Search results for: quest based learning

Authors: E. J. Spamer, J. M. Van Zyl, MHA Combrinck

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The Unit for Open Distance Learning (UODL) at the North-West University (NWU), South Africa was established in 2013 with its main function to deliver open distance learning (ODL) programmes to approximately 30 000 students from the Faculties of Education Sciences, Health Sciences, Theology and Arts and Culture. Quality operational and administrative processes are key components in the delivery of these programmes and they need to function optimally for students to be successful in their studies. Operational and administrative processes include aspects such as applications, registration, dissemination of study material, availability of electronic platforms, the management of assessment, and the dissemination of important information. To be able to ensure and enhance quality during these processes, it is vital to determine students’ perceptions with regards to these mentioned processes. A questionnaire was available online and also distributed to the 63 tuition centres. The purpose of this research was to determine the perceptions of ODL students from NWU regarding operational and administrative processes. 1903 students completed and submitted the questionnaire. The data was quantitatively analysed and discussed. Results indicated that the majority of students are satisfied with the operational and administrative processes; however, the results also indicated some areas that need improvement. The data gathered is important to identify strengths and areas for improvement and form part of a bigger strategy of qualitative assurance at the UODL.

Keywords: administrative support, ODL programmes, quantitative study, students' perceptions

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Authors: Ivana Hrebickova, Maria Sevcikova, Romana Slamberova

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The present study was aimed at evaluation of cognitive changes following scheduled neonatal methamphetamine exposure in combination with long-term exposure in adulthood of female Wistar rats. Pregnant mothers were divided into two groups: group with indirect exposure (methamphetamine in dose 5 mg/ml/kg, saline in dose 1 ml/kg) during early lactation period (postnatal day 1–11) - progeny of these mothers were exposed to the effects of methamphetamine or saline indirectly via the breast milk; and the second group with direct exposure – all mothers were left intact for the entire lactation period, while progeny was treated with methamphetamine (5 mg/ml/kg) by injection or the control group, which was received needle pick (shame, not saline) at the same time each day of period of application (postnatal day 1–11). Learning ability and memory consolidation were tested in the Morris Water Maze, which consisted of three types of tests: ‘Place Navigation Test ‘; ‘Probe Test ‘; and ‘Memory Recall Test ‘. Adult female progeny were injected daily, after completion last trial with saline or methamphetamine (1 mg/ml/kg). We compared the effects of indirect/direct neonatal methamphetamine exposure and adult methamphetamine treatment on cognitive function of female rats. Statistical analyses showed that neonatal methamphetamine exposure worsened spatial learning and ability to remember the position of the platform. The present study demonstrated that direct methamphetamine exposure has more significant impact on process of learning and memory than indirect exposure. Analyses of search strategies (thigmotaxis, scanning) used by females during the Place Navigation Test and Memory Recall Test confirm all these results.

Keywords: methamphetamine, Morris water maze, neonatal exposure, strategies, Wistar rats

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Priyanka Patil, Logan Manikam

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Background: The first 1000 days of life are a critical window and can result in adverse health consequences due to inadequate nutrition. South-Asian (SA) communities face significant health disparities, particularly in maternal and child health. Community-based interventions, often employing Participatory-Learning and Action (PLA) approaches, have effectively addressed health inequalities in lower-income nations. The aim of this study was to assess the feasibility of implementing a PLA intervention to improve infant feeding and care practices in SA communities living in London. Methods: Comprehensive analyses were conducted to assess the feasibility/fidelity of this pilot randomized controlled trial. Summary statistics were computed to compare key metrics, including participant consent rates, attendance, retention, intervention support, and perceived effectiveness, against predefined progression rules guiding toward a definitive trial. Secondary outcomes were analyzed, drawing insights from multiple sources, such as The Children’s-Eating-Behaviour Questionnaire (CEBQ), Parental-Feeding-Style Questionnaires (PFSQ), Food-diary, and the Equality-Impact-Assessment (EIA) tool. A video analysis of children's mealtime behavior trends was conducted. Feedback interviews were collected from study participants. Results: Process-outcome measures met predefined progression rules for a definitive trial, which deemed the intervention as feasible and acceptable. The secondary outcomes analysis revealed no significant changes in children's BMI z-scores. This could be attributed to the abbreviated follow-up period of 6 months, reduced from 12 months, due to COVID-19-related delays. CEBQ analysis showed increased food responsiveness, along with decreased emotional over/undereating. A similar trend was observed in PFSQ. The EIA tool found no potential discrimination areas, and video analysis revealed a decrease in force-feeding practices. Participant feedback revealed improved awareness and knowledge sharing. Conclusion: This study demonstrates that a co-adapted PLA intervention is feasible and well-received in optimizing infant-care practices among South-Asian community members in a high-income country. These findings highlight the potential of community-based interventions to enhance health outcomes, promoting health equity.

Keywords: child health, childhood obesity, community-based, infant nutrition

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Authors: Yi-Ju Lee

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This study identified the hypothesised relationship between creative experience, and revisit intention of handmade oriental parasol umbrella in Kaohsiung, Taiwan. A face-to-face questionnaire survey was administered in Meinong town, Kaohsiung. The components of creative experience were found as “sense of achievement”, “unique learning” and “interaction with instructors” in creative tourism. The result also revealed significant positive relationships between creative experience and revisit intention in handmade activities. This paper provides additional suggestions for enhancing revisit intention and guidance regarding creative tourism.

Keywords: creative tourism, sense of achievement, unique learning, interaction with instructors, folk art

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Authors: Joel S. Mtebe, Aron Kondoro, Mussa M. Kissaka, Elia Kibga

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Sub-Saharan Africa is described as the second fastest growing mobile phone penetration in the world more than in the United States or the European Union. Mobile phones have been used to provide a lot of opportunities to improve people’s lives in the region such as in banking, marketing, entertainment, and paying various bills such as water, TV, and electricity. However, the potential of using mobile phones to enhance teaching and learning has not been explored. This study presents an experience of developing and delivering SMS quizzes questions that were used to assess mastery of the subject content knowledge of science and mathematics secondary school teachers in Tanzania. The SMS quizzes were used as a follow up support mechanism to 500 teachers who participated in a project to upgrade subject content knowledge of science and mathematics subjects. Quizzes of 10-15 questions were sent to teachers each week for 8 weeks and the results were analyzed using SPSS. The results showed that chemistry and biology had better performance compared to mathematics and physics. Teachers reported some challenges that led to poor performance, invalid answers, and non-responses and they are presented. This research has several practical implications for those who are implementing or planning to use mobile phones for teaching and learning especially in rural secondary schools in sub-Saharan Africa.

Keywords: mobile learning, elearning, educational technolgies, SMS, secondary education, assessment

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Authors: Alejandra Yanez, Teresa Benavides, Zita Lopez

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Even before COVID-19, one of the most important challenges that Higher Education faces today is the need for innovative educational methodologies and flexibility. We could all agree that one of the objectives of Higher Education is to provide students with a variety of intellectual and practical skills that, at the same time, will help them develop competitive advantages such as adaptation and critical thinking. Among the strategic objectives of Universidad de Monterrey (UDEM) has been to provide flexibility and satisfaction to students in the delivery modes of the academic offer. UDEM implemented a methodology that combines face to face with synchronous and asynchronous as delivery modes. UDEM goal, in this case, was to implement new technologies and different teaching methodologies that will improve the students learning experience. In this study, the experience of students during courses implemented in new delivery mode was compared with students in courses with traditional delivery modes. Students chose openly either way freely. After everything students around the world lived in 2020 and 2021, one can think that the face to face (traditional) delivery mode would be the one chosen by students. The results obtained in this study reveal that both delivery modes satisfy students and favor their learning process. We will show how the combination of delivery modes provides flexibility, so the proposal is that universities can include them in their academic offer as a response to the current student's learning interests and needs.

Keywords: flexibility, new delivery modes, student satisfaction, academic offer

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Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

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This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

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Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

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The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error.

Keywords: rough sets, gene expression programming, rough neural networks, classification

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Authors: Gholam Reza Parvizi, Hossein Azad, Ali Reza Kargar

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This article investigated the impact of task-based language teaching (TBLT) on writing performance of the Iranian intermediate EFL learners. There were two groups of forty students of the intermediate female learners studying English in Jahad-e-Daneshgahi language institute, ranging in age from thirteen to nineteen. They participated in their regular classes in the institute and were assigned to two groups including an experimental group of task-based language teaching and a control group for the purpose of homogeneity, all students in two groups took an achievement test before the treatment. As a pre-test; students were assigned to write a task at the beginning of the course. One of the classes was conducted through talking a TBLT approach on their writing, while the other class followed regular patterns of teaching, namely traditional approach for TBLT group. There were some tasks chosen from learners’ textbook. The task selection was in accordance with learning standards for ESL and TOFEL writing sections. At the end of the treatment, a post-test was administered to both experimental group and the control group. Scoring was done on the basis of scoring scale of “expository writing quality scale”. The researcher used paired samples t-test to analyze the effect of TBLT teaching approach on the writing performance of the learners. The data analysis revealed that the subjects in TBLT group performed better on the writing performance post-test than the subjects in control group. The findings of the study also demonstrated that TBLT would enhance writing performance in the group of learners. Moreover, it was indicated that TBLT has been effective in teaching writing performance to Iranian EFL learners

Keywords: task-based language teaching, task, language teaching approach, writing proficiency, EFL learners

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Authors: M. Assaad, J. Mäkiö, T. Mäkelä, M. Kankaanranta, N. Fachantidis, V. Dagdilelis, A. Reid, C. R. del Rio, E. V. Pavlysh, S. V. Piashkun

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To bring science and society together in Europe, thus increasing the continent’s international competitiveness, STEM (science, technology, engineering and mathematics) education must be more relatable to European youths in their everyday life. STIMEY (Science, Technology, Innovation, Mathematics, Engineering for the Young) project researches and develops a hybrid educational environment with multi-level components that is being designed and developed based on a well-researched pedagogical framework, aiming to make STEM education more attractive to young people aged 10 to 18 years in this digital era. This environment combines social media components, robotic artefacts, and radio to educate, engage and increase students’ interest in STEM education and careers from a young age. Additionally, it offers educators the necessary modern tools to deliver STEM education in an attractive and engaging manner in or out of class. Moreover, it enables parents to keep track of their children’s education, and collaborate with their teachers on their development. Finally, the open platform allows businesses to invest in the growth of the youths’ talents and skills in line with the economic and labour market needs through entrepreneurial tools. Thus, universities, schools, teachers, students, parents, and businesses come together to complete a circle in which STEM becomes part of the daily life of youths through a hybrid educational environment that also prepares them for future careers.

Keywords: e-learning, entrepreneurship, pedagogy, robotics, serious gaming, social media, STEM education

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Authors: Grace karimi Muriithi, Behnaz Papari, Ali Arsalan, Christopher Shannon Edrington

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Complexity and nonlinearity of the control system design is increasing for DC microgrid applications when the cyber concept associated with the technology constraints will added to the picture. Controllers’ functionality during the critical operation mode is required to guaranteed specifically for a high profile applications such as NAVY DC ship power system (SPS) as an small-scaled DC microgrid. Thus, SPS is susceptible to cyber-attacks and, accordingly, can provide the disastrous effects. In this study, a machine learning (ML) approach is demonstrated to offer the promising performance of SPS for developing an effective and robust functionality over attacks time. Simulation results analysis demonstrate that the proposed method can improve the controllability successfully.

Keywords: controlability, cyber attacks, distribute control, machine learning

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Authors: Siti Aisyah

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This article discusses character education which is a very basic education for early childhood with the aim of instilling moral values to prevent unacceptable behaviours. Children can absorb good character when they are in a supportive environment, for that schools should understand and implement character education in the learning process. In the school environment, good character education and habituation can be developed. All parties in the school should be involved, especially the teachers. This research discusses how teachers apply characters on the values of responsibility, honesty, discipline, love and compassion, caring, courage, independence, hard work, mutual cooperation, courtesy, justice, self-control and tolerance. The respondents of this study were teachers involving 200 children from all over Indonesia. The methodology used was a survey method with the result that more than 80% of teachers have been able to exhibit the expected behaviours. The survey was conducted based on observations, types of tasks and assessed performance. The character values can be optimally taught in the school environment based on the teacher's ability to implement them. Through the character education in schools, children can also instil a positive outlook on life.

Keywords: teachers, character strengthening, early childhood, behavior

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Authors: Dimitrios Vlachopoulos, George Tsokkas

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Although it is not intended to identify distance education students as a homogeneous group, recent research has demonstrated that there are some demographic and personality common traits among most of them that provide the basis for the description of a typical distance learning student. The purpose of this paper is to describe these common traits and to facilitate their learning journey within a distance education program. The described research is an initiative of the Distance Education Unit at the European University Cyprus (Laureate International Universities) in the context of its action for the improvement of the students’ performance.

Keywords: distance education students, successful student performance, European University Cyprus, common traits

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Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

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This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

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In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning

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Authors: Ralph Barnes

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The internationalization and globalization of education are now a huge, multi-million dollar industry. The movement of international students across the globe has provided a rich vein of revenue for universities and institutions of higher learning to exploit and harvest. A concerted effort has been made by universities worldwide to court students from overseas, with some countries relying up to one-third of student fees, coming from international students. Australian universities and English Language Centres are coming under increased government scrutiny in respect to such areas as the academic progression of international students, management and understanding of student visa requirements and the design of higher education courses and effective assessment regimes. As such, universities and other higher education institutions are restructuring themselves more as service providers rather than as strictly education providers. In this paper, the high-touch, tailored academic model currently followed by some Australian educational institutions to support international students, is examined and challenged. Academic support services offered to international students need to be coordinated, sustained and reviewed regularly, in order to assess their effectiveness. Maintaining the delivery of high-quality educational programs and learning outcomes for this high income-generating student cohort is vital, in order to continue the successful academic and social engagement by international students across the Australian university and higher education landscape.

Keywords: ESL, engagement, tertiary, learning

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Authors: Shih-Yu Wang, Shun-Wen Hsiao

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In the field of cybersecurity, there exists many vendors giving malware samples classified results, namely naming after the label that contains some important information which is also called AV label. Lots of researchers relay on AV labels for research. Unfortunately, AV labels are too cluttered. They do not have a fixed format and fixed naming rules because the naming results were based on each classifiers' viewpoints. A way to fix the problem is taking a majority vote. However, voting can sometimes create problems of bias. Thus, we create a novel ensemble approach which does not rely on the cacophonous naming result but depend on group identification to aggregate everyone's opinion. To achieve this purpose, we develop an scoring system called Pairwise Consensus Score (PCS) to calculate result similarity. The entire method architecture combine Genetic Algorithm and PCS to find maximum consensus in the group. Experimental results revealed that our method outperformed the majority voting by 10% in term of the score.

Keywords: genetic algorithm, ensemble learning, malware family, malware labeling, AV labels

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Authors: Jun Wang, Chenglei Diao, Emanuele Pagone, Jialuo Ding, Stewart Williams

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As a crucial component in aircraft, landing gear systems are responsible for supporting the plane during parking, taxiing, takeoff, and landing. Given the need for high load-bearing capacity over extended periods, 300M ultra-high strength steel (UHSS) is often the material of choice for crafting these systems due to its exceptional strength, toughness, and fatigue resistance. In the quest for cost-effective and sustainable manufacturing solutions, Wire Arc Additive Manufacturing (WAAM) emerges as a promising alternative for fabricating 300M UHSS landing gears. This is due to its advantages in near-net-shape forming of large components, cost-efficiency, and reduced lead times. Cranfield University has conducted an extensive preliminary study on WAAM 300M UHSS, covering feature deposition, interface analysis, and post-heat treatment. Both Gas Metal Arc (GMA) and Plasma Transferred Arc (PTA)-based WAAM methods were explored, revealing their feasibility for defect-free manufacturing. However, as-deposited 300M features showed lower strength but higher ductility compared to their forged counterparts. Subsequent post-heat treatments were effective in normalising the microstructure and mechanical properties, meeting qualification standards. A 300M UHSS landing gear demonstrator was successfully created using PTA-based WAAM, showcasing the method's precision and cost-effectiveness. The demonstrator, measuring Ф200mm x 700mm, was completed in 16 hours, using 7 kg of material at a deposition rate of 1.3kg/hr. This resulted in a significant reduction in the Buy-to-Fly (BTF) ratio compared to traditional manufacturing methods, further validating WAAM's potential for this application. A "cradle-to-gate" environmental impact assessment, which considers the cumulative effects from raw material extraction to customer shipment, has revealed promising outcomes. Utilising Wire Arc Additive Manufacturing (WAAM) for landing gear components significantly reduces the need for raw material extraction and refinement compared to traditional subtractive methods. This, in turn, lessens the burden on subsequent manufacturing processes, including heat treatment, machining, and transportation. Our estimates indicate that the carbon footprint of the component could be halved when switching from traditional machining to WAAM. Similar reductions are observed in embodied energy consumption and other environmental impact indicators, such as emissions to air, water, and land. Additionally, WAAM offers the unique advantage of part repair by redepositing only the necessary material, a capability not available through conventional methods. Our research shows that WAAM-based repairs can drastically reduce environmental impact, even when accounting for additional transportation for repairs. Consequently, WAAM emerges as a pivotal technology for reducing environmental impact in manufacturing, aiding the industry in its crucial and ambitious journey towards Net Zero. This study paves the way for transformative benefits across the aerospace industry, as we integrate manufacturing into a hybrid solution that offers substantial savings and access to more sustainable technologies for critical component production.

Keywords: WAAM, aircraft landing gear, microstructure, mechanical performance, life cycle assessment

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Authors: Alaa Yehia, Ayatollah Yehia, Sherif Yehia

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There is a recurring issue of low enrollments across many civil engineering departments in postsecondary institutions. While there have been moments where enrollments begin to increase, civil engineering departments find themselves facing low enrollments at around 60% over the last five years across the Middle East. There are many reasons that could be attributed to this decline, such as low entry-level salaries, over-saturation of civil engineering graduates in the job market, and a lack of construction projects due to the impending or current recession. However, this recurring problem alludes to an intrinsic issue of the curriculum. The societal shift to the usage of high technology such as machine learning (ML) and artificial intelligence (AI) demands individuals who are proficient at utilizing it. Therefore, existing curriculums must adapt to this change in order to provide an education that is suitable for potential and current students. In this paper, In order to provide potential solutions for this issue, the analysis considers two possible implementations of high technology into the civil engineering curriculum. The first approach is to implement a course that introduces applications of high technology in Civil Engineering contexts. While the other approach is to intertwine applications of high technology throughout the degree. Both approaches, however, should meet requirements of accreditation agencies. In addition to the proposed improvement in civil engineering curriculum, a different pedagogical practice must be adapted as well. The passive learning approach might not be appropriate for Gen Z students; current students, now more than ever, need to be introduced to engineering topics and practice following different learning methods to ensure they will have the necessary skills for the job market. Different learning methods that incorporate high technology applications, like AI, must be integrated throughout the curriculum to make the civil engineering degree more attractive to prospective students. Moreover, the paper provides insight on the importance and approach of adapting the Civil Engineering curriculum to address the current low enrollment crisis that civil engineering departments globally, but specifically in the Middle East, are facing.

Keywords: artificial intelligence (AI), civil engineering curriculum, high technology, low enrollment, pedagogy

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Marcos Devaner, Marcela Alves, Cledson Braga, Fabiano Alves, Wilton Bezerra

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This article discusses the inclusion of people with disabilities in the process of testing an accessible system solution for distance education. The accessible system, team profile, methodologies and techniques covered in the testing process are presented. The testing process shown in this paper was designed from the experience with user. The testing process emerged from lessons learned from past experiences and the end user is present at all stages of the tests. Also, lessons learned are reported and how it was possible the maturing of the team and the methods resulting in a simple, productive and effective process.

Keywords: experience report, accessible systems, software testing, testing process, systems, e-learning

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Authors: Kari Björn

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Introduction of games has expanded the application area of computer-aided learning tools to wide variety of age groups of learners. Serious games engage the learners into a real-world -type of simulation and potentially enrich the learning experience. Institutional background of a Bachelor’s level engineering program in Information and Communication Technology is introduced, with detailed focus on one of its majors, Health Technology. As part of a Customer Oriented Software Application thematic semester, one particular course of “eHealth Business and Solutions” is described and reflected in a gamified framework. Learning a consistent view into vast literature of business management, strategies, marketing and finance in a very limited time enforces selection of topics relevant to the industry. Health Technology is a novel and growing industry with a growing sector in consumer wearable devices and homecare applications. The business sector is attracting new entrepreneurs and impatient investor funds. From engineering education point of view the sector is driven by miniaturizing electronics, sensors and wireless applications. However, the market is highly consumer-driven and usability, safety and data integrity requirements are extremely high. When the same technology is used in analysis or treatment of patients, very strict regulatory measures are enforced. The paper introduces a course structure using gamification as a tool to learn the most essential in a new market: customer value proposition design, followed by a market entry game. Students analyze the existing market size and pricing structure of eHealth web-service market and enter the market as a steering group of their company, competing against the legacy players and with each other. The market is growing but has its rules of demand and supply balance. New products can be developed with an R&D-investment, and targeted to market with unique quality- and price-combinations. Product cost structure can be improved by investing to enhanced production capacity. Investments can be funded optionally by foreign capital. Students make management decisions and face the dynamics of the market competition in form of income statement and balance sheet after each decision cycle. The focus of the learning outcome is to understand customer value creation to be the source of cash flow. The benefit of gamification is to enrich the learning experience on structure and meaning of financial statements. The paper describes the gamification approach and discusses outcomes after two course implementations. Along the case description of learning challenges, some unexpected misconceptions are noted. Improvements of the game or the semi-gamified teaching pedagogy are discussed. The case description serves as an additional support to new game coordinator, as well as helps to improve the method. Overall, the gamified approach has helped to engage engineering student to business studies in an energizing way.

Keywords: engineering education, integrated curriculum, learning experience, learning outcomes

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Authors: Prakash Singh

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This study focused on the importance of collegiality to improve the effectiveness of a poorly resourced school (PRS). In an effective school that embraces collegiality as its culture, one can expect to find a teaching staff and a management team that shares responsibilities and accountabilities through the development of a common purpose and vision, regardless of whether the school is considered to be poorly resourced or not. Working together in collegial teams is a more effective way to accomplish tasks and to create a climate for effective learning, even for learners in PRSs from poor communities. The main aim of this study was therefore to determine whether collegiality as a leadership strategy could extract the best from people in a PRS, and consequently create the most effective and efficient educational climate possible. The responses received from the teachers and the principal at the PRS supports the notion that collegiality does have a positive influence on learning, as demonstrated by the improved academic achievement of the learners. The teachers were now more involved in the school. They agreed that this was a positive development. Their descriptions of increased involvement, shared accountability and shared decision-making identified important aspects of collegiality that transformed the school from being dysfunctional. Hence, it is abundantly clear that a collegial leadership style can help extract the best from people because the most effective and efficient educational climate can be created at a school when collegiality is employed. Collegial leadership demonstrates that even in PRSs, there are boundless opportunities to improve teaching and learning.

Keywords: collegiality, collegial leadership, effective educational climate, poorly resourced school

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Authors: Massimiliano Condotta, Giovanni Borga, Chiara Scanagatta

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Urban systems are places where the various actors involved interact and enter in conflict, in particular with reference to topics such as traffic congestion and security. But topics of discussion, and often clash because of their strong complexity, are air and noise pollution. For air pollution, the complexity stems from the fact that atmospheric pollution is due to many factors, but above all, the observation and measurement of the amount of pollution of a transparent, mobile and ethereal element like air is very difficult. Often the perceived condition of the inhabitants does not coincide with the real conditions, because it is conditioned - sometimes in positive ways other in negative ways - from many other factors such as the presence, or absence, of natural elements such as trees or rivers. These problems are seen with noise pollution as well, which is also less considered as an issue even if it’s problematic just as much as air quality. Starting from these opposite positions, it is difficult to identify and implement valid, and at the same time shared, mitigation solutions for the problem of urban pollution (air and noise pollution). The LOOPER (Learning Loops in the Public Realm) project –described in this paper – wants to build and test a methodology and a platform for participatory co-design, planning, and construction process inside a learning loop process. Novelties in this approach are various; the most relevant are three. The first is that citizens participation starts since from the research of problems and air quality analysis through a participatory data collection, and that continues in all process steps (design and construction). The second is that the methodology is characterized by a learning loop process. It means that after the first cycle of (1) problems identification, (2) planning and definition of design solution and (3) construction and implementation of mitigation measures, the effectiveness of implemented solutions is measured and verified through a new participatory data collection campaign. In this way, it is possible to understand if the policies and design solution had a positive impact on the territory. As a result of the learning process produced by the first loop, it will be possible to improve the design of the mitigation measures and start the second loop with new and more effective measures. The third relevant aspect is that the citizens' participation is carried out via Urban Living Labs that involve all stakeholder of the city (citizens, public administrators, associations of all urban stakeholders,…) and that the Urban Living Labs last for all the cycling of the design, planning and construction process. The paper will describe in detail the LOOPER methodology and the technical solution adopted for the participatory data collection and design and construction phases.

Keywords: air quality, co-design, learning loops, noise pollution, urban living labs

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Authors: Charles St. Geste, Michael Neumann, Catherine Twohig

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Our primary aim is to examine farmer-centered participatory action learning as a tool to improve agricultural production, build resilience to climate shocks and, more broadly, advance community-driven solutions for sustainable development in rural communities across Haiti. For over six years, sixty plus farmers from Deslandes, Haiti, organized in three traditional work groups called konbits, have designed and tested low-input agroecology techniques as part of the Konbit Vanyan Kapab Pwoje Agroekoloji. The project utilizes a participatory action learning approach, emphasizing social inclusion, building on local knowledge, experiential learning, active farmer participation in trial design and evaluation, and cross-community sharing. Mixed methods were used to evaluate changes in knowledge and adoption of agroecology techniques, confidence in advancing agroecology locally, and innovation among Konbit Vanyan Kapab farmers. While skill and knowledge in application of agroecology techniques varied among individual farmers, a majority of farmers successfully adopted techniques outside of the trial farms. The use of agroecology techniques on trial and individual farms has doubled crop production in many cases. Farm income has also increased, and farmers report less damage to crops and property caused by extreme weather events. Furthermore, participatory action strategies have led to greater local self-determination and greater capacity for sustainable community development. With increased self-confidence and the knowledge and skills acquired from participating in the project, farmers prioritized sharing their successful techniques with other farmers and have developed a farmer-to-farmer training program that incorporates participatory action learning. Using adult education methods, farmers, trained as agroecology educators, are currently providing training in sustainable farming practices to farmers from five villages in three departments across Haiti. Konbit Vanyan Kapab farmers have also begun testing production of value-added food products, including a dried soup mix and tea. Key factors for success include: opportunities for farmers to actively participate in all phases of the project, group diversity, resources for application of agroecology techniques, focus on group processes and overcoming local barriers to inclusive decision-making.

Keywords: agroecology, participatory action learning, rural Haiti, sustainable community development

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Authors: Olga Karamalak

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The paper demonstrates a research about theoretical understanding of writing in the electronic environment as dynamic, interactive, dialogical, and distributed activity aimed at “other-orientation” and consensual domain creation. The purpose is to analyze the personal e-mail correspondence in the academic environment from this perspective. The focus is made on the dynamics of interaction between the correspondents such as contact setting, orientation and co-functions; and the text of an e-letter is regarded as indices of the write’s state or affordances in terms of ecological linguistics. The establishment of consensual domain of interaction brings about a new stage of cognition emergence which may lead to distributed learning. The research can play an important part in the series of works dedicated to writing in the electronic environment.

Keywords: consensual domain of interactions, distributed writing and learning, e-mail correspondence, interaction, orientation, co-function

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

Abstract:

Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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