Search results for: the creative learning process

Authors: Eleanor F. Willard

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The attrition rate on distance learning courses can be high. This research examines how online students often react when faced with poor results. Using q methodology, it was found that the emotional response level and the type of social support sought by students were key influences on their attitude to failure. As educational and psychological researchers, we are adept at measuring learning and achievement, but examining attitudes towards barriers to learning are not so well researched. The distance learning student has differing needs from onsite learners and, as the attrition rate is notoriously high in the online student population, examining learners’ attitude towards adversity and barriers is important. Self-report measures such as questionnaires are useful in terms of ascertaining levels of constructs such as resilience and academic confidence. Interviewing, too, can gain in depth detail of the opinions of such a population, but only in individuals. The aim of this research was to ascertain what the feelings and attitudes of online students were when faced with a setback. This was achieved using q methodology due to its use of both quantitative and qualitative methodology and its suitability for exploratory research. The emphasis with this methodology is the attitudes, not the individuals. The work was focused upon a population of distance learning students who attended a school on site for one week as part of their studies. They were engaged in a psychology masters conversion course and, as such, were graduate students. The Q sort had 30 items taken from the Academic Resilience Scale (ARS-30). The scale items represent three constructs; perseverance, reflecting (including adaptive help-seeking) and negative affect. These are widely acknowledged as being relevant concepts underpinning psychological resilience. The q sort was conducted with 19 students in total. This is done by participants arranging statement cards regarding how similar to themselves they believe each statement to be. This was done after reading a vignette describing an experience of academic failure. Commonalities and differences between the sorts from all participants are then analyzed in terms of correlations and response patterns. Following data collection, the participants' responses were initially analyzed and the key perspectives (factors) to emerge were labelled ‘persevering individuals’ and ‘emotional networkers’. The differences between the two perspectives centre around the level of emotion felt when faced with barriers and the extent that students enlist the help of others inside and outside of the university. The dominant factor to emerge from the sorts of ‘persevering individuals’ demonstrated that many distance learners are tenacious. However, for other students, the level of emotional and social support is pivotal in helping them complete their studies when facing adversity. This was demonstrated by the ‘emotional networkers’ perspective. This research forms a starting point for further work on engaging and retaining online students at university and can potentially provide insight into how universities can lower attrition rates on distance learning courses.

Keywords: academic resilience, distance learning, online learning, q methodology

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Authors: Regina Rahimi, Delores Liston

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The advent of COVID-19 has heightened awareness of the need for social emotional learning (SEL) throughout all educational contexts. Given this, schools (most often p12 settings) have begun to embrace practices for addressing social-emotional learning. While there is a growing body of research and literature on common practices of SEL, there is no ‘standard’ for its implementation. Our work proposed here recognizes there is no universal approach for addressing SEL and rather, seeks to explore how SEL can be approached in and through diverse contexts. We assert that left unrecognized and unaddressed by teachers, issues with social and emotional well-being profoundly negatively affect students’ academic performance and exacerbate teacher stress. They contribute to negative student-teacher relationships, poor classroom management outcomes, and compromised academic outcomes. Therefore, teachers and administrators have increasingly turned to developing pedagogical and classroom practices that support the social and emotional dimensions of students. Substantive quantitative evidence indicates professional development training to improve awareness and foster positive teacher-student relationships can provide a protective function for psycho-social outcomes and a promotive factor for improved learning outcomes for students. Our work aims to add to the growing body of literature on improving student well-being by providing a unique examination of SEL through a lens of diverse contexts. Methodology: This presentation hopes to present findings from an edited volume that will seek to highlight works that examine SEL practices in a variety of academic settings. The studies contained within the work represent varied forms of qualitative research. Conclusion: This work provides examples of SEL in higher education/postsecondary settings, a variety of P12 academic settings (public; private; rural, urban; charter, etc.), and international contexts. This work demonstrates the variety of ways educational institutions and educators have used SEL to address the needs of students, providing examples for others to adapt to their own diverse contexts. This presentation will bring together exemplar models of SEL in diverse practice settings.

Keywords: social emotional learning, teachers, classrooms, diversity

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Authors: Cao Jiayu, Lan Ximing, Huang Jingjia, Burra Venkata Durga Kumar

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The first virus to attack personal computers was born in early 1986, called C-Brain, written by a pair of Pakistani brothers. In those days, people still used dos systems, manipulating computers with the most basic command lines. In the 21st century today, computer performance has grown geometrically. But computer viruses are also evolving and escalating. We never stop fighting against security problems. Stack overflow is one of the most common security vulnerabilities in operating systems. It may result in serious security issues for an operating system if a program in it has a vulnerability with administrator privileges. Certain viruses change the value of specific memory through a stack overflow, allowing computers to run harmful programs. This study developed a mechanism to detect and respond to time whenever a stack overflow occurs. We demonstrate the effectiveness of standard machine learning algorithms and control flow enforcement techniques in predicting computer OS security using generating suspicious vulnerability functions (SVFS) and associated suspect areas (SAS). The method can minimize the possibility of stack overflow attacks occurring.

Keywords: operating system, security, stack overflow, buffer overflow, machine learning, control-flow enforcement technology

Procedia PDF Downloads 111

Authors: Osman Okumuş

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The pandemic has significantly affected every aspect of life. Especially in recenttimes, as a result of this effect, we have come closer to technology. Distance education has taken the place of formal education rather than supporting formal education. Thiscreatednewexperiencesforbothteachersandstudents. This research focused on revealing the experiences of the same students in distance and formal education, especially in history lessons. In the study, which was designed as a case study, 20 students were interviewed through a semi-structured interview form prepared by the researcher. The results show that both learning environments provide students with important experiences. However, despite the fact that the students developed their digital competencies and experienced different learning environments, they focused on formal education in the name of socialization.

Keywords: history lessons, distance education, pandemic., formal education

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Authors: Lina Tenjo, Ilba Rodríguez

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The role of cultural identity in bilingual programs has been barely discussed in regards to SLA. This research focuses on providing relevant information that helps in having more knowledge about the experiences that an elementary student has during the second language learning process in a bilingual program within a multicultural context. This study explores the experience of 18 students in a dual language program, in a public elementary school in Northern Virginia, USA. It examines their dual language experience and the different ways this experience contributes to the formation of their cultural identity. The findings were studied with the purpose of determining the relationship between participants and certain aspects of cultural identity in a multicultural context. The reflections that originate from the voices of children are the key source that helps us to better understand the particular needs that young learners have during their participation in a DLP.

Keywords: acculturation, bilingual education, culture, dual language program, identity, second language acquisition

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

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Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

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Authors: Irem Ela Yildizeli

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Advertising today has become an indispensable phenomenon both for businesses and consumers. Due to the conditions of rapid changes in the market and growth of competitiveness, the success of many of firms that produce similar merchandise depends largely on how professionally and effective they use marketing communication elements which also must have some sense of shared values between the message provider and the receiver within cultural and global trend. This paper demonstrates how consumer behaviour and communication through cultural values evaluate advertising styles. Using samples of award-winning ads from both author's and other professional's creative works, the study reveals a significant correlation between the cultural elements and advertisement reception for language and cultural norms respectively. The findings of this study draw attention to the change of communication in the beginning of the 21st century which has shaped a new style of Participatory and Experience Design in advertising.

Keywords: advertising, advertising style, culture, experience design, participatory design

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Authors: Kinda Alsamara

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Foreign language education is a complex endeavor that often presents educators with a range of challenges and difficulties. This study shed light on the specific challenges encountered in the context of teaching Arabic as a foreign language at the Australian National University (ANU). Drawing from real-world experiences and insights, we explore the multifaceted nature of these challenges and discuss strategies that educators have employed to address them. The challenges in teaching the Arabic language encompass various dimensions, including linguistic intricacies, cultural nuances, and diverse learner backgrounds. The complex Arabic script, grammatical structures, and pronunciation patterns pose unique obstacles for learners. Moreover, the cultural context embedded within the language demands a nuanced understanding of cultural norms and practices. The diverse backgrounds of learners further contribute to the challenge of tailoring instruction to meet individual needs and proficiency levels. This study also underscores the importance of technology in tackling these challenges. Technological tools and platforms offer innovative solutions to enhance language acquisition and engagement. Online resources, interactive applications, and multimedia content can provide learners with immersive experiences, aiding in overcoming barriers posed by traditional teaching methods. Furthermore, this study addresses the role of instructors in mitigating challenges. Educators often find themselves adapting teaching approaches to accommodate different learning styles, abilities, and motivations. Establishing a supportive learning environment and fostering a sense of community can contribute significantly to overcoming challenges related to learner diversity. In conclusion, this study provides a comprehensive overview of the challenges faced in teaching Arabic as a foreign language at ANU. By recognizing these challenges and embracing technological and pedagogical advancements, educators can create more effective and engaging learning experiences for students pursuing Arabic language proficiency.

Keywords: Arabic, Arabic online, blended learning, teaching and learning, Arabic language, educational aids, technology

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Setyarini Sri, Nursalim Agus

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Integrating critical thinking and digital based learning is one of demands in teaching English in 21st century. Child-friendly digital storytelling (CFDS) is an innovative learning model to promote young learners’ critical thinking. Therefore, this study aims to (1) investigate how child-friendly digital storytelling is implemented to promote young learners’ critical thinking in speaking English; (2) find out the benefits gained by the students in their learning based on CFDS. Classroom Action Research (CAR) took place in two cycles in which each of the cycle covered four phases namely: Planning, Acting, Observing, and Evaluating. Three classes of seventh graders were selected as the subjects of this study. Data were collected through observation, interview with some selected students as respondents, and document analysis in the form individual recorded storytelling. Sentences, phrases, words found in the transcribed data were identified and categorized based on Bloom taxonomy. The findings from the first cycle showed that the students seemed to speak critically that can be seen from the way they understood the story and related the story to their real life. Meanwhile, the result investigated from the second cycle likely indicated their higher level of critical thinking since the students spoke in English critically through comparing, questioning, analyzing, and evaluating the story by giving arguments, opinions, and comments. Such higher levels of critical thinking were also found in the students’ final project of individual recorded digital story. It is elaborated from the students’ statements in the interview who claimed CFDS offered opportunity to the students to promote their critical thinking because they comprehended the story deeply as they experienced in their real life. This learning model created good learning atmosphere and engaged the students directly so that they looked confident to retell the story in various perspectives. In term of the benefits of child-friendly digital storytelling, the students found it beneficial for some enjoyable classroom activities through watching beautiful and colorful pictures, listening to clear and good sounds, appealing moving motion and emotionally they were involved in that story. In the interview, the students also stated that child-friendly digital storytelling eased them to understand the meaning of the story as they were motivated and enthusiastic to speak in English critically.

Keywords: critical thinking, child-friendly digital storytelling, English speaking, promoting, young learners

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Authors: Wu Hong, Pan Cuilian, Wu Panzifan

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The student’s attention during the class is one of the most important indicators for the learning evaluation; the level of attention is directly related to the results of primary education. In recent years, extensive research has been conducted across China on improving primary school students’ attention during class. During the specific teaching activities in primary school, students have the characteristics of short concentration periods, high probability of distraction, and difficulty in long-term immersive learning. In physical education teaching, where there are mostly outdoor activities, this characteristic is particularly prominent due to the large changes in the environment and the strong sense of freshness among students. It is imperative to overcome this characteristic in a targeted manner, improve the student’s experience in the course, and raise the degree of systematization. There are many ways to improve the systematization of teaching and learning, but most of them lack quantitative indicators, which makes it difficult to evaluate the improvements before and after changing the teaching methods. Based on the situation above, we use the case analysis method, combined with a literature review, to study the negative impact of low systematization levels in primary school physical education teaching, put forward targeted improvement suggestions, and make a quantitative evaluation of the method change.

Keywords: attention, adolescent, evaluation, systematism, training-method

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Authors: John Knight, Fuchun Li, Yan Xu

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Based on a new method of the nonparametric estimator of the drift function, we propose a consistent test for the parametric specification of the drift function in the short rate diffusion process using observations from a panel of yields. The test statistic is shown to follow an asymptotic normal distribution under the null hypothesis that the parametric drift function is correctly specified, and converges to infinity under the alternative. Taking the daily 7-day European rates as a proxy of the short rate, we use our test to examine whether the drift of the short rate diffusion process is linear or nonlinear, which is an unresolved important issue in the short rate modeling literature. The testing results indicate that none of the drift functions in this literature adequately captures the dynamics of the drift, but nonlinear specification performs better than the linear specification.

Keywords: diffusion process, nonparametric estimation, derivative security price, drift function and volatility function

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Authors: Sami M. Alshareef

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The rapid growth of smart grid technology has brought significant advancements to the power industry. However, with the increasing interconnectivity and reliance on information and communication technologies, smart grids have become vulnerable to cyber-attacks, posing significant threats to the reliable operation of power systems. Among the critical components of smart grids, the Automatic Generation Control (AGC) system plays a vital role in maintaining the balance between generation and load demand. Therefore, protecting the AGC system from cyber threats is of paramount importance to maintain grid stability and prevent disruptions. Traditional security measures often fall short in addressing sophisticated and evolving cyber threats, necessitating the exploration of innovative approaches. Machine learning, with its ability to analyze vast amounts of data and learn patterns, has emerged as a promising solution to enhance AGC system security. Therefore, this research proposal aims to address the challenges associated with detecting and mitigating cyber-attacks on AGC in smart grids by leveraging machine learning techniques on automatic generation control of two-area power systems. By utilizing historical data, the proposed system will learn the normal behavior patterns of AGC and identify deviations caused by cyber-attacks. Once an attack is detected, appropriate mitigation strategies will be employed to safeguard the AGC system. The outcomes of this research will provide power system operators and administrators with valuable insights into the vulnerabilities of AGC systems in smart grids and offer practical solutions to enhance their cyber resilience.

Keywords: machine learning, cyber-attacks, automatic generation control, smart grid

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Authors: Yong-Jun Jeon, Hyung-Pil Park, Min-Jae Song, Baeg-Soon Cha

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Following the recent trend of having increased demand in producing lighter-weight car bodies for improvement of automobile safety and gas mileage, there is a forming method that makes use of hot-stamping technique, which satisfies all conditions mentioned above. Hot-stamping is a forming technique with advantages of excellent formability, good dimensional precision and others since it is a process in which steel plates are heated up to temperatures of at least approximately 900°C after which forming is conducted in die at room temperature followed by rapid cooling. In addition, it has characteristics of allowing for improvement in material strength through achievement of quenching effect by having simultaneous forming and rapid cooling of material of high temperatures. However, there is insufficient information on the changes in material strength according to changes in material temperature with regards to material heating method and forming process in hot-stamping. Accordingly, this study aims to design and press die for T-type product of the scale models of the center pillar and to understand the changes in material strength in relation to changes in forming methods of hot-stamping process. Thus in order to understand the changes in material strength due to quenching effect among the hot-stamping process, material strength and material forming precision were to be studied while varying the forming and forming method when forming. For test methods, material strength was observed by using boron steel that has boron additives, which was heated up to 950°C, after which it was transferred to a die and was cooled down to material temperature of 400°C followed by air cooling process. During the forming and cooling process here, experiment was conducted with forming parameters of 2 holding rates and 3 flange heating rates wherein changing appearance in material strength according to changes forming method were observed by verifying forming strength and forming precision for each of the conditions.

Keywords: hot-stamping, formability, quenching, forming, press die, forming methods

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Authors: Mangala. Sadasivan, Mary Hughes, Bryan Kelly

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Medical Education must focus on bridging the gap between teaching and learning when training pre-clinical year students in skills needed to keep up with medical knowledge and to meet the demands of health care in the future. The authors were interested in showing that a 3-S Model (building strength, developing stamina, and increasing speed) using a bridged curriculum design helps connect teaching and learning and improves students’ retention of basic science and clinical knowledge. The authors designed three learning modules using the 3-S Model within a systems course in a pre-clerkship medical curriculum. Each module focused on a bridge (concept map) designed by the instructor for specific content delivered to students in the course. This with-in-subjects design study included 304 registered MSU osteopathic medical students (3 campuses) ranked by quintile based on previous coursework. The instructors used the bridge to create self-directed learning exercises (building strength) to help students master basic science content. Students were video coached on how to complete assignments, and given pre-tests and post-tests designed to give them control to assess and identify gaps in learning and strengthen connections. The instructor who designed the modules also used video lectures to help students master clinical concepts and link them (building stamina) to previously learned material connected to the bridge. Boardstyle practice questions relevant to the modules were used to help students improve access (increasing speed) to stored content. Unit Examinations covering the content within modules and materials covered by other instructors teaching within the units served as outcome measures in this study. This data was then compared to each student’s performance on a final comprehensive exam and their COMLEX medical board examinations taken some time after the course. The authors used mean comparisons to evaluate students’ performances on module items (using 3-S Model) to non-module items on unit exams, final course exam and COMLEX medical board examination. The data shows that on average, students performed significantly better on module items compared to non-module items on exams 1 and 2. The module 3 exam was canceled due to a university shut down. The difference in mean scores (module verses non-module) items disappeared on the final comprehensive exam which was rescheduled once the university resumed session. Based on Quintile designation, the mean scores were higher for module items than non-module items and the difference in scores between items for Quintiles 1 and 2 were significantly better on exam 1 and the gap widened for all Quintile groups on exam 2 and disappeared in exam 3. Based on COMLEX performance, all students on average as a group, whether they Passed or Failed, performed better on Module items than non-module items in all three exams. The gap between scores of module items for students who passed COMLEX to those who failed was greater on Exam 1 (14.3) than on Exam 2 (7.5) and Exam 3 (10.2). Data shows the 3-S Model using a bridge effectively connects teaching and learning

Keywords: bridging gap, medical education, teaching and learning, model of learning

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Authors: Jeffrey M. Byford

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The conceptualized theory of decision making on historical events often does not conform to uniform beliefs among students. When presented the opportunity, many students have differing opinions and rationales associated with historical events and outcomes. The intent of this paper was to provide students with the economic, social and political dilemmas associated with the autonomy of East Berlin. Students ranked seven possible actions from the most to least acceptable. In addition, students were required to provide both positive and negative factors for each decision and relative ranking. Results from this activity suggested that while most students chose a financial action towards West Berlin, some students had trouble justifying their actions.

Keywords: content-centered learning, cold war, Berlin, decision-making

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Authors: Ayse Ozturk

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The study extends the prior research on modeling competencies by positioning students’ cognitive and language resources as the fundamentals for pursuing their own inquiry and expression lines through mathematical modeling. This strategy aims to answer the question that guides this study, “How do students’ group approaches to modeling tasks affect their modeling competencies over a unit of instruction?” Six bilingual tenth-grade students worked on open-ended modeling problems along with the content focused on quantities over six weeks. Each group was found to have a unique cognitive approach for solving these problems. Three different problem-solving strategies affected how the groups’ modeling competencies changed. The results provide evidence that the discussion around groups’ solutions, coupled with their reflections, advances group interpreting and validating competencies in the mathematical modeling process

Keywords: cognition, collective learning, mathematical modeling competencies, problem-solving

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Eva de Mingo-Fernández, Lourdes Rubio Rico, Carmen Ortega-Segura, Montserrat Querol-García, Raúl González-Jauregui

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Today, one of the great challenges that the university faces is to get closer to society and transfer knowledge. The competency-based training approach favours a continuous interaction between practice and theory, which is why it is essential to establish real experiences with reflection and debate and to contrast them with personal and professional knowledge. Service-learning (SL) consists of an integration of academic learning with service in the community, which enables teachers to transfer knowledge with social value and students to be trained on the basis of experience of real needs and problems with the aim of solving them. SLE combines research, teaching, and social value knowledge transfer with the real social needs and problems of a community. Goal: The objective of this study was to design, implement, and evaluate a service-learning program in the subject of adult nursing for second-year nursing students. Methodology: After establishing collaboration with eight associations of people with different pathologies, the students were divided into eight groups, and each group was assigned an association. The groups were made up of 10-12 students. The associations willing to participate were for the following conditions: diabetes, multiple sclerosis, cancer, inflammatory bowel disease, fibromyalgia, heart, lung, and kidney diseases. The methodological design consisting of 5 activities was then applied. Three activities address personal and individual reflections, where the student initially describes what they think it is like to live with a certain disease. They then express their reflections resulting from an interview conducted by peers, in person or online, with a person living with this particular condition, and after sharing the results of their reflections with the rest of the group, they make an oral presentation in which they present their findings to the other students. This is followed by a service task in which the students collaborate in different activities of the association, and finally, a third individual reflection is carried out in which the students express their experience of collaboration. The evaluation of this activity is carried out by means of a rubric for both the reflections and the presentation. It should be noted that the oral presentation is evaluated both by the rest of the classmates and by the teachers. Results: The evaluation of the activity, given by the students, is 7.80/10, commenting that the experience is positive and brings them closer to the reality of the people and the area.

Keywords: academic learning integration, knowledge transfer, service-learning, teaching methodology

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Seedwell Sithole

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One of the persistent problems in accounting education is how to effectively support students’ learning. A promising technique to this issue is to investigate the extent that learning is determined by the design of instructional material. This study examines the academic performance of students using three instructional designs in financial accounting. Student’s performance scores and reported mental effort ratings were used to determine the instructional effectiveness. The findings of this study show that accounting students prefer graph and text designs that are integrated. The results suggest that spatially separated graph and text presentations in accounting should be reorganized to align with the requirements of human cognitive architecture.

Keywords: accounting, cognitive load, education, instructional preferences, students

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Authors: Jade Bitomsky

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We all consume films on a daily basis. Yet, we frequently fail to recognize that these narratives shape our social, political, cultural, and economic values and attitudes. Narratives influence our perception; specifically, for this research, our perception of women within the genre of film noir. This creative research project examines to what extent film noir has perpetuated the male gaze and how noir’s representation of women has scripted female gender identity through perpetuated performative acts of femininity. Evolving from this research will be a deconstruction and (re)presentation of the femininity in noir. It will go beyond reiterated examinations, which developed awareness of Hollywood’s oppressive cinematic structures, to subvert the usual phallic diegesis and construct a female gaze in neo-noir screenplay, The Lonely Drive.

Keywords: femme fatale, film noir (classic), male gaze, neo-noir (contemporary), scopophilia

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

Procedia PDF Downloads 128

Authors: L. A. Toro, N. Prieto, J. J. Vargas

Abstract:

There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit.

Keywords: control charts, multivariate exponentially moving average (MEWMA), exponentially moving average (EWMA), industrial control process

Procedia PDF Downloads 349

Authors: G. Krishna Mohana Rao, P. Ravi Kumar

Abstract:

Milling is the process of removing unwanted material with suitable tool. Even though the milling process is having wider application, the vibration of machine tool and work piece during the process produces chatter on the products. Various methods of preventing the chatter have been incorporated into machine tool systems. Damper is cut into equal number of parts. Each part is called as finger. Multiple fingers were inserted in the hollow portion of the shank to reduce tool vibrations. In the present work, nonlinear static and dynamic analysis of the damper inserted end milling cutter used to reduce the chatter was done. A comparison is made for the milling cutter with multiple dampers. Surface roughness was determined by machining with multiple finger inserted milling cutters.

Keywords: damping inserts, end milling, vibrations, nonlinear dynamic analysis, number of fingers

Procedia PDF Downloads 515

Authors: Sajjad Esmaeili, Robert Krause, Lukas Gerlich, Alireza Mohammadian Kia, Benjamin Uhlig

Abstract:

This investigation aims to develop the feasible and qualitative process parameters for the thin films fabrication into ultra-large through-silicon-vias (TSVs) as vertical interconnections. The focus of the study is on TSV metallization and its challenges employing new materials for the purpose of rapid signal propagation in the microsystems technology. Cobalt metal-organic chemical vapor deposition (Co-MOCVD) process enables manufacturing an adhesive and excellent conformal ultra-thin film all the way through TSVs in comparison with the conventional non-conformal physical vapor deposition (PVD) process of copper (Cu) seed layer. Therefore, this process provides a Cu seed-free layer which is capable of direct Cu electrochemical deposition (Cu-ECD) on top of it. The main challenge of this metallization module is to achieve the proper alternative seed layer with less roughness, sheet resistance and granular organic contamination (e.g. carbon) which intensify the Co corrosion under the influence of Cu electrolyte.

Keywords: Cobalt MOCVD, direct Cu electrochemical deposition (ECD), metallization technology, through-silicon-via (TSV)

Procedia PDF Downloads 153

Authors: Peter Akayuure, Michael Johnson Nabie

Abstract:

Similar to many colonized nations in the world, one indelible mark left by colonial masters after Ghana’s independence in 1957 has been the fact that many contexts used to teach statistics and probability concepts are often alien and do not resonate with the social domain of our indigenous Ghanaian child. This has seriously limited the understanding, discoveries, and applications of mathematics for national developments. With the recent curriculum demands of making the Ghanaian child mathematically literate, this qualitative study involved video recordings and mathematical analysis of play sessions of an indigenous girl game called Ampe with the aim to demystify the concepts in probability theorem, which is applied in mathematics related fields of study. The mathematical analysis shows that the game of Ampe, which is widely played by school girls in Ghana, is suitable for learning concepts of the probability theorems. It was also revealed that as a girl game, the use of Ampe provides good lessons to educators, textbook writers, and teachers to rethink about the selection of mathematics tasks and learning contexts that are sensitive to gender. As we undertake to transform teacher education and student learning, the use of indigenous games should be critically revisited.

Keywords: Ampe, mathematical analysis, probability theorem, Ghanaian girl game

Procedia PDF Downloads 366

Authors: Navajyoti Panda, R. S. Pawar

Abstract:

In this study, process parameters like punch angle, die opening, grain direction, and pre-bend condition of the strip for deep draw of high strength low alloy steel HSLA 420 are investigated. The finite element method (FEM) in association with the Taguchi and the analysis of variance (ANOVA) techniques are carried out to investigate the degree of importance of process parameters in V-bending process for HSLA 420&ST12 grade material. From results, it is observed that punch angle had a major influence on the spring-back. Die opening also showed very significant role on spring back. On the other hand, it is revealed that grain direction had the least impact on spring back; however, if strip from flat sheet is taken, then it is less prone to spring back as compared to the strip from sheet metal coil. HyperForm software is used for FEM simulation and experiments are designed using Taguchi method. Percentage contribution of the parameters is obtained through the ANOVA techniques.

Keywords: bending, spring-back, v-bending, FEM, Taguchi, HSLA 420 and St12 materials, HyperForm, profile projector

Procedia PDF Downloads 186