Search results for: learning curve

Authors: Sergio Curilef, Boris Atenas

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Systems with long-range interactions are very common in nature. They are observed from the atomic scale to the astronomical scale and exhibit anomalies, such as inequivalence of ensembles, negative heat capacity, ergodicity breaking, nonequilibrium phase transitions, quasistationary states, and anomalous diffusion. These anomalies are exacerbated when special initial conditions are imposed; in particular, we use the so-called water bag initial conditions that stand for a uniform distribution. Several theoretical and practical implications are discussed here. A potential energy inspired by dipole-dipole interactions is proposed to build the dipole-type Hamiltonian mean-field model. As expected, the dynamics is novel and general to the behavior of systems with long-range interactions, which is obtained through molecular dynamics technique. Two plateaus sequentially emerge before arriving at equilibrium, which are corresponding to two different quasistationary states. The first plateau is a type of quasistationary state the lifetime of which depends on a power law of N and the second plateau seems to be a true quasistationary state as reported in the literature. The general behavior of the model according to its dynamics and thermodynamics is described. Using numerical simulation we characterize the mean kinetic energy, caloric curve, and the diffusion law through the mean square of displacement. The present challenge is to characterize the distributions in phase space. Certainly, the equilibrium state is well characterized by the Gaussian distribution, but quasistationary states in general depart from any Gaussian function.

Keywords: dipole-type interactions, dynamics and thermodynamics, mean field model, quasistationary states

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Authors: G. Singer, M. Golan

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Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Karthika Chandran, Pulkit Prakash, Amitabh Das, Santhosh P. N.

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Rare earth orthoferrites (RFeO₃) exhibit interesting and useful magnetic properties like multiferroicity, magnetodielectric coupling, spin reorientation (SR) and exchange bias. B site doped RFeO₃ are attracting attention due to the complex and tuneable magnetic transitions. In this work, 45% Mn-doped HoFeO₃ polycrystalline sample (HoFe₀.₅₅Mn₀.₄₅O₃) was synthesized by a solid-state reaction method. The magnetic structure and transitions were studied by magnetization measurements and neutron powder diffraction methods. The neutron diffraction patterns were taken at 13 different temperatures from 7°K to 300°K (7°K and 25°K to 300°K in 25°K intervals). The Rietveld refinement was carried out by using a FULLPROF suite. The magnetic space groups and the irreducible representations were obtained by SARAh module. The room temperature neutron diffraction refinement results indicate that the sample crystallizes in an orthorhombic perovskite structure with Pnma space group with lattice parameters a = 5.6626(3) Ǻ, b = 7.5241(3) Ǻ and c = 5.2704(2) Ǻ. The temperature dependent magnetization (M-T) studies indicate the presence of two magnetic transitions in the system ( TN Fe/Mn~330°K and TSR Fe/Mn ~290°K). The inverse susceptibility vs. temperature curve shows a linear behavior above 330°K. The Curie-Weiss fit in this region gives negative Curie constant (-34.9°K) indicating the antiferromagnetic nature of the transition. The neutron diffraction refinement results indicate the presence of mixed magnetic phases Γ₄(AₓFᵧG

Keywords: neutron powder diffraction, rare earth orthoferrites, Rietveld analysis, spin reorientation

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

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Authors: Juhanita Jiman

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This paper aims to demonstrate how androgogical approach can help educators to facilitate animation students with better understanding of their acquired technical knowledge and skills while introducing them to crucial content and ethical values. In this borderless world, it is important for the educators to know that they are dealing with young adults who are heavily influenced by their surroundings. Naturally, educators are not only handling academic issues, they are also burdened with social obligations. Appropriate androgogical approach can be beneficial for both educators and students to tackle these problems. We used to think that teaching pedagogy is important at all level of age. Unfortunately, pedagogical approach is not entirely applicable to university students because they are no longer children. Pedagogy is a teaching approach focusing on children, whereas andragogy is specifically focussing on teaching adults and helping them to learn better. As adults mature, they become increasingly independent and responsible for their own actions. In many ways, the pedagogical model is not really suitable for such developmental changes, and therefore, produces tension, dissatisfaction, and resistance in individual student. The ever-changing technology has resulted in animation students to be very competitive in acquiring their technical skills, making them forget and neglecting the importance of the core values of a story. As educators, we have to guide them not only to excel in achieving knowledge, skills and technical expertise but at the same time, show them what is right or wrong and encourage them to inculcate moral values in their work.

Keywords: andragogy, animation, artistic contents, productive learning environment

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Authors: Yan-Lin Liu, Cheng-Ting Shih, Jay Wu

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Tablet devices have been introduced into the medical environment in recent years. The performance of display can be varied based on the use of different hardware specifications and types of display technologies. Therefore, the differences between tablet devices and medical diagnostic LCDs have to be verified to ensure that image quality is not jeopardized for clinical diagnosis in a picture archiving and communication system (PACS). In this study, a set of randomized object test patterns (ROTPs) were developed, which included randomly located spheres in abdominal CT images. Five radiologists were asked to independently review the CT images on different generations of iPads and a diagnostic monochrome medical LCD monitor. Receiver operating characteristic (ROC) analysis was performed by using a five-point rating scale, and the average area under curve (AUC) and average reading time (ART) were calculated. The AUC values for the second generation iPad, iPad mini, iPad Air, and monochrome medical monitor were 0.712, 0.717, 0.725, and 0.740, respectively. The differences between iPads were not significant. The ARTs were 177 min and 127 min for iPad mini and medical LCD monitor, respectively. A significant difference appeared (p = 0.04). The results show that the iPads were slightly inferior to the monochrome medical LCD monitor. However, tablet devices possess advantages in portability and versatility, which can improve the convenience of rapid diagnosis and teleradiology. With advances in display technology, the applicability of tablet devices and mobile devices may be more diversified in PACS.

Keywords: tablet devices, PACS, receiver operating characteristic, LCD monitor

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Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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Authors: Hoora Dehghani, Meisam Shahbazi, Reza Zare

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During the previous decade, a significant evolution has occurred in the number of private educational centers and, accordingly, the increase in the number of providers and students of these centers around the world. The number of language teaching institutes in Iran that are considered private educational sectors is also growing exponentially as the request for learning foreign languages has extremely increased in recent years. This fact caused competition among the institutions in improving better services tailored to the students’ demands to win the competition. Along with the growth in the industry of education, higher education institutes should apply the marketing-related concepts and view students as customers because students’ outlooks are similar to consumers with education. Studying the influential factors in the selection of an institute has multiple benefits. Firstly, it acknowledges the institutions of the students’ choice factors. Secondly, the institutions use the obtained information to improve their marketing methods. It also helps institutions know students’ outlooks that can be applied to expand the student know-how. Moreover, it provides practical evidence for educational centers to plan useful amenities and programs, and use efficient policies to cater to the market, and also helps them execute the methods that increase students’ feeling of contentment and assurance. Thus, this study explored the influencing factors in the selection of a language learning institute by language learners and examined and compared the importance among the varying age groups and genders. In the first phase of the study, the researchers selected 15 language learners as representative cases within the specified age ranges and genders purposefully and interviewed them to explore the comprising elements in their language institute selection process and analyzed the results qualitatively. In the second phase, the researchers identified elements as specified items of a questionnaire, and 1000 English learners across varying educational contexts rated them. The TOPSIS method was used to analyze the data quantitatively by representing the level of importance of the items for the participants generally and specifically in each subcategory; genders and age groups. The results indicated that the educational quality, teaching method, duration of training course, establishing need-oriented courses, and easy access were the most important elements. On the other hand, offering training in different languages, the specialized education of only one language, the uniform and appropriate appearance of office staff, having native professors to the language of instruction, applying Computer or online tests instead of the usual paper tests respectively as the least important choice factors in selecting a language institute. Besides, some comparisons among different groups’ ratings of choice factors were made, which revealed the differences among different groups' priorities in choosing a language institute.

Keywords: choice factors, EFL institute selection, english learning, need analysis, TOPSIS

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Authors: Mei Xuan Tan, Eng Ying Bong

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Academic discussion during and after instructional teaching is an integral part of learning. Such discussion between the instructor and student or peer-to-peer discussion can be in several different forms. It could be face-to-face discussion, via email and use of online discussion forum. In this study, the effectiveness of using WhatsApp for academic discussion for a first year half-credit Chemistry course was examined. This study was run over two years with two different batches of students. Participation in the study was voluntary and student volunteers were recruited within the first week of the term. The activity in the WhatsApp group was monitored by two instructors teaching the course. At the end of the course, the students participated in an online survey to evaluate their experience of using WhatsApp for academic discussion. There were a total of 26 questions. The survey had a total of 4 sections with regards to the use of WhatsApp for academic discussion: 1) Familiarity with WhatsApp, 2) Effectiveness of using WhatsApp for discussion, 3) Challenges and 4) Overall experience. The main purpose of using an IM platform for academic discussion was to encourage after-class discussion amongst the students. 32% of the participants had used other online platform, such as Piazza and forums in Learning Management System (LMS), for after-class academic discussion with their instructors and peers. This was a low percentage considering that some courses use such online platform as their main forum amongst instructors and students. At the end of our study, over 83% of the participants felt that WhatsApp was a more effective platform compared to other online forum. One interesting finding was the effect of WhatsApp discussion on face-to-face interaction with instructors. 28% of the students agreed that the use of WhatsApp as a discussion forum had encouraged them to approach their instructors during or after class. 51% of students answered neutral. This could be interpreted that the use of WhatsApp had not affected the frequent (or lack of) face-to-face interaction with their instructors. A second survey question, similar but phrased differently from the first, was also asked to evaluate the aspect of face-to-face interaction with instructors. 34% disagreed that the use of WhatsApp had reduced the frequency of face-to-face interaction. This could imply that the frequency remained the same or might have increased. The 38% who agreed to a decrease in face-to-face interaction have either asked the questions in WhatsApp or had their questions answered by a query from another student in the group chat. These outcomes suggested that the use of technology aided and complemented face-to-face interaction between instructors and students. The study also looked at the challenges of using WhatsApp for academic discussion. Some challenges included difficulty in referring back to previous discussion and students finding some discussions irrelevant to them. In conclusion, the use of IM platform for academic discussion was desirable for the students, but it should not be the only channel as face-to-face consultation and online forum for lengthy discussion are still important for after-class learning of students.

Keywords: chemistry, pedogogy, technological tools, undergraduate

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Authors: Nasir Sulman

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In Pakistan, the inclusion of persons with disabilities in higher education institutions has significantly been increased with every passing year and anyone can observe a sizeable number of these students in each faculty. The study executes to conduct a baseline survey for measuring faculty understanding about the special needs, experiences of students with disabilities and support provided by university administration in order to teach these students effectively. The researcher has used mixed methods and the University of Karachi was selected through non-probability-based sampling method. This university is one of the largest universities in Pakistan where more than 40,000 students have been enrolled. Data was gathered through a questionnaire and focused group discussion from three stakeholders including students with disabilities, faculty members and members of the university administration. The key findings show that students with disabilities experience a number of problems related to accommodating their special needs. However, the most encouraging factors identified are the attitude, support, and motivation they received from various faculty members and university administration. On the basis of the findings of the study the researcher has prepared a faculty guidebook and established a ‘Model Learning Assistance Centre for Students with Disabilities’ in the Department of Special Education, University of Karachi. Both these efforts will be helpful for improving the support services for students with disabilities to strengthen the existing laws, policies, and practices in institutions of higher education.

Keywords: persons with disabilities, higher education, learning assistance center, faculty guidebook

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Authors: Colleen McMillan, Alice Schmidt-Hanbidge, Beth Archer-Kuhn, Heather Boynton, Judith Hughes

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It is well known that social work students enter the profession with higher scores of adverse childhood experiences (ACE). Add to that the fact that COVID-19 has forced higher education institutions to shift to online teaching and learning, where students, faculty and field educators in social work education have reported increased stressors as well as posing challenges in developing relationships with students and being able to identify mental health challenges including those related to trauma. This multi-institutional project included three Canadian post-secondary institutions at five sites (the University of Waterloo, the University of Calgary and the University of Manitoba) and partners; Desire To Learn (D2L), The Centre for Teaching Excellence at the University of Waterloo and the Taylor Institute for Teaching and Learning. A sequential mixed method research design was used. Survey data was collected from students, faculty and field education staff from the 3 universities using the Qualtrics Insight Platform, followed by virtual focus group data with students to provide greater clarity to the quantitative data. Survey data was analyzed using SPSS software, while focus group data was transcribed verbatim and organized with N-Vivo 12. Thematic analysis used line-by-line coding and constant comparative methods within and across focus groups. The following three objectives of the study were achieved: 1) Establish a Canadian baseline on trauma informed pedagogy and student experiences of trauma informed teaching in the online higher education environment during a pandemic; 2) Identify and document educator and student experiences of online learning regarding the ability to process trauma experiences; and, 3) Transfer the findings into a trauma informed pedagogical model for Social Work as a first step toward developing a universal trauma informed teaching model. The trauma informed pedagogy model would be presented in relation to the study findings.

Keywords: trauma informed pedagogy, higher education, social work, mental health

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Authors: Aljabryn Dalal Hamad

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The present explanatory study concerns with the relation between Diabetes Mellitus and Food Balance in the Kingdom of Saudi Arabia during 2005-2010, using published data. Results illustrated that Saudi citizen daily protein consumption (DPC) during 2005-2007 (g/capita/day) is higher than the average global consumption level of protein with 15.27%, daily fat consumption (DFC) with 24.56% and daily energy consumption (DEC) with 16.93% and increases than recommended level by International Nutrition Organizations (INO) with 56% for protein, 60.49% for fat and 27.37% for energy. On the other hand, DPC per capita in Saudi Arabia decreased during the period 2008-2010 from 88.3 to 82.36 gram/ day. Moreover, DFC per capita in Saudi Arabia decreased during the period 2008-2010 from 3247.90 to 3176.43 Cal/capita/ day, and daily energy consumption (DEC) of Saudi citizen increases than world consumption with 16.93%, while increases with 27.37% than INO. Despite this, DPC, DFC and DEC per capita in Saudi Arabia still higher than world mean. On the other side, results illustrated that the number of diabetic patients in Saudi Arabia during the same period (2005-2010). The curve of diabetic patient’s number in Saudi Arabia during 2005-2010 is regular ascending with increasing level ranged between 7.10% in 2005 and 12.44% in 2010. It is essential to devise Saudi National programs to educate the public about the relation of food balances and diabetes so it could be avoided, and provide citizens with healthy dietary balances tables.

Keywords: Diabetes mellitus, food balance, energy, fat, protein, Saudi Arabia

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Authors: Miguel Angel Garcia-Ruiz, Pedro Cesar Santana-Mancilla, Laura Sanely Gaytan-Lugo

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This paper describes the design and application of a short in-class project conducted in Algoma University’s Human-Computer Interaction (HCI) course taught at the Bachelor of Computer Science. The project was based on the Maker Movement (people using and reusing electronic components and everyday materials to tinker with technology and make interactive applications), where students applied low-cost and easy-to-use electronic components, the Arduino Uno microcontroller board, software tools, and everyday objects. Students collaborated in small teams by completing hands-on activities with them, making an interactive walking cane for blind people. At the end of the course, students filled out a Technology Acceptance Model version 2 (TAM2) questionnaire where they evaluated microcontroller boards’ applications in HCI classes. We also asked them about applying the Maker Movement in HCI classes. Results showed overall students’ positive opinions and response about using microcontroller boards in HCI classes. We strongly suggest that every HCI course should include practical activities related to tinkering with technology such as applying microcontroller boards, where students actively and constructively participate in teams for achieving learning objectives.

Keywords: maker movement, microcontrollers, learning, projects, course, technology acceptance

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Authors: Madhukar Kasarla, Sumit Fogla, Kiran Panuganti, Gaurav Jain, Abhijit Ramanujam, Astha Jain, Shashank Kraleti, Sharat Musham, Arun Chaudhury

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Patients have often chosen to travel for care — making pilgrimages to academic meccas and state-of-the-art hospitals for sophisticated surgery. This culture is still persistent in the landscape of US healthcare, with hundred thousand of visitors coming to the shores of United States to seek the high quality of medical care. One of the major challenges in this form of medical tourism has been the language barrier. Thus, an Iraqi patient, with immediate needs of communicating the healthcare needs to the treating team in the hospital, may face huge barrier in effective patient-doctor communication, delaying care and even at times reducing the quality. To circumvent these challenges, we are proposing the use of a state-of-the-art tool, Talk2All, which can translate nearly one hundred international languages (and even sign language) in real time. The tool is an easy to download app and highly user friendly. It builds on machine learning principles to decode different languages in real time. We suggest that the use of Talk2All will tremendously enhance communication in the hospital setting, effectively breaking the language barrier. We propose that vigorous incorporation of Talk2All shall overcome practical challenges in international medical and surgical tourism.

Keywords: language translation, communication, machine learning, medical tourism

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Authors: Marsha D. Loda

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College enrollment is declining and generation Z, today’s college students, are struggling. Before the pandemic, researchers characterized this generational cohort as unique. Gen Z has been called the most achievement-oriented generation, as they enjoy greater economic status, are more racially and ethnically diverse, and better educated than any other generation. However, they are also the most likely generation to suffer from depression and anxiety. Gen Z has grown up largely with usually well-intentioned but overprotective parents who inadvertently kept them from learning life skills, likely impacting their ability to cope with and to effectively manage challenges. The unprecedented challenges resulting from the pandemic up ended their world and left them emotionally reeling. One of the ramifications of this for higher education is how to reengage current Gen Z students in the classroom. This research presents qualitative findings from 24 single-spaced pages of verbatim comments from college students. Research questions concerned what helps them learn and what they abhor, as well as how to engage them with the university outside of the classroom to aid in retention. Students leave little doubt about what they want to experience in the classroom. In order of mention, students want discussion, to engage with questions, to hear how a topic relates to real life and the real world, to feel connections with the professor and fellow students, and to have an opportunity to give their opinions. They prefer a classroom that involves conversation, with interesting topics and active learning. “professor talks instead of lecturing” “professor builds a connection with the classroom” “I am engaged because it feels like a respectful conversation” Similarly, students are direct about what they dislike in a classroom. In order of frequency, students dislike teachers unenthusiastically reading word or word from notes or presentations, repeating the text without adding examples, or addressing how to apply the information. “All lecture. I can read the book myself” “Not taught how to apply the skill or lesson” “Lectures the entire time. Lesson goes in one ear and out the other.” Pertaining to engagement outside the classroom, Gen Z challenges higher education to step outside the box. They don’t want to just hear from professionals in their field, they want to meet and interact with them. Perhaps because of their dependence on technology and pandemic isolation, they seem to reach out for assistance in forming social bonds. “I believe fun and social events are the best way to connect with students and get them involved. Cookouts, raffles, socials, or networking events would all most likely appeal to many students”. “Events… even if they aren’t directly related to learning. Maybe like movie nights… doing meet ups at restaurants”. Qualitative research suggests strategy. This research is rife with strategic implications to improve learning, increase engagement and reduce drop-out rates among Generation Z higher education students. It also compliments existing research on student engagement. With college enrollment declining by some 1.3 million students over the last two years, this research is both timely and important.

Keywords: college enrollment, generation Z, higher education, pandemic, student engagement

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Authors: Liz Carmem Silva-Pereira, Raffael Alencar Mesquita Rodrigues, Francisco Helton Mendes Barbosa, Emerson de Freitas Ferreira

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Due to the Covid-19 pandemic declared in March 2020 by the World Health Organization, the way of social coexistence across the planet was affected, especially in educational processes, from the implementation of the remote modality as a teaching strategy. This teaching-learning modality caused a change in the routine and learning of basic education students, which resulted in serious consequences for the return to face-to-face teaching in 2021. 2022, at the Federal Institute of Education, Science and Technology of Pará (IFPA) – Campus Paragominas had their training process severely affected, having studied the initial half of their training in the remote modality, which compromised the carrying out of practical classes, technical visits and field classes, essential for the student formation on the environmental technician. With the objective of promoting the recomposition of these students' learning after returning to the face-to-face modality, an educational strategy was developed in the last period of the course. As teaching methodologies were used for research as an educational principle, the integrative project and the parallel recovery action applied jointly, aiming at recomposing the basic knowledge of the natural sciences, together with the technical knowledge of the environmental area applied to the course. The project assisted 58 finalist students of the environmental technical course. A research instrument was elaborated with parameters of evaluation of the environmental quality for study in 19 collection points, in the Uraim River urban hydrographic basin, in the Paragominas City – Pará – Brazilian Amazon. Students were separated into groups under the professors' and laboratory assistants’ orientation, and in the field, they observed and evaluated the places' environmental conditions and collected physical data and water samples, which were taken to the chemistry and biology laboratories at Campus Paragominas for further analysis. With the results obtained, each group prepared a technical report on the environmental conditions of each evaluated point. This work methodology enabled the practical application of theoretical knowledge received in various disciplines during the remote teaching modality, contemplating the integration of knowledge, people, skills, and abilities for the best technical training of finalist students. At the activity end, the satisfaction of the involved students in the project was evaluated, through a form, with the signing of the informed consent term, using the Likert scale as an evaluation parameter. The results obtained in the satisfaction survey were: on the use of research projects within the disciplines attended, 82% of satisfaction was obtained; regarding the revision of contents in the execution of the project, 84% of satisfaction was obtained; regarding the acquired field experience, 76.9% of satisfaction was obtained, regarding the laboratory experience, 86.2% of satisfaction was obtained, and regarding the use of this methodology as parallel recovery, 71.8% was obtained of satisfaction. In addition to the excellent performance of students in acquiring knowledge, it was possible to remedy the deficiencies caused by the absence of practical classes, technical visits, and field classes, which occurred during the execution of the remote teaching modality, fulfilling the desired educational recomposition.

Keywords: integrative project, parallel recovery, research as an educational principle, teaching-learning

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Authors: G. Espinosa Garza, I. Loera, N. Antonyan

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In the last decades, teaching in particular engineering subjects is going through a significative problem. A quick evaluation of the entrepreneurial surroundings makes it more difficult for students to identify the course contents with real situations related with their future professions. Proposing teaching through challenges or problem-based projects, and real-life situations is turning into an important challenge for any university-level educator. The objective of this work is to present the educational experience and the investigation results taken through the Project Viability course, done by a group of professors and students from the Technologic of Monterrey. Currently, in Mexico, the orange peels are considered a dispose and they are not being utilized as an alternative to create subproducts. However, there is a great opportunity in its use as a raw material with the goal to originate the waste from the local citric firms or business. The project challenge consisted in the development of edible products from the orange peel with the intention to generate new healthy products. With this project, apart from the obtainment of the original results, the accomplishment consisted in creating a learning atmosphere, where students together with the professors were able to plan, evaluate, and implement the project related with the creative, innovative, and sustainable processes with the goal to apply it in the development of local solutions. In the present article, the pedagogic methodologies that allowed to carry out this project will be discussed.

Keywords: engineering subjects, learning project, orange peel, sustainable process

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Authors: Nuno Biga

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The integration of serious games with gamification in management education and training has gained significant importance in recent years as innovative strategies are sought to improve target audience engagement and learning outcomes. This research builds on the author's previous work in this field and presents a case study that evaluates the ex-post impact of a sample of applications of the BIGAMES management simulator in the training of top managers from various hospital institutions. The methodology includes evaluating the reaction of participants after each edition of BIGAMES Accident & Emergency (A&E) carried out over the last 3 years, as well as monitoring the career path of a significant sample of participants and their feedback more than a year after their experience with this simulator. Control groups will be set up, according to the type of role their members held when they took part in the BIGAMES A&E simulator: Administrators, Clinical Directors and Nursing Directors. Former participants are invited to answer a questionnaire structured for this purpose, where they are asked, among other questions, about the importance and impact that the BIGAMES A&E simulator has had on their professional activity. The research methodology also includes an exhaustive literature review, focusing on empirical studies in the field of education and training in management and business that investigate the effectiveness of gamification and serious games in improving learning, team collaboration, critical thinking, problem-solving skills and overall performance, with a focus on training contexts in the health sector. The results of the research carried out show that gamification and serious games that simulate real scenarios, such as Business Interactive Games - BIGAMES©, can significantly increase the motivation and commitment of participants, stimulating the development of transversal skills, the mobilization of group synergies and the acquisition and retention of knowledge through interactive user-centred scenarios. Individuals who participate in game-based learning series show a higher level of commitment to learning because they find these teaching methods more enjoyable and interactive. This research study aims to demonstrate that, as executive education and training programs develop to meet the current needs of managers, gamification and serious games stand out as effective means of bridging the gap between traditional teaching methods and modern educational and training requirements. To this end, this research evaluates the medium/long-term effects of gamified learning on the professional performance of participants in the BIGAMES simulator applied to healthcare. Based on the conclusions of the evaluation of the effectiveness of training using gamification and taking into account the results of the opinion poll of former A&E participants, this research study proposes an integrated approach for the transversal application of the A&E Serious Game in various educational contexts, covering top management (traditionally the target audience of BIGAMES A&E), middle and operational management in healthcare institutions (functional area heads and professionals with career development potential), as well as higher education in medicine and nursing courses. The integrated solution called “BIGAMES A&E plus”, developed as part of this research, includes the digitalization of key processes and the incorporation of AI.

Keywords: artificial intelligence (AI), executive training, gamification, higher education, management simulators, serious games (SG), training effectiveness

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Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

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Authors: Yang Zheng, Wei Sun

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This paper describes a new approach which can be used to interpret the experimental creep deformation data obtained from miniaturized thin plate bending specimen test to the corresponding uniaxial data based on an inversed application of the reference stress method. The geometry of the thin plate is fully defined by the span of the support, l, the width, b, and the thickness, d. Firstly, analytical solutions for the steady-state, load-line creep deformation rate of the thin plates for a Norton’s power law under plane stress (b → 0) and plane strain (b → ∞) conditions were obtained, from which it can be seen that the load-line deformation rate of the thin plate under plane-stress conditions is much higher than that under the plane-strain conditions. Since analytical solution is not available for the plates with random b-values, finite element (FE) analyses are used to obtain the solutions. Based on the FE results obtained for various b/l ratios and creep exponent, n, as well as the analytical solutions under plane stress and plane strain conditions, an approximate, numerical solutions for the deformation rate are obtained by curve fitting. Using these solutions, a reference stress method is utilised to establish the conversion relationships between the applied load and the equivalent uniaxial stress and between the creep deformations of thin plate and the equivalent uniaxial creep strains. Finally, the accuracy of the empirical solution was assessed by using a set of “theoretical” experimental data.

Keywords: bending, creep, thin plate, materials engineering

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Authors: Xinghang Lv, Tao Peng, Jia Chen, Junping Liu, Xinrong Hu, Ruhan He, Minghua Jiang, Wenli Cao

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As one of the most dangerous vulnerabilities, effective detection of buffer overflow vulnerabilities is extremely necessary. Traditional detection methods are not accurate enough and consume more resources to meet complex and enormous code environment at present. In order to resolve the above problems, we propose the method for Buffer overflow detection based on Abstract syntax tree, Control flow graph, and Data dependency graph (BodeACD) in C/C++ programs with source code. Firstly, BodeACD constructs the function samples of buffer overflow that are available on Github, then represents them as code representation sequences, which fuse control flow, data dependency, and syntax structure of source code to reduce information loss during code representation. Finally, BodeACD learns vulnerability patterns for vulnerability detection through deep learning. The results of the experiments show that BodeACD has increased the precision and recall by 6.3% and 8.5% respectively compared with the latest methods, which can effectively improve vulnerability detection and reduce False-positive rate and False-negative rate.

Keywords: vulnerability detection, abstract syntax tree, control flow graph, data dependency graph, code representation, deep learning

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Authors: Walid Abdallah, Noureddine Boudriga

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Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

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Authors: T. S. Terence Ma, Irene T. Ho

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Conducting adequate assessment of the challenges students with ASD (Autism Spectrum Disorder) face and the support they need is imperative for promoting their school adjustment. Students with ASD often show deficits in communication, social interaction, emotional regulation, and self-management in learning. While targeting these areas in intervention is often helpful, we argue that not enough attention has been paid to weak self-regulation being a key factor underlying their manifest difficulty in all these areas. Self-regulation refers to one’s ability to moderate their behavioral or affective responses without assistance from others. Especially for students with high functioning autism, who often show problems not so much in acquiring the needed skills but rather in applying those skills appropriately in everyday problem-solving, self-regulation becomes a key to successful adjustment in daily life. Therefore, a greater understanding of the construct of self-regulation, its relationship with other daily skills, and its role in school functioning for students with ASD would generate insights on how students’ school adjustment could be promoted more effectively. There were two focuses in this study. Firstly, we examined the extent to which self-regulation is a distinct construct that is differentiable from other daily skills and the most salient indicators of this construct. Then we tested a model of relationships between self-regulation and other daily school skills as well as their relative and combined effects on school adjustment. A total of 1,345 Grade1 to Grade 6 students with ASD attending mainstream schools in Hong Kong participated in the research. In the first stage of the study, teachers filled out a questionnaire consisting of 136 items assessing a wide range of student skills in social, emotional and learning areas. Results from exploratory factor analysis (EFA) with 673 participants and subsequent confirmatory factor analysis (CFA) with another group of 672 participants showed that there were five distinct factors of school skills, namely (1) communication skills, (2) pro-social behavior, (3) emotional skills, (4) learning management, and (5) self-regulation. Five scales representing these skill dimensions were generated. In the second stage of the study, a model postulating the mediating role of self-regulation for the effects of the other four types of skills on school adjustment was tested with structural equation modeling (SEM). School adjustment was defined in terms of the extent to which the student is accepted well in school, with high engagement in school life and self-esteem as well as good interpersonal relationships. A 5-item scale was used to assess these aspects of school adjustment. Results showed that communication skills, pro-social behavior, emotional skills and learning management had significant effects on school adjustment only indirectly through self-regulation, and their total effects were found to be not high. The results indicate that support rendered to students with ASD focusing only on the training of well-defined skills is not adequate for promoting their inclusion in school. More attention should be paid to the training of self-management with an emphasis on the application of skills backed by self-regulation. Also, other non-skill factors are important in promoting inclusive education.

Keywords: autism, assessment, factor analysis, self-regulation, school adjustment

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Authors: Mohosina Jabin Toma, Gerald Tembrevilla, Marina Milner-Bolotin

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Science education in Bangladesh is losing its appeal at an alarming rate due to the lack of science laboratory equipment, excessive teacher-student ratio, and outdated teaching strategies. Research-based educational technologies aim to address some of the problems faced by teachers who have limited access to laboratory resources, like many Bangladeshi teachers. Physics Education Technology (PhET) research team has been developing science and mathematics interactive simulations to help students develop deeper conceptual understanding. Still, PhET simulations are rarely used in Bangladesh. The purpose of this study is to explore Bangladeshi teachers’ challenges in learning to implement PhET-enhanced pedagogies and examine teachers’ views on PhET’s pedagogical opportunities in secondary science education. Since it is a new technology for Bangladesh, seven workshops on PhET were conducted in Dhaka city for 129 in-service and pre-service teachers in the winter of 2023 prior to data collection. This study followed an explanatory mixed method approach that included a pre-and post-workshop survey and five semi-structured interviews. Teachers participated in the workshops voluntarily and shared their experiences at the end. Teachers’ challenges were also identified from workshop discussions and observations. The interviews took place three to four weeks after the workshop and shed light on teachers’ experiences of using PhET in actual classroom settings. The results suggest that teachers had difficulty handling new technology; hence, they recommended preparing a booklet and Bengali YouTube videos on PhET to assist them in overcoming their struggles. Teachers also faced challenges in using any inquiry-based learning approach due to the content-loaded curriculum and exam-oriented education system, as well as limited experience with inquiry-based education. The short duration of classes makes it difficult for them to design PhET activities. Furthermore, considering limited access to computers and the internet in school, teachers think PhET simulations can bring positive changes if used in homework activities. Teachers also think they lack pedagogical skills and sound content knowledge to take full advantage of PhET. They highly appreciated the workshops and proposed that the government designs some teacher training modules on how to incorporate PhET simulations. Despite all the challenges, teachers believe PhET can enhance student learning, ensure student engagement and increase student interest in STEM Education. Considering the lack of science laboratory equipment, teachers recognized the potential of PhET as a supplement to hands-on activities for secondary science education in Bangladesh. They believed that if PhET develops more curriculum-relevant sims, it will bring revolutionary changes to how Bangladeshi students learn science. All the participating teachers in this study came from two organizations, and all the workshops took place in urban areas; therefore, the findings cannot be generalized to all secondary science teachers. A nationwide study is required to include teachers from diverse backgrounds. A further study can shed light on how building a professional learning community can lessen teachers’ challenges in incorporating PhET-enhanced pedagogy in their teaching.

Keywords: educational technology, inquiry-based learning, PhET interactive simulations, PhET-enhanced pedagogies, science education, science laboratory equipment, teacher professional development

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Authors: Pisit Poolprasert, Auttpol Nakwa, Keerati Tanruean, Ezra Mongkolchaichana, Ezra Mongkolchaichana, Sinlapachai Senarat, Mark Tunmore

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This present preliminary study aimed to observe the butterfly species diversity in two selected subdistricts i.e., Mae Klong and Umphang of Umphang district, Tak province, northern Thailand, during May to September 2018. A survey method using sweep net was employed along line transects between 10.00-12.00 a.m. and 13.00-15.00 p.m. A total of 337 butterflies representing 37 species, 26 genera in five families were encountered. The family Nymphalidae held the highest species richness (15 species), followed by Papilionidae (9 species) and Pieridae (6 speices), respectively. Herein, four uncommon species, namely Junonia iphita iphita, Tanaecia julii odilina, Penthema darlisa melema, and Papilio alcmenor alcmenor, were discovered in this time. The Shannon diversity (H’) for all samples obtained from the pooled data set of this observation valued 2.563 with relatively high values of Evenness (J’= 0.710) and Simpson index and (D = 0.829). For similarity index (Ss), the assemblage recorded of butterfly species between Mae Klang-Umphang shared about 0.629, implying that the environmental conditions in both surveyed zones were alike. Additionally, the accumulation curve in both locations of the butterfly was still increasing gradually when the collection ended, indicating that the lepidopteran species would be raised if we continue to survey more in next month. Nevertheless, to gain more butterfly taxa, observing different plant communities covering every season and using several survey techniques should be considered for further investigation.

Keywords: butterfly, biodiversity, tak province, thailand

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Authors: Lorna Martha Dreyer

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Consistent with the changing paradigm on the rights of people with disabilities and in pursuit of social justice, there is internationally an increase in students with disabilities enrolling at Higher Education Institutions (HEIs). This trend challenges HEI’s to transform and attain Education for All (EFA) as a global imperative. However, while physical and sensory disabilities are observable, students with specific learning disabilities (SLD) do not present with any visible indications and are often referred to as “hidden” or “invisible” disabilities. This qualitative case study aimed to illuminate the experiences of students with SLDs at a South African university. The research was, therefore, guided by Vygotsky’s social-cultural theory (SCT). This research was conducted within a basic qualitative research methodology embedded in an interpretive paradigm. Data was collected through an online background survey and semi-structured interviews. Thematic qualitative content analysis was used to analyse the collected data systematically. From a social justice perspective, the major findings suggest that there are several factors that impede equal education for students with SLDs at university. Most participants in this small-scale study experienced a lack of acknowledgment and support from lecturers. They reported valuing the support of family and friends more than that of lecturers. It is concluded that lecturers need to be reflective of their pedagogical practices if authentic inclusion is to be realised.

Keywords: higher education, inclusive education, pedagogy, social-cultural theory, specific learning disabilities

Procedia PDF Downloads 149