Search results for: teaching and learning English

Authors: May Ziade

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Postgraduate studies in English language and literature at the University of Sydney provided opportunity for research into one of the most significant singer/songwriters of our time, Bob Dylan, and his masterpiece from the mid-1960s, ‘Desolation Row’. With a title alluding to Jack Kerouac’s Desolation Angels as well as John Steinbeck’s Cannery Row, ‘Desolation Row’ is Bob Dylan’s magnum opus. Recorded on August 4 1965, it takes pride of place as the last track on the revolutionary 1965 album of rock poetics, Highway 61 Revisited. From its inception, its epic proportions (ten long verses) and rich and baffling imagery got our attention - it amused, fascinated and beguiled. The song’s surreal and dreamlike landscape and its cast of characters, drawn from history, fiction, mythology, theology, and popular culture, lured us in and begged interpretation. What were they doing there? Where is Desolation Row? Do they want to escape from or go to ‘Desolation Row’? What was Dylan writing about and what were his influences? Through literary analysis and historical research, this paper will examine the song’s lyrics, the mid-60s context and Dylan’s vast influences to make sense, offer explanations and make connections. In particular, research findings place the Beat poets and oeuvre as a significant literary influence but it is a rich, multilayered text that straddles traditions and emerges as a paradox – a paradox that has endured and endeared itself to many. As it turns 50 this year, what better way to acknowledge this momentous occasion than at an international English language conference.

Keywords: analysis, Bob Dylan, beat context, desolation row

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Authors: Ayman M. Mansour, Bilal Hawashin, Mohammed A. Mansour

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In this paper, we propose a cooperative learning mechanism in a multi-agent intelligent system. The basic idea is that intelligent agents are capable of collaborating with one another by sharing their knowledge. The agents will start collaboration by providing their knowledge rules to the other agents. This will allow the most important and insightful detection rules produced by the most experienced agent to bubble up for the benefit of the entire agent community. The updated rules will lead to improving the agents’ decision performance. To evaluate our approach, we designed a five–agent system and implemented it using JADE and FuzzyJess software packages. The agents will work with each other to make a decision about a suspicious medical case. This system provides quick response rate and the decision is faster than the manual methods. This will save patients life.

Keywords: intelligent, multi-agent system, cooperative, fuzzy, learning

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Authors: C. W. Kan, H. F. Cheung, Y. S. Lee

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This study examines the results of colour fading of cotton fabric by plasma-induced ozone treatment, with an aim to provide learning materials for fashion designers when designing colour fading effects in fashion products. Cotton knitted fabrics were dyed with red reactive dye with a colour depth of 1.5% and were subjected to ozone generated by a commercially available plasma machine for colour fading. The plasma-induced ozone treatment was conducted with different parameters: (i) air concentration = 10%, 30%, 50% and 70%; (ii) water content in fabric = 35% and 45%, and (iii) treatment time = 10 minutes, 20 minutes and 30 minutes. Finally, the colour properties of the plasma–induced ozone treated fabric were measured by spectrophotometer under illuminant D₆₅ to obtain the CIE L*, CIE a* and CIE b* values.

Keywords: learning materials, colour fading, colour properties, fashion products

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Authors: Alica Höpken, Hergen Pargmann

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The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

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Authors: R. Bawazir, P. Jones

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Social Stories are widely used to teach social and communication skills or concepts to individuals on the autistic spectrum. This paper presents a theoretical framework for using Social Stories in conjunction with the creative arts. The paper argues that Bandura’s social learning theory can be used to explain the mechanisms behind Social Stories and the way they influence changes in response, while Gardner’s multiple intelligences theory can be used simultaneously to demonstrate the role of the creative arts in learning. By using Social Stories with the creative arts for individuals on the autistic spectrum, the aim is to meet individual needs and help individuals with autism to develop in different areas of learning and communication.

Keywords: individuals on the autistic spectrum, social stories, the creative arts, theoretical framework

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Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of the acquisition of new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used, is the analysis of the dynamics of different areas of the brain during a cognitive activity to find the relationships between the different areas analyzed in order to better understand the functioning of neural networks. Also, the latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neuro-developmental difficulties for their subsequent assessment and cure. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho pedagogical plans that allow obtaining an optimal integral development of the affected people.

Keywords: dyscalculia, neurodevelopment, evoked potentials, Learning disabilities, neural networks

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Authors: Sally A. Brown

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Over the last 30 years, researchers have developed models or frameworks to provide a more structured understanding of the reading comprehension process. Cognitive information processing models and social cognitive theories both provide frameworks to inform reading comprehension instruction. The purpose of this paper is to (a) provide an overview of the historical development of reading comprehension theory, (b) review the literature framed by cognitive information processing, social cognitive, and integrated reading comprehension theories, and (c) demonstrate how these frameworks inform instruction. As integrated models of reading can guide the interpretation of various factors related to student learning, an integrated framework designed by the researcher will be presented. Results indicated that features of cognitive processing and social cognitivism theory—represented in the integrated framework—highlight the importance of the role of the teacher. This model can aid teachers in not only improving reading comprehension instruction but in identifying areas of challenge for students.

Keywords: explicit instruction, integrated models of reading comprehension, reading comprehension, teacher’s role

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Authors: Venkata Narayana Chejarla, Nafisa Parvez Shaik, Gopi Vara Prasad Marabathula, Deva Kumar Bejjam

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Yoga is an advanced, well-recognized method with roots in Indian philosophy. Yoga benefits both the body and the psyche. Yoga is a regular exercise that helps people relax and sleep better while also enhancing their balance, endurance, and concentration. Yoga can be learned in a variety of settings, including at home with the aid of books and the internet as well as in yoga studios with the guidance of an instructor. Self-learning does not teach the proper yoga poses, and doing them without the right instruction could result in significant injuries. We developed "Online Yoga Asana Trainer using Deep Learning" so that people could practice yoga without a teacher. Our project is developed using Tensorflow, Movenet, and Keras models. The system makes use of data from Kaggle that includes 25 different yoga poses. The first part of the process involves applying the movement model for extracting the 17 key points of the body from the dataset, and the next part involves preprocessing, which includes building a pose classification model using neural networks. The system scores a 98.3% accuracy rate. The system is developed to work with live videos.

Keywords: yoga, deep learning, movenet, tensorflow, keras, CNN

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Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

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Authors: Eu Tteum Ha, Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks.

Keywords: Bayesian network, IoT, learning, situation -awareness, smart home

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Authors: Satu Lautamäki

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This paper presents findings from a multidisciplinary course (bachelor level) implemented at Seinäjoki University of Applied Sciences, Finland. The course aims to develop innovative thinking of students, by having projects given by companies, using design thinking methods as a tool for creativity and by integrating students into multidisciplinary teams working on the given projects. The course is obligatory for all first year bachelor students across four faculties (business and culture, food and agriculture, health care and social work, and technology). The course involves around 800 students and 30 pedagogical coaches, and it is implemented as an intensive one-week course each year. The paper discusses the pedagogy, structure and coordination of the course. Also, reflections on methods for the development of creative skills are given. Experts in contemporary, global context often work in teams, which consist of people who have different areas of expertise and represent various professional backgrounds. That is why there is a strong need for new training methods where multidisciplinary approach is at the heart of learning. Creative learning takes place when different parties bring information to the discussion and learn from each other. When students in different fields are looking for professional growth for themselves and take responsibility for the professional growth of other learners, they form a mutual learning relationship with each other. Multidisciplinary team members make decisions both individually and collectively, which helps them to understand and appreciate other disciplines. Our results show that creative and multidisciplinary project learning can develop diversity of knowledge and competences, for instance, students’ cultural knowledge, teamwork and innovation competences, time management and presentation skills as well as support a student’s personal development as an expert. It is highly recommended that higher education curricula should include various studies for students from different study fields to work in multidisciplinary teams.

Keywords: multidisciplinary learning, creative skills, innovative thinking, project-based learning

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Authors: Kien Tsong Chau, Zarina Samsudin, Wan Ahmad Jaafar Wan Yahaya

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The paper examined the capability of a prototype of a tangible multimedia system that was augmented with tangible objects in motivating young preschoolers in learning. Preschoolers’ learning behaviour is highly captivated and motivated by external physical stimuli. Hence, conventional multimedia which solely dependent on digital visual and auditory formats for knowledge delivery could potentially place them in inappropriate state of circumstances that are frustrating, boring, or worse, impede overall learning motivations. This paper begins by discussion with the objectives of the research, followed by research questions, hypotheses, ARCS model of motivation adopted in the process of macro-design, and the research instrumentation, Persuasive Multimedia Motivational Scale was deployed for measuring the level of motivation of subjects towards the experimental tangible multimedia. At the close, a succinct description of the findings of a relevant research is provided. In the research, a total of 248 preschoolers recruited from seven Malaysian kindergartens were examined. Analyses revealed that the tangible multimedia system improved preschoolers’ learning motivation significantly more than conventional multimedia. Overall, the findings led to the conclusion that the tangible multimedia system is a motivation conducive multimedia for preschoolers.

Keywords: tangible multimedia, preschoolers, multimedia, tangible objects

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Authors: Chen Guo, Heng Tang, Ben Niu

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Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

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Authors: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

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Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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Authors: Jinan Fiaidhi, Sabah Mohammed

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Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.

Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning

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Authors: Calassandra Nwokoro

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Introduction: The nursing profession requires a lot of commitment, effort, and time to efficiently manage patients and provide them quality healthcare services, this work load may eventually cause the nurses to become burned out and experience psychological distress. This study assessed the prevalence of burnout, risk factors, and the coping strategies among nurses working in Lagos University Teaching Hospital (LUTH), Lagos state, Nigeria. Methodology: A descriptive cross-sectional study design was conducted among 308 nurses working in LUTH. Simple random sampling was used in selection of study respondents. The questionnaire comprised three parts; the sociodemographic characteristics of the respondents, the extent of burnout syndrome using the Maslach Burnout Inventory, and the coping strategies used among the respondents using the BRIEF-COPE Inventory. Results: This study revealed relatively high levels of burnout among the nurses in LUTH with a prevalence of 16.9%, 31.2% and 20.1% for high emotional exhaustion, high depersonalization and reduced professional accomplishment respectively. It also showed that burnout was significantly associated with long working hours. Religion was found to be the most commonly used coping strategy overall, while emotional support was the most frequently used coping strategy among nurses who had burnout. Conclusion: This study has revealed a relatively high prevalence of burnout among the nurses in Lagos University Teaching Hospital. In order to minimize the negative health impacts of burnout, the government should collaborate with psychologists and psychiatrists to implement regular stress management and stress inoculation programs for nurses and other health professionals in the country.

Keywords: burnout, nurses, coping strategies, healthcare

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Authors: N. Mesatywa

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The aim of the article is to emphasize and motivate for the role of integrating cultures in institutions of learning. The article has used a literature review methodology. Findings indicate that cultures espouse immense social capital that can: facilitate and strengthen moral education that will help learners in mitigating moral decadence and HIV/AIDS; embrace and strengthen the tenets of peace and tranquility among learners from different backgrounds; can form education against xenophobia; can facilitate the process of cultural paradigm shift that will slow down cultural attrition and decadence; can bring back cultural strength, cultural revival, cultural reawakening and cultural emancipation, etc. The article recommends governments to finance cultural activities in institutions of learning; to allow cultural practitioners to be part and parcel of cultural education; and challenge people to pride in the social capital of their indigenous cultures.

Keywords: cultures, cultural practitioners, integration, traditional healers

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Jay-R M. Mendoza

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In almost all mathematics classrooms, students demonstrated discrepancies in their knowledge, skills, and understanding. OECD reports predicted that this continued to aggravate as not all teachers were sufficiently trained to handle this concentration. In response, the paper explored the potential of reSolve’s professional learning module 3 (PLM3) as an affordable and accessible professional development (PD) resource. Participants’ hands-on experience and exposure to PLM3 were audio recorded. After it was transcribed and examined and their work samples were analysed, there were four issues emerged: (1) criticality of conducting preliminary data collections and increasing the validity of inferences about what students can and cannot do by addressing the probabilistic nature of their performance; (2) criticality of the conclusion: a > b and/or (a-b) ∈ Z⁺ among students’ algebraic reasoning; (3) enabling and extending prompts provided by reSolve were found useful; and (4) dynamic adaptation of reSolve PLM3 through developing transferable skills and collaboration among teachers. PLM3 provided valuable insights on assessment, teaching, and planning to include all students in mathematics experiences.

Keywords: algebraic reasoning, building teacher capacity, including all students in mathematics experiences, professional development

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Authors: Sonia Arradaza-Pajaron

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This study examined the relationship between transformational leadership (TL) and self-efficacy (SE) of academic heads in the implementation of a customized English language curriculum (CELC) among technological state universities and colleges in Leyte provinces and Biliran, Philippines. Results manifested that academic leaders practiced transformational leadership and are self-efficacious enough but with only moderate level in the effectiveness of CELC implementation. It was found out; further, that of the four identified transformational leadership components, except idealized influence, three of which demonstrated a significant relationship with CELC component variables, although in varying degree. Moreover, self-efficacy sources, especially vicarious experiences and verbal persuasion manifested moderate to high significant relationships with effective CELC curriculum implementation. Further, verbal persuasion and physiological/emotional condition manifested significant relationship with CELC-resource and CELC-contextual/community influence, respectively. Regression analysis showed that TL-individualized consideration component explained wider extent when correlated with CELC contextual/community components, while self-efficacy source-verbal persuasion demonstrated a wider extent with the three CELC components, namely; resource, process and physiological/emotional condition. Results further revealed that TL-individualized consideration manifested lesser influence with CELC implementation, while SE-verbal persuasion demonstrated stronger influence or effect on CELC-process, CELC-physiological/emotional, while lesser influence with CELC-resource. This implies that academic leaders, in order to carry out effective curriculum implementation, should provide more emphasis on school culture, its beliefs, practices and academic atmosphere but most of all empower human resources who are considered the backbone of the work place and can be directly affected by any curriculum shifts and challenges. To realize this, more values-skilled training programs must be designed for academic heads are needed to equip them with the necessary leadership skills, beliefs in their capacity to lead and their own enhance emotional well-being in leading subordinates and facilitating curriculum implementation.

Keywords: Customized English Language curriculum, CELC, self-efficacy, transformational leadership, values-skilled training

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Giuseppe Liverano

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Today’s school iscalledupon to take the lead role in supporting students towards the formation of conscious identity and a sense of responsible citizenship, through the development of key competencies for lifelong learning A rolethatrequiresit to be ready for change and to respond to the ever new needs of students, by adopting new pedagogical and didactic models and new didactic devices. Information and Communication Technologies, in this sense, reveal themselves to be usefulresourcesthatpermit to focus attention on the learning of eachindividualstudentunderstoodas a dynamic and relational process of constructing shared and participatedmeanings. The use of collaborative writing apps represents a democratic and shared knowledge way of constructionthroughICTs. It promotes the learning of reading-writing, literacy, and the development of transversal competencies in an inclusive perspective peer-to-peer comparison and reflectionthatstimulates the transfer of thought into speech and writing, the transformation of knowledge through a trialogicalapproach to learning generates enthusiasm and strengthensmotivationItrepresents a “different” way of expressing the training needs which come from several disciplinary fields of subjects with different cultures. The contribution aims to reflect on the formative value of collaborative writing through apps and analyse some proposals on line at school during the time of pandemic in order to highlight their critical aspects and pedagogical perspectives.

Keywords: collaborative writing, formative value, online, apps, pandemic

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Authors: Afsha Shaikh, Mohammed Azizur Rahman, Ibrahim Hassan, Mayur Pal

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Student engagement in learning is instantly affected by the sources of learning methods available for them, such as videos showing the applications of the concept or showing a practical demonstration. Specific to the engineering discipline, there exist enormous challenging concepts that can be simplified when they are connected to real-world scenarios. For this study, the concept of heat exchangers was used as it is a part of multidisciplinary engineering fields. To make the learning experience enjoyable and impactful, 3-D printed heat exchanger models were created for students to use while working on in-class activities and assignments. Students were encouraged to use the 3-D printed heat exchanger models to enhance their understanding of theoretical concepts associated with its applications. To assess the effectiveness of the method, feedback was received by students pursuing undergraduate engineering via an anonymous electronic survey. To make the feedback more realistic, unbiased, and genuine, students spent nearly two to three weeks using the models in their in-class assignments. The impact of these tools on their learning was assessed through their performance in their ungraded assignments as well as their interactive discussions with peers. ‘Having to apply the theory learned in class whilst discussing with peers on a class assignment creates a relaxed and stress-free learning environment in classrooms’; this feedback was received by more than half the students who took the survey and found 3-D models of heat exchanger very easy to use. Amongst many ways to enhance learning and make students more engaged through interactive models, this study sheds light on the importance of physical tools that help create a lasting mental representation in the minds of students. Moreover, in this technologically enhanced era, the concept of augmented reality was considered in this research. E-drawings application was recommended to enhance the vision of engineering students so they can see multiple views of the detailed 3-D models and cut through its different sides and angles to visualize it properly. E-drawings could be the next tool to implement in classrooms to enhance students’ understanding of engineering concepts.

Keywords: student engagement, life-long-learning, visual demonstration, 3-D printed models, engineering education

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Authors: Hasan Coruk, Recep Cakir

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This study aimed at investigating the effect of multimedia containing ‘the structure and properties of matter’ unit on students’ academic achievement level and self-efficacy relating to science and technology course. The study used an experimental design with pre-test and post-test groups. The data collection tools were ‘Science and Technology Course Achievement Test’ and ‘Science and Technology Self-Efficacy Scale’. The sample of the study consisted of 8th grade students at a primary school in Tokat Province. The study was carried out with 42 students from two classes, 21 (8 males, 13 females) from experimental group and 21 (13 males and 8 females) from control group. The data were analyzed in SPSS.18 software. The findings of the study indicated that the use of multimedia increased the students’ academic achievement in science and technology course in comparison with traditional teaching methods. It was also determined that there was not a significant difference in students’ course-oriented self-efficacy levels regarding the two methods. Necessary and feasible suggestions were put forward for whom it concerns.

Keywords: multimedia learning, science and technology, the structure-properties of matter, self-efficacy, academic achievement

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Authors: Patrick Ebong Ebewo

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The trend of high unemployment levels globally is a growing concern, which suggests that university students especially those studying the creative industries are most likely to face unemployment upon completion of their studies. Therefore the effort of university in fostering entrepreneurial knowledge is equally important to the development of student’s soft skill. The purpose of this paper is to assess the significance of perceived university environment and perceived educational support that influencing University students’ intention in starting their own business in the future. Thus, attempting to answer the question 'How does perceived university environment affect students’ attitude towards entrepreneurship as a career option, perceived entrepreneurial abilities, subjective norm and entrepreneurial intentions?' The study is based on the Theory of Planned Behaviour model adapted from previous studies and empirically tested on graduates at the Tshwane University of Technology. A sample of 150 graduates from the Arts and Design graduates took part in the study and data collected were analysed using structural equation modelling (SEM). Our findings seem to suggest the indirect impact of perceived university environment on entrepreneurial intention through perceived environment support and perceived entrepreneurial abilities. Thus, any increase in perceived university environment might influence students to become entrepreneurs. Based on these results, it is recommended that: (a) Tshwane University of Technology and other universities of technology should establish an ‘Entrepreneurship Internship Programme’ as a tool for stimulated work integrated learning. Post-graduation intervention could be implemented by the development of a ‘Graduate Entrepreneurship Program’ which should be embedded in the Bachelor of Technology (B-Tech now Advance Diploma) and Postgraduate courses; (b) Policymakers should consider the development of a coherent national policy framework that addresses entrepreneurship for the Arts/creative industries sector. This would create the enabling environment for the evolution of Higher Education Institutions from merely Teaching, Learning & Research to becoming drivers for creative entrepreneurship.

Keywords: business venture, entrepreneurship education, entrepreneurial intent, university environment

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Authors: Luqman Shah

Abstract:

The myopic gender discrimination is now a well-documented and recognized fact. However, gender is only one facet of an individual’s multiple identities. The aim of this work is to investigate gender discrimination highlighted in print media in the subcontinent with a specific focus on Pakistan and India. In this study, an approach is adopted by using the James Paul Gee model for the identification of gender discrimination. As a matter of fact, gender discrimination is not consistent in its nature and intensity across global societies and varies as social, geographical, and cultural background change. The World has been changed enormously in every aspect of life, and there are also obvious changes towards gender discrimination, prejudices, and biases, but still, the world has a long way to go to recognize women as equal as men in every sphere of life. The history of the world is full of gender-based incidents and violence. Now the time came that this issue must be seriously addressed and to eradicate this evil, which will lead to harmonize society and consequently heading towards peace and prosperity. The study was carried out by a mixed model research method. The data was extracted from the contents of five Pakistani English newspapers out of a total of 23 daily English newspapers, and likewise, five Indian daily English newspapers out of 52 those were published 2018-2019. Two news stories from each of these newspapers, in total, twenty news stories were taken as sampling for this research. Content and semiotic analysis techniques were used to analyze through James Paul Gee's seven building tasks of language. The resources of renowned e-papers are utilized, and the highlighted cases in Pakistani newspapers of Indian gender-based stories and vice versa are scrutinized as per the requirement of this research paper. For analysis of the written stretches of discourse taken from e-papers and processing of data for the focused problem, James Paul Gee 'Seven Building Tasks of Language' is used. Tabulation of findings is carried to pinpoint the issue with certainty. Findings after processing the data showed that there is a gross human rights violation on the basis of gender discrimination. The print media needs a more realistic representation of what is what not what seems to be. The study recommends the equality and parity of genders.

Keywords: gender discrimination, print media, Paul Gee model, subcontinent

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

Abstract:

Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

Abstract:

In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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Authors: Shahad Alkhalifah, Jonh Wright

Abstract:

Autism spectrum disorder (ASD) is a pervasive developmental disorder associated, for 42% to 88% of people with ASD, with sensory processing disorders. Sensory processing disorders (SPD) impact daily functioning, and it is, therefore, essential to be able to diagnose them accurately. Currently, however, there is no assessment tool available for the Saudi Arabia (SA) population that would cover a wider enough age range. Therefore, this study aimed to assess the psychometric properties of the Sensory Processing Measure Preschool-Home Form (SPM-P) when used in English, with a population of English-speaking Saudi participants. This was chosen due to time limitations and the urgency in providing practitioners with appropriate tools. Using a convenience sampling approach group of caregivers of typically developing (TD) children and a group of caregivers for children with ASD were recruited (N = 40 and N = 16, respectively), and completed the SPM-P Home Form. Participants were also invited to complete it again after two weeks for test-retest reliability, and respectively, nine and five agreed. Reliability analyses suggested some issues with a few items when used in the Saudi culture, and, along with interscale correlations, it highlighted concerns with the factor structure. However, it was also found that the SPM-P Home has good criterion-based validity, and it is, therefore, suggested that it can be used until a tool is developed through translation and cultural adaptation. It is also suggested that the current factor structure of SPM-P Home is reassessed using a large sample.

Keywords: autism, sensory, assessment, reliability, sensory processing dysfunction, preschool, validity

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Authors: Muhammad Anwar

Abstract:

The prime objective of the study is to investigate the research productivity of the PAKISTAN STUDIES Bilingual / Bi-annual Pakistan Studies, English / Urdu Research Journal from 2015 to 2020. The present study also finds the frequency of publications, author contributions; paper length, references and most productive authors and degree of collaboration also have been checked. The current study finds 271 research articles have been contributed by faculty members of university of Balochistan, Quetta. The highest number of papers has been published 75(27.67%) in 2020 and 59(21.77%) papers were published in 2019. The current study finds the vol.10 and vol.11 were Contributed 36(13.28%) and 45(16.00%) research articles respectively. This present study recognizes those 179(66.05%) two authors and 62(22.87%) authors were counted in three. The results revealed the degree of collaboration was 0.97. The study further discloses the length of the paper where the majority of the 122(45.07%) papers were range of 11-15 and 73(26.93%) articles were range of 6-10. The utmost prolfic author was Dr.Noor Ahmed from the Pakistan study center with 15 papers ranked 1st and Dr.Kaleem Bareach contributed 14 articles ranked 2nd.

Keywords: research, bibliometric, bilingual, bi-annual, Pakistan, university, Balochistan

Procedia PDF Downloads 89