Search results for: quality of learning

Authors: Li Donghong, Peng Fuyang, Yang Guanghua, Su Xiaoyan

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Using open source code is a popular method of software development. How to evaluate the quality of software becomes more important. This paper introduces an evaluation model. The model evaluates the quality from four dimensions: technology, production, management, and development. Each dimension includes many indicators. The weight of indicator can be modified according to the purpose of evaluation. The paper also introduces a method of using the model. The evaluating result can provide good advice for evaluating or purchasing the software.

Keywords: evaluation model, software quality, open source code, evaluation indicator

Procedia PDF Downloads 391

Authors: Frank Fan

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PharmD (Doctor of Pharmacy) education has confronted an increasing challenge — curricular overload, a phenomenon resulting from the expansion of curricular requirements, as PharmD education strives to produce graduates who are practice-ready. The aftermath of the global pandemic has amplified the need for healthcare professionals, leading to a growing trend of assigning more responsibilities to them to address the global healthcare shortage. For instance, the pharmacist’s role has expanded to include not only compounding and distributing medication but also providing clinical services, including minor ailments management, patient counselling and vaccination. Consequently, PharmD programs have responded by continually expanding their curricula adding more requirements. While these changes aim to enhance the education and training of future professionals, they have also led to unintended consequences, including curricular overload, student burnout, and a potential decrease in program quality. To address the issue and ensure program quality, there is a growing need for evidence-based curriculum reforms. My research seeks to integrate Cognitive Load Theory, emerging machine learning algorithms within artificial intelligence (AI), and statistical approaches to develop a quantitative framework for optimizing curriculum design within the PharmD program at the University of Toronto, the largest PharmD program within Canada, to provide quantification and measurement of issues that currently are only discussed in terms of anecdote rather than data. This research will serve as a guide for curriculum planners, administrators, and educators, aiding in the comprehension of how the pharmacy degree program compares to others within and beyond the field of pharmacy. It will also shed light on opportunities to reduce the curricular load while maintaining its quality and rigor. Given that pharmacists constitute the third-largest healthcare workforce, their education shares similarities and challenges with other health education programs. Therefore, my evidence-based, data-driven curriculum analysis framework holds significant potential for training programs in other healthcare professions, including medicine, nursing, and physiotherapy.

Keywords: curriculum, curriculum analysis, health professions education, reflective writing, machine learning

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Authors: Sutanto, Alexander Christy, N. Sutrisno

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The demand of a product is linearly dependent on the price and quality of the product. It is analog to the demand of the employee in job-seeker problem. This paper address a closed-loop supply chain (CLSC) where a university plays role as manufacturer that produce graduates as job-seeker according to the demand and promote them to a certain corporation through a trial. Unemployed occurs when the job-seeker failed the trial or dismissed. A third party accomodates the unemployed and sends them back to the university to increase their quality through training.

Keywords: CLSC, price, quality, job-seeker problem

Procedia PDF Downloads 276

Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Tsai-Pei Liu, Zhi-Rou Zheng, Tzu-Tzu Wen

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Innovative logistics system simulation teaching is to extract the characteristics of the system through simulation methodology. The system has randomness and interaction problems in the execution time. Therefore, the simulation model can usually deal with more complex logistics process problems, giving students different learning modes. Students have more autonomy in learning time and learning progress. System simulation has become a new educational tool, but it still needs to accept many tests to use it in the teaching field. Although many business management departments in Taiwan have started to promote, this kind of simulation system teaching is still not popular, and the prerequisite for popularization is to be supported by students. This research uses an extension of Integration Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the acceptance of students in universities of science and technology to use system simulation as a learning tool. At the same time, it is hoped that this innovation can explore the effectiveness of the logistics system simulation after the introduction of teaching. The results indicated the significant influence of performance expectancy, social influence and learning value on students’ intention towards confirmed the influence of facilitating conditions and behavioral intention. The extended UTAUT2 framework helps in understanding students’ perceived value in the innovative logistics system teaching context.

Keywords: UTAUT2, logistics system simulation, learning value, Taiwan

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Othman Ali Alghtani, Yahya Abdul-Ekhalq Ali, Abdullah Abdul-Ekhalq Ali, Ahmed Al Sadiq Abdul Majeed, Najwa Attian Al-Mohammadi, Obead Mozel Alharbi, Sabri Mohamed Ismail, Omar Ibrahim Asiri

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This study assessed the curricula of secondary schools given requirements to enhance the quality of life of students. The components of quality of life were described to build a list of standards and indicators. A questionnaire assessing the dimensions of mental (cognitive and emotional), physical, digital, and social health, and environmental awareness was prepared. A descriptive-analytical approach was used on a sample of 258 teachers and educational supervisors in Tabuk. The results indicated shortcomings in the secondary school curricula regarding developing standards and indicators of components of quality of life. Results also indicated that secondary school curricula incorporated few practices to improve student’s quality of life. No significant differences were found regarding the core subject, job, gender, and years of experience.

Keywords: assessing curricula, teacher practices, quality of life, teaching practices

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Authors: Ivan Traina, Luigi Sangalli, Fabio Tognon, Angelo Lascioli

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Currently, challenges regarding preschooler children are mainly focused on a sedentary lifestyle. Also, motor activity in infancy is seen as a tool for the separate acquisition of cognitive and socio-emotional skills rather than considering neuromotor development as a tool for improving learning abilities. The paper utilized an observational research method to shed light on the results of practicing neuromotor exercises in preschool children with disability as well as provide implications for practice.

Keywords: children with disability, learning abilities, inclusion, neuromotor development

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Authors: Vimi Jham

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The banking sector has undergone tremendous change in all forms of service it provides to its customers. The efforts of the banks is to avoid customer defection and lead to customer satisfaction. The purpose of the study was to examine the linkages among the constructs such as customer perceived service quality, perceived service recovery quality and customer satisfaction in the banking industry. The moderating effect of negative brand perception due to service failure on recovery satisfaction were investigated. Random sampling methods are used to draw the sample from the population. Data was collected from 262 banking customers and were analyzed with the help of structural equation modelling approach using Smart PLS to understand the relationship among variables being studied. The results of the study contribute to the research by proving that customer service recovery satisfaction is dependent on customer perceived service quality and the moderating effect of negative brand perception due to service failure was insignificant.

Keywords: service recovery satisfaction, perceived service recovery quality, perceived service quality, structural equation modelling

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Authors: Jing Jin

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The incorporation of artificial intelligence (AI) and machine learning (ML) in aircraft manufacturing and aerospace supply chains leads to the generation of a substantial amount of data among various tiers of suppliers and OEMs. Identifying the high-quality information challenges decision-makers. The application of AI/ML models necessitates access to 'high-quality' information to yield desired outputs. However, the process of information sharing introduces complexities, including distortion through various communication channels and biases introduced by both human and AI entities. This phenomenon significantly influences the quality of information, impacting decision-makers engaged in configuring supply chain systems. Traditionally, distorted information is categorized as 'low-quality'; however, this study challenges this perception, positing that distorted information, contributing to stakeholder goals, can be deemed high-quality within supply chains. The main aim of this study is to identify and evaluate the dimensions of information quality crucial to the UK aerospace supply chain. Guided by a central research question, "What information quality dimensions are considered when defining information quality in the UK aerospace supply chain?" the study delves into the intricate dynamics of information quality in the aerospace industry. Additionally, the research explores the nuanced impact of information distortion on stakeholders' decision-making processes, addressing the question, "How does the information distortion phenomenon influence stakeholders’ decisions regarding information quality in the UK aerospace supply chain system?" This study employs deductive methodologies rooted in positivism, utilizing a cross-sectional approach and a mono-quantitative method -a questionnaire survey. Data is systematically collected from diverse tiers of supply chain stakeholders, encompassing end-customers, OEMs, Tier 0.5, Tier 1, and Tier 2 suppliers. Employing robust statistical data analysis methods, including mean values, mode values, standard deviation, one-way analysis of variance (ANOVA), and Pearson’s correlation analysis, the study interprets and extracts meaningful insights from the gathered data. Initial analyses challenge conventional notions, revealing that information distortion positively influences the definition of information quality, disrupting the established perception of distorted information as inherently low-quality. Further exploration through correlation analysis unveils the varied perspectives of different stakeholder tiers on the impact of information distortion on specific information quality dimensions. For instance, Tier 2 suppliers demonstrate strong positive correlations between information distortion and dimensions like access security, accuracy, interpretability, and timeliness. Conversely, Tier 1 suppliers emphasise strong negative influences on the security of accessing information and negligible impact on information timeliness. Tier 0.5 suppliers showcase very strong positive correlations with dimensions like conciseness and completeness, while OEMs exhibit limited interest in considering information distortion within the supply chain. Introducing social network analysis (SNA) provides a structural understanding of the relationships between information distortion and quality dimensions. The moderately high density of ‘information distortion-by-information quality’ underscores the interconnected nature of these factors. In conclusion, this study offers a nuanced exploration of information quality dimensions in the UK aerospace supply chain, highlighting the significance of individual perspectives across different tiers. The positive influence of information distortion challenges prevailing assumptions, fostering a more nuanced understanding of information's role in the Industry 4.0 landscape.

Keywords: information distortion, information quality, supply chain configuration, UK aerospace industry

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Durri Andriani, Irsan Tahar, Lilian Sarah Hiariey

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

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

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Authors: Geraldine O’Neill

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Work-integrated learning is a valuable learning experience for students in higher education. However, the fairness of the assessment process has been identified as a challenge. This study explored solutions to this challenge through interviews with expert authors in the field and workshops across nine different disciplines in Ireland. In keeping with the use of a participatory and action research methodology, the key stakeholders in the process, the students, educators, and practitioners, identified some solutions. The solutions included the need to: clarify the assessments’ expectations; enhance the flexibility of the competencies, reduce the number of competencies; use grading scales with lower specificity; support practitioner training, and empower students in the assessment process. The results are discussed as they relate to interactional, procedural, and distributive fairness.

Keywords: competencies, fairness, grading scales, work-integrated learning

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

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Authors: Ahmed T. Alahmar

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There is an increasing trend toward student-centred interactive e-learning methods and students’ feedback is a valuable tool for improving learning methods. The aim of this study was to explore the attitude of second year pharmacy students at the University of Babylon, Iraq, towards lectures, exams and e-learning. Materials and methods: Ninety pharmacy students were surveyed by paper questionnaire about their preference for lecture format, use of e-files, theoretical lectures versus practical experiments, lecture and lab time. Students were also asked about their predilection for Moodle-based online exams, different types of exam questions, exam time and other extra academic activities. Results: Students prefer to read lectures on paper (73.3%), use of PowerPoint file (76.7%), short lectures of less than 10 pages (94.5%), practical experiments (66.7%), lectures and lab time of less than two hours (89.9% and 96.6 respectively) and intra-lecture discussions (68.9%). Students also like to have paper-based exam (73.3%), short essay (40%) or MCQ (34.4%) questions and also prefer to do extra activities like reports (22.2%), seminars (18.6%) and posters (10.8%). Conclusion: Second year pharmacy students have different attitudes toward traditional and electronic leaning and assessment methods. Using multimedia, e-learning and Moodle are increasingly preferred methods among some students.

Keywords: pharmacy, students, lecture, exam, e-learning, Moodle

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Authors: Meiju Chen, Chaoyu Guo, Chia Wei Tang

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The present study's purpose is twofold: first, to construct indicators for learning-oriented school education and, second, to conduct a survey to examine how learning-oriented education has been implemented in junior high schools after the launch of the 12-year compulsory curriculum. For indicator system construction, we compiled relevant literature to develop a preliminary indicator list model and then conducted two rounds of a questionnaire survey to gain comprehensive feedback from experts to finalize our indicator model. In the survey's first round, 12 experts were invited to evaluate the indicators' appropriateness. Based on the experts' consensus, we determined our final indicator list and used it to develop the Fuzzy Delphi questionnaire to finalize the indicator system and each indicator's relative value. For the fact-finding survey, we collected 454 valid samples to examine how the concept of learning-oriented education is adopted and implemented in the junior high school context. We also used this data in our importance-performance analysis to explore the strengths and weaknesses of school education in Taiwan. The results suggest that the indicator system for learning-oriented school education must consist of seven dimensions and 34 indicators. Among the seven dimensions, 'student learning' and 'curriculum planning and implementation' are the most important yet underperforming dimensions that need immediate improvement. We anticipate that the indicator system will be a useful tool for other countries' evaluation of schools' performance in learning-oriented education.

Keywords: learning-oriented education, school education, fuzzy Delphi method, importance-performance analysis

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Debalina Datta, Pratyaypratim Datta, Kunal Kanti Majumdar

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Introduction: In India 8.1% of total population is elderly. The standard of living and meaningfulness of life are indirectly measured by assessing quality of life of elderly. So, it is important to improve quality of life. Quality of life is an individual’s understanding of his/ her life situation with respect to his/ her values and cultural context as well as in relation to his/her goals, expectations and concerns. The present study was planned to assess the quality of life of geriatric people in urban West Bengal, India. Materials and methods: It was a community based cross sectional observational study conducted among people aged 60 years and above in Kolkata and Sonarpur region of West Bengal, India. Data collection was done by house to house visit using Quality of Life- BREF questionnaire (WHOQOL-BERF) developed by WHO. Analysis of quality of life of physical, psychological, social relationship and environmental domain was done using SPSS (version 16.0). Results: Transformed score (0-100 scale) was used for each domain. Mean of physical, psychological, social relationship and environmental domain were found to be 42.25, 40.84, 39.62 and 48.36 respectively. There was no significant difference in score between Kolkata and Sonarpur people in any domain except social relationship domain, where people living at Sonarpur scored significantly better. Conclusion: Rehabilitation of old age people can be done by improving their quality of life. Social interaction with people of all ages, allowing them to take important family decision, engaging them in different social activities can help a lot.

Keywords: quality of life, elderly, Urban West Bengal, India

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Authors: Yi-Chuan Cheng, Li-Chi Huang, Yu-Shan Chang

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Introduction: The main purpose of this study was to explore the relationship between the learning number, care knowledge, care skills and maternal confidence in preterm infant care in Taiwan. Background: Preterm infants care has been stressful for mother caring at home. Many programs have been applied for improving the infant care maternal confident. But less to know the learning behavior in mothers of preterm infant. Methods: The sample consisted of 55 mothers with preterm infants were recruited in a neonatal intermediate unit at a medical center in central Taiwan. The self-reported questionnaires including knowledge and skills of preterm infant care scales and maternal confidence scale were used to evaluation, which were conducted during hospitalization, before hospital discharge, and one month after discharge. We performed by using Pearson correlation of the collected data using SPSS 18. Results: The study showed that the learning number and knowledge in preterm infant care was a significant positive correlation (r = .40), and the skills and confidence preterm infant care was positively correlated (r = .89). Conclusions: Study results showed the mother had more learning number in preterm infant care will be stronger knowledge, and the skills and confidence in preterm infant care were also positively correlated. Thus, we found the learning behavior change significant care knowledge. And the maternal confidence change significant with skill on preterm infant’s care. But bondage still needs further study and develop the participation in hospital-based instructional programs, which could lead to greater long-term retention of learning.

Keywords: learning behavior, care knowledge, care skills, maternal confidence

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Deborah Asibu Abu

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Motivation is the process that initiates, guides, and maintains goal-oriented behaviors. It is one of the most important ingredients in teaching and learning yet it does not come by chance; it involves necessary strategies appropriate to achieve a common goal. In learning, the psychological attention of the student is very important. This helps them to imagine whatever is being taught for a simple understanding, nonetheless, many students will be able to imagine how the environment is in social studies or how the bones or plant is, in integrated Science but will find it difficult to imagine what subject-verb agreement or phrases and clauses actually looks like until they are motivated or with the use of TLM’s to stir their interest to learn and forever remember. For students to be able to receive the motivation they need, there must be an effective relationship between the teacher and the student as well as the use of strategies for effectual execution of achievable goals. Every teacher must understand the importance of motivation by applying various kinds of teaching methodology, especially in the English Language as a subject. Hence this paper suggests some important factors necessary for student’s motivation in teaching and learning English Language, it handles what teaching method is, types of motivation, educational curriculum structure of many, what suitable teaching methods can achieve, appropriate teachers’ disposition, learning environment as tool for motivation and some other domestic factors that can also influence student motivation.

Keywords: english language, teacher-student relationship, curriculum structure, learning environment

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Authors: Brenda Ravenscroft, Ulemu Luhanga, Bev King

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A comprehensive evaluation of online learning needs to include a blend of educational design, technology use, and online instructional practices that integrate technology appropriately for developing and delivering quality online courses. Research shows that classroom-based evaluation tools do not adequately capture the dynamic relationships between content, pedagogy, and technology in online courses. Furthermore, studies suggest that using classroom evaluations for online courses yields lower than normal scores for instructors, and may affect faculty negatively in terms of administrative decisions. In 2014, the Faculty of Arts and Science at Queen’s University responded to this evidence by seeking an alternative to the university-mandated evaluation tool, which is designed for classroom learning. The Faculty is deeply engaged in e-learning, offering large variety of online courses and programs in the sciences, social sciences, humanities and arts. This paper describes the process by which a new student survey instrument for online courses was developed and piloted, the methods used to analyze the data, and the ways in which the instrument was subsequently adapted based on the results. It concludes with a critical reflection on the challenges of evaluating e-learning. The Student Evaluation of Online Teaching Effectiveness (SEOTE), developed by Arthur W. Bangert in 2004 to assess constructivist-compatible online teaching practices, provided the starting point. Modifications were made in order to allow the instrument to serve the two functions required by the university: student survey results provide the instructor with feedback to enhance their teaching, and also provide the institution with evidence of teaching quality in personnel processes. Changes were therefore made to the SEOTE to distinguish more clearly between evaluation of the instructor’s teaching and evaluation of the course design, since, in the online environment, the instructor is not necessarily the course designer. After the first pilot phase, involving 35 courses, the results were analyzed using Stobart's validity framework as a guide. This process included statistical analyses of the data to test for reliability and validity, student and instructor focus groups to ascertain the tool’s usefulness in terms of the feedback it provided, and an assessment of the utility of the results by the Faculty’s e-learning unit responsible for supporting online course design. A set of recommendations led to further modifications to the survey instrument prior to a second pilot phase involving 19 courses. Following the second pilot, statistical analyses were repeated, and more focus groups were used, this time involving deans and other decision makers to determine the usefulness of the survey results in personnel processes. As a result of this inclusive process and robust analysis, the modified SEOTE instrument is currently being considered for adoption as the standard evaluation tool for all online courses at the university. Audience members at this presentation will be stimulated to consider factors that differentiate effective evaluation of online courses from classroom-based teaching. They will gain insight into strategies for introducing a new evaluation tool in a unionized institutional environment, and methodologies for evaluating the tool itself.

Keywords: evaluation, online courses, student survey, teaching effectiveness

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Authors: Jinan Laurentia Woo

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This paper explores the intersection of technology, music education, and social-emotional learning (SEL) with a focus on Latin America. It delves into the impact of music education on social-emotional skills development, highlighting the universal significance of music across various life stages. The integration of artificial intelligence (AI) in music education is discussed, emphasizing its potential to enhance learning experiences. The paper also examines the implementation of SEL strategies in Latin American public schools, emphasizing the importance of fostering social-emotional well-being in educational settings. Challenges such as unequal access to technology and education in the region are addressed, calling for further research and investment in tech-assisted music education.

Keywords: music education, social emotional learning, educational technology, Latin America, artificial intelligence, music

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Authors: Clement Yeboah, Eva Laryea

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A pretest-posttest within subjects experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant, indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant, indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop an interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, computer game-based learning, statistics achievement, statistics anxiety

Procedia PDF Downloads 79

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

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

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

Procedia PDF Downloads 115

Authors: Daniela Monteiro

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Considering the entry into force of the new EU audit reform regarding statutory auditors, in effect in all member states since 2016, this research aims to identify which audit regulation rules are associated with a high-level audit quality on both its dimensions, i.e., the actual quality and the perceived quality, in relation to public interest entities, within the European Union, and whether those rules have the same impact on both dimensions. Its measurement was based on the following proxies: the quality of financial information through earnings management and the impact of qualified opinions on financial costs. We considered in the research regulation subjects such as auditors’ rotation and provision of services (NAS) and also the level of market concentration. The criteria to include these issues in the research was its contemplation of the new rules. We studied the period before the audit reform (2009-2015) when the regulation measures were less uniform. Besides the consideration of both dimensions of audit quality and several regulation measures, we believe our conclusions configure an important contribution to this research field, considering the involvement of the first 15 member states of the European Union. The results consolidate the assumption that the balance between competence and independence is not the only challenge related to the regulation of the audit profession. The evidence demonstrates that the balance between actual and perceived quality is also a relevant matter. The major conclusion is that the challenge is to keep balanced both actual and perceived audit quality whilst ensuring the independence and competence of auditors.

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Procedia PDF Downloads 184

Authors: R. Rezaeipour

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One of the key aspects of power quality improvement in power system is the mitigation of voltage sags/swells and flicker. Custom power devices have been known as the best tools for voltage disturbances mitigation as well as reactive power compensation. Dynamic voltage restorer (DVR) which is the most efficient and effective modern custom power device can provide the most commercial solution to solve several problems of power quality in distribution networks. This paper deals with analysis and simulation technique of DVR based on instantaneous power theory which is a quick control to detect signals. The main purpose of this work is to remove three important disturbances including voltage sags/swells and flicker. Simulation of the proposed method was carried out on two sample systems by using MATLAB software environment and the results of simulation show that the proposed method is able to provide desirable power quality in the presence of wide range of disturbances.

Keywords: DVR, power quality, voltage sags, voltage swells, flicker

Procedia PDF Downloads 348

Authors: Sherly Ferro Lensun, Donal Matheos Ratu, Elni Jeini Usoh, Helena M. L. Pandi, Mayske Rinny Liando

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This study aims to gain a deep understanding of the process and the increase resulting in mastery of Kanji with a Language Learning Strategies through multimedia. This research aims to gain scientific data on process and the result of improving kanji mastery by using Chokusetsu strategy in Kanji learning. The method used in this research is Action Research developed by Kemmis and Mc. Taggart is known as Spiral Model. This model consists of following stages: planning, implementation, observation, and reflection. The research results in following findings: (1) Kanji mastery comprises 4 major aspects, those are reading, writing, the use in sentence, and memorizing, and those aspects show gradual improvement from time to time. (2) Students have more participation in learning activities which can be identified from some positive behaviours such giving respond in finishing exercise in class. (3) Students’ better attention to the lesson shown by active behaviour in giving more questions or asking for more explanation to the lecturers, memorizing Kanji card, finishing the task of making Kanji card/house, doing the exercises more seriously, and finishing homework assignment punctually. (4) More attractive learning activities and tasks in the forms of more engaging colour and pictures enables students to conduct self-evaluation on their learning process.

Keywords: Kanji, action research, language learning strategies, multimedia

Procedia PDF Downloads 177

Authors: Sakib Hasn, Shahid Anwar

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This study highlights the vital importance of assessing the Simulator Sickness Questionnaire and presence measures as virtual reality (VR) incorporation into language teaching gains popularity. To address user discomfort, which prevents efficient learning in VR environments, the measurement of SSQ becomes crucial. Additionally, evaluating presence metrics is essential to determine the level of engagement and immersion, both crucial for rich language learning experiences. This paper designs a VR-based Chinese language application and proposes a thorough test technique aimed at systematically analyzing SSQ and presence measures. Subjective tests and data analysis were carried out to highlight the significance of addressing user discomfort in VR language education. The results of this study shed light on the difficulties posed by user discomfort in VR language learning and offer insightful advice on how to improve VR language learning applications. Furthermore, the outcome of the research explores ‘VR-based language education,’ ‘inclusive language learning platforms," and "cross-cultural communication,’ highlighting the potential for VR to facilitate language learning across diverse cultural backgrounds. Overall, the analysis results contribute to the enrichment of language learning experiences in the virtual realm and underscore the need for continued exploration and improvement in this field.

Keywords: virtual reality (VR), language education, simulator sickness questionnaire, presence metrics, VR-based Chinese language education

Procedia PDF Downloads 83

Authors: Mohsen Maraoui

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In the face of the growth in the quantity of data produced, several methods and techniques appear to remedy the problems of processing and analyzing large amounts of information mainly in the field of teaching. In this paper, we propose an intelligent cloud-based teaching system for E-learning content services. This system makes easy the manipulation of various educational content forms, including text, images, videos, 3 dimensions objects and scenes of virtual reality and augmented reality. We discuss the integration of institutional and external services to provide personalized assistance to university members in their daily activities. The proposed system provides an intelligent solution for media services that can be accessed from smart devices cloud-based intelligent service environment with a fully integrated system.

Keywords: cloud computing, e-learning, indexation, IoT, learning in Arabic language, smart tools

Procedia PDF Downloads 136