Search results for: educational data mining

Authors: S. A. N. Danushka, T. A. Weerasinghe

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The diverse necessities of instruction could be addressed effectively with the support of new dimensions of ICT integrated learning such as blended learning –which is a combination of face-to-face and online instruction which ensures greater flexibility in student learning and congruity of course delivery. As blended learning has been the ‘new normality' in education, many experimental and quasi-experimental research studies provide ample of evidence on its successful implementation in many fields of studies, but it is hard to justify whether blended learning could work similarly in the delivery of technology-teacher development programmes (TTDPs). The present study is bound with the particular research uncertainty, and having considered existing research approaches, the study methodology was set to decide the efficient instructional strategies for flipped classroom learning in TTDPs. In a quasi-experimental pre-test and post-test design with a mix-method research approach, the major study objective was tested with two heterogeneous samples (N=135) identified in a virtual learning environment in a Sri Lankan university. Non-randomized informal ‘before-and-after without control group’ design was employed, and two data collection methods, identical pre-test and post-test and Likert-scale questionnaires were used in the study. Selected two instructional strategies, self-directed learning (SDL) and self-regulated learning (SRL), were tested in an appropriate instructional framework with two heterogeneous samples (pre-service and in-service teachers). Data were statistically analyzed, and an efficient instructional strategy was decided via t-test, ANOVA, ANCOVA. The effectiveness of the two instructional strategy implementation models was decided via multiple linear regression analysis. ANOVA (p < 0.05) shows that age, prior-educational qualifications, gender, and work-experiences do not impact on learning achievements of the two diverse groups of learners through the instructional strategy is changed. ANCOVA (p < 0.05) analysis shows that SDL is efficient for two diverse groups of technology-teachers than SRL. Multiple linear regression (p < 0.05) analysis shows that the staged self-directed learning (SSDL) model and four-phased model of motivated self-regulated learning (COPES Model) are efficient in the delivery of course content in flipped classroom learning.

Keywords: COPES model, flipped classroom learning, self-directed learning, self-regulated learning, SSDL model

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

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STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data.

Keywords: rule induction, decision table, missing data, noise

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Authors: Nourah Alshalhoub

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As Saudi Arabia is one of the countries moving toward more inclusive schools, there are few researchers who have examined the new model of inclusive practice; that is, a model introduced by the Tatweer project. Tatweer is an initiative supported by the Saudi government to develop education with a particular focus on inclusion. This on-going doctoral work aims to find out the nature of inclusive practice that Taweer introduced to create effective practice to include students with different abilities. While stakeholders are important elements to the implementation of inclusive education practice, the study’s goal is to find out and explore their understandings and perspectives. This study considers the perspectives of stakeholders, who are involved and influential on the implementation of the practice, from different dimensions. Tatweer project’s managers, head teachers, teachers and teaching assistants will be interviewed to find out how do they understand inclusive education concept and what perspective do they hold. Reliant on this material, this work seeks to inquire into what meaning inclusion and inclusive practice holds in Tatweer and to what extent this educational models let students with different abilities be more included. Four primary schools in Riyadh were purposively selected and data will be collected through semi-structured interviews. Semi-structured interview was selected as a study tool because it is a relevant and helpful method in understanding the thoughts, views, and beliefs of the stakeholders individually, and investigating issues more thoroughly in the context of Saudi Arabia.

Keywords: inclusive education, perspective, understanding, definition, inclusion

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Authors: Matthew Armitt

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The revolutionary school of architectural teaching – VKhUTEAMAS (1923-1926) was a new approach for a new society bringing architectural education to the masses and masses to the growing industrial production. The school's pedagogical contribution of the 1920s made it an important school of the modernist movement, engaging pedagogy as a mode of experimentation. The teachers and students saw design education not just as a process of knowledge transfer but as a vehicle for design innovation developing an approach without precedent. This process of teaching and learning served as a vehicle for venturing into the unknown through a discipline of architectural teaching called “Space” developed by the Soviet architect Nikolai Ladovskii (1881-1941). The creation of “Space” was paramount not only for its innovative pedagogy but also as an experimental laboratory for developing new architectural language. This paper discusses whether the historical teaching of “Space” can function in the construction of the modern teaching identity today to promote value, richness, quality, and diversity inherent in architectural design education. The history of “Space” teaching remains unknown within academic circles and separate from the current architectural teaching debate. Using VKhUTEMAS and the teaching of “Space” as a pedagogical lens and drawing upon research carried out in the Russian Federation, America, Canada, Germany, and the UK, this paper discusses how historically different models of teaching and learning can intersect through examining historical based educational research by exploring different design studio initiatives; pedagogical methodologies; teaching and learning theories and problem-based projects. There are strong arguments and desire for pedagogical change and this paper will promote new historical and educational research to widen the current academic debate by exposing new approaches to architectural teaching today.

Keywords: VKhUTEMAS, discipline space, modernist pedagogy, teaching identity

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Authors: Ayesha Siddiqua

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Background: Hypertension is a silent killer disease and a common risk factor for considerable morbidity and mortality. Its effects can be seen on the organs like Heart; Brain; Kidneys. In Saudi Arabia, hypertension affects a sizeable enough proportion of the population, with a prevalence of 27.9% in urban and 22.4 in rural population. Despite these features, the magnitude and epidemiological characteristics of this disease have been rarely studied in Saudi Arabia. To fill this gap, we conducted a survey in Abha to study the KAP of hypertension. KAP study shows what people know about certain things, their feelings and behavior towards the disease management. An improvement in the Knowledge and Attitudes towards disease management can reform the kinds of practices which are followed. Objectives: To assess the level of Knowledge, Attitude and Practice of patients who suffer from Hypertension. To improve the Quality of life of patients. Methods: A prospective cross-sectional survey was conducted on a sample size of 130 Hypertensive patients of both the genders enrolled by simple random sampling technique admitted in the Aseer Central Hospital of Abha during the period from October 2016 to December 2016. Results: Altogether 130 hypertensive patients were enrolled in this study with equal no. of Males and Females. Most of the respondents were aged between 18-40 years (45%). On assessing the KAP of the patients, we found that the Knowledge and Attitude score was good but the Practice scores were moderate in both the genders. Conclusion: Our study concludes that a significant proportion of hypertensive patients show less Practice towards the disease management which can lead to severe complications in time being and also result in damage of other vital organs. So there is a need of intense educational intervention for the patients which can be done by Patient counselling by the clinical pharmacist. Strategies to modify lifestyle which help in control of hypertension can include providing leaflets as well as direct educational programs.

Keywords: Attitude, hypertension, Knowledge, practices

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Tiera Tanksley, Amanda M. McLeroy

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The advent of technology and social media has ushered in a new era of communication, providing platforms for news dissemination and cause advocacy. However, this digital landscape has also exposed a distressing phenomenon termed "Black death," or trauma porn. This paper delves into the profound effects of repeated exposure to traumatic content on Black youth via social media, exploring the psychological impacts and potential reinforcing of stereotypes. Employing Critical Race Technology Theory (CRTT), the study sheds light on algorithmic anti-blackness and its influence on Black youth's lives and educational experiences. Through ethnographic content analysis, the research investigates common manifestations of Black death encountered online by Black adolescents. Findings unveil distressing viral videos, traumatic images, racial slurs, and hate speech, perpetuating stereotypes. However, amidst the distress, the study identifies narratives of activism and social justice on social media platforms, empowering Black youth to engage in positive change. Coping mechanisms and community support emerge as significant factors in navigating the digital landscape. The study underscores the need for comprehensive interventions and policies informed by evidence-based research. By addressing algorithmic anti-blackness and promoting digital resilience, the paper advocates for a more empathetic and inclusive online environment. Understanding coping mechanisms and community support becomes imperative for fostering mental well-being among Black adolescents navigating social media. In education, the implications are substantial. Acknowledging the impact of Black death content, educators play a pivotal role in promoting media literacy and digital resilience. Creating inclusive and safe online spaces, educators can mitigate negative effects and encourage open discussions about traumatic content. The application of CRTT in educational technology emphasizes dismantling systemic biases and promoting equity. In conclusion, this study calls for educators to be cognizant of the impact of Black death content on social media. By prioritizing media literacy, fostering digital resilience, and advocating for unbiased technologies, educators contribute to an inclusive and just educational environment for all students, irrespective of their race or background. Addressing challenges related to Black death content proactively ensures the well-being and mental health of Black adolescents, fostering an empathetic and inclusive digital space.

Keywords: algorithmic anti-Blackness, digital resilience, media literacy, traumatic content

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Authors: Tesfaye Alamirew Dessie

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Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters.

Keywords: MPC, PID, ARX, pasteurization

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Authors: Mohammed Abubakar Mawoli

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Theory of Planned Behavior (TPB) is a useful tool for predicting entrepreneurial intentions among individuals or groups of people. In view of the Nigerian government’s renewed educational policies and programs to prepare Nigerian undergraduates towards self-reliance and employers of labor after graduation, it becomes pertinent to empirically examine and predict the undergraduate’s entrepreneurial intentions at graduation. Thus, this study primarily examines the undergraduates entrepreneurial intentions using TPB, which includes perceived desirability, perceived social norm, and perceived feasibility factors. In so doing, a questionnaire research method was adopted in which 219 copies of a questionnaire distributed to final year undergraduates were belonging to five departments with a total population of 487 students. A combination of relative frequency, mean standard deviation and multiple regression statistical tools were employed for data analysis. The study found that TPB components exert a significant composite effect on undergraduate’s entrepreneurial intentions. Based on individual contribution of the independent variables, Perceived Desirability is the strongest predictor of the undergraduate’s entrepreneurial intentions, while Perceived Social Norm is a strong predictor of the undergraduate’s entrepreneurial intentions. However, Perceived Feasibility is not a strong predictor of student’s entrepreneurial intentions. The study therefore, recommends that the Perceived desirability, which is formed and shaped by ones level of education and skills acquisition, be improved upon to create the expected positive impact on graduates entrepreneurial intentions and possible venture creation.

Keywords: entrepreneurship, entrepreneurship education, entrepreneurial intentions, planned behaviour, prediction, Nigeria

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Authors: Rana Rimawi, Ayman Baklizi

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Skewed distributions are important models that are frequently used in applications. Generalized distributions form a class of skewed distributions and gain widespread use in applications because of their flexibility in data analysis. More specifically, the Generalized Logistic Distribution with its different types has received considerable attention recently. In this study, based on progressively type-II censored data, we will consider point estimation in type II Generalized Logistic Distribution (Type II GLD). We will develop several estimators for its unknown parameters, including maximum likelihood estimators (MLE), Bayes estimators and linear estimators (BLUE). The estimators will be compared using simulation based on the criteria of bias and Mean square error (MSE). An illustrative example of a real data set will be given.

Keywords: point estimation, type II generalized logistic distribution, progressive censoring, maximum likelihood estimation

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Authors: Omneya Messallam

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Technology has been developed dramatically in most of the educational disciplines. For instance, digital rendering subject, which is being taught in both Interior and Architecture fields, is witnessing almost annually updated software versions. A lot of students and educators argued that there will be no need for manual rendering techniques to be learned. Therefore, the Interior Design Visual Presentation 1 course (ID133) has been chosen from the first level of the Interior Design (ID) undergraduate program, as it has been taught for six years continually. This time frame will facilitate sound observation and critical analysis of the use of appropriate teaching methodologies. Furthermore, the researcher believes in the high value of the manual rendering techniques. The course objectives are: to define the basic visual rendering principles, to recall theories and uses of various types of colours and hatches, to raise the learners’ awareness of the value of studying manual render techniques, and to prepare them to present their work professionally. The students are female Arab learners aged between 17 and 20. At the outset of the course, the majority of them demonstrated negative attitude, lacking both motivation and confidence in manual rendering skills. This paper is a reflective appraisal of deploying two student-centred teaching pedagogies which are: Project-based learning (PBL) and Outcome-based education (OBE) on ID133 students. This research aims of developing some teaching strategies to enhance the quality of teaching in this given course over an academic semester. The outcome of this research emphasized the positive influence of applying such educational methods on improving the quality of students’ manual rendering skills in terms of: materials, textiles, textures, lighting, and shade and shadow. Furthermore, it greatly motivated the students and raised the awareness of the importance of learning the manual rendering techniques.

Keywords: project-based learning, outcome-based education, visual presentation, manual render, personal competences

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Authors: Emanuele A. Solagna, Ricardo S, Tozetto, Roberto dos S. Rabello

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Nowadays, physical processes are becoming digitized by the evolution of communication, sensing and storage technologies which promote the development of smart cities. The evolution of this technology has generated multiple challenges related to the generation of big data and the active participation of electronic devices in society. Thus, devices can send information that is captured and processed over large areas, but there is no guarantee that all the obtained data amount will be effectively stored and correctly persisted. Because, depending on the technology which is used, there are parameters that has huge influence on the full delivery of information. This article aims to characterize the project, currently under development, of a platform that based on data science will perform a performance and effectiveness evaluation of an industrial network that implements LoRaWAN technology considering its main parameters configuration relating these parameters to the information loss.

Keywords: Internet of Things, LoRa, LoRaWAN, smart cities

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Authors: Elisa Santana-Monagas, Juan L. Núñez, Jaime León, Samuel Falcón, Celia Fernández, Rocío P. Solís

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Teaching practices have been widely studied in relation to students' outcomes, positioning themselves as one of their strongest catalysts and influencing students' emotional experiences. In the higher education context, teachers become even more crucial as many students ground their decisions on which courses to enroll in based on opinions and ratings of teachers from other students. Unfortunately, sometimes universities do not provide the personal, social, and academic stimulation students demand to be actively engaged. To evaluate their teachers, universities often rely on students' evaluations of teaching (SET) collected via Likert scale surveys. Despite its usefulness, such a method has been questioned in terms of validity and reliability. Alternatively, researchers can rely on qualitative answers to open-ended questions. However, the unstructured nature of the answers and a large amount of information obtained requires an overwhelming amount of work. The present work presents an alternative approach to analyse such data: sentiment analysis. To the best of our knowledge, no research before has included results from SA into an explanatory model to test how students' sentiments affect their emotional engagement in class. The sample of the present study included a total of 225 university students (Mean age = 26.16, SD = 7.4, 78.7 % women) from the Educational Sciences faculty of a public university in Spain. Data collection took place during the academic year 2021-2022. Students accessed an online questionnaire using a QR code. They were asked to answer the following open-ended question: "If you had to explain to a peer who doesn't know your teacher how he or she communicates in class, what would you tell them?". Sentiment analysis was performed using Microsoft's pre-trained model. The reliability of the measure was estimated between the tool and one of the researchers who coded all answers independently. The Cohen's kappa and the average pairwise percent agreement were estimated with ReCal2. Cohen's kappa was .68, and the agreement reached was 90.8%, both considered satisfactory. To test the hypothesis relations among SA and students' emotional engagement, a structural equation model (SEM) was estimated. Results demonstrated a good fit of the data: RMSEA = .04, SRMR = .03, TLI = .99, CFI = .99. Specifically, the results showed that student’s sentiment regarding their teachers’ teaching positively predicted their emotional engagement (β == .16 [.02, -.30]). In other words, when students' opinion toward their instructors' teaching practices is positive, it is more likely for students to engage emotionally in the subject. Altogether, the results show a promising future for sentiment analysis techniques in the field of education. They suggest the usefulness of this tool when evaluating relations among teaching practices and student outcomes.

Keywords: sentiment analysis, students' evaluation of teaching, structural-equation modelling, emotional engagement

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Authors: Nicole Christiansen, Hanne Marie Motzfeldt

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In recent years, more and more HR professionals have been using cyber-vetting in job recruitment in an effort to find the perfect match for the company. These practices are growing rapidly, accessing a vast amount of data from social networks, some of which is privileged and protected information. Thus, there is a risk that the right to privacy is becoming a duty to manage your private data. This paper investigates to which degree a job applicant's fundamental rights are protected adequately in current and future legislation in the EU. This paper argues that current data protection regulations and forthcoming regulations on the use of AI ensure sufficient protection. However, even though the regulation on paper protects employees within the EU, the recruitment sector may not pay sufficient attention to the regulation as it not specifically targeting this area. Therefore, the lack of specific labor and employment regulation is a concern that the social partners should attend to.

Keywords: AI, cyber vetting, data protection, job recruitment, online privacy

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Authors: Ayanda P. Deliwe, Storm B. Watson

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The COVID-19 pandemic has disrupted the established practices of higher education institutions (HEIs). Most higher education institutions worldwide had to shift from traditional face-to-face to online learning. The online environment and new online tools are disrupting the way in which higher education is presented. Furthermore, the structures of higher education institutions have been impacted by rapid advancements in information and communication technologies. Emerging technologies should not be viewed in a negative light because, as opposed to the traditional curriculum that worked to create productive and efficient researchers, emerging technologies encourage creativity and innovation. Therefore, using technology together with traditional means will enhance teaching and learning. Emerging technologies in higher education not only change the experience of students, lecturers, and the content, but it is also influencing the attraction and retention of students. Higher education institutions are under immense pressure because not only are they competing locally and nationally, but emerging technologies also expand the competition internationally. Emerging technologies have eliminated border barriers, allowing students to study in the country of their choice regardless of where they are in the world. Higher education institutions are becoming indifferent as technology is finding its way into the lecture room day by day. Academics need to utilise technology at their disposal if they want to get through to their students. Academics are now competing for students' attention with social media platforms such as WhatsApp, Snapchat, Instagram, Facebook, TikTok, and others. This is posing a significant challenge to higher education institutions. It is, therefore, critical to pay attention to emerging technologies in order to see how they can be incorporated into the classroom in order to improve educational quality while remaining relevant in the work industry. This study aims to understand how emerging technologies have been utilised at Nelson Mandela University in presenting teaching and learning activities since April 2020. The primary objective of this study is to analyse how academics are incorporating emerging technologies in their teaching and learning activities. This primary objective was achieved by conducting a literature review on clarifying and conceptualising the emerging technologies being utilised by higher education institutions, reviewing and analysing the use of emerging technologies, and will further be investigated through an empirical analysis of the use of emerging technologies at Nelson Mandela University. Findings from the literature review revealed that emerging technology is impacting several key areas in higher education institutions, such as the attraction and retention of students, enhancement of teaching and learning, increase in global competition, elimination of border barriers, and highlighting the digital divide. The literature review further identified that learning management systems, open educational resources, learning analytics, and artificial intelligence are the most prevalent emerging technologies being used in higher education institutions. The identified emerging technologies will be further analysed through an empirical analysis to identify how they are being utilised at Nelson Mandela University.

Keywords: artificial intelligence, emerging technologies, learning analytics, learner management systems, open educational resources

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Authors: Reda Abdel Azim, Tariq Shehab

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The main objective of this study is to develop a subsurface fracture map of naturally fractured reservoirs by overcoming the limitations associated with different data sources in characterising fracture properties. Some of these limitations are overcome by employing a nested neuro-stochastic technique to establish inter-relationship between different data, as conventional well logs, borehole images (FMI), core description, seismic attributes, and etc. and then characterise fracture properties in terms of fracture density and fractal dimension for each data source. Fracture density is an important property of a system of fracture network as it is a measure of the cumulative area of all the fractures in a unit volume of a fracture network system and Fractal dimension is also used to characterize self-similar objects such as fractures. At the wellbore locations, fracture density and fractal dimension can only be estimated for limited sections where FMI data are available. Therefore, artificial intelligence technique is applied to approximate the quantities at locations along the wellbore, where the hard data is not available. It should be noted that Artificial intelligence techniques have proven their effectiveness in this domain of applications.

Keywords: naturally fractured reservoirs, artificial intelligence, fracture intensity, fractal dimension

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Authors: Tim Nedyalkov

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A business decision to move to the cloud brings fundamental changes in how an organization develops and delivers its Information Technology solutions. The accelerated pace of digital transformation across businesses and government agencies increases the reliance on cloud-based services. They are collecting, managing, and retaining large amounts of data in cloud environments makes information security and data privacy protection essential. It becomes even more important to understand what key factors drive successful cloud adoption following the commencement of the Privacy Amendment Notifiable Data Breaches (NDB) Act 2017 in Australia as the regulatory changes impact many organizations and industries. This quantitative correlational research investigated the governance, risk management, and compliance factors contributing to cloud security success. The factors influence the adoption of cloud computing within an organizational context after the commencement of the NDB scheme. The results and findings demonstrated that corporate information security policies, data storage location, management understanding of data governance responsibilities, and regular compliance assessments are the factors influencing cloud computing adoption. The research has implications for organizations, future researchers, practitioners, policymakers, and cloud computing providers to meet the rapidly changing regulatory and compliance requirements.

Keywords: cloud compliance, cloud security, data governance, privacy protection

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Authors: U. Ali Rahoma, Nabil Esawy, Fawzia Ibrahim Moursy, A. H. Hassan, Samy A. Khalil, Ashraf S. Khamees

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The design of any solar energy conversion system requires the knowledge of solar radiation data obtained over a long period. Satellite data has been widely used to estimate solar energy where no ground observation of solar radiation is available, yet there are limitations on the temporal coverage of satellite data. Reanalysis is a “retrospective analysis” of the atmosphere parameters generated by assimilating observation data from various sources, including ground observation, satellites, ships, and aircraft observation with the output of NWP (Numerical Weather Prediction) models, to develop an exhaustive record of weather and climate parameters. The evaluation of the performance of reanalysis datasets (ERA-5) for North Africa against high-quality surface measured data was performed using statistical analysis. The estimation of global solar radiation (GSR) distribution over six different selected locations in North Africa during ten years from the period time 2011 to 2020. The root means square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) of reanalysis data of solar radiation range from 0.079 to 0.222, 0.0145 to 0.198, and 0.055 to 0.178, respectively. The seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons—the performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 is used for preliminary solar resource assessment and power estimation. The correlation coefficient (R2) varies from 0.93 to 99% for the different selected sites in North Africa in the present research. The goal of this research is to give a good representation for global solar radiation to help in solar energy application in all fields, and this can be done by using gridded data from European Centre for Medium-Range Weather Forecasts ECMWF and producing a new model to give a good result.

Keywords: solar energy, solar radiation, ERA-5, potential energy

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Authors: Fan Gao, Lior Pachter

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The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 15 to 18 times faster than Cell Ranger on mouse and human samples. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signal and interaction traces for cell groups. We use scATAK tool to explore the chromatin regulatory landscape of a healthy adult human brain and unveil cell-type specific features, and show that it provides a convenient and computational efficient approach for pre-processing single-cell ATAC-seq data.

Keywords: single-cell, ATAC-seq, bioinformatics, open chromatin landscape, chromatin interactome

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Authors: Alana E. Jansen

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With such a large proportion of organisations relying on the use of team-based structures, it is surprising that so few teams would be classified as high-performance teams. While the impact of team composition on performance has been researched frequently, there have been conflicting findings as to the effects, particularly when examined alongside other team factors. To broaden the theoretical perspectives on this topic and potentially explain some of the inconsistencies in research findings left open by other various models of team effectiveness and high-performing teams, the present study aims to use the Job-Demands-Resources model, typically applied to burnout and engagement, as a framework to examine how team composition factors (particularly diversity in team member characteristics) can facilitate or hamper team effectiveness. This study used a virtual interview design where participants were asked to both rate and describe their experiences, in one high-performing and one low-performing team, over several factors relating to demands, resources, team composition, and team effectiveness. A semi-structured interview protocol was developed, which combined the use of the Likert style and exploratory questions. A semi-targeted sampling approach was used to invite participants ranging in age, gender, and ethnic appearance (common surface-level diversity characteristics) and those from different specialties, roles, educational and industry backgrounds (deep-level diversity characteristics). While the final stages of data analyses are still underway, thematic analysis using a grounded theory approach was conducted concurrently with data collection to identify the point of thematic saturation, resulting in 35 interviews being completed. Analyses examine differences in perceptions of demands and resources as they relate to perceived team diversity. Preliminary results suggest that high-performing and low-performing teams differ in perceptions of the type and range of both demands and resources. The current research is likely to offer contributions to both theory and practice. The preliminary findings suggest there is a range of demands and resources which vary between high and low-performing teams, factors which may play an important role in team effectiveness research going forward. Findings may assist in explaining some of the more complex interactions between factors experienced in the team environment, making further progress towards understanding the intricacies of why only some teams achieve high-performance status.

Keywords: diversity, high-performing teams, job demands and resources, team effectiveness

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Authors: Jiangbo Shi, Zeyu Gao, Chen Li

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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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Authors: Khadem Hichem

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With the new technology challenges, dynamics and challenges of the contemporary world, most teachers are struggling to maintain effective and successful teaching /learning environment for learners. Teachers as a key to the success of reforms in the educational setting, they must improve their competencies to teach effectively. Many researchers emphasis on the ongoing professional development of the teacher by enhancing their experiences and encouraging their responsibility for learning, and thus promoting self-reliance, collaboration, and reflection. In short, teachers are considered as learners and they need to learn together. The educational system must support, both conceptually and financially, the teachers’ development as lifelong learners Teachers need opportunities to grow in language proficiency and in knowledge. Changing nature of language and culture in the world, all teachers must have opportunities to update their knowledge and practices. Many researchers in the field of foreign or additional languages indicate that teachers keep side by side of effective instructional practices and they need special support with the challenging task of developing and administering proficiency tests to their students. For significant change to occur, each individual teacher’s needs must be addressed. The teacher must be involved experientially in the process of development, since, by itself, knowledge of how to change does not mean change will be initiated. For improvement to occur, new skills have to be guided, practiced, and reflected upon in collaboration with colleagues. Clearly, teachers are at different places developmentally; therefore, allowances for various entry levels and individual differences need to be built into the professional development structure. Objectives must be meaningful to the participant and teacher improvement must be stated terms of student knowledge, student performance, and motivation. The most successful professional development process acknowledges the student-centered nature of good teaching. This paper highlights the importance of teacher professional development process and institutional supports as way to enhance good teaching and learning environment.

Keywords: teacher professional development, teacher competencies, institutional support, teacher education

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Authors: T. Goldstein, E. Serok, J. D. Kark

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Background and Aim: The prevalence of obesity is increasing worldwide and in Israel. To meet this challenge, our study tests a new educational approach through a controlled school-based trial to achieve an improvement in eating habits, aerobic activity, and reduced obesity in Grades 2-3. Methods and Design: A cluster randomized controlled trial allocated 4 elementary schools (3rd and 2nd-grade classes each) to intervention or control groups. This allocation was switched with the next cohort of children. Recruitment was in first grade, randomization at the beginning of second grade, evaluation of results at the end of second grade and the beginning of third grade — intervention: 5 joint parent-children classroom activities on health topics and 5 educational workshops for parents only. Alfred Adler's concepts were guiding principles. Subjects: Of 743 children in 23-second grade classes, parents provided informed consent for 508 (68%). Information of retention health habits continued for third grade. Additional parental approvals were required. Parents provided informed consent for third-grade follow-up for 432. Results: At the end of 2nd grade, the amount of aerobic activity increased in the intervention group in comparison with the control group, the difference being marginally statistically significant (p=0.061). There is a significant difference between the groups in the percentage of "no activity being done" reported at the end of second grade when in the experimental group, the percentage is lower than the control. There are differences between genders in the percentage of aerobic activity at the end of second grade (p=0.044) and in the third grade (p < 0.0001). Height increased significantly (p=0.030 ), and waist circumference declined significantly (p=0.021) in the intervention compared with the control group. There were no significant between-group differences in BMI and weight. Conclusion: There were encouraging changes in aerobic activity and in anthropometric measurements. To maintain changes over longer periods, refreshing these nutrition and activity themes annually in school using the model is required.

Keywords: aerobic activity, child obesity, Alfred Adler, schoolchildren

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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: Fatin Mansour Daas

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Language education responds to and is reflective of emerging social and political trends. Language policies and practices are shaped by political, economic, social and cultural considerations. Following this, Israeli language education policy as implemented in Arab schools in Israel is influenced by the particular political and social situation of Arab-Palestinian citizens of Israel. This national group remained in their homeland following the war in 1948 between Israel and its Arab neighbors and became Israeli citizens following the establishment of the State of Israel. This study examines language policy in Arab schools in Israel from 1948 until the present time in light of the unique experience of the Palestinian Arab homeland minority in Israel with a particular focus on questions of politics and identity. The establishment of the State of Israel triggered far-reaching political, social and educational transformations within Arab Palestinian society in Israel, including in the area of language and language studies. Since 1948, the linguistic repertoire of Palestinian Arabs in Israel has become more complex and diverse, while the place and status of different languages have changed. Following the establishment of the State of Israel, only Hebrew and Arabic were retained as the official languages, and Israeli policy reflected this in schools as well: with the advent of the Jewish state, Hebrew language education among Palestinians in Israel has increased. Similarly, in Arab Palestinian schools in Israel, English is taught as a third language, Hebrew as a second language, and Arabic as a first language – even though it has become less important to native Arabic speakers. This research focuses on language studies and language policy in the Arab school system in Israel from 1948 onwards. It will analyze the relative focus of language education between the different languages, the rationale of various language education policies, and the pedagogic approach used to teach each language and student achievements vis-à-vis language skills. This study seeks to understand the extent to which Arab schools in Israel are multi-lingual by examining successes, challenges and difficulties in acquiring the respective languages. This qualitative study will analyze five different components of language education policy: (1) curriculum, (2) learning materials; (3) assessment; (4) interviews and (5) archives. Firstly, it consists of an analysis examining language education curricula, learning materials and assessments used in Arab schools in Israel from 1948-2018 including a selection of language textbooks for the compulsory years of study and the final matriculation (Bagrut) examinations. The findings will also be based on archival material which traces the evolution of language education policy in Arabic schools in Israel from the years 1948-2018. This archival research, furthermore, will reveal power relations and general decision-making in the field of the Arabic education system in Israel. The research will also include interviews with Ministry of Education staff who provide instructional oversight in the instruction of the three languages in the Arabic education system in Israel. These interviews will shed light on the goals of language education as understood by those who are in charge of implementing policy.

Keywords: language education policy, languages, multilingualism, language education, educational policy, identity, Palestinian-Arabs, Arabs in Israel, educational school system

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Authors: R. Verwiebe, L. Seewann, M. Wolf

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This presentation addresses marginalization and discrimination as experienced by young academics with a migrant background in the Austrian labor market. Focusing on second generation academics of Central Eastern European and Turkish descent we explore two major issues. First, we ask whether their career-entry and everyday professional life entails origin-specific barriers. As educational residents, they show competences which, when lacking, tend to be drawn upon to explain discrimination: excellent linguistic skills, accredited high-level training, and networks. Second, we concentrate on how this group reacts to discrimination and overcomes experiences of marginalization. To answer these questions, we utilize recent sociological and social psychological theories that focus on the diversity of individual experiences. This distinguishes us from a long tradition of research that has dealt with the motives that inform discrimination, but has less often considered the effects on those concerned. Similarly, applied coping strategies have less often been investigated, though they may provide unique insights into current problematic issues. Building upon present literature, we follow recent discrimination research incorporating the concepts of ‘multiple discrimination’, ‘subtle discrimination’, and ‘visual social markers’. 21 problem-centered interviews are the empirical foundation underlying this study. The interviewees completed their entire educational career in Austria, graduated in different universities and disciplines and are working in their first post-graduate jobs (career entry phase). In our analysis, we combined thematic charting with a coding method. The results emanating from our empirical material indicated a variety of discrimination experiences ranging from barely perceptible disadvantages to directly articulated and overt marginalization. The spectrum of experiences covered stereotypical suppositions at job interviews, the disavowal of competencies, symbolic or social exclusion by new colleges, restricted professional participation (e.g. customer contact) and non-recruitment due to religious or ethnical markers (e.g. headscarves). In these experiences the role of the academics education level, networks, or competences seemed to be minimal, as negative prejudice on the basis of visible ‘social markers’ operated ‘ex-ante’. The coping strategies identified in overcoming such barriers are: an increased emphasis on effort, avoidance of potentially marginalizing situations, direct resistance (mostly in the form of verbal opposition) and dismissal of negative experiences by ignoring or ironizing the situation. In some cases, the academics drew into their specific competences, such as an intellectual approach of studying specialist literature, focus on their intercultural competences or planning to migrate back to their parent’s country of origin. Our analysis further suggests a distinction between reactive (i.e. to act on and respond to experienced discrimination) and preventative strategies (applied to obviate discrimination) of coping. In light of our results, we would like to stress that the tension between educational and professional success experienced by academics with a migrant background – and the barriers and marginalization they continue to face – are essential issues to be introduced to socio-political discourse. It seems imperative to publicly accentuate the growing social, political and economic significance of this group, their educational aspirations, as well as their experiences of achievement and difficulties.

Keywords: coping strategies, discrimination, labor market, second generation university graduates

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Authors: Abdullah Mukhtar, Shehroz Mukhtar, Amina Mukhtar, Choudhry Shahid, Hafiz Raza Razzaq, Saif Ur Rahman

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The aim of this study is to find out the negative and positive impacts of social media platforms on the attitude toward learning and the educational environment of the student community. Social Media platforms have become a source of collaboration with one another throughout the globe, making it a small world. This study performs a focalized investigation of the adverse and constructive factors that have a strong impact not only on psychological adjustments but also on the academic performance of peers. This study is quantitative research adopting a random sampling method in which the participants were the students at the university. The researcher distributed 1000 questionnaires among the university students from different departments and asked them to fill in the data on the Lickert Scale. The participants are from the age group of 18-24 years. The study applies user and gratification theory in order to examine the behavior of students practicing social media in their academic and personal lives. The findings of the study reveal that the use of social media platforms in the Pakistani context has less positive impact as compared to negative impacts on the behavior of students towards learning. The research suggests that usage of online social media platforms should be taught to students; awareness must the created among the users of social media by means of seminars, workshops and by media itself to overcome the negative impacts of social media, leading towards sustainable education in Pakistan.

Keywords: social media, positive impacts, negative impacts, sustainable education, learning behaviour

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Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

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Authors: Siriwan Chatmaneerungcharoen

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An emerging body of ‘21st century skills’-such as adaptability, complex communication skills, technology skills and the ability to solve non-routine problems--are valuable across a wide range of jobs in the national economy. Within the Thai context, a focus on the Philosophy of Sufficiency Economy is integrated into Science Education. Thai science education has advocated infusing 21st century skills and Philosophy of Sufficiency Economy into the school curriculum and several educational levels have launched such efforts. Therefore, developing science teachers to have proper knowledge is the most important factor to success of the goals. The purposes of this study were to develop 40 Cooperative Science teachers’ Technological Pedagogical Content Knowledge (TPACK) and to develop Professional Development Model integrated with Co-teaching Model and Coaching System (Co-TPACK). TPACK is essential to career development for teachers. Forty volunteer In-service teachers who were science cooperative teachers participated in this study for 2 years. Data sources throughout the research project consisted of teacher refection, classroom observations, Semi-structure interviews, Situation interview, questionnaires and document analysis. Interpretivist framework was used to analyze the data. Findings indicate that at the beginning, the teachers understood only the meaning of Philosophy of Sufficiency Economy but they did not know how to integrate the Philosophy of Sufficiency Economy into their science classrooms. Mostly, they preferred to use lecture based teaching and experimental teaching styles. While the Co- TPACK was progressing, the teachers had blended their teaching styles and learning evaluation methods. Co-TPACK consists of 3 cycles (Student Teachers’ Preparation Cycle, Cooperative Science Teachers Cycle, Collaboration cycle (Co-teaching, Co-planning, and Co-Evaluating and Coaching System)).The Co-TPACK enhances the 40 cooperative science teachers, student teachers and university supervisor to exchange their knowledge and experience on teaching science. There are many channels that they used for communication including online. They have used more Phuket context-integrated lessons, technology-integrated teaching and Learning that can explicit Philosophy of Sufficiency Economy. Their sustained development is shown in their lesson plans and teaching practices.

Keywords: technological pedagogical content knowledge, philosophy of sufficiency economy, professional development, coaching system

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