Search results for: incremental learning

Authors: Mohammed Alhammad

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The movement towards the inclusion of students with special educational needs (SEN) in mainstream schools has become widely accepted practice in many countries. However in Saudi Arabia, this is not happening. Instead the practice for students with learning difficulties (LD) is to study in special classrooms in mainstream schools and they are not included with their peers, except at break times and morning assembly, and on school trips. There are a number of barriers that face implementing inclusion for students with LD in mainstream classrooms: one such barrier is the training of teachers. The training, either pre- or in-service, that teachers receive is seen as playing an important role in leading to the successful implementation of inclusion. The aim of this presentation is to explore how pre-service training and in-service training are acting as barriers for implementing inclusion of students with LD in mainstream primary schools in Saudi Arabia from the perspective of teachers. The qualitative research approach was used to explore this barrier. Twenty-four teachers (general education teachers, special education teachers) were interviewed using semi-structured interview and a number of documents were used as method of data collection. The result showed teachers felt that not much attention was paid to inclusion in pre-services training for general education teachers and special education teachers in Saudi Arabia. In addition, pre-service training for general education teachers does not normally including modules on special education. Regarding the in-service training, no courses at all about inclusion are provided for teachers. Furthermore, training courses in special education are few. As result, the knowledge and skills required to implemented inclusion successfully.

Keywords: inclusion, learning difficulties, Saudi Arabia, training

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Cristina Costa-Lobo, Sandra Fernandes, Miguel Magalhães, José Dinis-Carvalho, Alfredo Regueiro, Ana Carvalho

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This paper is a report on the findings of the construction and the validation of a questionnaire monetized in a portuguese higher education context with undergraduate students. The Questionnaire for the Evaluation of Entrepreneurship Project Psychopedagogical Practices consists of six scales: Critical appraisal of the project, Developed Learning and Skills, Teamwork, Teacher and Tutor Roles, Evaluation of Student Performance, and Project Effectiveness as a Teaching-Learning Methodology. The proceedings of its construction are analyzed, and the validity and internal consistency analysis are described. Findings indicate good indicators of validity, good fidelity and an interpretable factorial structure.

Keywords: entrepreneurship project, higher education, psychopedagogical practices, teacher and tutor roles

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Authors: Marta Fonseca, Pedro Marvão, Beatriz Oliveira, Bruno Heleno, Pedro Carreiro-Martins, Nuno Neuparth, António Rendas

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Background: Concept maps (CMs) visually represent hierarchical connections among related ideas. They foster logical organization and clarify idea relationships, potentially aiding medical students in critical thinking (to think clearly and rationally about what to do or what to believe). However, there are inconsistent claims about the use of CMs in undergraduate medical education. Our three research questions are: 1) What studies have been published on concept mapping in undergraduate medical education? 2) What was the impact of CMs on students’ critical thinking? 3) How and why have these interventions had an educational impact? Methods: Eight databases were systematically searched (plus a manual and an additional search were conducted). After eliminating duplicate entries, titles, and abstracts, and full-texts were independently screened by two authors. Data extraction and quality assessment of the studies were independently performed by two authors. Qualitative and quantitative data were integrated using mixed-methods. The results were reported using the structured approach to the reporting in healthcare education of evidence synthesis statement and BEME guidance. Results: Thirty-nine studies were included from 26 journals (19 quantitative, 8 qualitative and 12 mixed-methods studies). CMs were considered as a tool to promote critical thinking, both in the perception of students and tutors, as well as in assessing students’ knowledge and/or skills. In addition to their role as facilitators of knowledge integration and critical thinking, CMs were considered both teaching and learning methods. Conclusions: CMs are teaching and learning tools which seem to help medical students develop critical thinking. This is due to the flexibility of the tool as a facilitator of knowledge integration, as a learning and teaching method. The wide range of contexts, purposes, and variations in how CMs and instruments to assess critical thinking are used increase our confidence that the positive effects are consistent.

Keywords: concept map, medical education, undergraduate, critical thinking, meaningful learning

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Authors: Adina Dudau, Georgios Kominis, Melinda Szocs

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This paper links distinct bodies of literature around innovation and public services by examining a case of perceived innovation failure. Through a mixed methodology investigating student attitudes to, and behaviour around, technological innovation in higher education, the paper makes a contribution to the public service innovation literature by focusing on the duality of innovation outcomes, suggestive of an innovation typology in public services. The study was conducted in a UK Russell Group university and it focused on a technological process innovation. The innovation consisted of the provision of feedback to students in the form of a digital video (mp4), tailored to each individual submission, with extended voice-over commentary from the course coordinator and visual cues intended to help students see the relevance of comments to their submissions. The sample of the study consisted of a class of 79 undergraduate students. To investigate student attainment, we designed a field (also known as quasi or natural) experiment, essentially a manipulation of a social setting (in this case, the form of feedback given to students), but as part of a naturally occurring social arrangement (a real course which students attend and in which they are assessed). A two group control group design (see figure 3) was utilised to examine the effectiveness of the feedback innovation (video feedback). Two outcome variables of the service innovation were measured: student satisfaction and student attainment. In other words, the study examined not only students’ perceptions of whether VF was deemed to be beneficial towards their subsequent assignments; but also evidence of actual incremental benefits in students’ performance from one assignment to the next after VF was provided. The results were baffling and indicating competing agendas in higher education.

Keywords: higher education, audio-video, feedback, innovation

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Authors: Anthony J. Rojas

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In flipped classroom environments, students are tasked with engaging in pre-class learning to maximize the effectiveness of in-class time. This study investigates the use of ‘Quests’, brief formative assessments administered at the start of class, to evaluate student understanding of assigned pre-class materials in an undergraduate chemistry course. Students completed Quests via Microsoft Forms, based on content from instructional videos and worksheets, and these assessments were mandatory, with no opportunity for make-up. This paper examines the correlation between Quest performance and overall course success, finding that students who performed well on the Quests consistently achieved higher final grades in the course. The findings suggest that Quests are effective in both reinforcing student engagement with pre-class content and predicting their broader academic performance. The implications of these results for flipped classroom strategies and student learning outcomes will be discussed.

Keywords: chemistry, flipped classroom, attendance, assessments

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Authors: Lana Burmistrova

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Introduction: The influence of visual impairments on several cognitive processes used in the music learning process is an increasingly important area in special education and cognitive musicology. Many children have several visual impairments due to the refractive errors and irreversible inhibitors. However, based on the compensatory neuroplasticity and functional reorganization, congenitally blind (CB) and early blind (EB) individuals use several areas of the occipital lobe to perceive and process auditory and tactile information. CB individuals have greater memory capacity, memory reliability, and less false memory mechanisms are used while executing several tasks, they have better working memory (WM) and short-term memory (STM). Blind individuals use several strategies while executing tactile and working memory n-back tasks: verbalization strategy (mental recall), tactile strategy (tactile recall) and combined strategies. Methods and design: The aim of the pilot study was to substantiate similar tendencies while executing attention, memory and combined auditory tasks in blind and sighted individuals constructed for this study, and to investigate attention, memory and combined mechanisms used in the music learning process. For this study eight (n=8) blind and eight (n=8) sighted individuals aged 13-20 were chosen. All respondents had more than five years music performance and music learning experience. In the attention task, all respondents had to identify pitch changes in tonal and randomized melodic pairs. The memory task was based on the mismatch negativity (MMN) proportion theory: 80 percent standard (not changed) and 20 percent deviant (changed) stimuli (sequences). Every sequence was named (na-na, ra-ra, za-za) and several items (pencil, spoon, tealight) were assigned for each sequence. Respondents had to recall the sequences, to associate them with the item and to detect possible changes. While executing the combined task, all respondents had to focus attention on the pitch changes and had to detect and describe these during the recall. Results and conclusion: The results support specific features in CB and EB, and similarities between late blind (LB) and sighted individuals. While executing attention and memory tasks, it was possible to observe the tendency in CB and EB by using more precise execution tactics and usage of more advanced periodic memory, while focusing on auditory and tactile stimuli. While executing memory and combined tasks, CB and EB individuals used passive working memory to recall standard sequences, active working memory to recall deviant sequences and combined strategies. Based on the observation results, assessment of blind respondents and recording specifics, following attention and memory correlations were identified: reflective attention and STM, reflective attention and periodic memory, auditory attention and WM, tactile attention and WM, auditory tactile attention and STM. The results and the summary of findings highlight the attention and memory features used in the music learning process in the context of blindness, and the tendency of the several attention and memory types correlated based on the task, strategy and individual features.

Keywords: attention, blindness, memory, music learning, strategy

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Authors: Kate Benedicta Amenador, Dianjian Wang, Bright Nkrumah

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This extensive bibliometric study explores the dynamic influence of artificial intelligence in the field of English as a Foreign Language (EFL) between 2012 and 2024. The study, which examined 4,500 articles from Google Scholar, Modern Language Association Linguistics Abstracts, Web of Science, Scopus, Researchgate, and library genesis databases, indicates that AI integration in EFL is on the rise. This notable increase is ascribed to a variety of transformative events, including increased academic funding for higher education and the COVID-19 epidemic. The results of the study identify leading contributors, prominent authors, publishers and sources, with the United States, China and the United Kingdom emerging as key contributors. The co-occurrence analysis of key terms reveals five clusters highlighting patterns in AI-enhanced language instruction and learning, including evaluation strategies, educational technology, learning motivation, EFL teaching aspects, and learner feedback. The study also discusses the impact of various AIs in enhancing EFL writing skills with software such as Grammarly, Quilbot, and Chatgpt. The current study recognizes limitations in database selection and linguistic constraints. Nevertheless, the results provide useful insights for educators, researchers and policymakers, inspiring and guiding a cross-disciplinary collaboration and creative pedagogical techniques and approaches to teaching and learning in the future.

Keywords: artificial intelligence, bibliometrics study, VOSviewer visualization, English as a foreign language

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Authors: Aaditya U. Jhamb

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Due to the current energy crisis that many countries are battling, energy-efficient buildings are the subject of extensive research in the modern technological era because of growing worries about energy consumption and its effects on the environment. The paper explores 8 factors that help determine energy efficiency for a building: (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution), with Tsanas and Xifara providing a dataset. The data set employed 768 different residential building models to anticipate heating and cooling loads with a low mean squared error. By optimizing these characteristics, machine learning algorithms may assess and properly forecast a building's heating and cooling loads, lowering energy usage while increasing the quality of people's lives. As a result, the paper studied the magnitude of the correlation between these input factors and the two output variables using various statistical methods of analysis after determining which input variable was most closely associated with the output loads. The most conclusive model was the Decision Tree Regressor, which had a mean squared error of 0.258, whilst the least definitive model was the Isotonic Regressor, which had a mean squared error of 21.68. This paper also investigated the KNN Regressor and the Linear Regression, which had to mean squared errors of 3.349 and 18.141, respectively. In conclusion, the model, given the 8 input variables, was able to predict the heating and cooling loads of a residential building accurately and precisely.

Keywords: energy efficient buildings, heating load, cooling load, machine learning models

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Authors: Afsaneh Jasmine Majidi

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Language testing is an important part of language teaching experience and language learning process as it presents assessment strategies for teachers to evaluate the efficiency of teaching and for learners to examine their outcomes. However, language testing is demanding and challenging because it should provide the opportunity for proper and objective decision. In addition to all the efforts test designers put to design valid and reliable tests, there are some other determining factors which are even more complex and complicated. These factors affect the educational system, individuals, and society, and the impact of the tests vary according to the scope of the test. Seemingly, the impact of a simple classroom assessment is not the same as that of high stake tests such as International English Language Testing System (IELTS). As the importance of the test increases, it affects wider domain. Accordingly, the impacts of high stake tests are reflected not only in teaching, learning strategies but also in society. Testing experts use the term ‘washback’ or ‘impact’ to define the different effects of a test on teaching, learning, and community. This paper first looks at the theoretical background of ‘washback’ and ‘impact’ in language testing by reviewing of relevant literature in the field and then investigates washback effects of IELTS test of on Iranian IELTS teachers and students. The study found significant relationship between the washback effect of IELTS test and teaching strategies of Iranian IELTS teachers as well as performance of Iranian IELTS candidates and their community.

Keywords: high stake tests, IELTS, Iranian Candidates, language testing, test impact, washback

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Authors: Akinbuluma Ayodeji Theophilus, Adewale Olutaiwo

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When building roads and paved surfaces, a strong foundation is always essential. A durable material that can withstand years of traffic while staying trustworthy must be used to build the foundation. A frequent problem in the construction of roads and pavements is the lack of high-quality, long-lasting materials for the pavement structure (base, subbase, and subgrade). Hence, this study examined the stabilization of lateritic soil samples from Ijoko with cement kiln dust and lime. The study adopted the experimental design. Laboratory tests were conducted on classification, swelling potential, compaction, California bearing ratio (CBR), and unconfined compressive tests, among others, were conducted on the laterite sample treated with cement kiln dust (CKD) and lime in incremental order of 2% up to 10% of dry weight soft soil sample. The results of the test showed that the studied soil could be classified as an A-7-6 and CL soil using the American Association of State Highway and transport officials (AASHTO) and the unified soil classification system (USCS), respectively. The plasticity (PI) of the studied soil reduced from 30.5% to 29.9% at the application of CKD. The maximum dry density on the application of CKD reduced from 1.9.7 mg/m3 to 1.86mg/m3, and lime application yielded a reduction from 1.97mg/m3 to 1.88.mg/m3. The swell potential on CKD application was reduced from 0.05 to 0.039%. The study concluded that soil stabilizations are effective and economic way of improving road pavement for engineering benefit. The degree of effectiveness of stabilization in pavement construction was found to depend on the type of soil to be stabilized. The study therefore recommended that stabilized soil mixtures should be used to subbase material for flexible pavement since is a suitable.

Keywords: lateritic soils, sand, cement, stabilization, road pavement

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Authors: Ali Faisal Saleem, Farheen Quadri, Mach Ondrej, Anita Zaidi

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Purpose: Pakistan is the final frontier for a polio-free world. Chronic malnutrition is associated with lack of effective gut immunity, and possibly associated with poliomyelitis in children received multiple OPV. We evaluate IPV dose administered together with OPV results in higher immunogenicity and mucosal immunity compared to OPV alone in chronically malnourished infants. Methods AND Materials: A community-based, unblinded-randomized-trial, conducted in 5 peri-urban, low-middle-income households of Karachi, in infants 9-12 months. Two study groups were non-malnourished (HAZ= -2 or more) and chronic malnourished (HAZ <-2SD), with 2-arms each i) OPV and ii) OPV and IPV. Two blood specimens (2ml) at baseline and at day 28 and two stool specimens (6 gm.) at day 29 and after 7 days. All infants received a bOPV challenge dose after first stool specimen. Calculates sample size was 210 in each arm. Serological (baseline compared to 28 days post-vaccine) and mucosal immunity after one week of bOPV challenge dose were study outcomes. Results: Baseline seroprevalence in malnourished infants were low compared to non-malnourished (P1, P2 and P3 (p=<0.001). There is significant rise in antibody titer and P1 seroprevalence in Mal A and B after receiving study vaccine; much higher in Mal B. Infants randomized to bOPV + IPV study vaccine showed incremental immune response against P1 (Mal B, 92.2%; Nor B, 98.4%), P2 (Mal B, 90.4%; Nor B, 94.7%), and P3 (Mal B, 85.6% and Nor B, 93.5%) was observed. A significant proportion of infants in malnourished (P1, 13%; P2, 24%; P3, 26%) and normally nourished group (P1, 5%; P2, 11%; P3, 14%) were found to be seronegative at baseline. Infants who received BOPV + IPV as their study vaccine showed a very high seroconversion response after vaccine (p=<0.001 for P1, P2 and P3). Majority of the specimens were negative at baseline (Mal A, 2%, Mal B, 1%; Nor A, 2%; Nor B, 1%), and remains negative after bOPV challenge dose (Mal A, 8%, Mal B, 6%; Nor A, 11%; Nor B, 10%). Conclusion: Malnourished-infants have low poliovirus-seroprevalence that increased remarkably after IPV. There is less viral shedding after IPV in infants.

Keywords: chronic malnutrition, infants, IPV, OPV

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Authors: W. Zekri

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This paper explores Zohra Drif’s memoir, Inside the Battle of Algiers, which narrates her desires as a student to become a revolutionary activist. She exemplified, in her narrative, the different roles, she and her fellows performed as combatants in the Casbah during the Algerian Revolution 1954-1962. This book review aims to evaluate the concept of women’s agency through education and language learning, and its impact on empowering women’s desires. Close-reading method and thematic analysis are used to explore the text. The analysis identified themes that refine the meaning of agency which are social and cultural supports, education, and language proficiency. These themes aim to contribute to the representation in Inside the Battle of Algiers of a woman guerrilla who engaged herself to perform national acts of resistance.

Keywords: agency, education, learning, women

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Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

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Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: M. Isabel Santos, Ana Breda, Ana Margarida Almeida

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Research on academic abilities of individuals with autism spectrum disorder (ASD) underlines the importance of mathematics interventions. Yet the proposal of digital applications for children and youth with ASD continues to attract little attention, namely, regarding the development of mathematical reasoning, being the use of the digital technologies an area of great interest for individuals with this disorder and its use is certainly a facilitative strategy in the development of their mathematical abilities. The use of digital technologies can be an effective way to create innovative learning opportunities to these students and to develop creative, personalized and constructive environments, where they can develop differentiated abilities. The children with ASD often respond well to learning activities involving information presented visually. In this context, we present the digital Learning Environment on Mathematics for Autistic children (LEMA) that was a research project conducive to a PhD in Multimedia in Education and was developed by the Thematic Line Geometrix, located in the Department of Mathematics, in a collaboration effort with DigiMedia Research Center, of the Department of Communication and Art (University of Aveiro, Portugal). LEMA is a digital mathematical learning environment which activities are dynamically adapted to the user’s profile, towards the development of mathematical abilities of children aged 6–12 years diagnosed with ASD. LEMA has already been evaluated with end-users (both students and teacher’s experts) and based on the analysis of the collected data readjustments were made, enabling the continuous improvement of the prototype, namely considering the integration of universal design for learning (UDL) approaches, which are of most importance in ASD, due to its heterogeneity. The learning strategies incorporated in LEMA are: (i) provide options to custom choice of math activities, according to user’s profile; (ii) integrates simple interfaces with few elements, presenting only the features and content needed for the ongoing task; (iii) uses a simple visual and textual language; (iv) uses of different types of feedbacks (auditory, visual, positive/negative reinforcement, hints with helpful instructions including math concept definitions, solved math activities using split and easier tasks and, finally, the use of videos/animations that show a solution to the proposed activity); (v) provides information in multiple representation, such as text, video, audio and image for better content and vocabulary understanding in order to stimulate, motivate and engage users to mathematical learning, also helping users to focus on content; (vi) avoids using elements that distract or interfere with focus and attention; (vii) provides clear instructions and orientation about tasks to ease the user understanding of the content and the content language, in order to stimulate, motivate and engage the user; and (viii) uses buttons, familiarly icons and contrast between font and background. Since these children may experience little sensory tolerance and may have an impaired motor skill, besides the user to have the possibility to interact with LEMA through the mouse (point and click with a single button), the user has the possibility to interact with LEMA through Kinect device (using simple gesture moves).

Keywords: autism spectrum disorder, digital technologies, inclusion, mathematical abilities, mathematical learning activities

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Authors: Vesile Evrim, Aliyu Awwal

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Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

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Authors: Dasom Kim, William Xiu Shun Wong, Yoonjin Hyun, Donghoon Lee, Minji Paek, Sungho Byun, Namgyu Kim

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There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers.

Keywords: data mining, document classifier, text mining, topic modeling

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Authors: Chirine Dannaoui, Maya Antoun

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This paper explores the intricate dynamics of play-based early childhood education (ECE) and the impact of professional development on teachers implementing play-based pedagogy, particularly in the context of vulnerable Syrian refugee children in Lebanon. By utilizing qualitative methodologies, including classroom observations and in-depth interviews with five early childhood educators and a field manager, this study delves into the challenges and transformations experienced by teachers in adopting play-based learning strategies. The research unveils the critical role of continuous and context-specific professional development in empowering teachers to implement play-based pedagogies effectively. When appropriately supported, it emphasizes how such educational approaches significantly enhance children's cognitive, social, and emotional development in crisis-affected environments. Key findings indicate that despite diverse educational backgrounds, teachers show considerable growth in their pedagogical skills through targeted professional development. This growth is vital for fostering a learning environment where vulnerable children can thrive, particularly in humanitarian settings. The paper also addresses educators' challenges, including adapting to play-based methodologies, resource limitations, and balancing curricular requirements with the need for holistic child development. This study contributes to the discourse on early childhood education in crisis contexts, emphasizing the need for sustainable, well-structured professional development programs. It underscores the potential of play-based learning to bridge educational gaps and contribute to the healing process of children facing calamity. The study highlights significant implications for policymakers, educators, schools, and not-for-profit organizations engaged in early childhood education in humanitarian contexts, stressing the importance of investing in teacher capacity and curriculum reform to enhance the quality of education for children in general and vulnerable ones in particular.

Keywords: play-based learning, professional development, vulnerable children, early childhood education

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Ayyaz Hussain, Tariq Sadad

Abstract:

Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

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Authors: Fiona Jensen, Barb Goodwin, Nancy Kleiman, Rhonda Usunier

Abstract:

Background: Clinical simulation is now a common component in many health profession curricula in preparation for clinical practice. In the Rady Faculty of Health Sciences (RFHS) college leads in simulation and interprofessional (IP) education, planned an eight hour simulated hospital day shift, where seventy students from six health professions across two campuses, learned with each other in a safe, realistic environment. Learning about interprofessional collaboration, an expected competency for many health professions upon graduation, was a primary focus of the simulation event. Method: Faculty representatives from the Colleges of Nursing, Medicine, Pharmacy and Rehabilitation Sciences (Physical Therapy, Occupation Therapy, Respiratory Therapy) and Pharmacy worked together to plan the IP event in a simulation facility in the College of Nursing. Each college provided a faculty mentor to guide the same profession students. Students were placed in interprofessional teams consisting of a nurse, physician, pharmacist, and then sharing respiratory, occupational, and physical therapists across the team depending on the needs of the patients. Eight patient scenarios were role played by health profession students, who had been provided with their patient’s story shortly before the event. Each team was guided by a facilitator. Results and Outcomes: On the morning of the event, all students gathered in a large group to meet mentors and facilitators and have a brief overview of the six competencies for effective collaboration and the session objectives. The students assuming their same profession roles were provided with their patient’s chart at the beginning of the shift, met with their team, and then completed professional specific assessments. Shortly into the shift, IP team rounds began, facilitated by the team facilitator. During the shift, each patient role-played a spontaneous health incident, which required collaboration between the IP team members for assessment and management. The afternoon concluded with team rounds, a collaborative management plan, and a facilitated de-brief. Conclusions: During the de-brief sessions, students responded to set questions related to the session learning objectives and expressed many positive learning moments. We believe that we have a sustainable simulation IP collaborative learning opportunity, which can be embedded into curricula, and has the capacity to grow to include more health profession faculties and students. Opportunities are being explored in the RFHS at the administrative level, to offer this event more frequently in the academic year to reach more students. In addition, a formally structured event evaluation tool would provide important feedback and inform the qualitative feedback to event organizers and the colleges about the significance of the simulation event to student learning.

Keywords: simulation, collaboration, teams, interprofessional

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Authors: Nina Dzamashvili Fogelström

Abstract:

This paper examines concepts of Game-Based Learning and Gamification. Conducted literature survey found an increased interest in the academia in these concepts, limited evidence of a positive effect on student motivation and academic performance, but also certain scepticism for adding games to traditional educational activities. A small-scale empirical study presented in this paper aims to evaluate student experience and usefulness of GameBased Learning and Gamification for a better understanding of the threshold concepts in software engineering project courses. The participants of the study were 22 second year students from bachelor’s program in software engineering at Blekinge Institute of Technology. As a part of the course instruction, the students were introduced to a digital game specifically designed to simulate agile software project. The game mechanics were designed as to allow manipulation of the agile concept of team velocity. After the application of the game, the students were surveyed to measure the degree of a perceived increase in understanding of the studied threshold concept. The students were also asked whether they would like to have games included in their education. The results show that majority of the students found the game helpful in increasing their understanding of the threshold concept. Most of the students have indicated that they would like to see games included in their education. These results are encouraging. Since the study was of small scale and based on convenience sampling, more studies in the area are recommended.

Keywords: agile development, gamification, game based learning, digital games, software engineering, threshold concepts

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Authors: Paula Escudeiro, Nuno Escudeiro, Márcia Campos, Francisca Escudeiro

Abstract:

In today's world, technical and scientific knowledge plays a vital role in education, research, and employment. Deaf students face unique challenges in educational settings, particularly when it comes to understanding technical and scientific terminology. The reliance on written and spoken languages can create barriers for deaf individuals who primarily communicate using sign language. This lack of accessibility can hinder their learning experience and compromise equity in education. To address this issue, the TechWhiz project has been developed as a comprehensive glossary of scientific and technical concepts explained in sign language. By providing deaf students with access to education in their first language, TechWhiz aims to enhance their learning achievements and promote inclusivity while also fostering equity in education for all students.

Keywords: deaf students, technical and scientific knowledge, automatic sign language, inclusive education

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Authors: Nathenal Thomas

Abstract:

In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Chantana Insra

Abstract:

The research objective focuses on creating a prototype media of the tactile texture of muscles for educational institutes to help visually impaired students learn massage extra learning materials further than the ordinary curriculum. This media is designed as an extra learning material. The population in this study was 30 blinded students between 4th - 6th grades who were able to read Braille language. The research was conducted during the second semester in 2012 at The Bangkok School for the Blind. The method in choosing the population in the study was purposive sampling. The methodology of the research includes collecting data related to visually impaired people, the production of the tactile texture media, human anatomy and Thai traditional massage from literature reviews and field studies. This information was used for analyzing and designing 14 tactile texture pictures presented to experts to evaluate and test the media.

Keywords: blind, tactile texture, muscle, visual arts and design

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Authors: Penelope Paliadelis, Asheley Jones, Glenn Campbell

Abstract:

This paper discusses the development of co-designed capability frameworks for two large multinational organizations led by a university department. The aim was to create evidence-based, integrated capability frameworks that could define, identify, and measure human skill capabilities independent of specific work roles. The frameworks capture and cluster human skills required in the workplace and capture their application at various levels of mastery. Identified capability gaps inform targeted learning opportunities for workers to enhance their employability skills. The paper highlights the value of this evidence-based framework development process in capturing, defining, and assessing desired human-focused capabilities for organizational growth and success.

Keywords: capability framework, human skills, work-integrated learning, credentialing, digital badging

Procedia PDF Downloads 79