Search results for: institutional learning outcomes

Authors: Qing Hui Wang, You Lin Fang, Min Chen

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In 2003 the percentage of students in Shanghai taking part in tertiary education reached 53% of the age cohort, which is at the universal level according to Trow's typology. This achievement led to a conscientious effort by the government to improve internal efficiency of local universities and colleges through a series of programs, the most important of which is the 085 Project. This paper considers the effects of the 085 Project- the 5 initiatives launched in 2008 on increasing the investment to improve institutional overall visibilities, teaching excellence, knowledge innovation, faculty development as well as internationalization. Using the approach of ERC (a theory of equality, reciprocity, and competition) model, it was found that the initiatives helped the lower tier universities and colleges make full play in the aspects of strategic planning and identifying themselves. It was also found that the rate of growth of social resources for universities as a whole increased more quickly after the implementation of the 085 Project.

Keywords: 085 Project, impact, internal efficiency, local universities and colleges

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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: Shangjun Zou, Zhiyuan Wang, Songhui You

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Introduction In recent years, the Chinese government has issued several policies to promote the institutional reform and innovation of the development of campus football, but many problems have been exposed in the process of policy implementation. Therefore, this paper attempts to conduct an empirical analysis of the campus football policy texts to reveal the dynamic development of the microsystem in the process of policy evolution. Methods The selected policy contents are coded by constructing a two-dimensional analysis framework of campus football policy tool-policy objective. Specifically, the X dimension consists of three oriented policy tools: environment, supply and demand, while the Y dimension is divided into six aspects of policy objectives, including institution, competition, player teaching, coach training, resource guarantee and popularization. And the distribution differences of textual analysis units on X and Y dimensions are tested by using SPSS22.0 so as to evaluate the characteristics and development trend of campus football policy on respective subjects. Results 1) In the policy evolution process of campus football stepping into the 2.0 Era, there were no significant differences in the frequency distribution of policy tools(p=0.582) and policy objectives(p=0.603). The collaborative governance of multiple participants has become the primary trend, and the guiding role of Chinese Football Association has gradually become prominent. 2) There were significant differences in the distribution of policy tools before the evolution at a 95% confidence level(p=0.041). With environmental tools always maintaining the dominant position, the overall synergy of policy tools increased slightly. 3) There were significant differences in the distribution of policy objectives after the evolution at a 90% confidence level(p=0.069). The competition system of policy objective has not received enough attention while the construction of institution and resource guarantee system has been strengthened. Conclusion The upgraded version of campus football should adhere to the education concept of health first, promote the coordinated development of youth cultural learning and football skills, and strive to achieve more solid popularization, more scientific institution, more comprehensive resource guarantee and adequate integration. At the same time, it is necessary to strengthen the collaborative allocation of policy tools and reasonable planning of policy objectives so as to promote the high quality and sustainable development of campus football in the New Era. Endnote The policy texts selected in this paper are “Implementation Opinions on Accelerating the Development of Youth Campus Football” and “Action Plans for the Construction of Eight Systems of National Youth Campus Football”, which were promulgated on August 13, 2015 and September 25, 2020 respectively.

Keywords: campus football, content analysis, evolution characteristics, policy objective, policy tool

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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: A. D. Zikiryahodjaev, L. V. Bolotina, A. S. Sukhotko

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Purpose. To evaluate the expediency and timeliness of performance of surgical treatment as a component of multi-therapy treatment of patients with stage IV breast cancers. Materials and Methods. This investigation comparatively analyzed the results of complex treatment with or without surgery in patients with metastatic breast cancer. We analyzed retrospectively treatment experience of 196 patients with generalized breast cancer in the department of oncology and breast reconstructive surgery of P.A. Herzen Moscow Cancer Research Institute from 2000 to 2012. The average age was (58±1,1) years. Invasive ductul carcinoma was verified in128 patients (65,3%), invasive lobular carcinoma-33 (16,8%), complex form - 19 (9,7%). Complex palliative care involving drug and radiation therapies was performed in two patient groups. The first group includes 124 patients who underwent surgical intervention as complex treatment, the second group includes 72 patients with only medical therapy. Standard systemic therapy was given to all patients. Results. Overall, 3-and 5-year survival in fist group was 43,8 and 21%, in second - 15,1 and 9,3% respectively [p=0,00002 log-rank]. Median survival in patients with surgical treatment composed 32 months, in patients with only systemic therapy-21. The factors having influencing an influence on the prognosis and the quality of life outcomes for of patients with generalized breast cancer were are also studied: hormone-dependent tumor, Her2/neu hyper-expression, reproductive function status (age, menopause existence). Conclusion.Removing primary breast tumor in patients with generalized breast cancer improve long-term outcomes. Three- and five-year survival increased by 28,7 and 16,3% respectively, and median survival–for 11 months. These patients may benefit from resection of the breast tumor. One explanation for the effect of this resection is that reducing the tumor load influences metastatic growth.

Keywords: breast cancer, combination therapy, factors of prognosis, primary tumor

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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: Efe Çukur

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This study aims to discuss and compare i) widespread and generic design, particularly industrial design education in relation to the specific needs of the automotive/transportation industry, and ii) an automotive/transportation design education model within and under to provide the conditions of design education and automotive industry, especially in Turkey and T.R.N.C. The automotive industry is the 11th largest in the world ($1.51 trillion). One of the most important departments in this industry, along with sales, marketing and engineering, is the design department. The automotive industry is known as the locomotive industry, but there is a non-automotive design department on the academic side of Turkey. This suggestion; includes the presentation of a program proposal that meets the needs of the industry for Turkey and T.R.N.C., the second largest automobile manufacturing country in Europe. On the education side, industrial design education has become a generic title. Automotive design studios are divided into several subgroups. Even in the higher graduate education, the automotive design departments get their subgroups like exterior design and interior design. Transportation design, which is a subfield of industrial design, is offered as higher education in transportation design departments, particularly in America and Europe. In these departments, the curriculum is shaped to the needs of the sectors. Higher education transportation design programs began in the mid-20th century. Until those high education programs...Until these high education programs, the industry has adapted architectures and engineers for designer workloads. Still today transportation design graduates are not the majority of the design studios. The content of the study is an in-depth comparison of these institutions and how the requirements, demands of the industry are met in this regard and revealed. Some of the institutions are selected from Europe and US. To be analyzed under the headings of staff, courses, syllabus, University-Industry collaboration, and location selection. The study includes short, mid, and long term proposals and a hypothesis for discussion. In short, the study will not only provide a wide comparative scope of information on generic and specialized aspects of design education in different countries but also propose a higher education model for automotive / transportation design with solid data of requirements, methodology, and structure regarding learning outcomes, and especially industry cooperation.

Keywords: design education, automotive - transportation design programs, transportation design, automotive industry in Turkey /T.R.N.C., automotive design education in Turkey /T.R.N.C.

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Authors: Xie Kefeng, Zhang He

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For the stability and control demand of offshore small floating platform, a 2-HUS/U parallel mechanism was presented as offshore platform. Inverse kinematics was obtained by institutional constraint equation, and the dynamic model of offshore 2-HUS/U parallel platform was derived based on rigid body’s Lagrangian method. The equivalent moment of inertia, damping and driving force/torque variation of offshore 2-HUS/U parallel platform were analyzed. A numerical example shows that, for parallel platform of given motion, system’s equivalent inertia changes 1.25 times maximally. During the movement of platform, they change dramatically with the system configuration and have coupling characteristics. The maximum equivalent drive torque is 800 N. At the same time, the curve of platform’s driving force/torque is smooth and has good sine features. The control system needs to be adjusted according to kinetic equation during stability and control and it provides a basis for the optimization of control system.

Keywords: 2-HUS/U platform, dynamics, Lagrange, parallel platform

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Authors: Ayyaz Hussain, Tariq Sadad

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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: Jatinderkumar R. Saini

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E-mail has become a key mechanism of electronic communication. This is truer for professional organizations that like to communicate with their subjects online and are slowly shifting to paper-less office. The current paper focuses specifically on academic institutions offering Engineering course in Gujarat state and attempts for textual analysis of the usernames of the institutional e-mail addresses. We found that the institutions tend to design the username segment of their e-mail addresses by choosing words or combination of words from specific categories. The paper also highlights the use of special characters, digits and random words in designing the usernames. On the sidelines, the paper lists the style of employing department names and designations for the design process. To the best of our knowledge, this is the first formal attempt to analyze the selection of words employed for designing username segment of e-mail addresses of Engineering institutions.

Keywords: e-mail address, institute, engineering, username

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Authors: Yu-Lin Chen, Kimberly Koester, Marissa Raymond-Flesh, Anika Thapar, Jay Thapar

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Purpose: Effectively communicating adolescent health topics to teens and their parents is crucial. This study emphasizes critically evaluating the optimal use of artificial intelligence tools (AI), which are increasingly prevalent in disseminating health information. By fostering a deeper understanding of AI voice preference in the context of health, the research aspires to have a ripple effect, enhancing the collective health literacy and decision-making capabilities of both teenagers and their parents. This study explores AI voices' potential within health learning modules for annual well-child visits. We aim to identify preferred voice characteristics and understand factors influencing perceived trustworthiness, ultimately aiming to improve health literacy and decision-making in both demographics. Methods: A cross-sectional study assessed preferences and trust perceptions of AI voices in learning modules among teens (11-18) and their parents/guardians in Northern California. The study involved the development of four distinct learning modules covering various adolescent health-related topics, including general communication, sexual and reproductive health communication, parental monitoring, and well-child check-ups. Participants were asked to evaluate eight AI voices across the modules, considering a set of six factors such as intelligibility, naturalness, prosody, social impression, trustworthiness, and overall appeal, using Likert scales ranging from 1 to 10 (the higher, the better). They were also asked to select their preferred choice of voice for each module. Descriptive statistics summarized participant demographics. Chi-square/t-tests explored differences in voice preferences between groups. Regression models identified factors impacting the perceived trustworthiness of the top-selected voice per module. Results: Data from 104 participants (teen=63; adult guardian = 41) were included in the analysis. The mean age is 14.9 for teens (54% male) and 41.9 for the parent/guardian (12% male). At the same time, similar voice quality ratings were observed across groups, and preferences varied by topic. For instance, in general communication, teens leaned towards young female voices, while parents preferred mature female tones. Interestingly, this trend reversed for parental monitoring, with teens favoring mature male voices and parents opting for mature female ones. Both groups, however, converged on mature female voices for sexual and reproductive health topics. Beyond preferences, the study delved into factors influencing perceived trustworthiness. Interestingly, social impression and sound appeal emerged as the most significant contributors across all modules, jointly explaining 71-75% of the variance in trustworthiness ratings. Conclusion: The study emphasizes the importance of catering AI voices to specific audiences and topics. Social impression and sound appeal emerged as critical factors influencing perceived trustworthiness across all modules. These findings highlight the need to tailor AI voices by age and the specific health information being delivered. Ensuring AI voices resonate with both teens and their parents can foster their engagement and trust, ultimately leading to improved health literacy and decision-making for both groups. Limitations and future research: This study lays the groundwork for understanding AI voice preferences for teenagers and their parents in healthcare settings. However, limitations exist. The sample represents a specific geographic location, and cultural variations might influence preferences. Additionally, the modules focused on topics related to well-child visits, and preferences might differ for more sensitive health topics. Future research should explore these limitations and investigate the long-term impact of AI voice on user engagement, health outcomes, and health behaviors.

Keywords: artificial intelligence, trustworthiness, voice, adolescent

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Authors: Nina Dzamashvili Fogelström

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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

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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: Max Butler

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Melanoma accounts for 80% of skin cancer-related deaths globally. The poor prognosis and increasing incidence of melanoma impose a significant burden on global healthcare systems. Early detection, precise diagnosis, and preventative strategies are critical to improving patient outcomes. Dermoscopy is the gold standard for specialist assessments of pigmented skin lesions, as it can differentiate between benign and malignant growths with greater accuracy than visual inspection. In the United Kingdom, guidelines from the National Institute of Clinical Excellence (NICE) state dermoscopy should be used in all specialist assessments of pigmented skin lesions. Compliance with this guideline is low, resulting in missed and delayed melanoma diagnoses. To address this problem, a quality improvement project was initiated at Buckinghamshire Healthcare Trust (BHT) within the plastic surgery department. The target group was a trainee and consultant plastic surgeons conducting outpatient skin cancer clinics. Analysis of clinic documentation over a one-month period found that only 62% (38/61) of patients referred with pigmented skin lesions were examined using dermoscopy. To increase dermoscopy rates, teaching was delivered to the department highlighting national guidelines and the evidence base for dermoscopic examination. In addition, clinic paperwork was redesigned to include a text box for dermoscopic examination. Reauditing after the intervention found a significant increase in dermoscopy rates (52/61, p = 0.014). In conclusion, implementing a quality improvement project with targeted teaching and documentation template templates successfully increased dermoscopy rates. This is a promising step toward improving early melanoma detection and patient outcomes.

Keywords: melanoma, dermoscopy, plastic surgery, quality improvement

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Authors: Nathenal Thomas

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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: Tosin Osasona

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The Nigeria Police Force bears the constitutional mandate as the primary policing agency for the protection of life and property within Nigeria; however, the police have an historical ill-reputation for corruption, ineptitude and impunity. Using the institutional theory of police as the framework of analysis, the paper argues that the performance of the police in Nigeria mirrors the dominant political, social and economic institutions and the structural environment of the Nigerian state. The article puts in perspective the deliberate political decision to underfund the police, leaving officers of the force the extra task of foraging for funds to undertake the duty that the Nigeria state primarily exists for; the article further explores the nexus between corruption in the police in Nigeria and the issue of funding. The article finds that the Nigerian state, by deliberately under-funding the police, while expecting the agency to perform its duties, has indirectly sanctioned the corruption of the force and approved the cooption of the institution of police and policing for private use in Nigeria.

Keywords: Police Corruption, Funding , Informal Taxation, POlice Checkpoint

Procedia PDF Downloads 153

Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

Procedia PDF Downloads 100

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

Procedia PDF Downloads 267

Authors: Penelope Paliadelis, Asheley Jones, Glenn Campbell

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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 72

Authors: Luisa Windhagen, Atsumi Hirose, Alex Bottle

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Global urbanization has resulted in the expansion of slums, leaving over 10 million Tanzanians in urban poverty and at risk of poor health. Whilst rural residence has historically been associated with an increased risk of adverse pregnancy outcomes, recent studies found higher perinatal mortality rates in urban Tanzania. This study aims to understand to what extent slum neighborhoods may account for the spatial disparities seen in Tanzania. We generated a slum indicator based on UN-HABITAT criteria to identify slum clusters within the 2015-2016 Tanzania Demographic and Health Survey. Descriptive statistics, disaggregated by urban slum, urban non-slum, and rural areas, were produced. Simple and multivariable logistic regression examined the association between cluster residence type and neonatal mortality and stillbirth. For neonatal mortality, we additionally built a multilevel logistic regression model, adjusting for confounding and clustering. The neonatal mortality ratio was highest in slums (38.3 deaths per 1000 live births); the stillbirth rate was three times higher in slums (32.4 deaths per 1000 births) than in urban non-slums. Neonatal death was more likely to occur in slums than in urban non-slums (aOR=2.15, 95% CI=1.02-4.56) and rural areas (aOR=1.78, 95% CI=1.15-2.77). Odds of stillbirth were over five times higher among rural than urban non-slum residents (aOR=5.25, 95% CI=1.31-20.96). The results suggest that slums contribute to the urban disadvantage in Tanzanian neonatal health. Higher neonatal mortality in slums may be attributable to lack of education, lower socioeconomic status, poor healthcare access, and environmental factors, including indoor and outdoor air pollution and unsanitary conditions from inadequate housing. However, further research is required to ascertain specific causalities as well as significant associations between residence type and other pregnancy outcomes. The high neonatal mortality, stillbirth, and slum formation rates in Tanzania signify that considerable change is necessary to achieve international goals for health and human settlements. Disparities in access to adequate housing, safe water and sanitation, high standard antenatal, intrapartum, and neonatal care, and maternal education need to urgently be addressed. This study highlights the spatial neonatal mortality shift from rural settings to urban informal settlements in Tanzania. Importantly, other low- and middle-income countries experiencing overwhelming urbanization and slum expansion may also be at risk of a reversing trend in residential neonatal health differences.

Keywords: urban health, slum residence, neonatal mortality, stillbirth, global urbanisation

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Authors: Dilek Dede

Abstract:

This study aims at scrutinized the dilemma between education place and working place with professional socialization dimension over the beginning teachers in Turkey is to be found the solution for the dilemma in Turkey. The research question is that how can be explained the gap between education place and working place for beginning teachers in Turkey. That expected to contribute to literature with the solutions for shorting the gap between working area and education area of the teaching profession in Turkey case. The study is constructed in two section. Firstly, socialization of the teaching profession and teaching modules have been discussed through the profession, education, working place indicators. In the second section, Secondly, two educational specialists from Turkey has been interviewed about their observation on trainee teachers compelling to participate the class for candidate teachers after university grade. Then, the dilemma between education area and working area of the teaching profession has been detected by of semi-structured and in-depth interviews, the literature on the relationality of institutions and regulation is discussed. The following outcomes have been accessed in accordance with the data set and literature linkage axis: Firstly, teachers coming from the distinctive programmes as an educational background. Hence, teachers who pertain to distinctive cultures work in the same environment. That cause cultural conflicts and complication of socialization of profession. Secondly, the insufficient partnership between schools and universities besides, the education classes lead to a struggle of culture among these two institutions. Thirdly, the education classes are designed as bureaucratic form instead of coalescence between head teachers and trainee teachers around a common culture. That become deep the dilemma. In conclusion, on condition that applied-oriented education that advocates in-service learning is promoted and this programme is supported with well-structured the in-service training through the partnership of universities and schools, the gap between the working-area and education-area might be shortened.

Keywords: beginning teachers, construction of a common, social mobilization in the teaching profession, teacher training institution, the relationality of the regulations and institutions

Procedia PDF Downloads 161