage teachers to continue using Madrasati effectively in their teaching.

Keywords: madrasati, decomposed theory of planned behaviour, continuance intention, attitude, subjective norms, perceived behavioural control

Procedia PDF Downloads 86

Authors: Elham Sharifineyestani, Mohammad Farshchin

Abstract:

Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

Procedia PDF Downloads 231

Authors: Carla Michelle Gastelum Knight

Abstract:

In an era where teacher educators and student teachers have almost unrestricted access to all kinds of sources through the internet, a research project carried out with a group of student-teachers has revealed how self-made videos are an exciting new way to motivate and engage students. The project was carried out at Universidad de Sonora, a public university in Northern Mexico, where 39 students of the Bachelor in Arts in English Language Teaching (B.A. in ELT) programme participated creating their own videos. In the process, they worked collaboratively, they exploited their creativity, they were highly motivated and showed more interest in the subject. The videos were shared in a private YouTube channel where students had the opportunity to review their peers’ work and where videos are available at any time for later viewing. This experience has led course instructor to face the challenge of planning and designing meaningful tasks that can and to find ways of exploiting the use of these resources for learning and training purposes.

Keywords: self-made materials, student-teachers, teacher education programme, teacher training

Procedia PDF Downloads 211