Search results for: meaningful learning

Authors: Hossam Ali-Hassan, Michael Bliemel

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This poster reflects upon our experiences using INNOV8, IBM’s Business Process Management (BPM) simulation game, in online MBA and undergraduate MIS classes over a period of 2 years. The game is designed to gives both business and information technology players a better understanding of how effective BPM impacts an entire business ecosystem. The game includes three different scenarios: Smarter Traffic, which is used to evaluate existing traffic patterns and re-route traffic based on incoming metrics; Smarter Customer Service where players develop more efficient ways to respond to customers in a call centre environment; and Smarter Supply Chains where players balance supply and demand and reduce environmental impact in a traditional supply chain model. We use the game as an experiential learning tool, where students have to act as managers making real time changes to business processes to meet changing business demands and environments. The students learn how information technology (IT) and information systems (IS) can be used to intelligently solve different problems and how computer simulations can be used to test different scenarios or models based on business decisions without having to actually make the potentially costly and/or disruptive changes to business processes. Moreover, when students play the three different scenarios, they quickly see how practical process improvements can help meet profitability, customer satisfaction and environmental goals while addressing real problems faced by municipalities and businesses today. After spending approximately two hours in the game, students reflect on their experience from it to apply several BPM principles that were presented in their textbook through the use of a structured set of assignment questions. For each final scenario students submit a screenshot of their solution followed by one paragraph explaining what criteria you were trying to optimize, and why they picked their input variables. In this poster we outline the course and the module’s learning objectives where we used the game to place this into context. We illustrate key features of the INNOV8 Simulation Game, and describe how we used them to reinforce theoretical concepts. The poster will also illustrate examples from the simulation, assignment, and learning outcomes.

Keywords: experiential learning, business process management, BPM, INNOV8, simulation, game

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Authors: K. R. Parvathy, R. Rao Bhavani , M. L. McLain, Kamal Bijlani, R. Jayakrishnan

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Augmented Reality (AR) technology is considered to be an important emerging technology used in education today. One potentially key use of AR in education is to teach socio-scientific issues (SSI), topics that inure students towards social conscience and critical thinking. This work uses multiple markers and virtual buttons that interact with each other, creating a life-like visual spectacle. Learning about issues such as global warming by using AR technology, students will have an increased sense of experiencing immersion, immediacy, and presence, thereby enhancing their learning as well as likely improving their ability to make better informed decisions about considerations of such issues. Another advantage of AR is that it is a low cost technology, making it advantageous for educators to adapt to their classrooms. Also in this work we compare the effectiveness of AR versus ordinary video by polling a group of students to assess the content understandability, effectiveness and interaction of both the delivery methods.

Keywords: augmented reality, global warming, multiple markers, virtual buttons

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Authors: Tukur Husaini Nahuche

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The parent resources play a vital role in the life of the offspring. It help give children basic necessities of life like food, clothing, and housing. In a like manner financial assets allow parents to move into neighborhood with more affluent school systems, to pay school bills, purchase expensive technologies like personal computer, save money for tutoring books, magazines, journals, Newspapers etc. Making of proper provision in the home environment conducive for learning after school hours and creation of other outdoor activities for them are what necessitate in enhancing and accelerating children’s learning opportunities. Indeed, this paper intends to discuss parental investment in education, parent income resources, parental education, occupation, and income as relatively influencing children’s access to quality education. With the hope that families would provide equal opportunities for children irrespective of their sex, intelligence, subject choice,etc.

Keywords: parental investment, children's access, quality education

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Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: Bhaveek Maini, Sanjay Dhanka, Surita Maini

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Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively.

Keywords: epileptic seizure recognition, logistic regression, Naïve Bayes, machine learning

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Authors: Devendra Kr Tayal, Vrinda Gupta, Aastha Bansal, Khushi Singh, Sristi Sharma, Hunny Gaur

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In today's world, suicide is a serious problem. In order to save lives, early suicide attempt detection and prevention should be addressed. A good number of at-risk people utilize social media platforms to talk about their issues or find knowledge on related chores. Twitter and Reddit are two of the most common platforms that are used for expressing oneself. Extensive research has already been done in this field. Through supervised classification techniques like Nave Bayes, Bernoulli Nave Bayes, and Multiple Layer Perceptron on a Reddit dataset, we demonstrate the early recognition of suicidal ideation. We also performed comparative analysis on these approaches and used accuracy, recall score, F1 score, and precision score for analysis.

Keywords: machine learning, suicide ideation detection, supervised classification, natural language processing

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Authors: Khaled Hamdan, Abid Amorri, Fatima Hamdan

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Involving students in English language learning process and achieving an adequate English language proficiency in the target language can be a great challenge for both teachers and students. This can prove even a far greater challenge to engage students with special needs (Dyslexia) if they have physical impairment and inadequate mastery of basic communicative language competence/proficiency in the target language. From this perspective, technology like robots can probably be used to enhance learning process for the special needs students who have extensive communication needs, who face continuous struggle to interact with their peers and teachers and meet academic requirements. Robots, precisely NAO, can probably provide them with the perfect opportunity to practice social and communication skills, and meet their English academic requirements. This research paper aims to identify to what extent robots can be used to improve students’ social interaction and communication skills and to understand the potential for robotics-based education in motivating and engaging UAEU dyslexic students to meet university requirements. To reach this end, the paper will explore several factors that come into play – Motion Level-involving cognitive activities, Interaction Level-involving language processing, Behavior Level -establishing a close relationship with the robot and Appraisal Level- focusing on dyslexia students’ achievement in the target language.

Keywords: dyslexia, robot technology, motion, interaction, behavior and appraisal levels, social and communication skills

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Authors: Raneem Osama Salem, Ayesha Nuzhat, Fatimah Nasser Al Shehri, Nasser Al Hamdan

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Background: Recently, most medical schools around the globe are using simulation for teaching and assessing students’ clinical skills and competence. There are many obstacles that could face students and faculty when simulation sessions are introduced into undergraduate curriculum. Objective: The aim of this study is to obtain the opinion of undergraduate medical students and our faculty regarding the role of simulation in undergraduate curriculum, the simulation modalities used, and perceived barriers in implementing stimulation sessions. Methods: To address the role of simulation, modalities used, and perceived challenges to implementation of simulation sessions, a self-administered pilot tested questionnaire with 18 items using a 5 point Likert scale was distributed. Participants included undergraduate male medical students (n=125) and female students (n=70) as well as the faculty members (n=14). Result: Various learning outcomes are achieved and improved through the technology enhanced simulation sessions such as communication skills, diagnostic skills, procedural skills, self-confidence, and integration of basic and clinical sciences. The use of high fidelity simulators, simulated patients and task trainers was more desirable by our students and faculty for teaching and learning as well as an evaluation tool. According to most of the students,' institutional support in terms of resources, staff and duration of sessions was adequate. However, motivation to participate in the sessions and provision of adequate feedback by the staff was a constraint. Conclusion: The use of simulation laboratory is of great benefit to the students and a great teaching tool for the staff to ensure students learning of the various skills.

Keywords: simulators, medical students, skills, simulated patients, performance, challenges, skill laboratory

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Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

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Authors: Akshat Mathur, Sunil Kumar Khatri

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This paper presents brief about how the use of Artificial intelligence in respect to GUI testing can reduce workload by using DL-fueled method. This paper also discusses about how graphical user interface and event driven software testing can derive benefits from the use of AI techniques. The use of AI techniques not only reduces the task and work load but also helps in getting better output than manual testing. Although results are same, but the use of Artifical intelligence techniques for GUI testing has proven to provide ideal results. DL-fueled framework helped us to find imperfections of the entire webpage and provides test failure result in a score format between 0 and 1which signifies that are test meets it quality criteria or not. This paper proposes DL-fueled method which helps us to find the genuine GUI bugs and defects and also helped us to scale the existing labour-intensive and skill-intensive methodologies.

Keywords: graphical user interface, GUI, artificial intelligence, deep learning, ML technology

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Ugabi John Ibak, Odey Boniface Ugbem

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Education is considered the bedrock of any meaningful developments and teacher education plays a critical role in this direction. Teacher education is the master keys that can alleviate poverty, promote peace, conserve the environment, improve the quality of life for all and help achieve all round sustain enable development in Nigeria and the world over. This paper X-rays the nature and character of the teaching profession, historical background to teacher education in Nigeria, national policy on education, problems of teacher education in Nigeria and prospects of teacher education for sustainable national development. The study shows that the misfortunes of the teacher education owes much to it historical antecedent. Also majorly, is the failure of government to adequately fund education at the various levels in the country. It was discovered that in the history of the nation no government has budgeted 13% of its annual budget (half of 26% UNESCO minimum) to education. This has resulted to poor infrastructure, inadequate equipment and poorly motivated personnel in all the nations public schools at all levels. Hence, the paper concludes that in spite of these overwhelming challenges, teachers have a lot of prospects both in the teaching profession and outside teaching.

Keywords: teacher education, teaching profession, sustainable national development, education, development

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Authors: Ayala Kobo-Greenhut, Amos Notea, Izhar Ben-Shlomo

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In R&D projects, there is no doubt about the need to change a current concept to an alternative one over time (i.e., concept leaping). Concept leaping is required since with most R&D projects uncertainty is present as they take place in dynamic environments. Despite the importance of concept leaping when needed, R&D teams may fail to do so (i.e., frozen concept). This research suggests a possible reason why frozen concept happens in the framework of quality engineering and control engineering. We suggest that frozen concept occurs since concept determines the derived plan and its implementation may be considered as equivalent to a closed-loop process, and is subject to the problem of not recognizing gaps as failures. We suggest that although implementing quality practices into an R&D project’s routine has many advantages, it intensifies the frozen concept problem since working according to quality practices relates to exploitation of learning behavior, while leaping to a new concept relates to exploring learning behavior.

Keywords: closed loop, control engineering, design, leaping, frozen concept, quality engineering, quality practices

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Authors: Mateusz Paszko, Konrad Pietrykowski, Krzysztof Skiba

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Due to low-altitude and relatively low flight speed, today’s combat assets like missile weapons equipped with infrared guidance systems are one of the most important threats to the helicopters performing combat missions. Especially meaningful in helicopter aviation is infrared emission by exhaust gases, regressed to the surroundings. Due to high temperature, exhaust gases are a major factor in detectability of a helicopter performing air combat operations. This study presents the results of simulating the flow of the mixture of exhaust and air in the flow duct of an injective exhaust cooler, adapted to cooperate with the PZL 10W turbine engine. The simulation was performed using a numerical model and the ANSYS Fluent software. Simulation computations were conducted for set flight conditions of the PZL W-3 Falcon helicopter. The conclusions resulting from the conducted numerical computations should allow for optimisation of the flow duct geometry in the cooler, in order to achieve the greatest possible temperature reduction of exhaust exiting into the surroundings. It is expected that the obtained results should be useful for further works related to the development of the final version of exhaust cooler for the PZL W-3 Falcon helicopter.

Keywords: exhaust cooler, helicopter, numerical simulation, stealth

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Authors: Lizhu Y. Davis, Gretchen Higginbottom, Vang Vang

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This presentation explores the adoption of library resources to engage students in a Visual Merchandising course during the 2016 spring semester. This study was a cross-disciplinary collaboration between the Fashion Merchandising Program and the Madden Library at California State University, Fresno. The goal of the project was to explore and assess the students’ use of library resources as a part of the Affordable Learning Solutions Initiative, a California State University (CSU) Office of the Chancellor Program that enables faculty to choose and provide high-quality, free or low-cost educational materials for their students. Students were interviewed afterwards and the results were generally favorable and provided insight into how students perceive and use library resources to support their research needs. This study reveals an important step in examining how open educational resources impact student learning.

Keywords: collaboration, library resources, open educational resources, visual merchandising

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Nkeiruka Queendarline Nwaizugbu

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The use of social media and mobile devices has become acceptable in virtually all areas of today’s world. Hence, this study is a survey that was carried out to find out if students and lecturers in public universities in Rivers State use social networking for educational purposes. The sample of the study comprised of 240 students and 99 lecturers from the University of Port Harcourt and the Rivers State University of science and Technology. The study had five research questions, two hypotheses and the instrument for data collection was a 4-point Likert-type rating scale questionnaire. The data was analysed using mean, standard deviation and z-test. The findings gotten from the analysed data shows that students participate in social networking using different types of web applications but they hardly use them for educational purposes. Some recommendations were also made.

Keywords: internet access, mobile learning, participation, social media, social networking, technology

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Shweta Jha, Nandita Chaube

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The concept of self has a major role to play in the study of personality which drives the current study in its present form. This is a case of Miss S, a 17-year-old Hindu, currently in eleventh standard, with no family history of mental illness but with a past history of inability to manage relationships, multiple emotional and sexual relationships, repeated self harming behaviour, and sexual abuse over a period of 2 months at the age of 10 years. She comes with a psychiatric history of one episode of dissociative fall followed by a stressful event which left the patient with many psychological disturbances matching the criterion of Cluster B and C traits. Current episode precipitated due to the relationship failure, predisposing factor is her personality traits, and poor social and family support. Considering the patient’s aspiration for positivity and demand of the therapy, ventilation sessions were carried out which made her capable of understanding and dealing with her negative emotions, also strengthened mother child bond, helped her maintain meaningful and healthy relationships, also helped her increase her problem solving ability and adaptive coping skills making her feel more positive and acceptable towards herself, family members and others.

Keywords: cluster B and C traits, personality, therapy, self

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Authors: Dawn Tsang

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This paper aims at synthesizing the difficulties in cognitive processes faced by translation majors in mainland China. The purpose is to develop possible solutions and innovation in terms of translation pedagogy, curriculum reform, and syllabus design. This research will base its analysis on students’ instant feedback and interview after training in translation and interpreting courses, and translation faculty’s teaching experiences. This research will take our translation majors as the starting point, who will be one of the focus groups. At present, our Applied Translation Studies Programme is offering translation courses in the following areas: practical translation and interpreting, translation theories, culture and translation, and internship. It is a four-year translation programme, and our students would start their introductory courses since Semester 1 of Year 1. The medium of instruction of our College is solely in English. In general, our students’ competency in English is strong. Yet in translation and especially interpreting classes, no matter it is students’ first attempt or students who have taken university English courses, students find class practices very challenging, if not mission impossible. Their biggest learning problem seems to be weakening cognitive processes in terms of lack of intercultural competence, incomprehension of English language and foreign cultures, inadequate aptitude and slow reaction, and inapt to utilize one’s vocabulary bank etc. This being so, the research questions include: (1) What specific and common cognitive difficulties are students facing while learning translation and interpreting? (2) How to deal with such difficulties, and what implications can be drawn on curriculum reform and syllabus design in translation? (3) How significant should cognitive study be placed on translation curriculum, i.e., the proportion of cognitive study in translation/interpreting courses and in translation major curriculum? and (4) What can we as translation educators do to maximize teaching and learning effectiveness by incorporating the latest development of cognitive study?. We have collected translation students’ instant feedback and conduct interviews with both students and teaching staff, in order to draw parallels as well as distinguishing from our own current teaching practices at United International College (UIC). We have collected 500 questionnaires for now. The main learning difficulties include: poor vocabulary bank, lack of listening and reading comprehension skills in terms of not fully understanding the subtext, aptitude in translation and interpreting etc. This being so, we propose to reform and revitalize translation curriculum and syllabi to address to these difficulties. The aim is to maximize teaching effectiveness in translation by addressing the above-mentioned questions with a special focus on cognitive difficulties faced by translation majors.

Keywords: cognitive difficulties, teaching and learning effectiveness, translation curriculum reform, translation pedagogy

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Authors: Shieh Shiow-fang

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Superstitious thoughts or actions often occur during athletic competitions. Often "superstitious rituals" have a positive impact on the performance of competitive athletes. Athletes affirm the many psychological benefits of religious beliefs mostly in a positive way. Method: By snowball sampling, we recruited 10 experienced competitive athletes as participants. We used in-person and online one-to-one in-depth interview to collect their experiences about sport superstition. The total interview time was 795 minutes. We analyzed the raw data with the grounded theory processes suggested by Strauss and Corbin (1990). Results: The factors affecting athlete performance are ritual beliefs, taboo awareness, learning norms, and spontaneous attribution behaviors. Conclusion: We concluded that sports superstition reflects several psychological implications. The analysis results of this paper can provide another research perspective for the future study of sports superstition behavior.

Keywords: superstition, taboo awareness, learning norms, competitive athlete

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Authors: Edgar Mauricio Ruiz Osuna, Claudia Yaneth Herrera Bolivar, Sandra Liliana Gomez Vasquez

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The increasing needs of professionals with skills in software development in industry are incremental, therefore, the relevance of an educational process in line with the strengthening of these competencies, are part of the responsibilities of universities with careers related to the area of Informatics and Systems. In this sense, it is important to consider that in the National Science, Technology and Innovation Plan for the development of the Electronics, Information Technologies and Communications (2013) sectors, it is established as a weakness in the SWOT Analysis of the Software sector and Services, Deficiencies in training and professional training. Accordingly, UNIMINUTO's Computer Technology Program has addressed the analysis of students' performance in software development, identifying various problems such as dropout in programming subjects, academic averages, as well as deficiencies in strategies and competencies developed in the area of programming. As a result of this analysis, it was determined to design a collaborative learning platform in basic programming using heat maps as a tool to support didactic feedback. The pilot phase allows to evaluate in a programming course the ALGORINFO platform as a didactic resource, through an interactive and collaborative environment where students can develop basic programming practices and in turn, are fed back through the analysis of time patterns and difficulties frequent in certain segments or program cycles, by means of heat maps. The result allows the teacher to have tools to reinforce and advise critical points generated on the map, so that students and graduates improve their skills as software developers.

Keywords: collaborative platform, learning, feedback, programming, heat maps

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Authors: Melissa C. LaDuke

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The overarching purpose of this study was to determine the relationship between the current format of online delivery for Defense Acquisition University (DAU) courses and Air Force Acquisition (AFA) personnel participation. AFA personnel (hereafter named “student”) were particularly of interest, as they have been mandated to take anywhere from 3 to 30 online courses to earn various DAU specialization certifications. Participants in this qualitative case study were AFA personnel who pursued DAU certifications in science and technology management, program/contract management, and other related fields. Air Force personnel were interviewed about their experiences with online courses. The data gathered were analyzed and grouped into 12 major themes. The themes tied into the theoretical framework and spoke to either teacher-centered or student-centered educational practices within Defense Acquisitions University. Based on the results of the data analysis, various factors contributed to student perceptions of DAU courses, including the online course construct and relevance to their job. The analysis also found students want to learn the information presented but would like to be able to apply the information learned in meaningful ways.

Keywords: educational theory, computer-based training, interview, student perceptions, online course design, teacher positionality

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Authors: Laabassi Ayache, Boudehane Asma

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Macrophyte pond has developed strongly in the field of wastewater treatment for irrigation in rural areas and small communities. Their association allows, in some cases, to increase the hydraulic capacity while maintaining the highest level of quality. The present work is devoted to the treatment of domestic wastewater under climatic conditions of Algeria (semi-arid) through a system using two tanks planted with Salvinia natans. The performance study and treatment efficiency of the system overall shows that the latter provides a significant removal of nitrogen pollution: total Kjeldahl nitrogen NTK (85.2%), Ammonium NH₄⁺-N (79%), Nitrite NO₂⁻-N (40%) also, a major meaningful reduction of biochemical oxygen demand BOD₅ was observed at the output of the system (96.9 %). As BOD₅, the chemical oxygen demand (COD) removal was higher than 95% at the exit of the two tanks. A moderately low yield of phosphate-phosphorus (PO₄³-P) was achieved with values not exceeding 37%. In general, the quality of treated effluent meets the Algerian standard of discharge and which allows us to select a suitable species in constructed wetland treatment systems under semi-arid climate.

Keywords: nutrient removal, Salvinia natans, semi-arid climate, wastewater treatment

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Authors: Abdulkabir Abdullahi

Abstract:

The paper is a pre-use evaluation of grammar activities in three primary English course books (two of which are international primary English course books and the other a popular Nigerian primary English course book). The titles are - Cambridge Global English, Collins International Primary English, and Nigeria Primary English – Primary English. Grammar points and grammar activities in the three-course books were identified, grouped, and evaluated. The grammar activity which was most common in the course books, simple past tense, was chosen for evaluation, and the units which present simple past tense activities were selected to evaluate the extent to which the treatment of simple past tense in each of the course books help the young learners of English as a second language in Nigeria, aged 8 – 11, level A1 to A2, who lack the basic grammatical knowledge, to know grammar/communicate effectively. A bespoke checklist was devised, through the modification of existing checklists for the purpose of the evaluation, to evaluate the extent to which the grammar activities promote the communicative effectiveness of Nigerian learners of English as a second language. The results of the evaluation and the analysis of the data reveal that the treatment of grammar, especially the treatment of the simple past tense, is evidently insufficient. While Cambridge Global English’s, and Collins International Primary English’s treatment of grammar, the simple past tense, is underpinned by state-of-the-art theories of learning, language learning theories, second language learning principles, second language curriculum-syllabus design principles, grammar learning and teaching theories, the grammar load is insignificantly low, and the grammar tasks do not promote creative grammar practice sufficiently. Nigeria Primary English – Primary English, on the other hand, treats grammar, the simple past tense, in the old-fashioned direct way. The book does not favour the communicative language teaching approach; no opportunity for learners to notice and discover grammar rules for themselves, and the book lacks the potency to promote creative grammar practice. The research and its findings, therefore, underscore the need to improve grammar contents and increase grammar activity types which engage learners effectively and promote sufficient creative grammar practice in EFL and ESL material design and development.

Keywords: evaluation, activity, second language, activity-types, creative grammar practice

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Authors: Eric Ho

Abstract:

Pedagogical approaches in Asia nowadays are imported from the West. In Confucian Heritage Culture (CHC), however, there is a dichotomy between the perceived benefits of Western pedagogies and the real classroom practices in Chinese societies. The success of Hong Kong students in large-scale international assessments has proved that both the strengths of both Western pedagogies and CHC educational approaches should be integrated for the sake of the students. University students aim to equip themselves with employability skills upon graduation. Formative assessments allow students to receive detailed, positive, and timely feedback and they can identify their strengths and weaknesses before they start working. However, there remains a question of whether university year 1 students who come from an examination-driven secondary education background are ready to respond to more formative assessments. The findings show that year 1 students are less concerned about competition in the university and more open to new teaching approaches that will allow them to improve as professionals in their major study areas.

Keywords: formative assessment, higher education, learning styles, Confucian heritage cultures

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Authors: Nudrat Kamal

Abstract:

Scholarship exploring the development of the literary tradition of feminist utopias, especially in the sociopolitical context of Europe and North America, has been important and meaningful, but it is, in large part, still restricted to Euro-American texts. This paper will explore Charlotte Perkins Gilman’s feminist utopia Herland (1915) with another feminist utopia written just a decade before, but in a widely different sociopolitical context: Bengali feminist writer Rokeya Sakhawat Hossain’s English short story Sultana’s Dream (1905). The paper will argue that both texts, despite being written in such different cultural and historical contexts, utilize and subvert the public versus private/domestic sphere dichotomy and the restricted gender roles associated with each sphere in order to conceive of specific formulations of feminist modernities that reflect the sociopolitical contexts and feminist movements both writers were writing within. By bringing Gilman’s Herland into conversation with Hossain’s Sultana’s Dream, the paper hopes to illuminate the new meanings that might emerge if the scholarship on Western feminist utopias was to open itself up to the feminist utopias written elsewhere.

Keywords: comparative literature, feminist utopias, modernity, South Asian literature, utopias

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

Abstract:

Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Sakineh Abbasi Bourondaragh

Abstract:

The aim of this research is effectiveness of integrative behavioral couples therapy on the communication patterns of couples applying for divorce. We selected (N=20) reports from Tabriz Family Judicial Complex (FJC) of couples which have conflict in their marital relationships. All of reports were released during 2012. First, they were randomly divided into two experimental and control groups and all the couples were given pre-test. They participated in twelve therapy sessions. Then the experimental group was exposed to an experimental intervention, but the control group was not received experimental intervention. The subjects were treated. At the end of treatment, a post-test was performed about subjects (each of two groups).The results showed that integrative behavioral couple therapy could increase and improve communication patterns. The findings also showed that integrative behavioral couples therapy had increased mutual constructive pattern and decreased demand/withdraw pattern and mutual avoidance pattern of CPQ sub-scale. Steady change indicator showed that the difference is clinically meaningful.

Keywords: integrative behavioral couple therapy, communication patterns, cognitive sciences, Family Judicial Complex

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