Search results for: adult learning.

Authors: Hřebíčková Sylva, Grasgruber Pavel, Ondráček Jan, Cacek Jan, Kalina Tomáš

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The aim of this study was to describe typical changes in several parameters of body composition – the amount of skeletal muscle mass (SMM), % body fat (BF) and body mass index (BMI) - in selected age categories (30+ years) of men and women in the Czech Republic, depending on the degree of sports activity. Study (n = 823, M = 343, F = 480) monitored differences in BF, SM and BMI in five age groups (from 30-39 years to 70+ years). Physically inactive individuals have (p < 0.05) higher % BF in comparison with physically active individuals (29.5 ± 0.59 vs. 27 ± 0.38%), higher BMI (27.3 ± 0.32 vs. 26.1 ± 0.20 kg/m2), but lower SM (39.0 ± 0.33 vs. 40.4 ± 0.21%). The results indicate that with an increasing age, there is a trend towards increasing values of BMI and % BF, and decreasing values of SMM.

Keywords: body composition, body fat, physical activity, skeletal muscle

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Ibrahim M. Elmojtaba, Rayan M. Altayeb

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Visceral leishmaniasis (VL) is a vector-borne disease caused by the protozoa parasite of the genus leishmania. The transmission of the parasite to humans and animals occurs via the bite of adult female sandflies previously infected by biting and sucking blood of an infectious humans or animals. In this paper we use a previously proposed model, and then applied two optimal controls, namely treatment and vaccination to that model to investigate optimal strategies for controlling the spread of the disease using treatment and vaccination as the system control variables. The possible impact of using combinations of the two controls, either one at a time or two at a time on the spread of the disease is also examined. Our results provide a framework for vaccination and treatment strategies to reduce susceptible and infection individuals of VL in five years.

Keywords: visceral leishmaniasis, treatment, vaccination, optimal control, numerical simulation

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Authors: Anum Rafiq

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Adolescence is a vulnerable period for youth across the world. It is a period of new learning with opportunities to understand and develop perspectives on health and well-being. With youth beginning to engage in intimate relationships at an earlier age in the 21st century, concentrating on the learning opportunity they have in school is paramount. The nature of what has been deemed important to teach in schools has changed throughout history, and the focus has shifted from home/family skills to teaching youth how to be competitive in the job market. Amidst this emphasis, opportunities for them exist to learn about building healthy intimate relationships, one of the foundational elements of most people’s lives. Using an Institutional Ethnography (IE), the lived experiences of youth in how they understand intimate relationships and how their learning experience is organized through the high school Health and Physical Education (H&PE) course is explored. An empirical inquiry into how the actual work of teachers and youth are socially organized by a biomedical, employment-related, and efficiency-based discourse is provided. Through thirty-two qualitative interviews with teachers and youth, a control of ruling relations such as institutional accountability circuits, performance reports, and timetabling over the experience of teachers and youth is found. One of the facets of the institutional accountability circuit is through the social organization of teaching and learning about healthy intimate relationships being framed through a biomedical discourse. In addition, the role of a hyper-focus on performance and evaluation is found as paramount in situating healthy intimacy discussions as inferior to neoliberally charged productivity measures such as employment skills. Lastly, due to the nature of institutional policies such as regulatory guidelines, teachers are largely influenced to avoid diving into discussions deemed risky or taboo by society, such as healthy intimacy in adolescence. The findings show how texts such as the H&PE curriculum, the Ontario College of Teachers (OCT) guidelines, Ministry of Education Performance Reports, and the timetable organize the day-to-day activities of teachers and students and reproduce different disjunctures for youth. This disjuncture includes some of their experiences being subordinated, difficulty relating to curriculum, and an experience of healthy living discussions being skimmed over across sites. The findings detail that the experience of youth in learning about healthy intimate relationships is not akin to the espoused vision outlined in policy documents such as the H&PE (2015) curriculum policy. These findings have implications for policymakers, activists, and school administration alike, which call for an investigation into who is in power when it comes to youth’s learning needs, as a pivotal period where youth can be equipped with life-changing knowledge is largely underutilized. A restructuring of existing institutional practices that allow for the social and institutional flexibility required to broach the topic of healthy intimacy in a comprehensive manner is required.

Keywords: health policy, intimate relationships, youth, education, ruling relations, sexual education, violence prevention

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Authors: Cathy Basterfield

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Issue: To engage in one’s own health care, health professionals need to be aware of an individual’s specific skills and abilities for best communication. One of the most discussed is health literacy. One of the assumed skills and abilities for adults is an individuals’ health literacy. Background: A review of publicly available health content appears to assume all adult readers will have a broad and full capacity to read at a high level of literacy, often at a post-school education level. Health information writers and clinicians need to recognise one critical area for why there may be little or no change in a person’s behaviour, or no-shows to appointments. Perhaps unintentionally, they are miscommunicating with the majority of the adult population. Health information contains many literacy domains. It usually includes technical medical terms or jargon. Many fact sheets and other information require scientific literacy with or without specific numerical literacy. It may include graphs, percentages, timing, distance, or weights. Each additional word or concept in these domains decreases the readers' ability to meaningfully read, understand and know what to do with the information. An attempt to begin to read the heading where long or unfamiliar words are used will reduce the readers' motivation to attempt to read. Critically people who have low literacy are overwhelmed when pages are covered with lots of words. People attending a health environment may be unwell or anxious about a diagnosis. These make it harder to read, understand and know what to do with the information. But access to health information must consider an even wider range of adults, including those with poor school attainment, migrants, and refugees. It is also homeless people, people with mental health illnesses, or people who are ageing. People with low literacy also may include people with lifelong disabilities, people with acquired disabilities, people who read English as a second (or third) language, people who are Deaf, or people who are vision impaired. Outcome: This paper will discuss Easy English, which is developed for adults. It uses the audiences’ everyday words, short sentences, short words, and no jargon. It uses concrete language and concrete, specific images to support the text. It has been developed in Australia since the mid-2000s. This paper will showcase various projects in the health domain which use Easy English to improve the understanding and functional use of written information for the large numbers of adults in our communities who do not have the health literacy to manage a range of day to day reading tasks. See examples from consent forms, fact sheets and choice options, instructions, and other functional documents, where Easy English has been developed. This paper will ask individuals to reflect on their own work practice and consider what written information must be available in Easy English. It does not matter how cutting-edge a new treatment is; when adults can not read or understand what it is about and the positive and negative outcomes, they are less likely to be engaged in their own health journey.

Keywords: health literacy, inclusion, Easy English, communication

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Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: W. Linthicum-Blackhorse, P. Martens

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This present study seeks to discover attitudinal commonalities and differences within a musician population relative to the communication of emotion via music. We hypothesized that instrument type, as well as age and gender, would bear significantly on musicians’ opinions. A survey was administered to 178 participants; 152 were current music majors (mean age 20.3 years, 62 female) and 26 were adult participants in a community choir (mean age 54.0 years, 12 female). The adult participants were all vocalists, while student participants represented the full range of orchestral instruments. The students were grouped by degree program, (performance, music education, or other) and instrument type (voice, brass, woodwinds, strings, percussion). The survey asked 'How important are each of the following areas to you for portraying emotion in music?' Participants were asked to rate each of 15 items on a scale of 1 (not at all important) to 10 (very important). Participants were also instructed to leave blank any item that they did not understand. The 15 items were: dynamic contrast, overall volume, phrasing, facial expression, staging (placement), pitch accuracy, tempo changes, bodily movement, your mood, your attitude, vibrato, rubato, stage/room lighting, clothing type, and clothing color. Contrary to our hypothesis, there was no overall effect of gender or age, and neither did any single response item show a significant difference due to these subject parameters. Among the student participants, however, one-way ANOVA revealed a significant effect of degree program on the rated importance of four items: dynamic contrast, tempo changes, vibrato, and rubato. Significant effects of instrument type were found in the responses to eight items: facial expression, staging, body movement, vibrato, rubato, lighting, clothing type, and clothing color. Post hoc comparisons (Tukey) show that some variation follows from obvious differences between instrument types (e.g. string players are more concerned with vibrato than everyone but woodwind players; vocalists are significantly more concerned with facial expression than everyone but string players), but other differences could point to communal mindsets toward vicarious cues within instrument type. These mindsets could be global (e.g. brass players deeming body movement significantly less important than string players, being less often featured as soloists and appearing less often at the front of the stage) or local (e.g. string players being significantly more concerned than all other groups about both clothing color and type, perhaps due to the strongly-expressed opinions of specific teachers). Future work will attempt to identify the source of these self-appraisals, whether enculturated via explicit pedagogy, or whether absorbed from individuals' observations and performance experience.

Keywords: performance, vicarious cues, communication, emotion

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Authors: Severino Machingambi

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Most academics entering Higher education as lecturers in South Africa do not have qualifications in Education or teaching. This creates serious problems since they are not sufficiently equipped with pedagogical approaches and theories that inform their facilitation of learning strategies. This, arguably, is one of the reasons why higher education institutions are experiencing high student failure rate. In order to mitigate this problem, it is critical that higher education institutions devise internal academic staff development programmes to capacitate academics with pedagogical skills and competencies so as to enhance the quality of student learning. This paper reported on how the Teaching and Learning Development Centre of a university used design-based research methodology to conceptualise and implement an academic staff development programme for new academics at a university of technology. This approach revolves around the designing, testing and refining of an educational intervention. Design-based research is an important methodology for understanding how, when, and why educational innovations work in practice. The need for a professional development course for academics arose due to the fact that most academics at the university did not have teaching qualifications and many of them were employed straight from industry with little understanding of pedagogical approaches. This paper examines three key aspects of the programme namely, the preliminary phase, the teaching experiment and the retrospective analysis. The preliminary phase is the stage in which the problem identification takes place. The problem that this research sought to address relates to the unsatisfactory academic performance of the majority of the students in the institution. It was therefore hypothesized that the problem could be dealt with by professionalising new academics through engagement in an academic staff development programme. The teaching experiment phase afforded researchers and participants in the programme the opportunity to test and refine the proposed intervention and the design principles upon which it was based. The teaching experiment phase revolved around the testing of the new academics professional development programme. This phase created a platform for researchers and academics in the programme to experiment with various activities and instructional strategies such as case studies, observations, discussions and portfolio building. The teaching experiment phase was followed by the retrospective analysis stage in which the research team looked back and tried to give a trustworthy account of the teaching/learning process that had taken place. A questionnaire and focus group discussions were used to collect data from participants that helped to evaluate the programme and its implementation. One of the findings of this study was that academics joining university really need an academic induction programme that inducts them into the discourse of teaching and learning. The study also revealed that existing academics can be placed on formal study programmes in which they acquire educational qualifications with a view to equip them with useful classroom discourses. The study, therefore, concludes that new and existing academics in universities should be supported through induction programmes and placement on formal studies in teaching and learning so that they are capacitated as facilitators of learning.

Keywords: academic staff, pedagogy, programme, staff development

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Authors: Martin K. Steiger, Lukas Heisler, Hans-Georg Brachtendorf

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Deep neural networks and their variants form the backbone of many AI applications. Based on the so-called residual networks, a continuous formulation of such models as ordinary differential equations (ODEs) has proven advantageous since different techniques may be applied that significantly increase the learning speed and enable controlled trade-offs with the resulting error at the same time. For the evaluation of such models, high-performance numerical differential equation solvers are used, which also provide the gradients required for training. However, whether classical gradient-based methods are even applicable or which one yields the best results has not been discussed yet. This paper aims to redeem this situation by providing empirical results for different applications.

Keywords: deep neural networks, gradient-based learning, image processing, ordinary differential equation networks

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Authors: Shavindrie Cooray

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It is vital that students are supported in having balanced conversations about topics that might be controversial. This process is crucial to the development of critical thinking skills. This can be difficult to attain in e-learning environments, with some research finding students report a perceived loss in the quality of knowledge exchange and performance. This research investigated if Systems Theory could be applied to structure the discussion, improve information sharing, and reduce conflicts when students are working in online environments. This research involved 160 participants across four categories of student groups at a college in the Northeastern US. Each group was provided with a shared problem, and each group was expected to make a proposal for a solution. Two groups worked face-to-face; the first face to face group engaged with the problem and each other with no intervention from a facilitator; a second face to face group worked on the problem using Systems tools to facilitate problem structuring, group discussion, and decision-making. There were two types of virtual teams. The first virtual group also used Systems tools to facilitate problem structuring and group discussion. However, all interactions were conducted in a synchronous virtual environment. The second type of virtual team also met in real time but worked with no intervention. Findings from the study demonstrated that the teams (both virtual and face-to-face) using Systems tools shared more information with each other than the other teams; additionally, these teams reported an increased level of disagreement amongst their members, but also expressed more confidence and satisfaction with the experience and resulting decision compared to the other groups.

Keywords: e-learning, virtual teams, systems approach, conflicts

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Authors: Li Jiuen Ong, Magdalin Cheong, Sri Rahayu, Lek Alexander, Pei Ting Tan

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Background: Previous programme evaluations from the diabetes training programme conducted in Changi General Hospital revealed that healthcare professionals (HCPs) are keen to receive advance diabetes training and education, specifically in medical, nutritional therapy. HCPs also expressed a preference for interactive activities over didactic teaching methods to enhance their learning. Since the War on Diabetes was initiated by MOH in 2016, HCPs are challenged to be actively involved in continuous education to be better equipped to reduce the growing burden of diabetes. Hence, streamlining training to incorporate an element of fun is of utmost importance. Aim: The nutrition programme incorporates game play using an interactive board game that aims to provide a more conducive and less stressful environment for learning. The board game could be adapted for training of community HCPs, health ambassadors or caregivers to cope with the increasing demand of diabetes care in the hospital and community setting. Methodology: Stages for game’s conception (Jaffe, 2001) were adopted in the development of the interactive board game ‘Sweet Score™ ’ Nutrition concepts and topics in diabetes self-management are embedded into the game elements of varying levels of difficulty (‘Easy,’ ‘Medium,’ ‘Hard’) including activities such as a) Drawing/ sculpting (Pictionary-like) b)Facts/ Knowledge (MCQs/ True or False) Word definition) c) Performing/ Charades To study the effects of game play on knowledge acquisition and perceived experiences, participants were randomised into two groups, i.e., lecture group (control) and game group (intervention), to test the difference. Results: Participants in both groups (control group, n= 14; intervention group, n= 13) attempted a pre and post workshop quiz to assess the effectiveness of knowledge acquisition. The scores were analysed using paired T-test. There was an improvement of quiz scores after attending the game play (mean difference: 4.3, SD: 2.0, P<0.001) and the lecture (mean difference: 3.4, SD: 2.1, P<0.001). However, there was no significance difference in the improvement of quiz scores between gameplay and lecture (mean difference: 0.9, 95%CI: -0.8 to 2.5, P=0.280). This suggests that gameplay may be as effective as a lecture in terms of knowledge transfer. All the13 HCPs who participated in the game rated 4 out of 5 on the likert scale for the favourable learning experience and relevance of learning to their job, whereas only 8 out of 14 HCPs in the lecture reported a high rating in both aspects. 16. Conclusion: There is no known board game currently designed for diabetes training for HCPs.Evaluative data from future training can provide insights and direction to improve the game format and cover other aspects of diabetes management such as self-care, exercise, medications and insulin management. Further testing of the board game to ensure learning objectives are met is important and can assist in the development of awell-designed digital game as an alternative training approach during the COVID-19 pandemic. Learning through gameplay increases opportunities for HCPs to bond, interact and learn through games in a relaxed social setting and potentially brings more joy to the workplace.

Keywords: active learning, game, diabetes, nutrition

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Nelson Kimeli Kemboi Yego, Juma Kasozi, Joseph Nkruzinza, Francis Kipkogei

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The study investigated the low uptake of health insurance in Kenya despite efforts to achieve universal health coverage through various health insurance schemes. Unsupervised machine learning techniques were employed to identify patterns in health insurance uptake based on sociodemographic factors among Kenyan households. The aim was to identify key demographic groups that are underinsured and to provide insights for the development of effective policies and outreach programs. Using the 2021 FinAccess Survey, the study clustered Kenyan households based on their health insurance uptake and sociodemographic features to reveal patterns in health insurance uptake across the country. The effectiveness of k-prototypes clustering, hierarchical clustering, and agglomerative hierarchical clustering in clustering based on sociodemographic factors was compared. The k-prototypes approach was found to be the most effective at uncovering distinct and well-separated clusters in the Kenyan sociodemographic data related to health insurance uptake based on silhouette, Calinski-Harabasz, Davies-Bouldin, and Rand indices. Hence, it was utilized in uncovering the patterns in uptake. The results of the analysis indicate that inclusivity in health insurance is greatly related to affordability. The findings suggest that targeted policy interventions and outreach programs are necessary to increase health insurance uptake in Kenya, with the ultimate goal of achieving universal health coverage. The study provides important insights for policymakers and stakeholders in the health insurance sector to address the low uptake of health insurance and to ensure that healthcare services are accessible and affordable to all Kenyans, regardless of their socio-demographic status. The study highlights the potential of unsupervised machine learning techniques to provide insights into complex health policy issues and improve decision-making in the health sector.

Keywords: health insurance, unsupervised learning, clustering algorithms, machine learning

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Authors: Chui Ka Shing

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This case study research attempts to examine the use of the Bar Model Method approach in learning and teaching mathematics in a primary school in Hong Kong. The objectives of the study are to find out to what extent (a) the Bar Model Method approach enhances the construction of students’ mathematics concepts, and (b) the school-based mathematics curriculum development with adopting the Bar Model Method approach. This case study illuminates the effectiveness of using the Bar Model Method to solve mathematics problems from Primary 1 to Primary 6. Some effective pedagogies and assessments were developed to strengthen the use of the Bar Model Method across year levels. Suggestions including school-based curriculum development for using Bar Model Method and further study were discussed.

Keywords: bar model method, curriculum development, mathematics education, problem solving

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Authors: Mohsine Jebbour

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Motivation and classroom interaction are of prime significance for second/foreign language learning to take place effectively. Thus, a considerable amount of motivation and classroom interaction helps ensure students’ success in and continuation of learning the TL. One way to enhance students’ motivation and classroom interaction in the Moroccan EFL classroom then is through the use of self-disclosure. For the purposes of this study, self-disclosure has been defined as the verbal communication of positive personal information including opinions, feelings, experiences, family and friendship stories to classmates and teachers. This paper is meant to demonstrate that positive self-disclosure can serve as an effective tool for helping students develop favorable attitudes toward the EFL classroom (i.e., English courses, teacher of English, and classroom activities) and promoting their intrinsic motivation (IM to know and IM toward stimulation). A further objective is that since self-disclosure is reciprocal, when teachers of English reveal their personal information, students will uncover their personal matters in return. This will help ensure effective classroom participation, foster teacher-student communication, and encourage students to practice and hence improve their oral proficiency (i.e., the speaking skill). A questionnaire was used to collect data in this study. 164 undergraduate students (99 females and 65 males) from the department of English at the faculty of letters and humanities, Dher el Mehraz, Sidi Mohammed Ben Abd Allah University completed a questionnaire that assessed self-disclosure in relation to motivation (i.e., attitudes toward the learning situation and intrinsic motivation) and classroom interaction (i.e., teacher-student interaction, participation, and out-of-class communication) on a 1 to 5 scale with (1) Strongly Disagree and (5) Strongly Agree. The level of agreement on the positive dimension of self-disclosure was ranked first by the respondents. The hypothesis set at the very beginning of the study, which posited that positive self-disclosure is essential to enhancing motivation and classroom interaction in the EFL context, was confirmed. In this regard, the findings suggest that implementing self-disclosure in the Moroccan EFL classroom may serve as an effective tool to have positive affect of teacher, class and classroom activities. This in turn will encourage the learners to attend classes, enjoy the language learning activity, complete classroom assignments, participate in class discussions, and interact with their teachers and classmates. It is hoped that teachers benefit from the results of this study and hence encourage the use of positive self-disclosure to develop English language learning in the Moroccan context where opportunities of using English outside the classroom are limited.

Keywords: EFL classroom, classroom interaction, motivation, self-disclosure

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Authors: Mariana Branco, Nahida Sobrino, Cristina Neves, Márcia Santos, Afonso Granja, João Rosa Oliveira, Joana Costa, Luísa Castro Leite

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This case reports a COVID-19 infection in an unvaccinated adult man with no history of COVID-19 and no relevant clinical history besides anabolic steroid use, undergoing weaning with tamoxifen after a bodybuilding competition. The patient presented a 4cm cervical mass 3 weeks after COVID-19 infection in his cohabitants. He was otherwise asymptomatic and tested negative to multiple RT-PCR tests. Nevertheless, the IgG COVID-19 antibody was positive, suggesting the previous infection. This report raises a potential link between anabolic steroid use and atypical COVID-19 onset. Objectives: The goals of this paper are to raise a potential link between anabolic steroid use and atypical COVID-19 onset but also to report an uncommon case of COVID-19 infection with consecutive negative gold standard tests. Methodology: The authors used CARE guidelines for case report writing. Introduction: This case reports a COVID-19 infection case in an unvaccinated adult man, with multiple serial negative reverse transcription polymerase chain reaction (RT-PCR) test results, presenting with single cervical lymphadenopathy. Although the association between COVID-19 and lymphadenopathy is well established, there are no cases with this presentation, and consistently negative RT-PCR tests have been reported. Methodologies: The authors used CARE guidelines for case report writing. Case presentation: This case reports a 28-year-old Caucasian man with no previous history of COVID-19 infection or vaccination and no relevant clinical history besides anabolic steroid use undergoing weaning with tamoxifendue to participation in a bodybuilding competition. He visits his primary care physician because of a large (4 cm) cervical lump, present for 3 days prior to the consultation. There was a positive family history for COVID-19 infection 3 weeks prior to the visit, during which the patient cohabited with the infected family members. The patient never had any previous clinical manifestation of COVID-19 infection and, despite multiple consecutive RT-PCR testing, never tested positive. The patient was treated with an NSAID and a broad-spectrum antibiotic, with little to no effect. Imagiological testing was performed via a cervical ultrasound, followed by a needle biopsy for histologic analysis. Serologic testing for COVID-19 immunity was conducted, revealing a positive Anti-SARS-CoV-2 IgG (Spike S1) antibody, suggesting the previous infection, given the unvaccinated status of our patient Conclusion: In patients with a positive epidemiologic context and cervical lymphadenopathy, physicians should still consider COVID-19 infection as a differential diagnosis, despite negative PCR testing. This case also raises a potential link between anabolic steroid use and atypical COVID-19 onset, never before reported in scientific literature.

Keywords: COVID-19, cervical lymphadenopathy, anabolic steroids, primary care

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Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

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The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

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Authors: Janet Holland

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Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation

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Authors: Meruyert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova, Madina Ermaganbetova

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This research paper explores the process of creating a proctoring system by evaluating the implementation of practical face recognition algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As an outcome, a proctoring system will be created, enabling the conduction of tests and ensuring academic integrity checks within the system. Due to the correct operation of the system, test works are carried out. The result of the creation of the proctoring system will be the basis for the automation of the informational, educational portal developed by machine learning.

Keywords: artificial intelligence, education portal, face recognition, machine learning, proctoring

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Authors: Mahdi Bazarganigilani

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Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

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Authors: Assel Jaxylykova, Alexnder Pak

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This study utilized a Self-Organizing Map (SOM) for unsupervised learning on the CONLL-2003 dataset for Named Entity Recognition (NER). The process involved encoding words into 300-dimensional vectors using FastText. These vectors were input into a SOM grid, where training adjusted node weights to minimize distances. The SOM provided a topological representation for identifying and clustering named entities, demonstrating its efficacy without labeled examples. Results showed an F1-measure of 0.86, highlighting SOM's viability. Although some methods achieve higher F1 measures, SOM eliminates the need for labeled data, offering a scalable and efficient alternative. The SOM's ability to uncover hidden patterns provides insights that could enhance existing supervised methods. Further investigation into potential limitations and optimization strategies is suggested to maximize benefits.

Keywords: named entity recognition, natural language processing, self-organizing map, CONLL-2003, semantics

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Tatyana Gavrilova, Vadim Onufriev

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Knowledge-based systems’ design requires the developer’s owning the advanced analytical skills. The efficient development of that skills within university courses needs a deep understanding of main pitfalls and drawbacks, which students usually make during their analytical work in form of visual modeling. Thus, it was necessary to hold an analysis of 5-th year students’ learning exercises within courses of 'Intelligent systems' and 'Knowledge engineering' in Saint-Petersburg Polytechnic University. The analysis shows that both lack of system thinking skills and methodological mistakes in course design cause the errors that are discussed in the paper. The conclusion contains an exploration of the issues and topics necessary and sufficient for the implementation of the improved practices in educational design for future curricula of teaching programs.

Keywords: knowledge based systems, knowledge engineering, students’ errors, visual modeling

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Authors: Dr. Rebecca Kimelman

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Teaching students to self-advocate has become a central topic in special education literature and practice. However, many special education programs do not address this important skill area. To this end, I created and implemented the Self Advocacy Made Easy (S.A.M.E.) program of instruction, intended to enhance the self-advocacy skills of young adults with mild to moderate disabilities. The effectiveness of S.A.M.E., the degree to which self-advocacy skills were acquired and demonstrated by the students, the level of parental support, and the impact of culture on the process, and teachers’ beliefs and attitudes about the role of self-advocacy skills for their students were measured using action research that employed mixed methodology. Conducted at an overseas American International School, this action research study sought answers to these questions by providing an in-depth portrayal of the S.A.M.E. program, as well as the attitudes and perceptions of the stakeholders involved in the study (thirteen students, their parents, teachers and counsellors). The findings of this study were very positive. The S.A.M.E. program was found to be a valid and valuable instructional tool for teaching self-advocacy skills to students with learning disabilities and ADHD. The study showed participation in the S.A.M.E. program led to an increased understanding of the important elements of self-advocacy, an increase in students’ skills and abilities to self-advocate, and a positive increase in students’ feelings about themselves. Inclusion in the Student-Led IEP meetings, an authentic student assessment within the S.A.M.E. program, also yielded encouraging results, including a higher level of ownership of one’s profile and learning needs, a higher level of student engagement and participation in the IEP meeting, and a growing student awareness of the relevance of the document and the IEP process to their lives. Without exception, every parent believed that participating in the Student-Led IEP led to a growth in confidence in their children, including that it taught them how to ‘own’ their disability and an improvement in their communication skills. Teachers and counsellors that participated in the study felt the program was worthwhile, and led to an increase in the students’ ability to acknowledge their learning profile and to identify and request the accommodations (such as extended time or use of a calculator) they need to overcome or work around their disability. The implications for further research are many, and include an examination of the degree to which participation in S.A.M.E. fosters student achievement, the long-term effects of participation in the program, and the degree to which student participation in the Student-Led IEP meeting increases parents’ level of understanding and involvement.

Keywords: self-advocacy, learning disabilities, ADHD, student-led IEP process

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Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

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New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Three research questions and hypotheses were formulated, and tested at a 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following recommendations was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and trainings should be organized to train teachers on the use of this technique and that schools and government should formulate policies and procedures towards responsible use of MCLT.

Keywords: education, communication, learning, mobile collaboration, technology

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Authors: Bhaskar Basu

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Universities and higher education institutes are finding it increasingly difficult to engage students fruitfully through traditional pedagogic tools. Web 2.0 technologies comprising social networking sites (SNSs) offer a platform for students to collaborate and share information, thereby enhancing their learning experience. Despite the potential and reach of SNSs, its use has been limited in academic settings promoting higher education. The purpose of this paper is to assess the perception of social networking sites among business school students in India and analyze its role in enhancing quality of student experiences in a business school leading to the proposal of an agenda for future research. In this study, more than 300 students of a reputed business school were involved in a survey of their preferences of different social networking sites and their perceptions and attitudes towards these sites. A questionnaire with three major sections was designed, validated and distributed among  a sample of students, the research method being descriptive in nature. Crucial questions were addressed to the students concerning time commitment, reasons for usage, nature of interaction on these sites, and the propensity to share information leading to direct and indirect modes of learning. It was further supplemented with focus group discussion to analyze the findings. The paper notes the resistance in the adoption of new technology by a section of business school faculty, who are staunch supporters of the classical “face-to-face” instruction. In conclusion, social networking sites like Facebook and LinkedIn provide new avenues for students to express themselves and to interact with one another. Universities could take advantage of the new ways  in which students are communicating with one another. Although interactive educational options such as Moodle exist, social networking sites are rarely used for academic purposes. Using this medium opens new ways of academically-oriented interactions where faculty could discover more about students' interests, and students, in turn, might express and develop more intellectual facets of their lives. hitherto unknown intellectual facets.  This study also throws up the enormous potential of mobile phones as a tool for “blended learning” in business schools going forward.

Keywords: business school, India, learning, social media, social networking, university

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Authors: Krisztina Bohacs, Klaudia Markus

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To author’s best knowledge we are in absence of studies on cognitive program evaluation and we are certainly short of programs that prove to have high effect sizes with strong retention results. The purpose of our study was to investigate the effectiveness of a comprehensive cognitive training program, namely BrainRx. This cognitive rehabilitation program target and remediate seven core cognitive skills and related systems of sub-skills through repeated engagement in game-like mental procedures delivered one-on-one by a clinician, supplemented by digital training. A larger sample of children with learning disability were given pretest and post-test cognitive assessments. The experimental group completed a twenty-week cognitive training program in a BrainRx center. A matched control group received another twenty-week intervention with Feuerstein’s Instrumental Enrichment programs. A second matched control group did not receive training. As for pre- and post-test, we used a general intelligence test to assess IQ and a computer-based test battery for assessing cognition across the lifespan. Multiple regression analyses indicated that the experimental BrainRx treatment group had statistically significant higher outcomes in attention, working memory, processing speed, logic and reasoning, auditory processing, visual processing and long-term memory compared to the non-treatment control group with very large effect sizes. With the exception of logic and reasoning, the BrainRx treatment group realized significantly greater gains in six of the above given seven cognitive measures compared to the Feuerstein control group. Our one-year retention measures showed that all the cognitive training gains were above ninety percent with the greatest retention skills in visual processing, auditory processing, logic, and reasoning. The BrainRx program may be an effective tool to establish long-term cognitive changes in case of students with learning disabilities. Recommendations are made for treatment centers and special education institutions on the cognitive training of students with special needs. The importance of our study is that targeted, systematic, progressively loaded and intensive brain training approach may significantly change learning disabilities.

Keywords: cognitive rehabilitation training, cognitive skills, learning disability, permanent structural cognitive changes

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