Search results for: learning efficiency

Authors: Nabila Daly, Hamdi Ellouzi, Hela Ltifi

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In last year’s, several recent researches’ that focus on e-learning approaches having as goal to improve pedagogy and student’s academy level assessment. E-learning-related works have become an important research file nowadays due to several problems that make it impossible for students join classrooms, especially in last year’s. Among those problems, we note the current epidemic problems in the word case of Covid-19. For those reasons, several e-learning-related works based on Artificial Intelligence techniques are proposed to improve distant education targets. In the current paper, we will present a short survey of the most relevant e-learning based on Artificial Intelligence techniques giving birth to newly developed e-learning tools that rely on new technologies.

Keywords: artificial intelligence techniques, decision, e-learning, support system, survey

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Authors: Amir Ahmad Dehghani, Morteza Nabizadeh

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This paper presents the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to flood hydrograph modeling of Shahid Rajaee reservoir dam located in Iran. This was carried out using 11 flood hydrographs recorded in Tajan river gauging station. From this dataset, 9 flood hydrographs were chosen to train the model and 2 flood hydrographs to test the model. The different architectures of neuro-fuzzy model according to the membership function and learning algorithm were designed and trained with different epochs. The results were evaluated in comparison with the observed hydrographs and the best structure of model was chosen according the least RMSE in each performance. To evaluate the efficiency of neuro-fuzzy model, various statistical indices such as Nash-Sutcliff and flood peak discharge error criteria were calculated. In this simulation, the coordinates of a flood hydrograph including peak discharge were estimated using the discharge values occurred in the earlier time steps as input values to the neuro-fuzzy model. These results indicate the satisfactory efficiency of neuro-fuzzy model for flood simulating. This performance of the model demonstrates the suitability of the implemented approach to flood management projects.

Keywords: adaptive neuro-fuzzy inference system, flood hydrograph, hybrid learning algorithm, Shahid Rajaee reservoir dam

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Authors: Amira Khelil, Habib Affes

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The XXIst century is characterized by promoting teamwork as one of the main drivers of firms` performance. Collective competence is becoming crucial in developing and maintaining a firm’s competitive advantage, as well as its contributions to organizational innovation. In other words, the improvement of collective competence for a firm is no longer a choice, but rather an obligation. Learning capabilities of a firm in the context of knowledge management are assumed to be the main drivers of collective competence. Although there are some efforts to consider these concepts together; they are mostly discussed separately in the management theory. Thus, this paper aims to offer a holistic approach for development collective competence on the basis of Knowledge Management and Organizational Learning Capabilities. A theoretical model that defines a relationship between knowledge management, organizational learning and collective competence is presented at the end of this paper.

Keywords: collective competence, exploitation learning, exploration learning, knowledge management, organizational learning capabilities

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Authors: Mari A. Virtanen, Elina Haavisto, Eeva Liikanen, Maria Kääriäinen

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Ubiquitous learning and the use of ubiquitous learning environments herald a new era in higher education. Ubiquitous environments fuse together authentic learning situations and digital learning spaces where students can seamlessly immerse themselves into the learning process. Definitions of ubiquitous learning are wide and vary in the previous literature and learning environments are not systemically described. The aim of this scoping review was to identify the criteria and the use of ubiquitous learning environments in higher education contexts. The objective was to provide a clear scope and a wide view for this research area. The original studies were collected from nine electronic databases. Seven publications in total were defined as eligible and included in the final review. An inductive content analysis was used for the data analysis. The reviewed publications described the use of ubiquitous learning environments (ULE) in higher education. Components, contents and outcomes varied between studies, but there were also many similarities. In these studies, the concept of ubiquitousness was defined as context-awareness, embeddedness, content-personalization, location-based, interactivity and flexibility and these were supported by using smart devices, wireless networks and sensing technologies. Contents varied between studies and were customized to specific uses. Measured outcomes in these studies were focused on multiple aspects as learning effectiveness, cost-effectiveness, satisfaction, and usefulness. This study provides a clear scope for ULE used in higher education. It also raises the need for transparent development and publication processes, and for practical implications of ubiquitous learning environments.

Keywords: higher education, learning environment, scoping review, ubiquitous learning, u-learning

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Authors: A. Meydanlioglu, F. Arikan

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In recent years, thanks to the development of information and communication technologies, the computer and internet have been used widely in higher education. Internet-based education is impacting traditional higher education as online components increasingly become integrated into face-to-face (FTF) courses. The goal of combined internet-based and traditional education is to take full advantage of the benefits of each platform in order to provide an educational opportunity that can promote student learning better than can either platform alone. Research results show that the use of hybrid learning is more effective than online or FTF models in higher education. Due to the potential benefits, an increasing number of institutions are interested in developing hybrid courses, programs, and degrees. Future research should evaluate the effectiveness of hybrid learning. This paper is designed to determine the impact of hybrid learning on higher education.

Keywords: e-learning, higher education, hybrid learning, online education

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Authors: Aleksandra Ristic

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The Coronavirus Disease – 2019 hit Europe in February 2020, and infections took place in four waves. It left consequences and demanded changes for the future. More than half of European countries responded quickly by declaring a state of emergency and introducing various containment measures that have had a major impact on individuals’ lives in recent years. Closing public lives was largely achieved by limited access and/or closing public institutions and services, including the closure of educational institutions. Teaching in classrooms converted to distance learning. In the research, we used a quantitative study to analyze various factors of distance learning that influenced pupils in different segments: teachers’ availability, family support, entire online conference learning, successful distance learning, time for themselves, reliable sources, teachers’ feedback, successful distance learning, online participation classes, motivation and teachers’ communication and theoretical review of the importance of digital skills, e-learning Index, World comparison of e-learning in the past, digital education plans for the field of Europe. We have gathered recommendations and distance learning solutions to improve the learning process by strengthening teachers and creating more tiered strategies for setting and achieving learning goals by the children.

Keywords: availability, digital skills, distance learning, resources

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Mingxi Xiao

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Children’s experiences in the early years build and shape the brain. The early years learning environment plays a significantly important role in children’s development. A well-constructed environment will facilitate children’s physical and mental well-being. This case study used an early learning centre in Australia called SDN Hurstville as an example, describing the learning environment in the centre, as well as analyzing the functions of the affordances. In addition, this report talks about the sustainability of learning in the centre, and how the environment supports cultural diversity and indigenous learning. The early years for children are significant. Different elements in the early childhood centre should work together to help children develop better. This case study found that the natural environment and the artificial environment are both critical to children; only when they work together can children have better development in physical and mental well-being and have a sense of belonging when playing and learning in the centre.

Keywords: early childhood center, early childhood education, learning environment, Australia

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Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

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Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

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Authors: Victor Oluwaseyi Olowonisi

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The paper aimed at presenting an overview on the concept of e-learning as it relates to higher education and how it provides opportunities for students, instructors and the government in developing the educational sector. It also touched on the benefits and challenges attached to e-learning as a new medium of reaching more students especially in the Nigerian context. The opportunities attributed to e-learning in the paper includes breaking boundaries barriers, reaching a larger number of students, provision of jobs for ICT experts, etc. In contrary, poor power supply, cost of implementation, poor computer literacy, technophobia (fear of technology), computer crime and system failure were some of the challenges of e-learning discussed in the paper. The paper proffered that the government can help the people gain more from e-learning through its financing. Also, it was stated that instructors/lecturers and students need to undergo training on computer application in order for e-learning to be more effective in developing higher education in Nigeria.

Keywords: e-learning, education, higher education, increasing literacy

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Authors: Pinn Tsin Isabel Yee

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Students are increasingly turning towards online learning materials to supplement their education. One such approach would be the gamified student engagement platforms (GSEPs) to instill a new learning culture. Data was collected from closed-ended questions via content analysis techniques. About 81.8% of college students from the Monash University Foundation Year agreed that GSEPs (Quizizz) was an effective tool for learning. Approximately 85.5% of students disagreed that games were a waste of time. GSEPs were highly effective among students to facilitate the learning process.

Keywords: engagement, gamified, Quizizz, technology

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Authors: Elson Szeto

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Over the past 15 years, digital technology has ubiquitously penetrated societies around the world. New values of e-learning are emerging in the preparation of future talents, while e-learning is a key driver of widening participation and knowledge transfer in Chinese higher education. As a vibrant, Chinese society in Asia, Hong Kong’s new generation university students, perhaps the digital natives, have been learning with e-learning since their basic education. They can acquire new knowledge with the use of different forms of e-learning as a generic competence. These students who embrace this competence further their study journeys in higher education. This project reviews the Government’s policy of Information Technology in Education which has largely put forward since 1998. So far, primary to secondary education has embraced advantages of e-learning capability to advance the learning of different subject knowledge. Yet, e-learning capacity in higher education is yet to be fully examined in Hong Kong. The study reported in this paper is a pilot investigation into e-learning capacity in Chinese higher education in the region. By conducting a qualitative case study of Hong Kong, the investigation focuses on (1) the institutional ICT settings in general; (2) the pedagogic responses to e-learning in specific; and (3) the university students’ satisfaction of e-learning. It is imperative to revisit the e-learning capacity for promoting effective learning amongst university students, supporting new knowledge acquisition and embracing new opportunities in the 21st century. As a pilot case study, data will be collected from individual interviews with the e-learning management team members of a university, teachers who use e-learning for teaching and students who attend courses comprised of e-learning components. The findings show the e-learning capacity of the university and the key components of leveraging e-learning capability as a university-wide learning settings. The findings will inform institutions’ senior management, enabling them to effectively enhance institutional e-learning capacity for effective learning and teaching and new knowledge acquisition. Policymakers will be aware of new potentials of e-learning for the preparation of future talents in this society at large.

Keywords: capability, e-learning, higher education, student learning

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Authors: Khlaid Albishi

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This paper participates in giving new vision and explains the learning and acquisition processes of English language by analyzing a certain context. Five important factors in English language acquisition and learning are discussed and suitable solutions are provided. The factors are compared with the learners' linguistic background at Bisha College of Technology BCT attempting to link the issues faced by students and the research done on similar situations. These factors are phonology, age of acquisition, motivation, psychology and courses of English. These factors are very important; because they interfere and affect specific learning processes at BCT context and general English learning situations.

Keywords: language acquisition, language learning, factors, Bisha college

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Authors: Reni Francis

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Learning has transcended the classroom boundaries to create a learner centric, interactive, and integrative teaching learning environment. This study analysed the impact of iLearn on the teaching, learning, and evaluation among 100 teacher trainees. The objectives were to cater to the different learning styles of the teacher trainees, to incorporate innovative teaching learning activities, to assist in peer tutoring, to implement different evaluation processes. i: Identifying the learning styles among the teacher trainees through VARK Learning style checklist was followed by planning the teaching-learning process to meet the learning styles of the teacher trainees. L: Leveraging innovations in teaching- learning by planning and creating modules incorporating innovative teaching learning and hence the concept based year plan was prepared. E: Engage learning through constructivism using different teaching methodology to engage the teacher trainees in the learning process through Workshop, Round Robin, Gallery walk, Co-Operative learning, Think-Pair-Share, EDMODO, Course Networking, Concept Map, Brainstorming Sessions, Video Clippings. A: Assessing the learning through an Open Book assignment, Closed book assignment, and Multiple Choice Questions and Seminar presentation. R: Remediation through peer tutoring through Mentor-mentee approach in the tutorial groups, Group work, Library Hours. N: Norming new standards. This was done in the form of extended remediation and tutorials to understand the need of the teacher trainee and support them for further achievements in learning through Face to face interaction, Supervised Study Circle, Mobile (Device) learning. The findings of the study revealed the positive impact of iLearn towards student achievement and enhanced social skills.

Keywords: academic achievement, innovative strategy, learning styles, social skills

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Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

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Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

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Authors: Motahareh Fadaei

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Kumiko Inagaki

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Qualitative changes to the undergraduate education have recently become the focus of attention in Japan. This is occurring against the backdrop of declining birthrate and increasing university enrollment, as well as drastic societal changes of advance toward globalization and a knowledge-based society. This paper describes the cases of Japanese universities that promoted various forms of experiential learning around the theme of social participation. The opportunity of learning through practical experience, where students turn their attention to social problems and take pains to consider means of resolving them, creates opportunities to demonstrate “human power” applicable to all sorts of activities the following graduation, thereby guaranteeing students’ continuous growth throughout their careers.

Keywords: career education, experiential learning, subjective learning, university education

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Authors: Loice K. Gudukeya, Charles Mbohwa

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The research investigates hydropower plant efficiency with a view to improving the power output while keeping the overall project cost per kilowatt produced within an acceptable range. It reviews the commonly used Pelton and Cross-flow turbines which are employed in the region for micro-hydro power plants. Turbine parameters such as surface texture, material used and fabrication processes are dealt with the intention of increasing the efficiency by 20 to 25 percent for the micro hydro-power plants.

Keywords: hydro, power plant, efficiency, manufacture

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Authors: Rasha Alshaye

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Blended learning has become an innovative model that combines face-to-face with e-learning approaches. The Saudi Electronic University (SEU) has adopted blended learning as a flexible approach that provides instructors and learners with a motivating learning environment to stimulate the teaching and learning process. This study investigates the perceptions of English language instructors, teaching the four English language skills at Saudi Electronic University. Four main domains were examined in this study; challenges that the instructors encounter while implementing the blended learning approach, enhancing student-instructor interaction, flexibility in teaching, and the lack of technical skills. Furthermore, the study identifies and represents the instructors’ aspirations and plans to utilize this approach in enhancing the teaching and learning experience. Main findings indicate that instructors at Saudi Electronic University experience some challenges while teaching the four language skills. However, they find the blended learning approach motivating and flexible for them and their students. This study offers some important understandings into how instructors are applying the blended learning approach and how this process can be enriched.

Keywords: blended learning, English language skills, English teaching, instructors' perceptions

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Authors: Long Chen

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With the proliferation of E-Learning, collaborative learning becomes more and more popular in various teaching and learning occasions. Studies over the years have proved that actively participating in classroom discussion can enhance student's learning experience, consolidating their knowledge and understanding of the class content. Collaborative learning can also allow students to share their resources and knowledge by exchanging, absorbing, and observing one another's opinions and ideas. Community Question Answering (CQA) services are particularly suitable paradigms for collaborative learning, since it is essentially an online collaborative learning platform where one can get information from multiple sources for he/her to choose from. However, current CQA services have only achieved limited success in collaborative learning due to the uncertainty of answers' quality. In this paper, we predict the quality of answers in a CQA service, i.e. Yahoo! Answers, for the use of online education and distance learning, which would enable a student to find relevant answers and potential answerers more effectively and efficiently, and thus greatly increase students' user experience in CQA services. Our experiment reveals that the quality of answers is influenced by a series of factors such as asking time, relations between users, and his/her experience in the past. We also show that by modelling user's profile with our proposed personalized features, student's satisfaction towards the provided answers could be accurately estimated.

Keywords: Community Question Answering, Collaborative Learning, Log File, Co-Training

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Authors: Olusegun Omoluwa, Kolawole Israel Anthony

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Pupils with learning disabilities are found in every classroom, but because learning disabilities cannot be seen, the condition is often too neglected. Unless these pupils are recognised and treated, they are likely to become educational discards. This study consequently attempted to determine effects of self-monitoring strategy on homework completion among pupils with learning disabilities. Ninety (90) participants were engaged in the study. Pre-test, post-test, control group quasi experimental design was adopted. Purposive sampling technique was used to select pupils with evidence of learning disabilities from three primary schools in Ondo State. Findings showed that self-monitoring strategy was significant in enhancing homework completion among pupils with learning disabilities. However, gender and self-esteem did not significantly contribute to homework completion. The study therefore recommended that measures such that would uncover unsettling academic, psychological and emotional deficiencies of these pupils through appropriate diagnosis should be undertaken by the parents and teachers, in order for them to have a sense of belonging in the society.

Keywords: self monitoring, home work completion, learning dissabilities, learning

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Authors: Wan Jen Chang

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Back ground: Taiwan's current adult lifelong learning services have expanded from vocational training to universal lifelong learning. However, both the professional knowledge training of learning guidance and consulting services and the provision of adult online learning consulting service systems still need to be established. Purpose: The purposes of this study are as follows: 1. Analyze the professional training mechanism for cultivating adult lifelong learning consultation and coaching; 2. Explore the feasibility of constructing a system that uses network technology to provide adult learning consultation services. Research design: This study conducts a literature analysis of counseling and coaching policy reports on lifelong learning in European countries and the United States. There are two focus discussions were conducted with 15 lifelong learning scholars, experts and practitioners as research subjects. The following two topics were discussed and suggested: 1. The current situation, needs and professional ability training mechanism of "Adult Lifelong Learning Consulting and Services"; 2. Strategies for establishing an "Adult Lifelong Learning Consulting and Service internet System". Conclusion: 1.Based on adult lifelong learning consulting and service needs, plan a professional knowledge training and certification system.2.Adult lifelong learning consulting and service professional knowledge and skills training should include the use of network technology to provide consulting service skills.3.To establish an adult lifelong learning consultation and service system, the Ministry of Education should promulgate policies and measures at the central level and entrust local governments or private organizations to implement them.4.The adult lifelong learning consulting and service system can combine the national qualifications framework, private sector and NPO to expand learning consulting service partners.

Keywords: adult lifelong learning, profesional knowledge, consulting and service, network system

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Authors: Tingting Zou

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This paper firstly presents an overview of the situation of Xinjiang and Hotan, and describes the current status and features of Uyghur students’ English education. Then it summarizes the research on the theories of Third Language Acquisition and Foreign Language Learning Motivation at home and abroad. Further, through the data collected by the questionnaire, the paper points out the three main problems and causes of Uyghur students’ English learning in Hotan, Xinjiang. Finally, the paper draws a conclusion and puts forward some suggestions on how to improve their English learning quality based on the theory of Foreign Language Learning Motivation.

Keywords: countermeasures and difficulties, English learning, Hotan Xinjiang, Uyghur students

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Authors: Aigerim Uyzbayeva, Valeriya Tyo, Nurlan Ibrayev

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Kazakhstan is currently one of the dynamically developing states in its region. The stable growth in all sectors of the economy leads to a corresponding increase in energy consumption. Thus, country consumes a significant amount of energy due to the high level of industralisation and the presence of energy-intensive manufacturing such as mining and metallurgy which in turn leads to low energy efficiency. With allowance for this the Government has set several priorities to adopt a transition of Republic of Kazakhstan to a “green economy”. This article provides an overview of Kazakhstan’s energy efficiency situation in for the period of 1991-2014. First, the dynamics of production and consumption of conventional energy resources are given. Second, the potential of renewable energy sources is summarised, followed by the description of GHG emissions trends in the country. Third, Kazakhstan’ national initiatives, policies and locally implemented projects in the field of energy efficiency are described.

Keywords: energy efficiency in Kazakhstan, greenhouse gases, renewable energy, sustainable development

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Authors: Zhidong Zhang, Yingchen Yang

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In this study, a diagnostic assessment model for Mastery Engineering Learning was established based on a group of undergraduate students who studied in an engineering course. A diagnostic assessment model can examine both students' learning process and report achievement results. One very unique characteristic is that the diagnostic assessment model can recognize the errors and anything blocking students in their learning processes. The feedback is provided to help students to know how to solve the learning problems with alternative strategies and help the instructor to find alternative pedagogical strategies in the instructional designs. Dynamics is a core course in which is a common course being shared by several engineering programs. This course is a very challenging for engineering students to solve the problems. Thus knowledge acquisition and problem-solving skills are crucial for student success. Therefore, developing an effective and valid assessment model for student learning are of great importance. Diagnostic assessment is such a model which can provide effective feedback for both students and instructor in the mastery of engineering learning.

Keywords: diagnostic assessment, mastery learning, engineering, bayesian network model, learning processes

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Duaa Al Maani, Andrew Roberts

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Students in higher education are expected to learn actively and independently. Whilst quite work has been done to understand the perceptions of students’ learning transition regarding independent learning, to author’s best knowledge, it seems relatively few published research on independent learning in studio-based subjects such as architecture. Another major issue in independent learning research concerned the inconsistency in terminology; there appears to be a paucity of research on its definition, challenges, and tools within the UK university sector. It is not always clear how independent learning works in practice, or what are the challenges that face students toward being independent learners. Accordingly, this paper seeks to highlight these problems by analyzing previous and current literature of independent learning, in addition, to measure students’ independence at the very begging of their first academic year and compare it with their level of learning independence at the end of the same year. Eighty-seven student enrolled in 2017/2018 at Cardiff University completed the Autonomous Learning Questionnaire in order to measure their level of learning independence. Students’ initial responses were very positive and showed high level of learning independence. Interestingly, these responses significantly decreased at the end of the year. Time management was the most obvious challenge facing students transition into higher education, and contrary to expectations, we found no effect of student maturity on their level of independence. Moreover, we found no significant differences among students’ gender, but we did find differences among nationalities.

Keywords: autonomous learning, first year, learning independence, transition

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Authors: Ambali Alade Odebowale, Andargachew Mekonnen Berhe, Haroldo T. Hattori, Andrey E. Miroshnichenko

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Solar thermophotovoltaic systems (STPV) have emerged as a promising solution to overcome the Shockley-Queisser limit, a significant impediment in the direct conversion of solar radiation into electricity using conventional solar cells. The STPV system comprises essential components such as an optical concentrator, selective emitter, and a thermophotovoltaic (TPV) cell. The pivotal element in achieving high efficiency in an STPV system lies in the design of a spectrally selective emitter or absorber. Traditional methods for designing and optimizing selective emitters are often time-consuming and may not yield highly selective emitters, posing a challenge to the overall system performance. In recent years, the application of machine learning techniques in various scientific disciplines has demonstrated significant advantages. This paper proposes a novel nanostructure composed of four-layered materials (SiC/W/SiO2/W) to function as a selective emitter in the energy conversion process of an STPV system. Unlike conventional approaches widely adopted by researchers, this study employs a machine learning-based approach for the design and optimization of the selective emitter. Specifically, a random forest algorithm (RFA) is employed for the design of the selective emitter, while the optimization process is executed using genetic algorithms. This innovative methodology holds promise in addressing the challenges posed by traditional methods, offering a more efficient and streamlined approach to selective emitter design. The utilization of a machine learning approach brings several advantages to the design and optimization of a selective emitter within the STPV system. Machine learning algorithms, such as the random forest algorithm, have the capability to analyze complex datasets and identify intricate patterns that may not be apparent through traditional methods. This allows for a more comprehensive exploration of the design space, potentially leading to highly efficient emitter configurations. Moreover, the application of genetic algorithms in the optimization process enhances the adaptability and efficiency of the overall system. Genetic algorithms mimic the principles of natural selection, enabling the exploration of a diverse range of emitter configurations and facilitating the identification of optimal solutions. This not only accelerates the design and optimization process but also increases the likelihood of discovering configurations that exhibit superior performance compared to traditional methods. In conclusion, the integration of machine learning techniques in the design and optimization of a selective emitter for solar thermophotovoltaic systems represents a groundbreaking approach. This innovative methodology not only addresses the limitations of traditional methods but also holds the potential to significantly improve the overall performance of STPV systems, paving the way for enhanced solar energy conversion efficiency.

Keywords: emitter, genetic algorithm, radiation, random forest, thermophotovoltaic

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