Search results for: online teaching and learning

Authors: Melaku Takele Abate, Abbi Lemma Wodajo, Adula Bekele Hunde

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This study aimed at exploring mathematics teachers' reactions, learning, school leaders’ support, and use of the Lesson Study for Transformative Assessment (LSforTA) program ideas in practice. The LSforTA program was new, and therefore, a local and grounded approach was needed to examine teachers’ knowledge and skills acquired using LSforTA. So, a design-based research approach was selected to evaluate and refine the LSforTA approach. The results showed that LSforTA increased teachers' knowledge and use of different levels of mathematics assessment tasks. The program positively affected teachers' practices of transformative assessment and enhanced their knowledge and skills in assessing students in a transformative way. The paper concludes how the LSforTA procedures were adapted in response to this evaluation and provides suggestions for future development and research.

Keywords: classroom assessment, feedback practices, lesson study, mathematics, design-based research

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Authors: Papitchaya Papangkorn

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Matching the expectations of teachers and learners is significant for successful language learning. Moreover, teachers should discover what their learners think and feel about what and how they want to learn. Therefore, this study investigates International College, Suan Sunandha Rajabhat University students’ preferences toward error corrections in order to help SSRUIC teachers match their expectations and their learners because it is important for successful language learning. This study examined the learners’ attitude and preference toward error correction through 50 first year SSRUIC students both male (25) and female (25) in Bangkok, Thailand. The data were collected from a questionnaire and interviews to investigate the necessity and frequency, timing, type of errors, method of corrective feedback, and person who gives error correction in order to answer the overall research question and sub-questions. The findings indicate five suggestions regarding the overall research question. Firstly, errors should be treated, and always be treated. Secondly, treating errors after finish speaking is the most appropriate time. Thirdly, “errors that may cause problems in an understanding of listener” and “frequent spoken errors” should be treated. Fourthly, repetition and explicit feedback were the most popular types of feedback among males, whereas metalinguistic feedback was the most favoured types amongst females. Finally, teachers were the most preferred person to deliver corrective feedback for the learners. Although the results of the study are difficult to generalize to a larger population, which are Thai EFL learners because of the small sample, the findings provide useful information that may contribute to understanding of SSRUIC learners’ preferences toward error corrections and it might reduce the gap between what teachers employ and what students expect when receiving corrective feedback. The reduction of this gap may be useful for the learning process and could enhance the efforts of both teachers and learners in a Thai context.

Keywords: attitude, corrective feedback, error, preference

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: Amal S. Al-Badi, Ali H. Al-Badi

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Many organizations are opting to adopt Open Source Software (OSS) as it is the current trend to rely on each other rather than on companies (Software vendors). It is a clear shift from organizations to individuals, the concept being to rely on collective participation rather than companies/vendors. The main objectives of this research are 1) to identify the current level of OSS usage in Sultan Qaboos University; 2) to identify the potential benefits of using OSS in educational institutes; 3) to identify the OSS applications that are most likely to be used within an educational institute; 4) to identify the existing and potential barriers to the successful adoption of OSS in education. To achieve these objectives a two-stage research method was conducted. First a rigorous literature review of previously published material was performed (interpretive/descriptive approach), and then a set of interviews were conducted with the IT professionals at Sultan Qaboos University in Oman in order to explore the extent and nature of their usage of OSS.

Keywords: open source software, social software, e-learning 2.0, Web 2.0, connectivism, personal learning environment (PLE), OpenCourseWare

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Li-Ching Hung

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The lack of empirical research on portfolio assessment in aboriginal EFL English classes of junior high schools in Taiwan may inhibit EFL teachers from appreciating the utility of this alternative assessment approach. This study addressed the following research questions: 1) understand how aboriginal EFL students and instructors of junior high schools in Taiwan perceive portfolio assessment, and 2) how portfolio assessment affects Taiwanese aboriginal EFL students’ learning outcomes. Ten classes of five junior high schools in Taiwan (from different regions of Taiwan) participated in this study. Two classes from each school joined the study, and each class was randomly assigned as a control group, and one was the experimental group. These five junior high schools consisted of at least 50% of aboriginal students. A mixed research design was utilized. The instructor of each class implemented a portfolio assessment for 15 weeks of the 2015 Fall Semester. At the beginning of the semester, all participants took a GEPT test (pretest), and in the 15th week, all participants took the same level of GEPT test (post-test). Scores of students’ GEPT tests were checked by the researcher as supplemental data in order to understand each student’s performance. In addition, each instructor was interviewed to provide qualitative data concerning students’ general learning performance and their perception of implementing portfolio assessments in their English classes. The results of this study were used to provide suggestions for EFL instructors while modifying their lesson plans regarding assessment. In addition, the empirical data were used as references for EFL instructors implementing portfolio assessments in their classes effectively.

Keywords: assessment, portfolio assessment, qualitative design, aboriginal ESL students

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Authors: Dada. O.Akinola, Uche. I. Ebele, Bamiro .A.Rafatu, Akinbami A. Akinsegun, Dada O. Adeyemi, Adeyemi. O. Ibukun, Okunowo O.Bolanle, Abdulateef O. Kareem, Ibrahim.N. Ismaila, Dosu Rihanat

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Background: Hyperglycaemia alters qualitatively and quantitatively all the full blood count parameters. The alterations among other factors are responsible for the macrovascular and microvascular complications associated with diabetes mellitus (DM). This study is aimed at correlating haematological parameters in DM subjects with their fasting blood glucose (FBG) and anthropometric parameters. Materials and Methods: This was a cross-sectional study of participants attending DM clinic of Lagos State University Teaching Hospital (LASUTH), Ikeja. The study recruited one hundred and two (102) DM subjects and one hundred (100) non-DM controls. Venous blood samples were collected for full blood count (FBC) assay while FBG was done, structured questionnaires were administered, and anthropometric measurements of all participants were done. Data were analyzed with Statistical Package for Social Science (SPSS) version 23. P was set at ≤0.05. Results: The mean age of DM patients was 64.32± 11.31 years. Using a haemoglobin concentration cut-off of 11g/dl, 39.2%, and 13% DM and control participants respectively had values lower than 11g/dl. A total of 22.5% and 3% of DM and controls respectively gave a history of previous blood transfusion.White blood cells count and platelet count means were (6.12±1.60 and 5.30±7.52,p=0.59) and (213.31±73.58 and 228.91±73.21,p = 0.26) *109/L in DM subjects and controls respectively. FBG and all the anthropometric data in DM subjects were significantly higher than in controls. Conclusions: The prevalence of anaemia in DM subjects was three times higher than in controls. The white blood cell count was higher but not statistically significant in DM compared with controls. But platelet count was higher but not statistically significant in controls compared with DM subjects.

Keywords: haematological profile, diabetes mellitus, anthropometric data, fasting blood glucose

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Authors: Precious Barnes, Abraham Mensah, Leonard Derkyi-Kwarteng, Benjamin Amoani, George Adjei, Ernest Adankwah, Faustina Pappoe, Kwabena Dankwah, Daniel Amoako-Sakyi, Samuel Victor Nuvor, Dorcas Obiri-Yeboah, Ewura Seidu Yahaya, Patrick Kafui Akakpo, Roland Osei Saahene

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Context: Breast cancer is a prevalent and aggressive type of cancer among African women, with high mortality rates in Ghana. Nuclear factor kappa B (NF-kB) is a transcription factor that has been associated with tumor progression in breast cancer. However, there is a lack of published data on NF-kB in breast cancer patients in Ghana or other African countries. Research Aim: The aim of this study was to assess the prognostic significance of NF-kB (p65) expression and its association with various clinicopathological features in breast cancer patients at the Cape Coast Teaching Hospital in Ghana. Methodology: A total of 90 formalin-fixed breast cancer tissues and 15 normal breast tissues were used in this study. The expression level of NF-kB (p65) was examined using immunohistochemical techniques. Correlation analysis between NF-kB (p65) expression and clinicopathological features was performed using SPSS version 25. Findings: The study found that NF-kB (p65) was expressed in 86.7% of breast cancer tissues. There was a significant relationship between NF-kB (p65) expression and tumor grade, proliferation index (Ki67), and molecular subtype. High-level expression of NF-kB (p65) was more common in tumor grade 3 compared to grade 1, and Ki67 > 20 had higher expression of NF-kB (p65) compared to Ki67 ≤ 20. Triple-negative breast cancer patients had the highest overexpression of NF-kB (p65) compared to other molecular subtypes. There was no significant association between NF-kB (p65) expression and other clinicopathological parameters. Theoretical Importance: This study provides important insights into the expression of NF-kB (p65) in breast cancer patients in Ghana, particularly in relation to tumor grade and proliferation index. The findings suggest that NF-kB (p65) could serve as a potential biological marker for cancer stage, progression, prognosis and as a therapeutic target. Data Collection and Analysis Procedures: Formalin-fixed breast cancer tissues and normal breast tissues were collected and analyzed using immunohistochemical techniques. Correlation analysis between NF-kB (p65) expression and clinicopathological features was performed using SPSS version 25. Question Addressed: This study addressed the question of the prognostic significance of NF-kB (p65) expression and its association with clinicopathological features in breast cancer patients in Ghana. Conclusion: This study, the first of its kind in Ghana, demonstrates that NF-kB (p65) is highly expressed among breast cancer patients at the Cape Coast Teaching Hospital, especially in triple-negative breast cancer patients. The expression of NF-kB (p65) is associated with tumor grade and proliferation index. NF-kB (p65) could potentially serve as a biological marker for cancer stage, progression, prognosis, and as a therapeutic target.

Keywords: breast cancer, Ki67, NF-kB (p65), tumor grade

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Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

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Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

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Authors: Cheeno Marlo Sayuno

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Room to Read is an international nonprofit organization aimed at empowering young readers through literature and literacy education. In particular, the organization is focused on girls’ education in schools and bettering their social status through crafting stories and making sure that these stories are accessible to them. In 2019, Room to Read visited the Philippines and partnered with Philippine children’s literature publishers Adarna House, Lampara Books, Anvil Publishing, and OMF-Hiyas with the goal of producing contextualized stories that Filipino children can read. The result is a set of 20 storybooks developed by Filipino writers and illustrators, the author of this paper included. The project led to narratives of experiences in storybook production from conceptualization to publication, towards translations and reimagining in online repository, storytelling, and audiobook formats. During the production process, we were particularly reminded of gender representations, child’s rights, and telling stories that can empower the children in vulnerable communities, who are the beneficiaries of the project. The storybooks, along with many others produced in Asia and the world, are available online through the literacycloud.org website of Room to Read. In this study, the goal is to survey the stories produced in Asia and look at how gender is represented in the storybooks. By analyzing both the texts and the illustrations of the storybooks produced across Asian countries, themes of portrayals of young boys and girls, their characteristics and narratives, and how they are empowered in the stories are identified, with the goal of mapping how Room to Read is able to address the problem of access to literacy among young girls and ensuring them that they can do anything, the way they are portrayed in the stories. The paper hopes to determine how gender is represented in Asian storybooks produced by the international nonprofit organization Room to Read. Thematic textual analysis was used as methodology, where the storybooks are analyzed qualitatively to identify arising themes of gender representation. This study will shed light on the importance of responsible portrayal of gender in storybooks and how it can impact and empower children. The results of the study can also aid writers and illustrators in developing gender-sensitive storybooks.

Keywords: room to read, asian storybooks, young girls, thematic analysis, child empowerment, literacy, education

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Authors: Fonteyne Lot

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SIMON (Study capacities and Interest Monitor is a freely accessible online self-assessment tool that allows secondary education pupils to evaluate their interests and capacities in order to choose a post-secondary major that maximally suits their potential. The tool consists of two broad domains that correspond with two general questions pupils ask: 'What study fields interest me?' and 'Am I capable to succeed in this field of study?'. The first question is addressed by a RIASEC-type interest inventory that links personal interests to post-secondary majors. Pupils are provided with a personal profile and an overview of majors with their degree of congruence. The output is dynamic: respondents can manipulate their score and they can compare their results to the profile of all fields of study. That way they are stimulated to explore the broad range of majors. To answer whether pupils are capable of succeeding in a preferred major, a battery of tests is provided. This battery comprises a range of factors that are predictive of academic success. Traditional predictors such as (educational) background and cognitive variables (mathematical and verbal skills) are included. Moreover, non-cognitive predictors of academic success (such as 'motivation', 'test anxiety', 'academic self-efficacy' and 'study skills') are assessed. These non-cognitive factors are generally not included in admission decisions although research shows they are incrementally predictive of success and are less discriminating. These tests inform pupils on potential causes of success and failure. More important, pupils receive their personal chances of success per major. These differential probabilities are validated through the underlying research on academic success of students. For example, the research has shown that we can identify 22 % of the failing students in psychology and educational sciences. In this group, our prediction is 95% accurate. SIMON leads more students to a suitable major which in turn alleviates student success and retention. Apart from these benefits, the instrument grants insight into risk factors of academic failure. It also supports and fosters the development of evidence-based remedial interventions and therefore gives way to a more efficient use of means.

Keywords: academic success, online self-assessment, student retention, vocational choice

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Authors: Salihu Mohammed Umar

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Perceived physical exercise benefits among staff of tertiary institutions in Adamawa State was investigated as a basis for formulating proper exercise intervention strategies. The study utilized descriptive survey design. The purpose of the study was to determine perceived exercise benefits among staff of tertiary institutions in Adamawa state, Nigeria. The instrument used for data collection was a questionnaire adapted from Exercise Benefit/Barrier Scale (EBBS) developed by Sechrist, Walker and Pender (1985) which was validated by five experts. Three hundred and thirty (330) copies of the questionnaire were distributed among study participants in six institutions of higher learning in Adamawa state. The scale comprised two components; Benefits and Barriers dimensions. To achieve this purpose, three research questions were posed. The instrument had a four response forced-choice Likert-type format with responses ranging from 4 = strongly agree (SA), 3 = Agree (A), 2 = Disagree (D) and 1 = Strongly Disagree (SD). The findings of the study revealed that both male and female staff in institutions of higher learning in Adamawa state perceived exercise as highly beneficial. However, male staff had higher perceived benefits score than their female counterparts. (Male: x̄ = 95.02. SD = 3.08) > female: x̄ = 94.04, SD = 4.35. There was also no significant difference in perceived exercise barriers between staff and students of tertiary institutions in Adamawa state. Based on the finding of the study, it was concluded that staff of tertiary institutions perceived exercise as highly beneficial. It was recommended that since staff of institutions of higher learning in Adamawa State irrespective of gender and religious affiliations have basic knowledge of perceived benefits of exercise, there is the need to explore programmes that will enable staff across the sub-groups to overcome barriers that could discourage physical exercise participation.

Keywords: perception, physical exercise, staff, benefits

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Authors: Lyne Chantal Boudreau, Claudine Auger, Arline Laforest

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As an English-speaking country, Canada faces challenges in French-language education. During both editions of a provincial congress on education planned and conducted under shared cultural leadership, three organizers created a Francophone space where, for the first time in the province of New Brunswick (the only officially bilingual province in Canada), a group of stakeholders from the school, post-secondary and community sectors have succeeded in contributing to reflections on specific topics by sharing winning practices to meet the challenges of learning in a minority Francophone environment. Shared cultural leadership is a hybrid between theories of leadership styles in minority communities and theories of shared leadership. Through shared cultural leadership, the goal is simply to guide leadership and to set up all minority leaderships in minority context through shared leadership. This leadership style requires leaders to transition from a hierarchical to a horizontal approach, that is, to an approach where each individual is at the same level. In this exploratory research, it has been demonstrated that shared leadership exercised under the T-learning model best fosters the mobilization of all partners in advancing in-depth knowledge in a particular field while simultaneously allowing learning of the elements related to the domain in question. This session will present how it is possible to mobilize the whole community through leaders who continually develop their knowledge and skills in their specific field but also in related fields. Leaders in this style of management associated to shared cultural leadership acquire the ability to consider solutions to problems from a holistic perspective and to develop a collective power derived from the leadership of each and everyone in a space where all are rallied to promote the ultimate advancement of society.

Keywords: education, minority context, shared leadership, t-leaning

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Authors: Luisa Carrer, Lorenzo De Masi

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In this paper, we exploit the massive amount of information from Facebook to build a measure of gender attitudes in Italy at a previously impossible resolution—the municipal level. We construct our index via a machine learning method to replicate a benchmark region-level measure. Interestingly, we find that most of the variation in our Gender Norms Index (GNI) is across towns within narrowly defined geographical areas rather than across regions or provinces. In a second step, we show how this local variation in norms can be leveraged for identification purposes. In particular, we use our index to investigate whether these differences in norms carry over to the policy activity of politicians elected in the Italian Parliament. We document that females are more likely to sit in parliamentary committees focused on gender-sensitive matters, labor, and social issues, but not if they come from a relatively conservative town. These effects are robust to conditioning the legislative term and electoral district, suggesting the importance of social norms in shaping legislators’ policy activity.

Keywords: gender equality, gender norms index, Facebook, machine learning, politics

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Authors: Yasmine Abu Adla, Racha Soubra, Milana Kasab, Mohamad O. Diab, Aly Chkeir

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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals, out of which 11 were chosen based on their intraclass correlation coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, 5 features were introduced to the linear discriminant analysis classifier, and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90%, respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: Ziying Wu, Danfeng Yan

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Multi-Access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based Vehicle-Aware Multi-Access Edge Computing Network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

Keywords: multi-access edge computing, computation offloading, 5th generation, vehicle-aware, deep reinforcement learning, deep q-network

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Authors: Pedram Behyar, Zahra Bayat

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The internet and web are changing ways of “far reaching scope and potential for transformation of the marketing functions”. The web is developing in a faster rate than any previous new communication medium. The website of destination has become a crucial branding channel, that is why all businesses are changing their way to communicate with their customers to encounter their needs and wants in better ways. Website provides numerous opportunities for businesses to strengthen their relationship with their customers. One of these opportunities is website component that enables internet users to make two-way communication with the businesses.

Keywords: marketing communication, brand image, usability, privacy and security, personalization and customization, responsiveness, customer online web experience

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Authors: Adhi Susilo

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This paper addresses the roles of distance education in the agricultural extension workers’ education. Agriculture plays an important role in both poverty reduction and economic growth. The technology of agriculture in the developing world should change continuously to keep pace with rising populations and rapidly changing social, economic, and environmental conditions. Therefore, agricultural extension workers should have several competencies in order to carry out their duties properly. One of the essential competencies that they must possess is the professional competency that is directly related to their duties in carrying out extension activities. Such competency can be acquired through studying at Universitas Terbuka (UT). With its distance learning system, agricultural extension workers can study at UT without leaving their duties. This paper presenting sociological analysis and lessons learnt from the specific context of Indonesia. Diversities in geographic, demographic, social cultural and economic conditions of the country provide specific challenges for its distance education practice and the process of social transformation to which distance education can contribute. Extension officers used distance education for personal benefits and increased professional productivity. An increase in awareness is important for the further adoption of distance learning for extension purposes. Organizations in both the public and private sector must work to increase knowledge of ICTs for the benefit of stakeholders. The use of ICTs can increase productivity for extensions officers and expand educational opportunities for learners. The use of distance education by extension to disseminate educational materials around the world is widespread. Increasing awareness and use of distance learning can lead to more productive relationships between extension officers and agricultural stakeholders.

Keywords: agricultural extension, demographic and geographic condition, distance education, ICTs

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Aadityaa Singla

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The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

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Authors: Tso Sheng-Yang, Wang Tien-Ni

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Introduction and Purpose: Previous researchers have documented children with ASD (Autism Spectrum Disorders) prefer to escaping internal privates and external privates when they face tough conditions they can’t control or they don’t like.Especially, when children with ASD need to learn challenging tasks, such us Chinese language, their inappropriate behaviors will occur apparently. Recently, researchers apply positive behavior support strategies for children with ASD to enhance their self-efficacy and therefore to reduce their adverse behaviors. Thus, the purpose of this research was to design a series of lecture based on art therapy and to evaluate its effectiveness on the child’s self-efficacy. Method: This research was the single-case design study that recruited a high school boy with ASD. Whole research can be separated into three conditions. First, baseline condition, before the class started and ended, the researcher collected participant’s competencies of self-efficacy every session. In intervention condition, the research used dramatic conventions to teach the child in Chinese language twice a week.When the data was stable across three documents, the period entered to the maintenance condition. In maintenance condition, the researcher only collected the score of self-efficacynot to do other interventions five times a month to represent the effectiveness of maintenance.The time and frequency of data collection among three conditions are identical. Concerning art therapy, the common approach, e.g., music, drama, or painting is to use art medium as independent variable. Due to visual cues of art medium, the ASD can be easily to gain joint attention with teachers. Besides, the ASD have difficulties in understanding abstract objectives Thus, using the drama convention is helpful for the ASD to construct the environment and understand the context of Classical Chinese. By real operation, it can improve the ASD to understand the context and construct prior knowledge. Result: Bassd on the 10-points Likert scale and research, we product following results. (a) In baseline condition, the average score of self-efficacyis 1.12 points, rangedfrom 1 to 2 points, and the level change is 0 point. (b)In intervention condition, the average score of self-efficacy is 7.66 points rangedfrom 7 to 9 points, and the level change is 1 point. (c)In maintenance condition, the average score of self-efficacy is 6.66 points rangedfrom 6 to 7 points, and the level change is 1 point. Concerning immediacy of change, between baseline and intervention conditions, the difference is 5 points. No overlaps were found between these two conditions. Conclusion: According to the result, we find that it is effective that using dramatic conventions a s teaching strategies to teach children with ASD. The result presents the score of self-efficacyimmediately enhances when the dramatic conventions commences. Thus, we suggest the teacher can use this approach and adjust, based on the student’s trait, to teach the ASD on difficult task.

Keywords: dramatic conventions, autism spectrum disorder, slef-efficacy, teaching strategy

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Authors: Kausik Halder, Nabendu Chaki, Ranjan Dasgupta

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We present a modelling framework that supports the engineering of early requirements specifications for design of learner centric dynamic Learning Management System. The framework is based on i* modelling tool and Means End Analysis, that adopts primitive concepts for modelling early requirements (such as actor, goal, and strategic dependency). We show how pedagogical and computational requirements for designing a learner centric Learning Management system can be adapted for the automatic early requirement engineering specifications. Finally, we presented a model on a Learner Quanta based adaptive Courseware. Our early requirement analysis shows that how means end analysis reveals gaps and inconsistencies in early requirements specifications that are by no means trivial to discover without the help of formal analysis tool.

Keywords: adaptive courseware, early requirement engineering, means end analysis, organizational modelling, requirement modelling

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Authors: Ronit Chakraborty, Sugata Banerji

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There has been a marked increase in the reported prevalence of Autism Spectrum Disorder (ASD) among children in the US over the past two decades. This research has analyzed the growth in state-level ASD prevalence against 45 different potentially explanatory factors, including socio-economic, demographic, healthcare, public policy, and political factors. The goal was to understand if these factors have adequate predictive power in modeling the differential growth in ASD prevalence across various states and if they do, which factors are the most influential. The key findings of this study include (1) the confirmation that the chosen feature set has considerable power in predicting the growth in ASD prevalence, (2) the identification of the most influential predictive factors, (3) given the nature of the most influential predictive variables, an indication that a considerable portion of the reported ASD prevalence differentials across states could be attributable to over and under diagnosis, and (4) identification of Florida as a key outlier state pointing to a potential under-diagnosis of ASD there.

Keywords: autism spectrum disorder, clustering, machine learning, predictive modeling

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Asmaa Shehata

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Prior research indicates that second language (L2) learners encounter difficulty in the distinguishing novel L2 contrasting sounds that are not contrastive in their native languages. L2 orthographic information, however, is found to play a positive role in the acquisition of non-native phoneme contrasts. While most studies have mainly involved a familiar written script (i.e., the Roman script), the influence of a foreign, unfamiliar script is still unknown. Therefore, the present study asks: Does unfamiliar L2 script play a role in creating distinct phonological representations of novel contrasting phonemes? It is predicted that subjects’ performance in the unfamiliar orthography group will outperform their counterparts’ performance in the control group. Thus, training that entails orthographic inputs can yield a significant improvement in L2 adult learners’ identification and lexical encoding of novel L2 consonant contrasts. Results are discussed in terms of their implications for the type of input introduced to L2 learners to improve their language learning.

Keywords: Arabic, consonant contrasts, foreign script, lexical encoding, orthography, word learning

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Authors: Silvia Platania, Martina Morando, Giuseppe Santisi

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Introduction: In the last thirty years, there is constant talk of sustainable consumption and a "transition" of consumer lifestyles towards greater awareness of consumer choices (United Nation, 1992). The 2019 coronavirus (COVID-19) epidemic that has hit the world population since 2020 has had significant consequences in all areas of people's lives; individuals have been forced to change their behaviors, to redefine their owngoals, priorities, practices, and lifestyles, to rebuild themselves in the new situation dictated by the pandemic. Method(Participants and procedure ): The data were collected through an online survey; moreover, we used convenience sampling from the general population. The participants were 669 Italians consumers (Female= 514, 76.8%; Male=155, 23.2%) that choice sustainability brands, aged between 18 and 65 years (Mₐ𝓰ₑ = 35.45; Standard Deviation, SD = 9.51).(Measure ): The following measures were used: The Muncy–Vitell Consumer Ethics Scale; Attitude Toward Business Scale; Perceived Consumer Effectiveness Scale; Consumers Perception on Sustainable Brand Attitudes. Results: Preliminary analyses were conducted to test our model. Pearson's bivariate correlation between variables shows that all variables of our model correlate significantly and positively, PCE with CPSBA (r = .56, p <.001). Furthermore, a CFA, according to Harman's single-factor test, was used to diagnose the extent to which common-method variance was a problem. A comparison between the hypothesised model and a model with one factor (with all items loading on a unique factor) revealed that the former provided a better fit for the data in all the CFA fit measures [χ² [6, n = 669] = 7.228, p = 0.024, χ² / df = 1.20, RMSEA = 0.07 (CI = 0.051-0.067), CFI = 0.95, GFI = 0.95, SRMR = 0.04, AIC = 66.501; BIC = 132,150). Next, amulti mediation was conducted to test our hypotheses. The results show that there is a direct effect of PCE on ethical consumption behavior (β = .38) and on ATB (β = .23); furthermore, there is a direct effect on the CPSBA outcome (β = .34). In addition, there is a mediating effect by ATB (C.I. =. 022-.119, 95% interval confidence) and by CES (C.I. =. 136-.328, 95% interval confidence). Conclusion: The spread of the COVID-19 pandemic has affected consumer consumption styles and has led to an increase in online shopping and purchases of sustainable products. Several theoretical and practical considerations emerge from the results of the study.

Keywords: decision making, sustainability, pandemic, multimediation model

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Authors: Man-Yun Liu, Emily Chia-Yu Su

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Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

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Authors: Dzhavidan Zeinalov, Bugra Sen, Firangiz Aslanova

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Most text-to-speech models cannot operate well in low-resource languages and require a great amount of high-quality training data to be considered good enough. Yet, with the improvements made in ASR systems, it is now much easier than ever to collect data for the design of custom text-to-speech models. In this work, our work on using the ASR model to collect data to build a viable text-to-speech system for one of the leading financial institutions of Azerbaijan will be outlined. NVIDIA’s implementation of the Tacotron 2 model was utilized along with the HiFiGAN vocoder. As for the training, the model was first trained with high-quality audio data collected from the Internet, then fine-tuned on the bank’s single speaker call center data. The results were then evaluated by 50 different listeners and got a mean opinion score of 4.17, displaying that our method is indeed viable. With this, we have successfully designed the first text-to-speech model in Azerbaijani and publicly shared 12 hours of audiobook data for everyone to use.

Keywords: Azerbaijani language, HiFiGAN, Tacotron 2, text-to-speech, transfer learning, whisper

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