Search results for: online learning platforms

Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Isaac Owusu Asante, Yushi Jiang, Hailin Tao

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Live streaming marketing, the new electronic commerce element, became an optional marketing channel following the COVID-19 pandemic. Many sellers have leveraged the features presented by live streaming to increase sales. Studies on live streaming have focused on gaming and consumers’ loyalty to brands through live streaming, using interview questionnaires. This study, however, was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during live streaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study introduces a new way of measuring interactions in live streaming commerce and proposes a way to manually gather data on consumer behaviors in live streaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness

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Authors: Vaida Jurgile

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Intercultural learning is one of the approaches taken to understand the cultural diversity of the modern world and to accept changes in cultural identity and otherness and the expression of tolerance. During intercultural learning, students develop their abilities to interact and communicate with their group members. These abilities help to understand social and cultural differences, to form one’s identity, and to give meaning to intercultural learning. Intercultural education recognizes that a true understanding of differences and similarities of another culture is necessary in order to lay the foundations for working together with others, which contributes to the promotion of intercultural dialogue, appreciation of diversity, and cultural exchange. Therefore, it is important to examine the concept of intercultural learning, revealed through students’ learning experiences and understanding of how this learning takes place and what significance this phenomenon has in higher education. At a scientific level, intercultural learning should be explored in order to uncover the influence of cultural identity, i.e., intercultural learning should be seen in a local context. This experience would provide an opportunity to learn from various everyday intercultural learning situations. Intercultural learning can be not only a form of learning but also a tool for building understanding between people of different cultures. The research object of the study is the process of intercultural learning. The aim of the dissertation is to develop a grounded theory of the process of learning in an intercultural study environment, revealing students’ learning experiences. The research strategy chosen in this study is a constructivist grounded theory (GT). GT is an inductive method that seeks to form a theory by applying the systematic collection, synthesis, analysis, and conceptualization of data. The targeted data collection was based on the analysis of data provided by previous research participants, which revealed the need for further research participants. During the research, only students with at least half a year of study experience, i.e., who have completed at least one semester of intercultural studies, were purposefully selected for the research. To select students, snowballing sampling was used. 18 interviews were conducted with students representing 3 different fields of sciences (social sciences, humanities, and technology sciences). In the process of intercultural learning, language expresses and embodies cultural reality and a person’s cultural identity. It is through language that individual experiences are expressed, and the world in which Others exist is perceived. The increased emphasis is placed on the fact that language conveys certain “signs’ of communication and perception with cultural value, enabling the students to identify the Self and the Other. Language becomes an important tool in the process of intercultural communication because it is only through language that learners can communicate, exchange information, and understand each other. Thus, in the process of intercultural learning, language either promotes interpersonal relationships with foreign students or leads to mutual rejection.

Keywords: intercultural learning, grounded theory, students, other

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Authors: Ya-Mei Chen

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Online crowdsourced translation, an innovative translation practice brought by Web 2.0 technologies and the democratization of information, has become increasingly popular in the Internet era. Carried out by grass-root internet users, crowdsourced translation contains fundamentally different features from its off-line traditional counterpart, such as voluntary participation and parallel collaboration. To better understand such a participatory and collaborative nature, this paper will use the online Chinese translation project of Global Voices as a case study to investigate the following issues: (1) the changes in volunteer translators’ and reviewers’ motivations for participation, (2) translators’ and reviewers’ use of translation strategies and (3) the correlations of translators’ and reviewers’ motivations and strategies with the organizational mission, the translation style guide, the translator-reviewer interaction, the mediation of the translation platform and various types of capital within the translation field. With an aim to systematically explore the above three issues, this paper will collect both quantitative and qualitative data and then draw upon Engestrom’s activity theory and Bourdieu’s field theory as a theoretical framework to analyze the data in question. An online anonymous questionnaire will be conducted to obtain the quantitative data. The questionnaire will contain questions related to volunteer translators’ and reviewers’ backgrounds, participation motivations, translation strategies and mutual relations as well as the operation of the translation platform. Concerning the qualitative data, they will come from (1) a comparative study between some English news texts published on Global Voices and their Chinese translations, (2) an analysis of the online discussion forum associated with Global Voices’ Chinese translation project and (3) the information about the project’s translation mission and guidelines. It is hoped that this research, through a detailed sociological analysis of a cause-driven crowdsourced translation project, can enable translation researchers and practitioners to adequately meet the translation challenges appearing in the digital age.

Keywords: crowdsourced translation, global voices, motivation, translation strategies

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Authors: Mariko Hara, Mari Aoki, Takako Ito, Masao Sugita

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Social distancing measures due to the COVID-19 crisis had a severe impact on music and art practices based in community settings. They had to re-think how to connect with their dispersed community using online tools. As the social distancing continues, there is an urgent need to investigate the possibilities of online community music and art practices. Are they sustainable actions that can have positive impacts on the community and the quality of lives of people over time? The Online Lindgren Café (hereafter ‘OLC’) is a monthly online literature event which started in June 2020. In the OLC, up to 14 members meet online to discuss the works of Astrid Lindgren and similar authors. Members come from various places in Japan and Norway, with a variety of expertise from music therapy, music education, psychotherapy, music sociology, storytelling, and theatre, and their family members join them. In these meetings, music and arts activities emerged in response to interests among the members. The resources and experiences of the members helped to develop these activities further. This paper first introduces one of the music and art activities in one specific event, a collaborative picture book-making with music, which was initiated and led by the second author. The third author chose the music, and the activity itself was recorded. This is followed by the description of a reflecting event, where the recording of the collaborative picture book-making activity was shared to facilitate further creations (drawings, haiku, and fabric weaving) as well as group reflections on the trajectories of the Online Lindgren Café. Finally, we will discuss the preliminary findings using the data collected at the reflecting event. Key findings suggest that the resource-driven approach of the OLC leveled the relationships among the intergenerational, multi-cultural, and interdisciplinary members. This enabled the members to set aside their professional and/or predominant identities, which allowed them to discover their own and others’ resources. The relaxed, unstructured, and liminal phenomenon at OLC can be regarded as a form of communitas, where members gain a sense of liberation and belonging in a different way from in-person communications. Participation from one’s home, as well as a video conferencing function that allowed the members to position themselves among the other participants in equal-sized windows, seems to have enabled members to feel safe to express themselves openly at the same time feel a sense of belonging. Furthermore, in the OLC, music and arts activities acted to inclusively connect and re-connect dispersed, intergenerational members with each other. For instance, in a music and drawing activity, music acted as a means for each member to engage in their own ‘drawing space’ while still feeling connected with the others. The positive experiences from these activities inspired the members to use similar approaches outside of the OLC. The finding suggests that, because of its resource-driven approach supported by the music and arts activities, the OLC could be developed further as a permeable and sustainable action even after any current social distancing measures are lifted.

Keywords: communitas, COVID-19, musical affordances, online community of practices, resource-driven approach

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Authors: Maria L. Ulloa, Ruth M. Crawford, Stephanie Kelly, Joey Domdom

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In recent decades health and social service delivery have become more collaborative and interdisciplinary. Emerging trends suggest the need for an integrative and interprofessional approach to meet the challenges faced by professionals navigating the complexities of health and social service practice environments. Terms such as multidisciplinary practice, interprofessional collaboration, interprofessional education and transprofessional practice have become the common language used across a range of social services and health providers in western democratic systems. In Aotearoa New Zealand, one example of an interprofessional collaborative approach to curriculum design and delivery in health and social service is the development of an innovative Masters of Professional Practice programme. This qualification is the result of a strategic partnership between two tertiary institutions – Whitireia New Zealand (NZ) and the Wellington Institute of Technology (Weltec) in Wellington. The Master of Professional Practice programme was designed and delivered from the perspective of a collaborative, interprofessional and relational approach. Teachers and students in the programme come from a diverse range of cultural, professional and personal backgrounds and are engaged in courses using a blended learning approach that incorporates the values and pedagogies of interprofessional education. Students are actively engaged in professional practice while undertaking the programme. This presentation describes the themes of exploratory qualitative formative observations of engagement in class and online, student assessments, student research projects, as well as qualitative interviews with the programme teaching staff. These formative findings reveal the development of critical practice skills around the common themes of the programme: research and evidence based practice, education, leadership, working with diversity and advancing critical reflection of professional identities and interprofessional practice. This presentation will provide evidence of enhanced learning experiences in higher education and learning in multi-disciplinary contexts.

Keywords: diversity, exploratory research, interprofessional education, professional identity

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Authors: Fahsai Pawapootanont, Sasithon Yuwakosol

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The objectives of this research study are as follows: 1) Study the information-seeking behavior of followers of influencers on Facebook in making hotel booking decisions and 2) Study the characteristics of travel influencers that affect their followers' hotel booking decisions. The Data was collected by interviewing 35 key informants, consisting of 25 Thai tourists who were followers of travel influencers and 10 travel influencers, as well as collecting data using online questionnaires from a sample of 400 Thai tourists and using statistical data analysis: percentage, standard deviation, mean, T-Test and One-Way Analysis of Variance: ANOVA. The results of the influence of travel influencers on Facebook on hotel booking decisions in Thailand revealed the following: People in different age groups have different information-seeking behaviours. Depends on experience and aptitude in using technology. The sample group did not seek information from only one source. There is also a search for information from various places in order to get comparative information and the most truthful information to make decisions. In addition, travel influencers should be those who present honest, clear, and complete content. And present services honestly. In addition to the characteristics of travel influencers affecting hotel booking decisions, Presentation formats and platforms also affect hotel booking decisions. But it must be designed and presented to suit the behavior of the group of people we want. As for the influence of travel influencers, it can be concluded that The influence of travel influencers can influence their followers' interests and hotel booking decisions. However, it was found that there are other factors that followers of travel influencers on Facebook will factor into their decision to book a hotel, such as Whether the hotel's comfort meets your needs or not; location, price, and promotions also play an important role in deciding to book a hotel.

Keywords: influencer, travel, facebook, hotel booking decisions, Thailand

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Authors: Yousef M. Alammari, Mahmoud A. Gaddoury, Reem A. Almohaini, Sara A. Alharbi, Lena S. Alsaleem, Lujain H. Allowaihiq, Maha H. Alrashid, Abdullah H. Alghamdi, Abdullah A. Alaryni

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Objective: The goal of this study was to establish the prevalence of neuropsychiatric symptoms in systemic lupus erythematosus (NPSLE) patients in Saudi Arabia and the variables that are linked to it. Methods: During June 2021, this cross-sectional study was carried out among SLE patients in Saudi Arabia. The Saudi Rheumatism Association exploited social media platforms to provide a self-administered online questionnaire to SLE patients. All data analyses were performed using the Statistical Packages for Social Sciences (SPSS) version 26. Results: Two hundred and five SLE patients participated in the study (females 91.3 % vs. males 8.7 %). In addition, 13.5 % of patients had a family history of SLE, and 26% had SLE for one to three years. Alteration or loss of sensation (53.4%), Fear (52.4%), and headache (48.1%) were the most prevalent signs of neuropsychiatric symptoms in systemic lupus erythematosus (NPSLE) patients. The prevalence of patients with NPSLE was 40%. In a multivariate regression model, fear, altered sensations, cerebrovascular illness, sleep disruption, and diminished interest in routine activities were identified as independent risk variables for NPSLE. Conclusion: Nearly half of SLE patients demonstrated NP manifestations, with significant symptoms including fear, alteration of sensation, cerebrovascular disease, sleep disturbance, and reduced interest in normal activities. To detect the pathophysiology of NPSLE, it is necessary to understand the relationship between neuropsychiatric morbidity and other relevant rheumatic disorders in the SLE population.

Keywords: neuropsychiatric, systemic lupus erythematosus, NPSLE, prevalence, SLE patients

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Authors: David S. Rosenstein

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The author believes that modern spoken languages should be equally difficult (or easy) to learn, since all normal children learning their native languages do so at approximately the same rate and with the same competence, progressing from easy to more complex grammar and syntax in the same way. Why then, do some languages seem more difficult than others? Perhaps people are referring to the written language, where it may be true that mastering Chinese requires more time than French, which in turn requires more time than Spanish. But this may be marginal, since Chinese and French children quickly catch up to their Spanish peers in reading comprehension. Rather, the real differences in difficulty derive from two sources: hardened L1 language habits trying to cope with contrasting L2 habits; and unfamiliarity with unique L2 characteristics causing faulty expectations. It would seem that effective L2 teaching and learning must take these two sources of difficulty into consideration. The author feels that the latter (faulty expectations) causes the greatest difficulty, making effective teaching and learning somewhat different for each given foreign language. Examples from Chinese and other languages are presented.

Keywords: learning different languages, language learning difficulties, faulty language expectations

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Authors: Nadeem Akhtar, An Ran, Cornelius B. Pratt

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China’s President Xi' vision of Belt and Road Initiative, an infrastructural project of development and connectivity, is attracting international students to Chinese universities, with Pakistan and India among the top-10 countries of origin of those students (Ministry of Education China, 2018). An additional factor in international students’ interest in Chinese universities is their improving global rankings of Chinese universities. Against that backdrop, this study addresses two overarching questions: (a) What factors explain South Asian students’ study-away experiences, particularly in their multicultural environments? and (b) What role do new media play in their adaptation to that environment? This study is guided by Stephen’s (2011) theoretical model, which suggests that social networks influence immigrants’ interactions with host and home culture. The present study used a structured questionnaire distributed through both WeChat and other online platforms to international students studying in Chinese universities. Preliminary results are threefold: (a) that the frequency of use of social media is a predictor of the level of adjustment of the students to their multicultural environment; (b) that social engagement with their international-student peers is a moderating factor in their experiential outcomes; and (c) length of stay in Chinese universities, surprisingly, was not a predictor of adaptation. A major implication of these findings is that, even though social media tend to be criticized for contributing to anomie and to diminishing social capital among youths and millennials, they can be poignant tools for cultural adaptation, particularly among international students in China. It remains to be seen if such outcomes occur among international students in other countries or world regions.

Keywords: adaptation, China's Belt and Road Initiative, international students, social media

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Chaitanya Vyas

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This paper identifies limitations of existing two e-Governance services viz. railway ticket booking and passport service in India. The comparison has been made as to how in the past these two citizen services were operating manually and how these services are taken online via e-Governance. Different e-Governance projects, investment aspects, and role of corporate are discussed. For Indian Railway online ticketing a comparison has been made between state run booking website and popular private firm run booking website. For passport service, observation through personal visit to passport center is described. Suggestions are made to improve these services further to improve citizen service experiences.

Keywords: e-Governance, citizen services, passport, Indian Railways

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Authors: Matthias Grünke

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In this single-case study, we evaluated the effects of a self-graphing intervention to help students improve their reading fluency. Our participant was a 10-year-old girl with a suspected learning disability in reading. We applied an ABAB reversal design to test the efficacy of our approach. The dependent measure was the number of correctly read words from a children’s book within five minutes. Our participant recorded her daily performance using a simple line diagram. Results indicate that her reading rate improved simultaneously with the intervention and dropped as soon as the treatment was suspended. The findings give reasons for optimism that our simple strategy can be a very effective tool in supporting students with learning disabilities to boost their reading fluency.

Keywords: single-case study, learning disabilities, elementary education, reading problems, reading fluency

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Authors: Enrique Barra, Aldo Gordillo, Juan Quemada

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This paper presents the Virtual Science Hub platform. It is an open source platform that combines a social network, an e-learning authoring tool, a video conference service and a learning object repository for science teaching enrichment. These four main functionalities fit very well together. The platform was released in April 2012 and since then it has not stopped growing. Finally we present the results of the surveys conducted and the statistics gathered to validate this approach.

Keywords: e-learning, platform, authoring tool, science teaching, educational sciences

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Authors: Luke Taylor, Samantha J. Hall, Kenneth I. Cumming, Jakki Bardsley, Scott S. P. Wildman

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Medway School of Pharmacy, as part of an Equality Diversity and Inclusivity (EDI) initiative run by the University of Kent, decided to take steps to try and negate disparities in numeracy competencies within students undertaking the Master of Pharmacy degree in order to combat a trend in pharmacy students’ numerical abilities upon entry. This included a research driven project 1) to identify if pharmacy students are aware of weaknesses in their numeracy capabilities, and 2) recognise where their numeracy skillset is lacking. In addition to gaining this student perspective, a number of actions have been implemented to support students in improving their numeracy competencies. Reflective and quantitative analysis has shown promising improvements for the final year cohort of 2014/15 when compared to previous years. The method of involving student feedback into the structure of numeracy teaching/support has proven to be extremely beneficial to both students and teaching staff alike. Students have felt empowered and in control of their own learning requirements, leading to increased engagement and attainment. School teaching staff have received quality data to help improve existing initiatives and to innovate further in the area of numeracy teaching. In light of the recognised improvements, further actions are currently being trialled in the area of numeracy support. This involves utilising Virtual Learning Environment platforms to provide individualised support as a supplement to the increased numeracy mentoring (staff and peer) provided to students. Mentors who provide group or one-to-one sessions are now given significant levels of training in dealing with situations that commonly arise from mentoring schemes. They are also provided with continued support throughout the life of their degree. Following results from this study, Medway School of Pharmacy hopes to drive increasing numeracy standards within Pharmacy (primarily through championing peer mentoring) as well as other healthcare professions including Midwifery and Nursing.

Keywords: attainment, ethnicity, numeracy, pharmacy, support

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Authors: Tan Swee Liang

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This paper studies the two-way relationship between fintech credit and banking efficiency using the Generalized panel Method of Moment (GMM) estimation in structural equation modeling (SEM). Banking system efficiency, defined as its ability to produce the existing level of outputs with minimal inputs, is measured using input-oriented data envelopment analysis (DEA), where the whole banking system of an economy is treated as a single DMU. Banks are considered an intermediary between depositors and borrowers, utilizing inputs (deposits and overhead costs) to provide outputs (increase credits to the private sector and its earnings). Analysis of the interrelationship between fintech credit and bank efficiency is conducted to determine the impact in different country groupings (ASEAN, Asia and OECD), in particular the banking system response to fintech credit platforms. Our preliminary results show that banks do respond to the greater pressure caused by fintech platforms to enhance their efficiency, but differently across the different groups. The author’s earlier research on ASEAN-5 high bank overhead costs (as a share of total assets) as the determinant of economic growth suggests that expenses may not have been channeled efficiently to income-generating activities. One practical implication of the findings is that policymakers should enable alternative financing, such as fintech credit, as a warning or encouragement for banks to improve their efficiency.

Keywords: fintech lending, banking efficiency, data envelopment analysis, structural equation modeling

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Authors: Suriyani Ariffin, Nor Aziah Alias, Khairil Iskandar Othman, Haslinda Yusoff

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An Integrated Cumulative Grade Point Average (iCGPA) is a mechanism and strategy to ensure the curriculum of an academic programme is constructively aligned to the expected learning outcomes and student performance based on the attainment of those learning outcomes that is reported objectively in a spider web. Much effort and time has been spent to develop a viable mechanism and trains academics to utilize the platform for reporting. The question is: How well do learners conceive the idea of their achievement via iCGPA and whether quality learner attributes have been nurtured through the iCGPA mechanism? This paper presents the architecture of an integrated CGPA mechanism purported to address a holistic evaluation from the evaluation of courses learning outcomes to aligned programme learning outcomes attainment. The paper then discusses the students’ understanding of the mechanism and evaluation of their achievement from the generated spider web. A set of questionnaires were distributed to a group of students with iCGPA reporting and frequency analysis was used to compare the perspectives of students on their performance. In addition, the questionnaire also explored how they conceive the idea of an integrated, holistic reporting and how it generates their motivation to improve. The iCGPA group was found to be receptive to what they have achieved throughout their study period. They agreed that the achievement level generated from their spider web allows them to develop intervention and enhance the programme learning outcomes before they graduate.

Keywords: learning outcomes attainment, iCGPA, programme learning outcomes, spider web, iCGPA reporting skills

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Authors: Guanghua Zhang, Fubao Wang, Weijun Duan

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Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output.

Keywords: convolution neural network, discriminator, generator, unsupervised learning

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Kumiko Inagaki

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In recent, years, Japanese universities have begun to face a dilemma: more than half of all high school graduates go on to attend an institution of higher learning, overwhelming Japanese universities accustomed to small student bodies. These universities have been forced to embrace qualitative changes to accommodate the increased number and diversity of students who enter their establishments, students who differ in their motivations for learning, their levels of eagerness to learn, and their perspectives on the future. One of these changes is an increase in awareness among Japanese educators of the importance of active learning, which deepens students’ understanding of course material through a range of activities, including writing, speaking, thinking, and presenting, in addition to conventional “passive learning” methods such as listening to a one-way lecture. 　The purpose of this study is to examine the effectiveness of the teaching method adapted to improve active learning. A teaching method designed to promote active learning was implemented in a social science course at one of the most popular universities in Japan. A questionnaire using a five-point response format was given to students in 2,305 courses throughout the university to evaluate the effectiveness of the method based on the following measures: ① the ratio of students who were motivated to attend the classes, ② the rate at which students learned new information, and ③ the teaching method adopted in the classes. The results of this study show that the percentage of students who attended the active learning course eagerly, and the rate of new knowledge acquired through the course, both exceeded the average for the university, the department, and the subject area of social science. In addition, there are strong correlations between teaching method and student motivation and between teaching method and knowledge acquisition rate. These results indicate that the active learning teaching method was effectively implemented and that it may improve student eagerness to attend class and motivation to learn.

Keywords: active learning, Japanese university, teaching method, university education

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Authors: Erhan Eroğlu

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This paper presents a new method called Mentor and Mentee Based Learning. This new method is becoming more and more common especially at workplaces. This study is significant as it clearly underlines how it works well. Education has always aimed at equipping people with the necessary knowledge and information. For many decades it went on teachers’ talk and chalk methods. In the second half of the nineteenth century educators felt the need for some changes in delivery systems. Some new terms like self- discovery, learner engagement, student centered learning, hands on learning have become more and more popular for such a long time. However, some educators believe that there is much room for better learning methods in many fields as they think the learners still cannot fulfill their potential capacities. Thus, new systems and methods are still being developed and applied at education centers and work places. One of the latest methods is assigning some mentors for the newly recruited employees and training them within a mentor and mentee program which allows both parties to see their strengths and weaknesses and the areas which can be improved. This paper aims at finding out the perceptions of the mentors and mentees on the programs they are offered at their workplaces and suggests some betterment alternatives. The study has been conducted via a qualitative method whereby some interviews have been done with both mentors and mentees separately and together. Results show that it is a great way to train inexperienced one and also to refresh the older ones. Some points to be improved have also been underlined. The paper shows that education is not a one way path to follow.

Keywords: learning, mentor, mentee, training

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Authors: Amjaad Alsalamah, Liyakathunsia Syed

Abstract:

Transportation is an essential need for many people to go to their work, school, and home. In particular, the main common method inside many cities is to drive the car. Driving a car can be an easy job to reach the destination and load all stuff in a reasonable time. However, deciding to find a parking lot for a car can take a long time using the traditional system that can issue a paper ticket for each customer. The old system cannot guarantee a parking lot for all customers. Also, payment methods are not always available, and many customers struggled to find their car among a numerous number of cars. As a result, this research focuses on providing an online smart parking system in order to save time and budget. This system provides a flexible management system for both parking owner and customers by receiving all request via the online system and it gets an accurate result for all available parking and its location.

Keywords: smart parking system, IoT, tracking system, process model, cost, time

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

Abstract:

In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Manuela Nayantara Jeyaraj

Abstract:

Having moved into the 21^st century, it is way past being arguable that innovative technology needs to be incorporated into conventional classroom teaching. Though the Western world has found presumable success in achieving this, it is still a concept under battle in developing countries such as Sri Lanka. Reaching the acme of implementing interactive virtual learning within classrooms is a struggling idealistic fascination within the island. In order to overcome this problem, this study is set to reveal facts that limit the implementation of virtual, interactive learning within the school classrooms and provide hacks that could prove the augmented use of the Virtual World to enhance teaching and learning experiences. As each classroom moves along with the usage of technology to fulfill its functionalities, a few intense hacks provided will build the administrative onuses on a virtual system. These hacks may divulge barriers based on social conventions, financial boundaries, digital literacy, intellectual capacity of the staff, and highlight the impediments in introducing students to an interactive virtual learning environment and thereby provide the necessary actions or changes to be made to succeed and march along in creating an intellectual society built on virtual learning and lifestyle. This digital learning environment will be composed of multimedia presentations, trivia and pop quizzes conducted on a GUI, assessments conducted via a virtual system, records maintained on a database, etc. The ultimate objective of this study could enhance every child's basic learning environment; hence, diminishing the digital divide that exists in certain communities.

Keywords: digital divide, digital learning, digitization, Sri Lanka, teaching methodologies

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

Abstract:

Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

Abstract:

A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Jaeseo Lim, Jooyong Park

Abstract:

Although there is ample evidence that student involvement enhances learning, college education is still mainly centered on lectures. However, in recent years, the effectiveness of discussions and the use of collective intelligence have attracted considerable attention. This study intends to examine the empirical effects of discussions on learning outcomes in various conditions. Eighty eight college students participated in the study and were randomly assigned to three groups. Group 1 was told to review material after a lecture, as in a traditional lecture-centered class. Students were given time to review the material for themselves after watching the lecture in a video clip. Group 2 participated in a discussion in groups of three or four after watching the lecture. Group 3 participated in a discussion after studying on their own. Unlike the previous two groups, students in Group 3 did not watch the lecture. The participants in the three groups were tested after studying. The test questions consisted of memorization problems, comprehension problems, and application problems. The results showed that the groups where students participated in discussions had significantly higher test scores. Moreover, the group where students studied on their own did better than that where students watched a lecture. Thus discussions are shown to be effective for enhancing learning. In particular, discussions seem to play a role in preparing students to solve application problems. This is a preliminary study and other age groups and various academic subjects need to be examined in order to generalize these findings. We also plan to investigate what kind of support is needed to facilitate discussions.

Keywords: discussions, education, learning, lecture, test

Procedia PDF Downloads 174