Search results for: embedded learning support

Authors: Songul Cinaroglu

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Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures.

Keywords: machine learning, lasso regression, random forest regression, support vector regression, hyperparameter tuning, health expenditure

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Authors: Anli Du Preez, James Bekker

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Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Ibraheem Alzahrani

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This paper seeks to discover the relationship between both the social constructivist learning theory and the collaborative learning environment. This relationship can be identified through given an example of the learning environment. Due to wiki characteristics, wiki can be used to understand the relationship between constructivist learning theory and collaborative learning environment. However, several evidences will come in this paper to support the idea of why wiki is the suitable method to explore the relationship between social constructivist theory and the collaborative learning and their role in learning. Moreover, learning activities in wiki classroom will be discussed in this paper to find out the result of the learners' interaction in the classroom groups, which will be through two types of communication; synchronous and asynchronous.

Keywords: social constructivist, collaborative, environment, wiki, activities

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Authors: Wieke Ellen Bouwes

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This study aims to unravel which factors enhance accessibility of video consultations (VCs) for patients with low digital literacy. Thirteen in-depth interviews with patients, hospital employees, eHealth experts, and digital support organizations were held. Patients with low digital literacy received in-home support during real-time video consultations and are observed during the set-up of these consultations. Key findings highlight the importance of patient acceptance, emphasizing video consultations benefits and avoiding standardized courses. The lack of a uniform video consultation system across healthcare providers poses a barrier. Familiarity with support organizations – to support patients in usage of digital tools - among healthcare practitioners enhances accessibility. Moreover, considerations regarding the Dutch General Data Protection Regulation (GDPR) law influence support patients receive. Also, provider readiness to use video consultations influences patient access. Further, alignment between learning styles and support methods seems to determine abilities to learn how to use video consultations. Future research could delve into tailored learning styles and technological solutions for remote access to further explore effectiveness of learning methods.

Keywords: video consultations, digital literacy skills, effectiveness of support, intra- and inter-organizational relationships, patient acceptance of video consultations

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Authors: Mona Hoyng

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In times of digitalization nowadays, the use of games became a crucial new way for digital game-based learning (DGBL) in higher education. In this regard, the development of a gameful experience (GE) among students is decisive when examining DGBL as the GE is a necessary precondition determining the effectiveness of games. In this regard, the purpose of this study is to provide deeper insights into the GE and to empirically investigate whether and how these meaningful learning experiences within games, i.e., GE, among students are created. Based on the theory of experience and flow theory, a double-mediation model was developed considering instructional support, group engagement, and flow as determinants of students’ GE. Based on data of 337 students taking part in a business simulation game at two different universities in Germany, regression-based statistical mediation analysis revealed that instructional support promoted students’ GE. This relationship was further sequentially double mediated by group engagement and flow. Consequently, in the context of DGBL, meaningful learning experiences within games in terms of GE are created and promoted through appropriate instructional support, as well as high levels of group engagement and flow among students.

Keywords: gameful experience, instructional support, group engagement, flow, education, learning

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Authors: Suwaree Yordchim

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The objective is to study the satisfaction on English with an online learning. Online learning system mainly consists of English lessons, exercises, tests, web boards, and supplementary lessons for language practice. The sample groups are 80 Thai students studying English for Business Communication, majoring in Hotel and Lodging Management. The data are analyzed by mean, standard deviation (S.D.) value from the questionnaires. The results were found that the most average of satisfaction on academic aspects are technological searching tool through E-learning system that support the students’ learning (4.51), knowledge evaluation on prepost learning and teaching (4.45), and change for project selections according to their interest, subject contents including practice in the real situations (4.45), respectively.

Keywords: English language learning, online system, online learning, supplementary lessons

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Authors: Matthew Montebello

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Student engagement within a virtual learning environment generates masses of data points that can significantly contribute to the learning analytics that lead to decision support. Ideally, similar data is collected during student interaction with a physical learning space, and as a consequence, data is present at a large scale, even in relatively small classes. In this paper, we report of such an occurrence during classes held in a HiFlex modality as we investigate the advantages of adopting such a methodology. We plan to take full advantage of the learner-generated data in an attempt to further enhance the effectiveness of the adopted learning environment. This could shed crucial light on operating modalities that higher education institutions around the world will switch to in a post-COVID era.

Keywords: HiFlex, big data in higher education, learning analytics, virtual learning environment

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Angela Meyer

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The operation and maintenance cost for wind parks make up a major fraction of the park’s overall lifetime cost. To minimize the cost and risk involved, an optimal operation and maintenance strategy requires continuous monitoring and analysis. In order to facilitate this, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behavior in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. The presented decision support system and method are applicable for any turbine type and manufacturer providing telemetry data of the turbine operating state. We demonstrate the successful detection and diagnosis of anomalous operating states in a case study at a German onshore wind park comprised of Vestas V112 turbines.

Keywords: anomaly detection, decision support, machine learning, monitoring, performance optimization, wind turbines

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Authors: Chryssi Vitsilaki, Apostolos Kostas, Ilias Efthymiou

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This paper presents selected best practices on online learning and teaching derived from a novel and innovating Lifelong Learning program through e-Learning, which has during the last five years been set up at the University of the Aegean in Greece. The university, capitalizing on an award-winning, decade-long experience in e-learning and blended learning in undergraduate and postgraduate studies, recently expanded into continuous education and vocational training programs in various cutting-edge fields. So, in this article we present: (a) the academic structure/infrastructure which has been developed for the administrative, organizational and educational support of the e-Learning process, including training the trainers, (b) the mode of design and implementation based on a sound pedagogical framework of open and distance education, and (c) the key results of the assessment of the e-learning process by the participants, as they are used to feedback on continuous organizational and teaching improvement and quality control.

Keywords: distance education, e-learning, life-long programs, synchronous/asynchronous learning

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Authors: T. Konst

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Most jobs include training and communication tasks, but often the people in these jobs lack pedagogical competences to plan, implement and assess learning. This paper aims to discuss how a learning approach called innovation pedagogy developed in higher education can be utilized for learning development in various organizations. The methods presented how to implement innovation pedagogy such as process consultation and train the trainer model can provide added value to develop pedagogical knowhow in organizations and thus support their internal learning and development.

Keywords: innovation pedagogy, learning, organizational development, process consultation

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Authors: Ofra Walter Btel Liran Hazan

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This study’s goal was to clarify whether psychological capital (PsyCap) mediated the relations between social support and subjective well-being among post-secondary students during the Covid-19 pandemic and to assess whether students diagnosed with a learning disability (LD) and/or attention deficit hyperactivity disorder (ADHD) differed from others in their reliance on social support and their level of PsyCap and subjective wellbeing. Participants were257 students, 152 diagnosed with LD/ADHD and the rest neurotypical. The study used four questionnaires: demographic and academic information; Psychological Capital Questionnaire (PCQ); Subjective Well-Being Index; social support questionnaire. The results indicated PsyCapmediated relations between social support and subjective wellbeing. Students diagnosed with LD/ADHD differed from neurotypicals in their PsyCap and subjective wellbeing levels but not in their social support. In addition, the relations between PsyCap and social support were stronger among students diagnosed with LD/ADHD. PsyCap was an important resource for all participants and was related to social support and subjective wellbeing, making it especially valuable for LD/ADHD students facing new and threatening situations, such as the Covid-19 pandemic.

Keywords: LD/ADHD post-secondary students, subjective wellbeing, social support, PsyCap, covid-19

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Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

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Authors: Fiona Wahr, Sitalakshmi Venkatraman

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Industry employers require new graduates to bring with them a range of knowledge, skills and abilities which mean these new employees can immediately make valuable work contributions. These will be a combination of discipline and professional knowledge, skills and abilities which give graduates the technical capabilities to solve practical problems whilst interacting with a range of stakeholders. Underpinning the development of these disciplines and professional knowledge, skills and abilities, are “enabling” knowledge, skills and abilities which assist students to engage in learning. These are academic and learning skills which are essential to common starting points for both the learning process of students entering the course as well as forming the foundation for the fully developed graduate knowledge, skills and abilities. This paper reports on a project created to introduce and strengthen these enabling skills into the first semester of a Bachelor of Information Technology degree in an Australian polytechnic. The project uses an action research approach in the context of ongoing continuous improvement for the course to enhance the overall learning experience, learning sequencing, graduate outcomes, and most importantly, in the first semester, student engagement and retention. The focus of this is implementing the new curriculum in first semester subjects of the course with the aim of developing the “enabling” learning skills, such as literacy, research and numeracy based knowledge, skills and abilities (KSAs). The approach used for the introduction and embedding of these KSAs, (as both enablers of learning and to underpin graduate attribute development), is presented. Building on previous publications which reported different aspects of this longitudinal study, this paper recaps on the rationale for the curriculum redevelopment and then presents the quantitative findings of entering students’ reading literacy and numeracy knowledge and skills degree as well as their perceived research ability. The paper presents the methodology and findings for this stage of the research. Overall, the cohort exhibits mixed KSA levels in these areas, with a relatively low aggregated score. In addition, the paper describes the considerations for adjusting the design and delivery of the new subjects with a targeted learning experience, in response to the feedback gained through continuous monitoring. Such a strategy is aimed at accommodating the changing learning needs of the students and serves to support them towards achieving the enabling learning goals starting from day one of their higher education studies.

Keywords: enabling skills, student retention, embedded learning support, continuous improvement

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Zahra Alqahtani

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The use of e-learning technologies is gaining momentum in all educational institutions of the world, including Saudi universities. In the e-learning context, there is a growing need and concern among Saudi universities to improve and enhance quality assurance for e-learning systems. Practicing quality assurance activities and applying quality standards in e-learning in Saudi universities is thought to reduce the negative viewpoints of some stakeholders and ensure stakeholders’ satisfaction and needs. As a contribution to improving the quality of e-learning method in Saudi universities, the main purpose of this study is to explore and investigate strategies for the development of quality assurance in e-learning in one university in Saudi Arabia, which is considered a good reference university using the best and ongoing practices in e-learning systems among Saudi universities. In order to ensure the quality of its e-learning methods, Saudi university has adopted Quality Matters Standards as a controlling guide for the quality of its blended and full e-course electronic courses. Furthermore, quality assurance can be further improved if a variety of perspectives are taken into consideration from the comprehensive viewpoints of faculty members, administrative staff, and students.This qualitative research involved the use of different types of interviews, as well as documents that contain data related to e-learning methods in the Saudi university environment. This exploratory case study was undertaken, from the perspectives of various participants, to understand the phenomenon of quality assurance using an inductive technique.The results revealed six main supportive factors that assist in ensuring the quality of e-learning in the Saudi university environment. Essentially, these factors are institutional support, faculty member support, evaluation of faculty, quality of e-course design, technology support, and student support, which together have a remarkable positive effect on quality, forming intrinsic columns connected by bricks leading to quality e-learning. Quality Matters standards are considered to have a strong impact on improving faculty members' skills and on the development of high-quality blended and full e-courses.

Keywords: E-learning, quality assurance, quality matters standards, KKU-supportive factors

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Authors: Zhou Hong, Gu Xiao-Qing, Lıu Hong-Jiao, Leng Jing

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Seamless learning is becoming a research hotspot in recent years, and the emerging of micro-lectures, flipped classroom has strengthened the development of seamless learning. Based on the characteristics of the seamless learning across time and space and the course structure of the flipped classroom, and the theories of language learning, we put forward the language learning model which can support ‘seamless flipped’ environment (abbreviated as ‘S-F’). Meanwhile, the characteristics of the ‘S-F’ learning environment, the corresponding framework construction and the activity design of diversified corpora were introduced. Moreover, a language learning app named ‘Daily Speaking’ was developed to facilitate the practice of the language learning model in ‘S-F’ environment. In virtue of the learning case of Shanghai language, the rationality and feasibility of this framework were examined, expecting to provide a reference for the design of ‘S-F’ learning in different situations.

Keywords: seamless learning, flipped classroom, seamless-flipped environment, language learning model

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Bhavani Veasuvalingam

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Case-based learning (CBL) is a popular instructional method aimed to bridge theory to clinical practice. This study aims to explore CBL mixed modality curriculum in influencing students’ learning styles and strategies that support learning. An explanatory sequential mixed method study was employed with initial phase, 44-itemed Felderman’s Index of Learning Style (ILS) questionnaire employed across year one medical students (n=142) using convenience sampling to describe the preferred learning styles. The qualitative phase utilised three focus group discussions (FGD) to explore in depth on the multimodal learning style exhibited by the students. Most students preferred combination of learning stylesthat is reflective, sensing, visual and sequential i.e.: RSVISeq style (24.64%) from the ILS analysis. The frequency of learning preference from processing to understanding were well balanced, with sequential-global domain (66.2%); sensing-intuitive (59.86%), active- reflective (57%), and visual-verbal (51.41%). The qualitative data reported three major themes, namely Theme 1: CBL mixed modalities navigates learners’ learning style; Theme 2: Multimodal learners active learning strategies supports learning. Theme 3: CBL modalities facilitating theory into clinical knowledge. Both quantitative and qualitative study strongly reports the multimodal learning style of the year one medical students. Medical students utilise multimodal learning styles to attain the clinical knowledge when learning with CBL mixed modalities. Educators’ awareness of the multimodal learning style is crucial in delivering the CBL mixed modalities effectively, considering strategic pedagogical support students to engage and learn CBL in bridging the theoretical knowledge into clinical practice.

Keywords: case-based learning, learnign style, medical students, learning

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Authors: Lorna Martha Dreyer

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Consistent with the changing paradigm on the rights of people with disabilities and in pursuit of social justice, there is internationally an increase in students with disabilities enrolling at Higher Education Institutions (HEIs). This trend challenges HEI’s to transform and attain Education for All (EFA) as a global imperative. However, while physical and sensory disabilities are observable, students with specific learning disabilities (SLD) do not present with any visible indications and are often referred to as “hidden” or “invisible” disabilities. This qualitative case study aimed to illuminate the experiences of students with SLDs at a South African university. The research was, therefore, guided by Vygotsky’s social-cultural theory (SCT). This research was conducted within a basic qualitative research methodology embedded in an interpretive paradigm. Data was collected through an online background survey and semi-structured interviews. Thematic qualitative content analysis was used to analyse the collected data systematically. From a social justice perspective, the major findings suggest that there are several factors that impede equal education for students with SLDs at university. Most participants in this small-scale study experienced a lack of acknowledgment and support from lecturers. They reported valuing the support of family and friends more than that of lecturers. It is concluded that lecturers need to be reflective of their pedagogical practices if authentic inclusion is to be realised.

Keywords: higher education, inclusive education, pedagogy, social-cultural theory, specific learning disabilities

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Authors: F. Zarrabi, J. R. Brown

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Although English is not a second language in Iran, it has become an inseparable part of many Iranian people’s lives and is becoming more and more widespread. This high demand has caused a significant increase in the number of private English language institutes in Iran. Although English is a compulsory course in schools and universities, the majority of Iranian people are unable to communicate easily in English. This paper reviews the current state of teaching and learning English as an international language in Iran. Attitudes and motivations about learning English are reviewed. Five different aspects of using English within the country are analysed, including: English in public domain, English in Media, English in organizations/businesses, English in education, and English in private language institutes. Despite the time and money spent on English language courses in private language institutes, the majority of learners seem to forget what has been learned within months of completing their course. That is, when they are students with the support of the teacher and formal classes, they appear to make progress and use English more or less fluently. When this support is removed, their language skills either stagnant or regress. The findings of this study suggest that a dependant approach to learning is potentially one of the main reasons for English language learning problems and this is encouraged by English course books and approaches to teaching.

Keywords: English in Iran, English language learning, English language teaching, evaluation

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Authors: Young-Su Ryu, Kyung-Won Park, Jae-Hoon Song, Ki-Won Kwon

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In this paper, an embedded frequency modulation (FM) transmission control software (SW) for a low power battery system is designed and implemented. The simultaneous translation systems for various languages are needed as so many international conferences and festivals are held in world wide. Especially in portable transmitting and receiving systems, the ability of long operation life is used for a measure of value. This paper proposes an embedded FM transmission control SW for low power battery system and shows the results of the SW implemented on a portable FM transmission system.

Keywords: FM transmission, simultaneous translation system, portable transmitting and receiving systems, low power embedded control SW

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Authors: Ajay Krishan Tiwari

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Studies on educational psychology have demonstrated the interest of the child's psychological and cognitive environment in the quality of their school commitment. The educational psychologist works with children and adolescents to remedy these factors. The task of the educational psychologist is to liberate the child and adolescent intellectually. Its purpose is to harmonize the child with the system of learning. Psychoanalytic support requires practice in creativity, reading, math, and meditation methods. The goal of educational psychology is to restore the desire and enjoyment of learning. The educational psychologist takes into account the concerns and personality traits that hinder student learning and restores self-esteem. Educational psychologists specialize in supporting children or adolescents who have a different approach to learning. Its role is to consider the child as a whole (cognitive, affective, physical, school, family factors, etc.). It welcomes the child's way of thinking and participates in its development. It is an essential point of contact between the child and his school environment.

Keywords: educational psychology, educational theories, psychologist, cognitive environment, psychoanalytic support, enjoyment of learning

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Authors: Adedolapo Taiwo Adeyemi, Success Ayodeji Fasanmi

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The emergence of the coronavirus pandemic and the rate of the spread affected a lot of activities across the world. This led to the introduction of online learning modes in several countries after institutions were shut down. Unfortunately, most public universities in Nigeria could not switch to the online mode because they were not prepared for it, as they do not have the technological capacity to support a full online learning mode. This study examines the readiness of university and the attitude of undergraduates towards online learning mode in Obafemi Awolowo University (OAU), Ile Ife. It investigated the skills and competencies of students for online learning as well as the university’s readiness towards online learning mode; the effort was made to identify challenges of online teaching and learning in the study area, and suggested solutions were advanced. OAU was selected because it is adjudged to be the leading Information and Communication Technology (ICT) driven institution in Nigeria. The descriptive survey research design was used for the study. A total of 256 academic staff and 1503 undergraduates were selected across six faculties out of the thirteen faculties in the University. Two set of questionnaires were used to get responses from the selected respondents. The result showed that students have the skills and competence to operate e-learning facilities but are faced with challenges such as high data cost, erratic power supply, and lack of gadgets, among others. The study found out that the university was not prepared for online learning mode as it lacks basic technological facilities to support it. The study equally showed that while lecturers possess certain skills in using some e-learning applications, they were limited by the unavailability of online support gadgets, poor internet connectivity, and unstable power supply. Furthermore, the assessment of student attitude towards online learning mode shows that the students found the online learning mode very challenging as they had to bear the huge cost of data. Lecturers also faced the same challenge as they had to pay a lot to buy data, and the networks were sometimes unstable. The study recommended that adequate funding needs to be provided to public universities by the government while the management of institutions must build technological capacities to support online learning mode in the hybrid form and on a full basis in case of future emergencies.

Keywords: universities, online learning, undergraduates, attitude

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Authors: Kai Yip Michael Tsang

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Science writing has played a crucial part in science assessments. This paper reports a study in an area that has received little research attention – how Language across the Curriculum (LAC, i.e. science language and literacy) learning activities in science lessons can increase the science knowledge development of English as a foreign language (EFL) students in Hong Kong. The data comes from a school-based interventional study in chemistry classrooms, with written data from questionnaires, assessments and teachers’ logs and verbal data from interviews and classroom observations. The effectiveness of the LAC teaching and learning activities in various chemistry classrooms were compared and evaluated, with discussion of some implications. Students in the treatment group with lower achieving students received LAC learning and teaching activities while students in the control group with higher achieving students received conventional learning and teaching activities. After the study, they performed better in control group in formative assessments. Moreover, they had a better attitude to learning chemistry content with a richer language support. The paper concludes that LAC teaching and learning activities yielded positive learning outcomes among chemistry learners with low English ability.

Keywords: science learning and teaching, content and language integrated learning, language across the curriculum, English as a foreign language

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Authors: M. Khaled Abduesslam, Mohammed Ali, Basher H. Alsdai, Muhammad Nizam Inayati

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This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New-England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification.

Keywords: IEEE 39 bus, least squares support vector machine, learning vector quantization, voltage collapse

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Authors: Tommy Krieger, Klaus Gruendler

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We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its flexibility it is also a valuable tool for comparison studies.

Keywords: democracy, democracy index, machine learning, support vector machines

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Authors: Jamaludin Jamaludin, Suparji Suparji, Lilik Anifah, I. Gusti Putu Asto Buditjahjanto, Eppy Yundra

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Learning media is an important and main component in the learning process. By using currently available media, cadets still have difficulty understanding how the piston engine works, so they are not able to apply these concepts appropriately. This study aims to examine the development of interactive media for piston engine courses in order to improve student learning outcomes. The research method used is a literature study of several articles, journals and proceedings of interactive media development results from 2010-2020. The results showed that the development of interactive media is needed to support the learning process and influence the cognitive abilities of students. With this interactive media, learning outcomes can be improved and the learning process can be effective.

Keywords: interactive media, learning outcomes, learning process, literature study

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Authors: Zheng Sun, Weiwei Cui, Xiaodong Ma, Hongxin Jin, Dongpao Hong, Jinsong Yang, Jingyi Sun

Abstract:

With the improvement of the performance and complexity of modern equipment, automatic test system (ATS) becomes widely used for condition monitoring and fault diagnosis. However, the conventional ATS mainly works in a serial mode, and lacks the ability of testing several equipments at the same time. That leads to low test efficiency and ATS redundancy. Especially for a large majority of equipment under test, the conventional ATS cannot meet the requirement of efficient testing. To reduce the support resource and increase test efficiency, we propose a method of design for the parallel test based on the embedded system in this paper. Firstly, we put forward the general framework of the parallel test system, and the system contains a central management system (CMS) and several distributed test subsystems (DTS). Then we give a detailed design of the system. For the hardware of the system, we use embedded architecture to design DTS. For the software of the system, we use test program set to improve the test adaption. By deploying the parallel test system, the time to test five devices is now equal to the time to test one device in the past. Compared with the conventional test system, the proposed test system reduces the size and improves testing efficiency. This is of great significance for equipment to be put into operation swiftly. Finally, we take an industrial control system as an example to verify the effectiveness of the proposed method. The result shows that the method is reasonable, and the efficiency is improved up to 500%.

Keywords: parallel test, embedded system, automatic test system, automatic test system (ATS), central management system, central management system (CMS), distributed test subsystems, distributed test subsystems (DTS)

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