Search results for: adaptive educational digital learning environments

Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

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Authors: Eda Ozkara San

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Nurse educators have been charged by several nursing organizations and accrediting bodies to provide innovative and evidence-based educational experiences, both didactic and clinical, to help students to develop the knowledge, skills, and attitudes needed to provide culturally competent nursing care to patients. Clinical simulation, which offers the opportunity for students to practice nursing skills in a risk-free, controlled environment and helps develop self-efficacy (confidence) within the nursing role. As one simulation method, the standardized patients (SPs) simulation helps educators to teach nursing students variety of skills in nursing, medicine, and other health professions. It can be a helpful tool for nurse educators to enhance cultural competence of nursing students. An alarming gap exists within the literature concerning the effectiveness of SP strategy to enhance cultural competence development of diverse student groups, who must work with patients from various backgrounds. This grant-supported, longitudinal, one-group, pretest and post-test educational intervention study aimed to examine the effect of the Diverse Standardized Patient Simulation (DSPS) cultural competence education strategy on students’ (n = 53) transcultural self-efficacy (TSE). The researcher-developed multidimensional DSPS strategy involved careful integration of transcultural nursing skills guided by the Cultural Competence and Confidence (CCC) model. As a carefully orchestrated teaching and learning strategy by specifically utilizing the SP pedagogy, the DSPS also followed international guidelines and standards for the design, implementation, evaluation, and SP training; and had content validity review. The DSPS strategy involved two simulation scenarios targeting underrepresented patient populations (Muslim immigrant woman with limited English proficiency and Irish-Italian American gay man with his partner (Puerto Rican) to be utilized in a second-semester, nine-credit, 15-week medical-surgical nursing course at an urban public US university. Five doctorally prepared content experts reviewed the DSPS strategy for content validity. The item-level content validity index (I-CVI) score was calculated between .80-1.0 on the evaluation forms. Jeffreys’ Transcultural Self-Efficacy Tool (TSET) was administered as a pretest and post-test to assess students’ changes in cognitive, practical, and affective dimensions of TSE. Results gained from this study support that the DSPS cultural competence education strategy assisted students to develop cultural competence and caused statistically significant changes (increase) in students’ TSE perceptions. Results also supported that all students, regardless of their background, benefit (and require) well designed cultural competence education strategies. The multidimensional DSPS strategy is found to be an effective way to foster nursing students’ cultural competence development. Step-by-step description of the DSPS provides an easy adaptation of this strategy with different student populations and settings.

Keywords: cultural competence development, the cultural competence and confidence model, CCC model, educational intervention, transcultural self-efficacy, TSE, transcultural self-efficacy tool, TSET

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Authors: Muhammed Kök & Adem Kantar

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Learning a foreign language can be affected by various factors such as age, aptitude, motivation, L2 disposition, etc. Among these factors, masculine gender roles stress (MGRS) that male learners possess is the least touched area that has been examined so far.MGRS can be defined as the traditional male role stress when the male learners feel the masculinity threat against their traditionally adopted masculinity norms. Traditional masculine norms include toughness, accuracy, completeness, and faultlessness. From this perspective, these norms are diametrically opposed to the language learning process since learning a language, by its nature, involves stages such as making mistakes and errors, not recalling words, pronouncing sounds incorrectly, creating wrong sentences, etc. Considering the potential impact of MGRS on the language learning process, the main purpose of this study is to investigate the mediating role of MGRS on the relationship between the EFL learners’ self-disclosure and English class anxiety. Data were collected from Turkish EFL learners (N=282) who study different majors in various state universities across Turkey. Data were analyzed by means of the Bootstraping method using the SPSS Process Macro plugin. The findings show that the indirect effect of self-disclosure level on the English Class Anxiety via MGRS was significant. We conclude that one of the reasons why Turkish EFL learners have English class anxiety might be the pressure that they feel because of their traditional gender role stress.

Keywords: masculine, gender role stress, english class anxiety, self-disclosure, masculinity norms

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Authors: Henry Okechukwu Onyeiwu

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In today’s Nigerian digital age, the landscape of public diplomacy has been significantly altered by the rise of social media platforms like YouTube, Facebook, Twitter, and Instagram. In recent years, social media platforms have emerged as powerful tools for public diplomacy, transforming how countries communicate with both domestic and global audiences. International broadcasting as a tool of public diplomacy has undergone a significant transformation. Traditional methods of state-run media and controlled broadcasting have evolved to incorporate the dynamic, interactive, and decentralized nature of digital platforms. Understanding how Nigerian governments engages in international broadcasting of public diplomacy, the influence of social media on broadcasting public diplomacy, focusing on the advantages and disadvantages of controlling media outlets for diplomatic purposes and also covers the changing nature of global communication in this digital era. As countries navigate the complexities of international relations, the effectiveness of controlled media in shaping public perception and engagement raises significant questions worth exploring. The vast amount of content available can make it challenging to capture and retain audience attention. The ease of spreading false information on social media requires international broadcasters to maintain credibility and counteract misleading narratives. Addressing these challenges requires a comprehensive research that integrates digital communication tools, cultural sensitivity, cybersecurity measures and ongoing evaluation to enhance Nigeria’s international broadcasting of public diplomacy. This study employed a mixed-methods approach, combining qualitative and quantitative research methods. A content analysis of Nigeria’s international broadcasting content was conducted to assess its themes, narratives, and engagement strategies. Additionally, surveys and interviews with communications professionals, diplomats, and social media users were carried out to gather insights on perceptions and effectiveness of public diplomacy initiatives. It has highlighted some of the present trends in technology and the international environmental in which public diplomacy must work, and show how the past can illuminate the road for those navigating this new world. The rise of the social network creates more opportunities than it closes for public diplomacy. This evolution highlights the increasing importance of engagement, mutual understanding, and cooperation in international relations. By Adopting a more inclusive and participatory approach, public diplomacy can more effectively address global challenges and build stronger, more resilient relationships between nations. As Nigeria navigates the complexities of its international relations, this abstract will provide a vital examination of how it can better utilize the dual platforms of international broadcasting and social media in its public diplomacy efforts. The outcome will bear significance not only for Nigeria but also for other nations grappling with similar challenges in the digital age. As social media continues to play a crucial role in public diplomacy, understanding the dynamics of controlled media outlets becomes ever more critical. This abstract shed light on the advantages and disadvantages of such control, ultimately contributing valuable insights to practitioners in the field of diplomacy as they adapt to the rapidly changing communication landscape.

Keywords: international broadcasting, public diplomacy, social media, international relation, polities

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Authors: Swarna Pundir, Prabuddha Hans

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As AI has already stepped into the daily life of human society, one cannot be ignorant about the data it collects and used it to provide a quality of services depending up on the individuals’ choices. It also helps in giving option for making decision Vs choice selection with a calculation based on the history of our search criteria. Over the past decade or so, the way Artificial Intelligence (AI) has impacted society is undoubtedly large.AI has changed the way we shop, the way we entertain and challenge ourselves, the way information is handled, and has automated some sections of our life. We have answered as to what AI is, but not why one may see it as useful. AI is useful because it is capable of learning and predicting outcomes, using Machine Learning (ML) and Deep Learning (DL) with the help of Artificial Neural Networks (ANN). AI can also be a system that can act like humans. One of the major impacts be Joblessness through automation via AI which is seen mostly in manufacturing sectors, especially in the routine manual and blue-collar occupations and those without a college degree. It raises some serious concerns about AI in regards of less employment, ethics in making moral decisions, Individuals privacy, human judgement’s, natural emotions, biased decisions, discrimination. So, the question is if an error occurs who will be responsible, or it will be just waved off as a “Machine Error”, with no one taking the responsibility of any wrongdoing, it is essential to form some rules for using the AI where both machines and humans are involved.

Keywords: AI, ML, DL, ANN

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Authors: Zainab Algharibi, Julie McAdam, Catherine Fagan

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The purpose of this paper is to shed light on how Omani children in the first grade experience their needs during their transition to primary school. Theoretically, the paper was built on two perspectives: Dewey's concept of continuity of experience and the boundary objects introduced by Vygotsky (CHAT). The methodology of the study is based on the crucial role of children’s agency which is a very important activity as an educational tool to enhance the child’s participation in the learning process and develop their ability to face various issues in their life. Thus, the data were obtained from 45 children in grade one from 4 different primary schools using drawing and visual narrative activities, in addition to researcher observations during the start of the first weeks of the academic year for the first grade. As the study dealt with children, all of the necessary ethical laws were followed. This paper is considered original since it seeks to deal with the issue of children's transition from kindergarten to primary school in Oman, if not in the Arab region. Therefore, it is expected to fill an important gap in this field and present a proposal that will be a door for researchers to enter this research field later. The analysis of drawing and visual narrative was performed according to the social semiotics approach in two phases. The first is to read out the surface message “denotation,” while the second is to go in-depth via the symbolism obtained from children while they talked and drew letters and signs. This stage is known as “signified”; a video was recorded of each child talking about their drawing and expressing themself. Then, the data were organised and classified according to a cross-data network. Regarding the researcher observation analyses, the collected data were analysed according to the model was developed for the "grounded theory". It is based on comparing the recent data collected from observations with data previously encoded by other methods in which children were drawing alongside the visual narrative in the current study, in order to identify the similarities and differences, and also to clarify the meaning of the accessed categories and to identify sub-categories of them with a description of possible links between them. This is a kind of triangulation in data collection. The study came up with a set of findings, the most vital being that the children's greatest interest goes to their social and psychological needs, such as friends, their teacher, and playing. Also, their biggest fears are a new place, a new teacher, and not having friends, while they showed less concern for their need for educational knowledge and skills.

Keywords: children’s academic needs, children’s social needs, transition, primary school

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Authors: Abdullah Bal

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This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

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Authors: Mutiara Amalina Khairisa, Purnama Arafah, Rahayu Listiana Ramli

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In recent years, so many cases of divorce increasingly occur. Betrayal in form of infidelity, less communication one another, economically problems, selfishness of two sides, intervening parents from both sides which frequently occurs in Asia, especially in Indonesia, the differences of both principles and beliefs, “Sense of Romantism” depletion, role confict, a large difference in the purpose of marriage,and sex satisfaction are expected as the primary factors of the causes of divorce. Every couple of marriage wants to reach happy life in their family but severe problems brought about by either of those main factors come as a reasonable cause of failure marriage. The purpose of this study is to find out how marital adjustment and supporting factors in ensuring the success of that previous marital adjusment are inseparable two things assumed as a framework can affect the success in marriage becoming a resolution to reduce the desires to divorce. Those two inseparable things are able to become an aspect of learning from the success of the different ethnics marriage to keep holding on wholeness.

Keywords: marital adjustment, marital sustainability, learning process, successful ethnicity differences marriage, basical cultural values

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Authors: Yu-Chi Chou

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The purpose of this study was to establish a logical path of an instructional model of empathy and social regulation, providing feasibility evidence on the model implementation in students with autism spectrum disorder (ASD). This newly developed Emotional Bug-Out Bag (BoB) curriculum was designed to enhance the empathy and socially shared regulation of students with ASD. The BoB model encompassed three instructional phases of basic theory lessons (BTL), action plan practices (APP), and final theory practices (FTP) during implementation. Besides, a learning flow (teacher-directed instruction, student self-directed problem-solving, group-based task completion, group-based reflection) was infused into the progress of instructional phases to deliberately promote the social regulatory process in group-working activities. A total of 23 junior high school students with ASD were implemented with the BoB curriculum. To examine the logical path for model implementation, data was collected from the participating students’ self-report scores on the learning nodes and understanding questions. Path analysis using structural equation modeling (SEM) was utilized for analyzing scores on 10 learning nodes and 41 understanding questions through the three phases of the BoB model. Results showed (a) all participants progressed throughout the implementation of the BoB model, and (b) the models of learning nodes and phases were positive and significant as expected, confirming the hypothesized logic path of this curriculum.

Keywords: autism spectrum disorder, empathy, regulation, socially shared regulation

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

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Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Hinda Berghiche, Hamida Benradia, Noureddine Soltani

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Pesticides are widely used in crop production and are known to induce a major contamination of ecosystems especially in aquatic environments. The leaching of a large amount of pollutants derived from agricultural activities (fertilizers, pesticides) might contaminate rivers which diverse into the likes and estuarine and coastal environments affecting several organisms such as crustacean species. In this context, there is searched for new selective insecticides with minimal toxic effects on the environment and human health such as growth insect regulators (GIRs). The current study aimed to examine the impact of novaluron (CE 20%), a potent benzoylphenylurea derivative insecticide on mosquito larvae, against non-target shrimp, Palaemon adspersus (Decapoda, Palaemonidae). The compound was tested at two concentrations (0.91 mg/L and 4.30 mg/L) corresponding respectively to the LC50 and LC90 determined against fourth-instar larvae of Culiseta longiareolata (Diptera, Culicidae). The molting hormone titer was determined in the haemolymph by an enzyme-immunoassay, while chitin was measured in peripheral integument at different stages during the molting cycle. Under normal conditions, the haemolymphatic ecdysteroid concentrations increased during the molting cycle to reach peak at stage D. In the treated series, we note absence of the peak at stage D and an increase at stages B, C and D as compared to the controls. Concerning the chitin amounts, we observe an increase from stage A to stage C followed by a decrease at stage D. Exposition of shrimps to novaluron resulted in a significant decrease of values at all molting stages with a dose-response effect. Thus, the insecticide can present secondary effects on this non-target arthropod species.

Keywords: toxicology, novaluron, crustacean, palaemon adspersus, ecdysteroids, cuticle, chitin

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Authors: Shakirat Abimbola Adesola, Shuaib Akintunde Asifat, Jelili Olalekan Amoo

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This paper presented the impact of students’ emotions on learning when receiving lectures and when taking tests. It was observed that students experience different types of emotions during the study, and this was found to have a significant effect on their academic performance. A total of one thousand six hundred and seventy-five (1675) students from the department of Computer Science in two Colleges of Education in South-West Nigeria took part in this study. The students were randomly selected for the research. Sample comprises of 968 males representing 58%, and 707 females representing 42%. A structured questionnaire, of Motivated Strategies for Learning Questionnaire (MSLQ) was distributed to the participants to obtain their opinions. Data gathered were analyzed using the IBM SPSS 20 to obtain ANOVA, descriptive analysis, stepwise regression, and reliability tests. The results revealed that emotion moderately shape students’ motivation and engagement in learning; and that self-regulation and self-determination do have significant impact on academic performance. It was further revealed that test anxiety has a significant correlation with academic performance.

Keywords: motivation, self-determination, test anxiety, academic performance, and academic engagement

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Authors: Saugata Bose, Ritambhra Korpal

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The internet has increased the copy-paste scenarios amongst students as well as amongst researchers leading to different levels of plagiarized documents. For this reason, much of research is focused on for detecting plagiarism automatically. In this paper, an initiative is discussed where Natural Language Processing (NLP) techniques as well as supervised machine learning algorithms have been combined to detect plagiarized texts. Here, the major emphasis is on to construct a framework which detects external plagiarism from monolingual texts successfully. For successfully detecting the plagiarism, n-gram frequency comparison approach has been implemented to construct the model framework. The framework is based on 120 characteristics which have been extracted during pre-processing the documents using NLP approach. Afterwards, filter metrics has been applied to select most relevant characteristics and then supervised classification learning algorithm has been used to classify the documents in four levels of plagiarism. Confusion matrix was built to estimate the false positives and false negatives. Our plagiarism framework achieved a very high the accuracy score.

Keywords: lexical matching, shallow NLP, supervised machine learning algorithm, word n-gram

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Authors: A. Aldawsari

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The complex issue of women working in a mixed-gender work environment has its roots in social and cultural factors. This research was done to identify and explore the social and cultural challenges Saudi women face in a mixed-gender work environment in Saudi Arabia. Over the years, Saudi women in mixed-gender work environments in Saudi Arabia have been of interest in various research areas, especially within the context of a hospital work environment. This research, which involves a female researcher interacting one-on-one with Saudi women, will address this issue as well as the effect of the 2030 Vision in Saudi Arabia, and it will aim to include several new fields of work environments for women in Saudi Arabia. The aim of this research is to examine the perceptions of Saudi women who work in a mixed gender environment regarding the general empowerment of women in these settings. The objective of this research is to explore the cultural and social challenges that influence Saudi women's rights to work in a mixed-gender environment in Saudi Arabia. The significance of this research lies in the fact that there is an urgency to resolve issue of female employment in Saudi Arabia, where Saudi women still suffer from inequality in employment opportunity. Although the Saudi government is seeking to empower women by integrating them into a mixed-gender work environment, which is a key goal and prominent social change advocated for in the 2030 Vision, this same goal is one of the main challenges in the face of achieving female empowerment. The methodology section focuses on appropriate methods that can be used to study the effect of social and cultural challenges on the employment of women. It then determines the conditions and limitations of the research by applying a qualitative research approach to the investigation and analysing the data collected from the interviews. A statistical analysis tool, such as NVivo, will be used for the qualitative analysis of the interviews. The study found that the factor most responsible for creating social and cultural challenges is family—whether close family or distant family—more so than tribe or community.

Keywords: women, work, mixed-gender, environment

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

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A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Mateusz Jeżowski, Jadwiga Fila, Paweł Poszytek

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This abstract summarizes the combined findings of several analyses carried out by the authors on the barriers to accessing international education mobility programs by people with fewer opportunities, especially those with a low level of cultural and social capital. Two flagship educational mobility initiatives of the European Union – the Erasmus+ Program and the European Solidarity Corps are aimed at equipping young people and participants of all ages with the qualifications and skills needed for their meaningful participation in a democratic society intercultural understanding and successful transition in the labour market. The heart of these programs is to come closer to people with fewer opportunities, including people with disabilities, migrants, as well as those facing socio-economic difficulties and displaying a low level of social and cultural capital. Still, people who participate in such initiatives usually demonstrate higher than average cultural and social capital, as understood by Pierre Bourdieu. First of all, the educational attainment of their parents is higher than the average. Secondly, they mostly live in large agglomerations, with good access to education and culture, which affects their foreign language skills as well as social and cultural competencies. Finally, participation in Erasmus+ Program or European Solidarity Corps is not their first educational mobility experience. It is therefore justified to state that their social and cultural capital, already high before taking part in Erasmus+ and European Solidarity Corps, becomes even higher once their international mobility activities have been over, at the expense of those people with fewer opportunities, who, in theory, could participate in those initiatives, nonetheless did not, for the following reasons: lack of sufficient information on such programs, financial obstacles or unappreciation of the value of international mobility. In their work, the authors will discuss what are, in the light of Bourdieu’s perception of social and cultural capital, the main obstacles for young people to participate in international mobility programs of the European Union and will offer comprehensive solutions rooted in their vast experience in management and implementation of Erasmus+ Program and European Solidarity Corps.

Keywords: cultural capital, educational mobility, Erasmus+, European solidarity corps, inclusion, social capital

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Syamimi Shamsuddin, Hanafiah Yussof, Fazah Akhtar Hanapiah, Salina Mohamed, Nur Farah Farhan Jamil, Farhana Wan Yunus

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Robot-based intervention for children with autism is an evolving research niche in human-robot interaction (HRI). Recent studies in this area mostly covered the role of robots in the clinical and experimental setting. Our previous work had shown that interaction with a robot pose no adverse effects on the children. Also, the presence of the robot, together with specific modules of interaction was associated with less autistic behavior. Extending this impact on school-going children, interactions that are in-tune with special education lessons are needed. This methodological paper focuses on how a robot can be incorporated in a current learning environment for autistic children. Six interaction scenarios had been designed based on the existing syllabus to teach communication skills, using the Applied Behavior Analysis (ABA) technique as the framework. Development of the robotic experience in class also covers the required set-up involving participation from teachers. The actual research conduct involving autistic children, teachers and robot shall take place in the next phase.

Keywords: autism spectrum disorder, ASD, humanoid robot, communication skills, robot-assisted learning

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Authors: Virginia Guillén Cañas, Miren Agurtzane Ortiz-Jauregi, Sonia Ruiz De Azua, Naiara Ozamiz

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We want to focus on relationship of the communicational skills in several key aspects of medicine. The most relevant competencies of a health professional are an adequate communication capacity, which will influence the satisfaction of professionals and patients, therapeutic compliance, conflict prevention, clinical outcomes’ improvement and efficiency of health services. We define empathy as it as Sympathy and connection to others and capability to communicate this understanding. Some outcomes favoring empathy are female gender, younger age, and specialty choice. Third gender or third sex is a concept in which allows a person not to be categorized in a dual way but as a continuous variable, giving the choice of moving along it. This point of view recognizes three or more genders. The subject of Ethics and Clinical Communication is dedicated to sensitizing students about the importance and effectiveness of a good therapeutic relationship. We are also interested in other communicational aspects related to empathy as active listening, assertivity and basic and advanced Social Skills. Objectives: 1. To facilitate the approach of the student in the Medicine Degree to the reality of the medical profession 2. Analyze interesting outcome variables in communication 3. Interactive process to detect the areas of improvement in the learning process of the Physician throughout his professional career needs. Design: A comparative study with a cross-sectional approach was conducted in successive academic year cohorts of health professional students at a public Basque university. Four communicational aspects were evaluated through these questionnaires in Basque, Spanish and English: The active listening questionnaire, the TECA empathy questionnaire, the ACDA questionnaire and the EHS questionnaire Social Skills Scale. Types of interventions for improving skills: Interpersonal skills training intervention, Empathy intervention, Writing about experiential learning, Drama through role plays, Communicational skills training, Problem-based learning, Patient interviews ´videos, Empathy-focused training, Discussion. Results: It identified the need for a cross cultural adaptation and no gender distinction. The students enjoyed all the techniques in comparison to the usual master class. There was medium participation but these participative methodologies are not so usual in the university. According to empathy, men have a greater empathic capacity to fully understand women (p < 0.05) With regard to assertiveness there have been no differences between men and women in self-assertiveness but nevertheless women are more heteroassertive than men. Conclusions: These findings suggest that educational interventions with adequate feedback can be effective in maintaining and enhancing empathy in undergraduate medical students.

Keywords: physician's communicational skills, patient satisfaction, third gender, cross cultural adaptation

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Authors: Durri Andriani, Irsan Tahar, Lilian Sarah Hiariey

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Every institution has to develop, implement, and control its policies to ensure the effectiveness of the institution. In doing so, all related stakeholders have to be involved to maximize the benefit of the policies and minimize the potential constraints and resistances. Open and distance education (ODE) institution is no different. As an education institution, ODE institution has to focus their attention to fulfilling academic needs of their students through open and distance measures. One of them is quality learning support system. Significant stakeholders in learning support system are tutors since they are the ones who directly communicate with students. Tutors are commonly seen as objects whose main responsibility is limited to implementing policies decided by management in ODE institutions. Nonetheless, tutors’ perceptions of tutorials are believed to influence tutors’ performances in facilitating learning support. It is therefore important to analyze tutors’ perception on various aspects of learning support. This paper presents analysis of tutors’ perceptions on policies of tutoriala in ODE institution using Policy Analysis Framework (PAF) modified by King, Nugent, Russell, and Lacy. Focus of this paper is on on-line tutors, those who provide tutorials via Internet. On-line tutors were chosen to stress the increasingly important used of Internet in ODE system. The research was conducted in Universitas Terbuka (UT), Indonesia. UT is purposely selected because of its large number (1,234) of courses offered and large area coverage (6000 inhabited islands). These posed UT in a unique position where learning support system has, to some extent, to be standardized while at the same time it has to be able to cater the needs of different courses in different places for students with different backgrounds. All 598 listed on-line tutors were sent the research questionnaires. Around 20% of the email addresses could not be reached. Tutors were asked to fill out open-ended questionnaires on their perceptions on definition of on-line tutorial, roles of tutors and students in on-line tutorials, requirement for on-line tutors, learning materials, and student evaluation in on-line tutorial. Data analyzed was gathered from 40 on-line tutors who sent back filled-out questionnaires. Data were analyzed qualitatively using content analysis from all 40 tutors. The results showed that using PAF as entry point in choosing learning support services as area of policy with delivery learning materials as the issue at UT has been able to provide new insights of aspects need to be consider in formulating policies in online tutorial and in learning support services. Involving tutors as source of information could be proven to be productive. In general, tutors had clear understanding about definition of online tutorial, roles of tutors and roles of students, and requirement of tutor. Tutors just need to be more involved in the policy formulation since they could provide data on students and problem faced in online tutorial. However, tutors need an adjustment in student evaluation which according tutors too focus on administrative aspects and subjective.

Keywords: distance education, on-line tutorial, tutorial policy, tutors’ perspectives

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Adel Pasztor

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International doctoral mobility is a largely under-researched component of academic mobility and migration. This is in stark contrast to the case of student mobility where much research has been undertaken on Erasmus students; or the growing research on academic staff mobility which can be viewed as a key part of highly skilled migration. The aim of this paper is to remedy the situation by specifically focusing on international doctoral students studying at elite higher education institutions in the United Kingdom. In doing so, in-depth qualitative interviews with doctoral students and recent graduates were carried out in order to identify the signifiers of an internationally mobile doctoral student and unpack the decision-making processes leading onto the choice of higher education institution abroad. Overall, a diverse range of degree subjects from within the humanities and the social sciences were covered with a relatively large spread of nationalities which include the following countries: Italy, Germany, Hungary, Latvia, Bulgaria, Turkey, Lebanon, Israel, Australia, USA, China, and Chile. The interview questions were designed to probe the motivations, choices, educational trajectories and career plans of international doctoral students relative to their social class background, gender, nationality or funding. It was clear from the interviews that there were two main types of international doctoral students: those who ‘did not think anything else was ever a serious possibility’, contrasted with the other, more opportune type, to whom ‘it happened to be a PhD’. There were marked differences between the two types since initial access to university, mainly because educational decisions such as the doctorate do not happen in a vacuum, rather are built on the individual’s higher education aspirations and previous educational choices. The results were in line with existing literature suggesting that those with higher educated parents and from schools strongly supporting the choice process fared better as they were able to make well informed, well thought through as well as strategic decisions for their future involving the very best universities within the national boundaries. Being ‘at the right place’ often meant access to prestigious doctoral scholarships thus, the route of the PhD has been chosen even if it did not necessarily enhance career opportunities. At the same time, the initial higher education choices of those with limited capital were played out locally, although they did aim for the best universities within their geographically constrained landscape of choice. Here, the majority of students referred to some ‘turning points’ in their lives which lead them towards considering international doctoral opportunities but essentially their proactive, do-it-yourself attitude was behind the life-changing educational opportunities.

Keywords: choice, doctoral students, international mobility, PhD, UK

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Authors: Kaoutar Ben Azzou, Hanaa Talei

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Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Suzi M. S. Cavalari, Tim Lewis

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Teletandem is a bilingual model of virtual exchange in which two partners from different countries( and speak different languages) meet synchronously and regularly over a period of 8 weeks to learn each other’s mother tongue (or the language of proficiency). At São Paulo State University (UNESP), participants should answer a questionnaire before starting the exchanges in which one of the questions refers to setting a goal to be accomplished with the help of the teletandem partner. In this context, the present presentation aims to examine the goal-setting activity of 79 Brazilians who participated in Portuguese-English teletandem exchanges over a period of four years (2012-2015). The theoretical background is based on goal setting and self-regulated learning theories that propose that appropriate efficient goals are focused on the learning process (not on the product) and are specific, proximal (short-term) and moderately difficult. The data set used was 79 initial questionnaires retrieved from the MulTeC (Multimodal Teletandem Corpus). Results show that only approximately 10% of goals can be considered appropriate. Features of these goals are described in relation to specificities of the teletandem context. Based on the results, three mechanisms that can help learners to set attainable goals are discussed.

Keywords: foreign language learning, goal setting, teletandem, virtual exchange

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Authors: Prasita Sooksamran, Wareerat Kaewurai

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STEM is an integrated teaching approach promoted by the Ministry of Education in Thailand. STEM Education is an integrated approach to teaching Science, Technology, Engineering, and Mathematics. It has been questioned by Thai teachers on the grounds of how to integrate STEM into the classroom. Therefore, the main objective of this study is to develop a science instructional model based on the STEM approach to enhance scientific mind and problem-solving skills for primary students. This study is participatory action research, and follows the following steps: 1) develop a model 2) seek the advice of experts regarding the teaching model. Developing the instructional model began with the collection and synthesis of information from relevant documents, related research and other sources in order to create prototype instructional model. 2) The examination of the validity and relevance of instructional model by a panel of nine experts. The findings were as follows: 1. The developed instructional model comprised of principles, objective, content, operational procedures and learning evaluation. There were 4 principles: 1) Learning based on the natural curiosity of primary school level children leading to knowledge inquiry, understanding and knowledge construction, 2) Learning based on the interrelation between people and environment, 3) Learning that is based on concrete learning experiences, exploration and the seeking of knowledge, 4) Learning based on the self-construction of knowledge, creativity, innovation and 5) relating their findings to real life and the solving of real-life problems. The objective of this construction model is to enhance scientific mind and problem-solving skills. Children will be evaluated according to their achievements. Lesson content is based on science as a core subject which is integrated with technology and mathematics at grade 6 level according to The Basic Education Core Curriculum 2008 guidelines. The operational procedures consisted of 6 steps: 1) Curiosity 2) Collection of data 3) Collaborative planning 4) Creativity and Innovation 5) Criticism and 6) Communication and Service. The learning evaluation is an authentic assessment based on continuous evaluation of all the material taught. 2. The experts agreed that the Science Instructional Model based on the STEM Education Approach had an excellent level of validity and relevance (4.67 S.D. 0.50).

Keywords: instructional model, STEM education, scientific mind, problem solving

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Authors: Beilei Xu, Wencheng Wu, Lei Lin, Rachel Melnyk, Ahmed Ghazi

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In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative cognitiveload-based feedback can also provide valuable insights during surgical training to optimize training efficiency and effectiveness. Various physiological measures have been evaluated for quantifying cognitive stress for different mental challenges. In this paper, we present a study using the cognitive stress measured by the task evoked pupillary response extracted from the time series eye-tracking measurements to predict task difficulties in a virtual reality based robotic surgery training environment. In particular, we proposed a differential-task-difficulty scale, utilized a comprehensive feature extraction approach, and implemented a multitask learning framework and compared the regression accuracy between the conventional single-task-based and three multitask approaches across subjects.

Keywords: surgical metric, task evoked pupillary response, multitask learning, TSFresh

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Authors: Simao E. Luis

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From the 1960s to the 1990s, the social-psychological framework of language attitudes that emerged from the Canadian research tradition was very influential. Integrativeness was one of the main variables in Gardner’s theory because refugees and immigrants were motivated to learn English and French to integrate into the Canadian community. Second language (L2) scholars have expressed concerns over integrativeness because it cannot explain the motivation of L2 learners in global contexts. This study aims to investigate student motivation to learn English as a foreign language in Mozambique, and to contribute to the ongoing validation of the L2 Motivational Self System theory in an under-researched country. One hundred thirty-seven (N=137) university students completed a well-established motivation questionnaire. The data were analyzed with SPSS, and descriptive statistics, correlations, multiple regressions, and MANOVA were conducted. Results show that many variables contribute to motivated learning behavior, particularly the L2 learning experience and attitudes towards the English language. Statistically significant differences were found between males and females, with males expressing more motivation to learn the English language for personal interests. Statistically significant differences were found between older and younger students, with older students reporting more vivid images of themselves as future English language users. These findings have pedagogical implications because motivational strategies are positively correlated with student motivated learning behavior. Therefore, teachers should design L2 tasks that can help students to develop their future L2 selves.

Keywords: English as a foreign language, L2 motivational self system, Mozambique, university students

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