Search results for: state of learning

Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Clement C. Aladi, Zhaoxia Yi

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This study investigates factors influencing the implementation of flexible curricula in e-learning in Information Systems (IS) higher education. Drawing from curriculum theorists and contemporary literature, and using the Technology, Pedagogy, and Content Knowledge (TPACK) framework, it explores teacher-related challenges and their impact on curriculum flexibility implementation. By using the PLS-SEM, the study uncovers these factors and hopes to contribute to enhancing curriculum flexibility in delivering online and blended learning in IS higher education.

Keywords: flexible curriculum, online learning, e-learning, technology

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Authors: Maria Gabrielle "Abby" Dalmacio

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Social-emotional learning is a concept that is garnering more importance when considering the development of young children. The aim of this scoping literature review is to explore the implementation of social-emotional learning programs conducted with first-generation immigrant young children ages 3-12 years in North America. This review of literature focuses on social-emotional learning programs taking place in early childhood education centres and elementary school settings that include the first-generation immigrant children population to determine if and how their understanding of social-emotional learning skills may be impacted by the curriculum being taught through North American educational pedagogy. Research on early childhood education and social-emotional learning reveals the lack of inter-cultural adaptability in social emotional learning programs and the potential for immigrant children as being assessed as developmentally delayed due to programs being conducted through standardized North American curricula. The results of this review point to a need for more research to be conducted with first-generation immigrant children to help reform social-emotional learning programs to be conducive for each child’s individual development. There remains to be a gap of knowledge in the current literature on social-emotional learning programs and how educators can effectively incorporate the intercultural perspectives of first-generation immigrant children in early childhood education.

Keywords: early childhood education, social-emotional learning, first-generation immigrant children, north america, inter-cultural perspectives, cultural diversity, early educational frameworks

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Ladda Silanoi

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One of the main purposes in establishment of ASEAN Community is educational development. All countries in ASEAN shall then prepare for plans and strategies for country development. Therefore, Thailand set up the policy concerning educational management for all educational institutions to understand about ASEAN Community. However, some educational institutions lack of precision in determining the curriculums of ASEAN Community, especially schools in rural areas, for example, schools along the common border with Laos, and Cambodia. One of the effective methods to promote the precision in ASEAN Community is to design additional learning courses. The important process of additional learning courses design is to provide learning outcomes of ASEAN Community for course syllabus determination. Therefore, the researcher is interested in developing teachers in the schools of common border with Laos, and Cambodia to provide learning outcomes and learning process. This research has the objective of developing the learning outcomes and learning process management of basic education along Thailand, Laos, and Cambodia Common Border for the ASEAN Community Preparation. Research methodology consists of 2 steps. Step 1: Delphi Technique was used to provide guidelines in development of learning outcomes and learning process. Step 2: Action Research procedures was employed to study the result of additional learning courses design. Result of the study: By using Delphi technique, consensus is expected to be achieved, from 50 experts in the study within 3 times of the survey. The last survey found that experts’ opinions were compatible on every item (inter-quartile range = 0) leading to the arrangement of training courses in step of Action Research. The result from the workshop found that teachers in schools of Srisaket and Bueng Kan provinces could be able to provide learning outcomes of all courses.

Keywords: learning outcome and learning process, basic education, ASEAN Community preparation, Thailand Laos and Cambodia common border

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Authors: Asish O. Mathew, Lewlyn L. R. Rodrigues

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The organizations in the knowledge economy era have recognized the importance of building knowledge assets for sustainable growth and development. In comparison to other industries, Information Technology (IT) enterprises, holds an edge in developing an effective Knowledge Management (KM) program, thanks to their in-house technological abilities. This paper tries to study the various knowledge-based incentive programs and its effect on Knowledge Sharing and Learning in the context of the Indian IT sector. A conceptual model is developed linking KM incentives, knowledge sharing, and learning. A questionnaire study is conducted to collect primary data from the knowledge workers of the IT organizations located in India. The data was analysed using Structural Equation Modeling using Partial Least Square method. The results show a strong influence of knowledge management incentives on knowledge sharing and an indirect influence on learning.

Keywords: knowledge management, knowledge management incentives, knowledge sharing, learning

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Authors: Peijen Pamela Chuang, Tzu-Hua Wang

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To address the notion of educational equity, undergo the global pandemic, a digital learning system was cross-disciplinarily designed by a 15-year-experienced teaching practitioner. A study was performed on students through the use of this pioneering e-learning system, in which Taiwanese students with different learning styles and special needs have a foreign language- English as the target subject. 121 students are particularly selected from an N= 580 sample spread across 20 inclusive and special education schools throughout districts of Taiwan. To bring off equity, the participants are selected from a mix of different socioeconomic statuses. Grouped data, such as classroom observation, individual learning preference, prerequisite knowledge, learning interest, and learning performance of the population, is carefully documented for further analyzation. The paper focuses on documenting the awareness and needs of this pedagogical methodology revolution, data analysis of UX (User Experience), also examination and system assessment of this system. At the time of the pilot run, this newly-developed e-learning system had successfully applied for and received a national patent in Taiwan. This independent research hoped to expand the awareness of the importance of individual differences in SDG4 (Substantial Development Goals 4) as a part of the ripple effect, and serve as a comparison for future scholars in the pedagogical research with an interdisciplinary approach.

Keywords: e-learning, educational equity, foreign language acquisition, inclusive education, individual differences, interdisciplinary innovation, learning preferences, SDG4

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Authors: Helena Sit

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Developing skills in using ICT in the language classroom has been discussed at all educational levels. Digital tools and learning management systems enable teachers to transform their instructional activities while giving learners the opportunity to engage with virtual communities. In the field of English as a second language (ESL) teaching and learning, the use of technology-enhanced learning and diverse pedagogical practices continues to grow. Whilst technology and multimodal learning is a way of the future for education, second language teachers now face the predicament as to whether implementing these newer ways of learning is, in fact, beneficial or disadvantageous to learners. Research has shown that integrating multimodality and technology can improve students’ engagement and participation in their English language learning. However, students can experience anxiety or misunderstanding when engaging with E-learning or digital-mediated learning. This paper aims to explore how ESL teaching and learning are transformed via the use of educational technology and what impact it has had on student teachers. Case study is employed in this research. The study reviews the growing presence of technology and multimodality in university language classrooms, discusses their impact on teachers’ pedagogical practices, and proposes scaffolding strategies to help design effective English language courses in the Australian education context. The study sheds light on how pedagogical integration today may offer a way forward for language teachers of tomorrow and provides implications to implement an evidence-informed approach that blends knowledge from research, practice and people experiencing the practice in the digital era.

Keywords: educational technology, ICT in higher education, curriculum design and innovation, teacher education, multiliteracies pedagogy

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Authors: Samar Alharbi, Yota Dimitriadi

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With the exponential growth of information and communication technologies in higher education, new online teaching strategies have become increasingly important for student engagement and learning. In particular, some institutions depend solely on asynchronous e-learning to provide courses for their students. The major challenge facing these institutions is how to improve the quality of teaching and learning in their asynchronous tools. One of the most important methods that can help e-learner to enhance their social learning and social presence in asynchronous learning setting is immediacy. This study explores tutors perceptions of their instructional immediacy practices as part of their communication actions in online learning environments. It was used a mixed-methods design under the umbrella of pragmatic philosophical assumption. The participants included tutors at an educational institution in a Saudi university. The participants were selected with a purposive sampling approach and chose an institution that offered fully online courses to students. The findings of the quantitative data show the importance of teachers’ immediacy practices in an online text-based learning environment. The qualitative data contained three main themes: the tutors’ encouragement of student interaction; their promotion of class participation; and their addressing of the needs of the students. The findings from these mixed methods can provide teachers with insights into instructional designs and strategies that they can adopt in order to use e-immediacy in effective ways, thus improving their students’ online learning experiences.

Keywords: asynchronous e-learning, higher education, immediacy, tutor

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Authors: Abdullah Bal

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This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan

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Email marketing is one of the most important segments of online marketing. It has been proved to be the most effective way to acquire and retain customers. The email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of email has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy.

Keywords: email marketing, email content, reinforcement learning, machine learning, Q-learning

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Authors: Krongthong Khairiree

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The purpose of this research study is to explore mathematics project-based learning approach and the use of technology in the context of school mathematics in Thailand. Data of the study were collected from 6 sample secondary schools and the students were 6-14 years old. Research findings show that through mathematics project-based learning approach and the use of GSP, students were able to make mathematics learning fun and challenging. Based on the students’ interviews they revealed that, with GSP, they were able to visualize and create graphical representations, which will enable them to develop their mathematical thinking skills, concepts and understanding. The students had fun in creating variety of graphs of functions which they can not do by drawing on graph paper. In addition, there are evidences to show the students’ abilities in connecting mathematics to real life outside the classroom and commercial products, such as weaving, patterning of broomstick, and ceramics design.

Keywords: mathematics, project-based learning, Geometer’s Sketchpad (GSP), commercial products

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Authors: Joao Carlos Koch Junior, Risa Takashima

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Learning disabilities have been extensively and increasingly studied in recent times. In spite of this, there is arguably a scarce number of studies addressing a key issue, which is the impact of foreign-language requirements on students with learning disabilities in higher education, and the lack of training or awareness of teachers regarding language learning disabilities. This study is an attempt to address this issue. An extensive review of the literature in multiple fields will be summarised. This, paired with a case-analysis of a university adopting a more inclusive approach towards special-needs students in its foreign-language programme, this presentation aims to establish a link between different studies and propose a number of suggestions to make language classrooms more inclusive.

Keywords: foreign language teaching, higher education, language teacher education, learning disabilities

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Authors: S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad

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Cubic equations of state like Redlich–Kwong (RK) EOS have been proved to be very reliable tools in the prediction of phase behavior. Despite their good performance in compositional calculations, they usually suffer from weaknesses in the predictions of saturated liquid density. In this research, RK equation was modified. The result of this study shows that modified equation has good agreement with experimental data.

Keywords: equation of state, modification, ammonia, genetic algorithm

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Authors: Rodrigo Aguiar, Adelino Ferreira

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Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

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Authors: Ibode Osa Felix

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The use of e-Learning is presently intensified in the academic world following the outbreak of the Covid-19 pandemic in early 2020. Hitherto, e-learning had made its debut in teaching and learning many years ago when it emerged as an aspect of Computer Based Teaching, but never before has its patronage become so important and popular as currently obtains. Previous studies revealed that there is an ongoing debate among researchers on the efficacy of the E-learning mode of instruction over the traditional teaching method. Therefore, the study examined the effect of E-learning and Conventional Mode of Instruction on Students Achievement in the English Language. The study is a quasi-experimental study in which 230 students, from three public secondary schools, were selected through a simple random sampling technique. Three instruments were developed, namely, E-learning Instructional Guide (ELIG), Conventional Method of Instructional Guide (CMIG), and English Language Achievement Test (ELAT). The result revealed that students taught through the conventional method had better results than students taught online. The result also shows that girls taught with the conventional method of teaching performed better than boys in the English Language. The study, therefore, recommended that effort should be made by the educational authorities in Nigeria to provide internet facilities to enhance practices among learners and provide electricity to power e-learning equipment in the secondary schools. This will boost e-learning practices among teachers and students and consequently overtake conventional method of teaching in due course.

Keywords: e-learning, conventional method of teaching, achievement in english, electricity

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Authors: Jianyong Xue, Olivier L. Georgeon, Salima Hassas

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During the initial phase of cognitive development, infants exhibit amazing abilities to generate novel behaviors in unfamiliar situations, and explore actively to learn the best while lacking extrinsic rewards from the environment. These abilities set them apart from even the most advanced autonomous robots. This work seeks to contribute to understand and replicate some of these abilities. We propose the Bottom-up hiErarchical sequential Learning algorithm with Constructivist pAradigm (BEL-CA) to design agents capable of learning autonomously and continuously through interactions. The algorithm implements no assumption about the semantics of input and output data. It does not rely upon a model of the world given a priori in the form of a set of states and transitions as well. Besides, we propose a toolkit to analyze the learning process at run time called GAIT (Generating and Analyzing Interaction Traces). We use GAIT to report and explain the detailed learning process and the structured behaviors that the agent has learned on each decision making. We report an experiment in which the agent learned to successfully interact with its environment and to avoid unfavorable interactions using regularities discovered through interaction.

Keywords: cognitive development, constructivist learning, hierarchical sequential learning, self-adaptation

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Authors: Min Bao

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The teacher is at the core of the three fundamental factors in international Chinese language teaching, the other two being the textbook and the method. Professional development of the teacher comprises a self-renewing process that is characterized by knowledge impartment and self-reflection, in which individual agency plays a significant role. Agency makes a positive contribution to teachers’ teaching practice and their life-long learning. This study, taking Chinese teaching and learning in Minsk State Linguistic University of Belarus as an example, attempts to understand agency by investigating the teacher’s strategic adaptation of textbooks to meet local needs. Firstly, through in-depth interviews, teachers’ comments on textbooks are collected and analyzed to disclose their strategies of adapting and localizing textbooks. Then, drawing on the theory of 'The chordal triad of agency', the paper reveals the process in which teacher agency is exercised as well as its rationale. The results verify the theory, that is, given its temporal relationality, teacher agency is constructed through a combination of experiences, purposes and aims, and context, i.e., projectivity, iteration and practice-evaluation as mentioned in the theory. Evidence also suggests that the three dimensions effect differently; It is usually one or two dimensions that are of greater effects on the construction of teacher agency. Finally, the paper provides four specific insights to teacher development in international Chinese language teaching: 1) when recruiting teachers, priority be given on candidates majoring in Chinese language or international Chinese language teaching; 2) measures be taken to assure educational quality of the two said majors at various levels; 3) pre-service teacher training program be tailored for improved quality, and 4) management of overseas Confucius Institutions be enhanced.

Keywords: international Chinese language teaching, teacher agency, textbooks, localization

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Authors: Fengjuan Wang, Azilawati Jamaludin

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Developmental Dyscalculia (DD) is defined as a particular learning difficulty with continuous challenges in learning requisite math skills that cannot be explained by intellectual disability or educational deprivation. Recent studies have increasingly recognized that DD is a heterogeneous, instead of monolithic, learning disorder with not only cognitive and behavioral deficits but so too neural dysfunction. In recent years, neuroimaging studies employed group comparison to explore the neural underpinnings of DD, which contradicted the heterogenous nature of DD and may obfuscate critical individual differences. This research aimed to investigate the neural heterogeneity of DD using case studies with functional near-infrared spectroscopy (fNIRS). A total of 54 aged 6-7 years old of children participated in this study, comprising two comprehensive cognitive assessments, an 8-minute resting state, and an 8-minute one-digit addition task. Nine children met the criteria of DD and scored at or below 85 (i.e., the 16th percentile) on the Mathematics or Math Fluency subtest of the Wechsler Individual Achievement Test, Third Edition (WIAT-III) (both subtest scores were 90 and below). The remaining 45 children formed the typically developing (TD) group. Resting-state data and brain activation in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), and intraparietal sulcus (IPS) were collected for comparison between each case and the TD group. Graph theory was used to analyze the brain network under the resting state. This theory represents the brain network as a set of nodes--brain regions—and edges—pairwise interactions across areas to reveal the architectural organizations of the nervous network. Next, a single-case methodology developed by Crawford et al. in 2010 was used to compare each case’s brain network indicators and brain activation against 45 TD children’s average data. Results showed that three out of the nine DD children displayed significant deviation from TD children’s brain indicators. Case 1 had inefficient nodal network properties. Case 2 showed inefficient brain network properties and weaker activation in the IFG and IPS areas. Case 3 displayed inefficient brain network properties with no differences in activation patterns. As a rise above, the present study was able to distill differences in architectural organizations and brain activation of DD vis-à-vis TD children using fNIRS and single-case methodology. Although DD is regarded as a heterogeneous learning difficulty, it is noted that all three cases showed lower nodal efficiency in the brain network, which may be one of the neural sources of DD. Importantly, although the current “brain norm” established for the 45 children is tentative, the results from this study provide insights not only for future work in “developmental brain norm” with reliable brain indicators but so too the viability of single-case methodology, which could be used to detect differential brain indicators of DD children for early detection and interventions.

Keywords: brain activation, brain network, case study, developmental dyscalculia, functional near-infrared spectroscopy, graph theory, neural heterogeneity

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Authors: Aiydh Aljeddani

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Sustainable learning is an emerging concept that aims at enhancing sustainability literacy and competency in educational contexts. Mobile learning is one of the means increasingly used in sustainable development education nowadays. Studies which have explored this issue in the Saudi educational context so far are rare. Therefore, the current study attempted to explore the current situation of the usage of mobile learning in the Saudi elementary and secondary schools as a means of promoting sustainable learning. It also focused on how mobile learning has been implemented in those schools to promote sustainable learning and what factors have contributed to the success/failure of the implementation of mobile learning and possible ways to improve the current practice. An interpretive approach was followed in this study to gain a thorough understanding of the explored issue in the Saudi educational context using the activity theory as a lens to do so. A qualitative case study methodology in which semi-structured interviews, documents analysis and nominal group were used to gather the data for this study. Two hundred and twenty-nine participants representing several main stakeholders in the educational system took part in this study. Those included six general education schools, head teachers, teachers, students’ parents, educational supervisors, one curriculum designer and academic curriculum specialists. Through the lens of activity theory, the results of the study showed that there were contradictions in the current practice between the elements of the activity system and within each of its elements. Furthermore, several sociocultural factors have influenced both the division of labour and the community's members. These have acted as obstacles which have impeded the usage of mobile learning to promote sustainable learning in this context. It was found that shifting from the current practice to sustainable learning via the usage of mobile learning requires appropriate interrelationship between the different elements of the activity system. The study finally offers a number of recommendations to improve on the current practices and suggests areas for further studies.

Keywords: activity theory, mobile learning, sustainability competency, sustainability literacy, sustainable learning

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Authors: J. Chow

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In laboratory settings, this study measured and reported the effects of undergraduate students watching animated videos on learning microeconomics as compared with the effectiveness of reading written texts. The study described an experiment on learning microeconomics in higher education using two different types of learning materials. It reported the effectiveness on microeconomics learning of watching animated videos and reading written texts. Undergraduate students in the university were randomly assigned to either a ‘video group’ or a ‘text group’ in the experiment. Previously-validated multiple-choice questions on fundamental concepts of microeconomics were administered. Both groups showed improvement between the pre-test and post-test. The experience of learning using text and video materials was also assessed. After controlling the student characteristics variables, the analyses showed that both types of materials showed comparable level of perceived learning experience. The effect size and statistical significance of these results supported the hypothesis that animated video is an effective alternative to text materials as a learning tool for students. The findings suggest that such animated videos may support teaching microeconomics in higher education.

Keywords: animated videos for education, laboratory experiment, microeconomics education, undergraduate economics education

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Authors: Yong Wook Kim

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The purpose of this study is to develop an instruction analysis checklist applicable to inclusive setting based on the Universal Design for Learning Guideline 2.0. To do this, two self-validation reviews, two expert validity reviews, and two usability evaluations were conducted based on the Universal Design for Learning Guideline 2.0. After validation and usability evaluation, a total of 36 items consisting of 4 items for each instruction was developed. In all questions, examples are presented for the purpose of reinforcing concrete. All the items were judged by the 3-point scale. The observation results were provided through a radial chart allowing SWOT analysis of the universal design for learning of teachers. The developed checklist provides a description of the principles and guidelines in the checklist itself as it requires a thorough understanding by the observer of the universal design for learning through prior education. Based on the results of the study, the instruction criteria, the specificity of the criteria, the number of questions, and the method of arrangement were discussed. As a future research, this study proposed the characteristics of application of universal design for learning for each subject, the comparison with the observation results through the self-report teaching tool, and the continual revision and supplementation of the lecture checklist.

Keywords: inclusion, universal design for learning, instruction analysis, instruction checklist

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Shinoy Vengaramkode Bhaskaran

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The advancement of cloud computing led to a variety of security issues for both consumers and industries. Whereas machine learning (ML) is one approach to securing Cloud-based systems. Various methods have been employed to prevent or detect attacks and security vulnerabilities on the Cloud using ML techniques. In this paper, we present an ML perspective on the methodologies and techniques of cloud security. Initially, an investigative study on cloud computing is conducted with a primary emphasis on the gaps with two research questions that are impeding the adoption of cloud technology, as well as the challenges associated with threat solutions. Next, some ideas are generated based on machine learning methods to mitigate certain types of attacks that are frequently discussed through the application of ML techniques. Finally, we review different machine learning algorithms and their adoption in cloud computing.

Keywords: artificial intelligence, machine learning, cloud computing infrastructure as a service, support vector machine, platform as a service

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Authors: Dalia Elleuch

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The use of machine learning in the field of healthcare has gained tremendous momentum in recent years, with the potential to revolutionize the way diseases are diagnosed and treated. In particular, the field of machine learning in the detection of degenerative diseases through language performance analysis has shown great promise and has been the subject of a growing body of research. As Alzheimer’s Disease (AD) is among the most prevalent neurodegenerative diseases, this review is designed to investigate the effectiveness of machine learning through speech analysis techniques to analyze linguistic data from patients with AD, with the goal of detecting early signs of the disease. A corpus comprising seven comparative studies with a total number of patients (n=1054) was analyzed. The finding reveals a high degree of accuracy, ranging between 83.32% and 97.18%. This systematic review sheds light on the potential of speech analysis and machine learning in the detection of AD, highlighting the need for further development and integration into clinical practice for improved patient outcomes.

Keywords: machine learning, detection, neurodegenerative diseases, Alzheimer’s disease, speech analysis

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Authors: Abraham Ampong

Abstract:

BACKGROUND AND SIGNIFICANCE OF THE STUDY: The development of information technology and its influence today is inevitable in the world of education. The development of information technology and communication (ICT) has an impact on the use of teaching aids such as computers and the Internet, for example, E-learning. E-learning is a learning process attained through electronic means. But learning is not merely technology because learning is essentially more about the process of interaction between teacher, student, and source study. The main purpose of the study is to investigate learners’ online learning experiences in a blended learning approach, evaluate how learners’ experience of an online learning environment affects the blended learning approach and examine the future of online learning in a blended learning environment. Blended learning pedagogies have been recognized as a path to improve teacher’s instructional strategies for teaching using technology. Blended learning is perceived to have many advantages for teachers and students, including any-time learning, anywhere access, self-paced learning, inquiry-led learning and collaborative learning; this helps institutions to create desired instructional skills such as critical thinking in the process of learning. Blended learning as an approach to learning has gained momentum because of its widespread integration into educational organizations. METHODOLOGY: Based on the research objectives and questions of the study, the study will make use of the qualitative research approach. The rationale behind the selection of this research approach is that participants are able to make sense of their situations and appreciate their construction of knowledge and understanding because the methods focus on how people understand and interpret their experiences. A case study research design is adopted to explore the situation under investigation. The target population for the study will consist of selected students from selected universities. A simple random sampling technique will be used to select the targeted population. The data collection instrument that will be adopted for this study will be questions that will serve as an interview guide. An interview guide is a set of questions that an interviewer asks when interviewing respondents. Responses from the in-depth interview will be transcribed into word and analyzed under themes. Ethical issues to be catered for in this study include the right to privacy, voluntary participation, and no harm to participants, and confidentiality. INDICATORS OF THE MAJOR FINDINGS: It is suitable for the study to find out that online learning encourages timely feedback from teachers or that online learning tools are okay to use without issues. Most of the communication with the teacher can be done through emails and text messages. It is again suitable for sampled respondents to prefer online learning because there are few or no distractions. Learners can have access to technology to do other activities to support their learning”. There are, again, enough and enhanced learning materials available online. CONCLUSION: Unlike the previous research works focusing on the strengths and weaknesses of blended learning, the present study aims at the respective roles of its two modalities, as well as their interdependencies.

Keywords: online learning, blended learning, technologies, teaching methods

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Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

Abstract:

As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: apartment housing, machine learning, multi-objective optimization, performance prediction

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