Search results for: evolving learning

Authors: Safa Kaka

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As more and more students are travelling to different countries to study and, in particular, to study English, the question of what motivates them to make such a large move has come under question. This is particularly pertinent in the case of Arab students who make up nearly 15% of the foreign student body in the UK. Given that the cultural differences between the UK and Arab nations are extremely wide, the decision to come to this country to study English must be driven by strong motivational forces. Numerous previous studies have considered what motivates foreign students to travel to the UK and other countries for their education or language learning but the specific motivators of Arab students have yet to be explored. This study undertakes to close that gap by examining the concepts and theories of motivation, both in general terms and in relation to English learning and foreign study. 70 Arab students currently studying in the UK were asked to participate in an online questionnaire which asked about their motivations for coming to the UK and for studying and learning English. A further six individuals were interviewed on a face to face basis. The outcomes have indicated that the factors which motivate the decision to come to the UK are similar to those that motivate the desire to learn English. In particular a motivation for self-improvement, career advancement and potential future benefits were cited by a number of respondents. Other indications were the ease of accessibility to the UK as an English speaking country, a motivation to experience different cultures and lifestyles and even political freedoms. Overall the motivations of Arab students were not found to be conspicuously different from those of other foreign students, although it was noted that their motivations did change, both positively and negatively following a period of time in the country. These changes were based on the expectations of the students pre-arrival and their actual experience of the country and its teaching approaches and establishments and were, as indicated both good and bad. The implications for the Arab student population and UK educational establishments are reviewed and future research pathways highlighted.

Keywords: motivation, Arab learners of English, language teaching, applied linguistics

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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: Hasna Mekkaoui Alaoui, Mariem Mekkaoui Alaoui

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In a context marked by a growing and pressing demand for online training within Moroccan universities, massive open online courses (Moocs) are undergoing constant evolution, amplified by the widespread use of digital technology and accentuated by the Coronavirus pandemic. However, despite their growing popularity and expansion, these courses are still lacking in terms of tools, enabling teachers and researchers to carry out a fine-grained analysis of the learning processes taking place within them. What's more, the circulation and sharing of knowledge within these environments is becoming increasingly important. The crucial aspect of traceability emerges here, as MOOCs record and generate traces from the most minute to the most visible. This leads us to consider traceability as a valuable approach in the field of educational research, where the trace is envisaged as a research tool in its own right. In this exploratory research project, we are looking at aspects of community knowledge sharing based on traces observed in the "Communication des organisations" Mooc. Focusing in particular on the mediating trace and its impact in identifying knowledge circulation processes in this learning space, we have mobilized the traces of video capsules as an index of knowledge circulation in the Mooc device. Our study uses a methodological approach based on thematic analysis, and although the results show that learners reproduce knowledge from different video vignettes in almost identical ways, they do not limit themselves to the knowledge provided to them. This research offers concrete perspectives for improving the dynamics of online devices, with a potentially positive impact on the quality of online university teaching.

Keywords: circulation, index, digital environments, mediation., trace

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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: Adnan Alhazmi

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Background: UDL can be understood as a framework that holds the potential to elaborate the alternatives and platforms for the students with intellectual disabilities within general education settings and aims at offering flexible pathways that can support all the students in gaining a mastering over the goals of learning. This system of learning addresses the problem of the variability of the learner by delineating the diverse ways in which the individuals can understand, conceive, express and deal with the information. Goal: The aim of the proposed research is to examine the impact of the implementation of UDL in teaching practices for the students with intellectual disabilities in Saudi Arabian schools. Method: This research has used a combination of quantitative and qualitative designs. Survey questionnaires were used to gather the data for under this analytical descriptive method. The application of the qualitative interpretive approach was applied with the help of the interview to gather a detailed understanding on the aim of the research. For this purpose, the semi-structured interviews were conducted. Thus, the primary data will be gathered with the help of survey and interview to examine the impact of universal design learning implementation on teaching practices for intellectually disabled students in Saudi Arabian schools. The survey was conducted to examine the prevailing teaching practices for the students with intellectual disabilities in Saudi Arabia and evaluate if the teaching experience influences the current practices or not. The surveys were distributed to 50 teachers who teach the students with intellectual disabilities. However, the interviews were conducted to explore barriers of implementing UDL in Saudi Arabia and provide suggested guideline for the implementation of UDL in Saudi Arabia. The interviews, therefore, were with 10 teachers teaching the same subject. Findings: A key findings highlighted in this study revealed that the UDL framework serves as a crucial guide for teachers within inclusive settings to undertake meaningful planning for the individuals with intellectual disabilities so that they are able to access, participate, and grow within the general education curriculum. Other findings of the study highlighted the need to prepare the educators and all faculty members to understand the purpose and need for inclusion, the UDL framework so that better information about academic and social expectations for individuals with intellectual disabilities can be delivered. Conclusion: On the basis of the preliminary study undertaken on the subject of research, it could be suggested that UDL can serve to be an effective support for undertaking a meaningful inclusion of students with intellectual disability (ID) in general educational settings. It holds the potential role of working as an institutional design framework that could be used for designing curriculum for students with intellectual disabilities.

Keywords: intellectual disability, inclusion, universal design for learning, teaching practice

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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: Jeffrey Siphiwe Mkhize

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Twelve years of schooling for most South African children, particularly those children from disadvantaged past, are confronted with numerous and diverse challenges. These challenges range from infrastructural limitations, language of teaching, poor resources and varying family backgrounds. Likewise, schools are categorized to signify schools’ geographic location, poverty lines, societal class and type of students that the school are likely to enroll. Such categorization perpetuates particular lines of identities that are indirectly reinforced by the same system that seeks to redress. South African universities prefer point systems to determine students’ suitability to gain access to their programmes. Once students are admitted based on the qualifying points there is an assumed equity in the manner in which they receive tuition. They are assumed as equal; noting the widened access to South African universities as means to redress past inequalities. Given the challenges, inequalities, it is necessary to view higher education as a site for knowledge construction that is accessible to all students. Epistemological access is key to all students irrespective of their socio-economic status. This paper seeks to contribute to the discourse of student engagement using lecturer-student relationship as a lens to understand this phenomenon. Data were generated using South African Survey of Student Engagement, focus group interviews, semi-structured one-on-one-interviews as well as document analysis. The focus was on students registered for the first year of a Bachelor of Education degree as well as lecturers that teach high risk modules in this qualification at the same level. The findings suggest that lecturers are challenged by overcrowded classrooms and over-enrolled modules; this challenge hampers their good intentions to become more efficient and innovative in their teaching. Students lack confidence in approaching lecturers for assistance. Collaborative learning has stronger results and students believe in self-support to deal with their challenges based on their individual strengths. Collaborative learning is key to student academic performance.

Keywords: collaborative learning, consultations, student engagement, student performance

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Authors: Abdelbasit Gadour

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A great number of children in mainstream schools across Libya are currently living with emotional, behavioral difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioral difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Libya find classroom behavior problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with emotional and behavioral difficulties (EBD). The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary schools.

Keywords: children, emotional and behavior difficulties, learning, teachers'

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Authors: Vânia Graça, Paula Quadros-Flores, Altina Ramos

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The use of Digital Technologies in teaching and learning processes is currently a reality, namely in initial teacher training. This study aims at knowing the digital reality of students in initial teacher training in order to improve training in the educational use of ICT and to promote digital technology integration strategies in an educational context. It is part of the IFITIC Project "Innovate with ICT in Initial Teacher Training to Promote Methodological Renewal in Pre-school Education and in the 1st and 2nd Basic Education Cycle" which involves the School of Education, Polytechnic of Porto and Institute of Education, University of Minho. The Project aims at rethinking educational practice with ICT in the initial training of future teachers in order to promote methodological innovation in Pre-school Education and in the 1st and 2nd Cycles of Basic Education. A qualitative methodology was used, in which a questionnaire survey was applied to teachers in initial training. For data analysis, the techniques of content analysis with the support of NVivo software were used. The results point to the following aspects: a) future teachers recognize that they have more technical knowledge about ICT than pedagogical knowledge. This result makes sense if we consider the objective of Basic Education, so that the gaps can be filled in the Master's Course by students who wish to follow the teaching; b) the respondents are aware that the integration of digital resources contributes positively to students' learning and to the life of children and young people, which also promotes preparation in life; c) to be a teacher in the digital age there is a need for the development of digital literacy, lifelong learning and the adoption of new ways of teaching how to learn. Thus, this study aims to contribute to a reflection on the teaching profession in the digital age.

Keywords: digital technologies, initial teacher training, pedagogical use of ICT, skills

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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: Norshaffika I. Zaiedy Nor, Andrew P. Smith

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Background: Training is essential to develop individuals’ expertise to meet current and future job demands and to improve work performance. At the same time, individuals’ well-being is crucial to ensure that they can fully and positively carry out their daily duties. In addition to the studies that have examined what constitutes well-being and the factors behind it, many researchers have investigated the predictors of training effectiveness and transfer of training. However, there has been very little integration between them. This study was an attempt to bridge the gap between training effectiveness predictors and well-being. Purpose: This research paper aimed to investigate the association between well-being among employees and psychosocial characteristics, together with training variables. Training variables consist of motivation to learn; learning; implementation intention; and cognitive dissonance. Methodology: In total, 210 workers who had undergone various training programs completed an online survey measuring various psychosocial characteristics, four training variables, and level of well-being. Findings: The results showed that certain types of positive psychosocial characteristics (e.g., positive personality, positive work behaviors, positive work and resources) predict motivation to learn, learning and implementation intention. Meanwhile, negative psychosocial characteristics (e.g. negative work demands and resources, negative coping) predict cognitive dissonance. Also, all the training variables had a moderate to high correlation with well-being. However, after controlling other variables (age, gender, education and psychosocial characteristics), none of the training variables predicted well-being. Self-determination theory, cognitive dissonance theory, and the DRIVE model were used to explain these findings. Conclusion: As there is limited research on the integration of training variables with well-being, this study gives a new perspective in the field of both training and well-being. Further investigations are needed to examine the relationships between them.

Keywords: cognitive dissonance, implementation intention, learning, motivation to learn, psychosocial characteristics, well-being

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Authors: Jacqueline Żammit

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Recent research indicates that the utilization of right-brain strategies holds significant implications for the acquisition of language skills. Nevertheless, the utilization of visual culture as a means to stimulate these strategies and amplify language retention among adults engaging in second language (L2) learning remains a relatively unexplored area. This investigation delves into the impact of visual culture on activating right-brain processes during the initial stages of language acquisition, particularly in the context of teaching Maltese as a second language (ML2) to adult learners. By employing a qualitative research approach, this study convenes a focus group comprising twenty-seven educators to delve into a range of visual culture techniques integrated within language instruction. The collected data is subjected to thematic analysis using NVivo software. The findings underscore a variety of impactful visual culture techniques, encompassing activities such as drawing, sketching, interactive matching games, orthographic mapping, memory palace strategies, wordless picture books, picture-centered learning methodologies, infographics, Face Memory Game, Spot the Difference, Word Search Puzzles, the Hidden Object Game, educational videos, the Shadow Matching technique, Find the Differences exercises, and color-coded methodologies. These identified techniques hold potential for application within ML2 classes for adult learners. Consequently, this study not only provides insights into optimizing language learning through specific visual culture strategies but also furnishes practical recommendations for enhancing language competencies and skills.

Keywords: visual culture, right-brain strategies, second language acquisition, maltese as a second language, visual aids, language-based activities

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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: Pinzhe Zhao

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This study focuses on predicting university students' cheating tendencies using psychological traits and machine learning techniques. Academic dishonesty is a significant issue that compromises the integrity and fairness of educational institutions. While much research has been dedicated to detecting cheating behaviors after they have occurred, there is limited work on predicting such tendencies before they manifest. The aim of this research is to develop a model that can identify students who are at higher risk of engaging in academic misconduct, allowing for earlier interventions to prevent such behavior. Psychological factors are known to influence students' likelihood of cheating. Research shows that traits such as test anxiety, moral reasoning, self-efficacy, and achievement motivation are strongly linked to academic dishonesty. High levels of anxiety may lead students to cheat as a way to cope with pressure. Those with lower self-efficacy are less confident in their academic abilities, which can push them toward dishonest behaviors to secure better outcomes. Students with weaker moral judgment may also justify cheating more easily, believing it to be less wrong under certain conditions. Achievement motivation also plays a role, as students driven primarily by external rewards, such as grades, are more likely to cheat compared to those motivated by intrinsic learning goals. In this study, data on students’ psychological traits is collected through validated assessments, including scales for anxiety, moral reasoning, self-efficacy, and motivation. Additional data on academic performance, attendance, and engagement in class are also gathered to create a more comprehensive profile. Using machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, the research builds models that can predict students’ cheating tendencies. These models are trained and evaluated using metrics like accuracy, precision, recall, and F1 scores to ensure they provide reliable predictions. The findings demonstrate that combining psychological traits with machine learning provides a powerful method for identifying students at risk of cheating. This approach allows for early detection and intervention, enabling educational institutions to take proactive steps in promoting academic integrity. The predictive model can be used to inform targeted interventions, such as counseling for students with high test anxiety or workshops aimed at strengthening moral reasoning. By addressing the underlying factors that contribute to cheating behavior, educational institutions can reduce the occurrence of academic dishonesty and foster a culture of integrity. In conclusion, this research contributes to the growing body of literature on predictive analytics in education. It offers a approach by integrating psychological assessments with machine learning to predict cheating tendencies. This method has the potential to significantly improve how academic institutions address academic dishonesty, shifting the focus from punishment after the fact to prevention before it occurs. By identifying high-risk students and providing them with the necessary support, educators can help maintain the fairness and integrity of the academic environment.

Keywords: academic dishonesty, cheating prediction, intervention strategies, machine learning, psychological traits, academic integrity

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Samuel Ntsanwisi

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This study investigates the transformative role of Artificial Intelligence (AI) and Virtual Reality (VR) in the preservation of endangered African languages. The study is contextualized within the film and television industry, highlighting disparities in screen representation for certain languages in South Africa, underscoring the need for increased visibility and preservation efforts; with globalization and cultural shifts posing significant threats to linguistic diversity, this research explores approaches to language preservation. By leveraging AI technologies, such as speech recognition, translation, and adaptive learning applications, and integrating VR for immersive and interactive experiences, the study aims to create a framework for teaching and passing on endangered African languages. Through digital documentation, interactive language learning applications, storytelling, and community engagement, the research demonstrates how these technologies can empower communities to revitalize their linguistic heritage. This study employs a dual-method approach, combining a rigorous literature review to analyse existing research on the convergence of AI, VR, and language preservation with primary data collection through interviews and surveys with ten filmmakers. The literature review establishes a solid foundation for understanding the current landscape, while interviews with filmmakers provide crucial real-world insights, enriching the study's depth. This balanced methodology ensures a comprehensive exploration of the intersection between AI, VR, and language preservation, offering both theoretical insights and practical perspectives from industry professionals.

Keywords: language preservation, endangered languages, artificial intelligence, virtual reality, interactive learning

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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: 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

Procedia PDF Downloads 134

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: 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: Waleed Al-Nuaimy, Luke Anastassiou, Manjinder Kainth

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As the process of teaching has evolved with the advent of new technologies over the ages, so has the process of learning. Educators have perpetually found themselves on the lookout for new technology-enhanced methods of teaching in order to increase learning efficiency and decrease ever expanding workloads. Shortly after the invention of the internet, web-based learning started to pick up in the late 1990s and educators quickly found that the process of providing learning material and marking assignments could change thanks to the connectivity offered by the internet. With the creation of early web-based virtual learning environments (VLEs) such as SPIDER and Blackboard, it soon became apparent that VLEs resulted in higher reported computer self-efficacy among students, but at the cost of students being less satisfied with the learning process . It may be argued that the impersonal nature of VLEs, and their limited functionality may have been the leading factors contributing to this reported dissatisfaction. To this day, often faced with the prospects of assigning colossal engineering cohorts their homework and assessments, educators may frequently choose optimally curated assessment formats, such as multiple-choice quizzes and numerical answer input boxes, so that automated grading software embedded in the VLEs can save time and mark student submissions instantaneously. A crucial skill that is meant to be learnt during most science and engineering undergraduate degrees is gaining the confidence in using, solving and deriving mathematical equations. Equations underpin a significant portion of the topics taught in many STEM subjects, and it is in homework assignments and assessments that this understanding is tested. It is not hard to see that this can become challenging if the majority of assignment formats students are engaging with are multiple-choice questions, and educators end up with a reduced perspective of their students’ ability to manipulate equations. Artificial intelligence (AI) has in recent times been shown to be an important consideration for many technologies. In our paper, we explore the use of new AI based software designed to work in conjunction with current VLEs. Using our experience with the software, we discuss its potential to solve a selection of problems ranging from impersonality to the reduction of educator workloads by speeding up the marking process. We examine the software’s potential to increase learning efficiency through its features which claim to allow more customized and higher-quality feedback. We investigate the usability of features allowing students to input equation derivations in a range of different forms, and discuss relevant observations associated with these input methods. Furthermore, we make ethical considerations and discuss potential drawbacks to the software, including the extent to which optical character recognition (OCR) could play a part in the perpetuation of errors and create disagreements between student intent and their submitted assignment answers. It is the intention of the authors that this study will be useful as an example of the implementation of AI in a practical assessment scenario insofar as serving as a springboard for further considerations and studies that utilise AI in the setting and marking of science and engineering assignments.

Keywords: engineering education, assessment, artificial intelligence, optical character recognition (OCR)

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Authors: Marilei Amadeu Sabino

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The aim of this study is to establish differences between the terms ‘false cognates’, ‘false friends’ and ‘deceptive cognates’, usually considered to be synonyms. It will be shown they are not synonyms, since they do not designate the same linguistic process or phenomenon. Despite their differences in meaning, many pairs of formally similar words in two (or more) different languages are true cognates, although they are usually known as ‘false’ cognates – such as, for instance, the English and Italian lexical items ‘assist x assistere’; ‘attend x attendere’; ‘argument x argomento’; ‘apology x apologia’; ‘camera x camera’; ‘cucumber x cocomero’; ‘fabric x fabbrica’; ‘factory x fattoria’; ‘firm x firma’; ‘journal x giornale’; ‘library x libreria’; ‘magazine x magazzino’; ‘parent x parente’; ‘preservative x preservativo’; ‘pretend x pretendere’; ‘vacancy x vacanza’, to name but a few examples. Thus, one of the theoretical objectives of this paper is firstly to elaborate definitions establishing a distinction between the words that are definitely ‘false cognates’ (derived from different etyma) and those that are just ‘deceptive cognates’ (derived from the same etymon). Secondly, based on Set Theory and on the concepts of equal sets, subsets, intersection of sets and disjoint sets, this study is intended to elaborate some theoretical and practical questions that will be useful in identifying more precisely similarities and differences between cognate words of different languages, and according to graphic interpretation of sets it will be possible to classify them and provide discernment about the processes of semantic changes. Therefore, these issues might be helpful not only to the Learning of Second and Foreign Languages, but they could also give insights into Foreign and Second Language Teaching Methodology. Acknowledgements: FAPESP – São Paulo State Research Support Foundation – the financial support offered (proc. n° 2017/02064-7).

Keywords: deceptive cognates, false cognates, foreign language learning, teaching methodology

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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

Procedia PDF Downloads 103