Search results for: activity learning

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 103

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: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

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Authors: R. Ajith, Kamal Bijlani

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Over the past few years, tremendous evolutions have happened in the educational discipline. Serious game, which is regarded as one of the most important inventions is being widely for learning purposes. Serious games can be used to negate the various drawbacks that the current evaluation and assessment methods have, like examination anxiety and the lack of proper feedback given to the learners. This paper proposes serious game as a tool for conducting evaluations and assessments. The examination anxiety faced by learners can be reduced, as they are provided with a game as an examination. The serious game also tracks learner’s actions, records them and provide feedback based on the predefined set of actions according to the course objectives. The appropriate feedback given to the learner will help in developmental activities in the learning process.

Keywords: serious games, evaluation, performance assessment, examination anxiety, performance feedback

Procedia PDF Downloads 582

Authors: S. Mamoucha, N.Tsafantakis, T. Ioannidis, S. Chatzipanagiotou, C. Nikolaou, L. Skaltsounis, N. Fokialakis, N. Christodoulakis

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Introduction: Pistacia lentiscus L. (well known as Mastic tree) is an evergreen sclerophyllous shrub that extensively thrives in the eastern Mediterranean area yet only the trees cultivated in the southern region of the Greek island Chios produces mastic resin. Different parts of P. lentiscus L. var. chia have been used in folk medicine for various purposes, such as tonic, aphrodisiac, antiseptic, antihypertensive and management of dental, gastrointestinal, liver, urinary, and respiratory tract disorders. Several studies have focused on the antibacterial activity of its resin (gum) and its essential oil. However, there is no study combining anatomy of the plant organs, phytochemical profile, and antibacterial screening of the plant. In our attempt to discover novel bioactive metabolites from the mastic tree, we screened its antibacterial activity not only against ATCC strains but also against clinical, resistant strains. Materials-methods: Leaves were investigated using Transmission (ΤΕΜ) and Scanning Εlectron Microscopy (SEM). Histochemical tests were performed on fresh and fixed tissue. Extracts prepared from dried, powdered leaves using 3 different solvents (DCM, MeOH and H2O) the waste water obtained after a hydrodistillation process for essential oil production were screened for their phytochemical content and antibacterial activity. Μetabolite profiling of polar and non-polar extracts was recorded by GC-MS and LC-HRMS techniques and analyzed using in-house and commercial libraries. The antibacterial screening was performed against Staphylococcus aureus ATCC25923, Escherichia coli ATCC25922, Pseudomonas aeruginosa ATCC27853 and against clinical, resistant strains Methicillin-resistant S. aureus (MRSA), Carbapenem-Resistant Metallo-β-Lactamase (carbapenemase) P. aeruginosa (VIM), Klebsiella pneumoniae carbapenemases (KPCs) and Acinetobacter baumanii resistant strains. The antibacterial activity was tested by the Kirby Bauer and the Agar Well Diffusion method. The zone of inhibition (ZI) of each extract was measured and compared with those of common antibiotics. Results: Leaf is compact with inosclereids and numerous idioblasts containing a globular, spiny crystal. The major nerves of the leaf contain a resin duct. Mesophyll cells showed accumulation of osmiophillic metabolites. Histochemical treatments defined secondary metabolites in subcellular localization. The phytochemical investigation revealed the presence of a large number of secondary metabolites, belonging to different chemical groups, such as terpenoids, phenolic compounds (mainly myricetin, kaempferol and quercetin glycosides), phenolic, and fatty acids. Among the extracts, the hydrostillation wastewater achieved the best results against most of the bacteria tested. MRSA, VIM and A. baumanii were inhibited. Conclusion: Extracts from plants have recently been of great interest with respect to their antimicrobial activity. Their use emerged from a growing tendency to replace synthetic antimicrobial agents with natural ones. Leaves of P. lentiscus L. var. chia showed a high antimicrobial activity even against drug - resistant bacteria. Future prospects concern the better understanding of mode of action of the antibacterial activity, the isolation of the most bioactive constituents and the clarification if the activity is related to a single compound or to the synergistic effect of several ones.

Keywords: antibacterial screening, leaf anatomy, phytochemical profile, Pistacia lentiscus var. chia

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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: 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: Sevil Arabaci Tamer, Gonul Gurol, Ibrahim Tekeoglu, Halil Harman, Ihsan Hakki Ciftci

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Neuroserpin (NSP) is a serine protease inhibitor and member of the serpin family. It is expressed in developing and adult nervous systems, and acts as an inhibitor of protease tissue plasminogen activator (tPA) and a regulator of neuronal growth and plasticity. Also NSP displays anti-inflammatory activity. But, its role in rheumatoid arthritis had never been studied before. So, the aim of the present study was to investigate the effect of neuroserpin in patients with RA. A total of 50 frozen (-20 ºC) serum samples 40 of them belonged to patients with RA, and 10 sample belonged to healthy subjects, were enrolled prospectively. We used DAS-28 to evaluate disease activity. The following clinical data gathered from the original patients' charts. Serum neuroserpin levels were measured by enzyme-linked immunosorbent assay. Our preliminary study results demonstrate, for the first time, that NSP levels are significantly different in RA patients relative to healthy subjects (P = 0.014). So, NSP contribute to pathological condition of RA. Thus, we believe that serum NSP levels can be as a marker in patients with RA. However other inflammatory diseases should be further investigated.

Keywords: neuroserpin, rheumatoid arthritis, tPA, tPA inhibitor

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Authors: Alireza Arab, Morteza Elsa, Amirhossein Moghanian

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In this study, experiments were carried out to achieve a promising multifunctional and modified silicate based bioactive glass (BG). The main aim of the study was investigating the effect of lithium (Li) and magnesium (Mg) substitution, on in vitro bioactivity of substituted-58S BG. Moreover, it is noteworthy to state that modified BGs were synthesized in 60SiO₂–(36-x)CaO–4P₂O₅–(x)Li₂O and 60SiO₂–(36-x)CaO–4P₂O₅–(x)MgO (where x = 0, 5, 10 mol.%) quaternary systems, by sol-gel method. Their performance was investigated through different aspects such as biocompatibility, antibacterial activity as well as their effect on alkaline phosphatase (ALP) activity, and proliferation of MC3T3 cells. The antibacterial efficiency was evaluated against methicillin-resistant Staphylococcus aureus bacteria. To do so, CaO was substituted with Li₂O and MgO up to 10 mol % in 58S-BGs and then samples were immersed in simulated body fluid up to 14 days and then, characterized by X-ray diffraction, Fourier transform infrared spectroscopy, inductively coupled plasma atomic emission spectrometry, and scanning electron microscopy. Results indicated that this modification led to a retarding effect on in vitro hydroxyapatite (HA) formation due to the lower supersaturation degree for nucleation of HA compared with 58s-BG. Meanwhile, magnesium revealed further pronounced effect. The 3-(4,5 dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) and ALP analysis illustrated that substitutions of both Li₂O and MgO, up to 5 mol %, had increasing effect on biocompatibility and stimulating proliferation of the pre-osteoblast MC3T3 cells in comparison to the control specimen. Regarding to bactericidal efficiency, the substitution of either Li or Mg for Ca in the 58s BG composition led to statistically significant difference in antibacterial behaviors of substituted-BGs. Meanwhile, the sample containing 5 mol % CaO/Li₂O substitution (BG-5L) was selected as a multifunctional biomaterial in bone repair/regeneration due to the improved biocompatibility, enhanced ALP activity and antibacterial efficiency among all of the synthesized L-BGs and M-BGs.

Keywords: alkaline, alkaline earth, bioactivity, biomedical applications, sol-gel processes

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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: 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: Yeng Min Yi, Rosli Md Illias, Salehhuddin Hamdan

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Industrial biocatalysis is mainly based on the use of cell free or intracellular enzyme systems. However, the expensive cost and relatively lower operational stability of free enzymes limit practical use in industries. Cell surface display system can be used as a cost-efficient alternative to overcome the laborious purification and substrate transport limitation. In this research, TibA autotransporter from E. coli was used to display Aspergillus fumigatus xylanase (xyn). The amplified xyn was fused in between N-terminal signal peptide and C-terminal β-barrel of TibA. The cloned was transformed and expressed in E. coli BL21 (DE3). Outer membrane localization of TibA-xyn fusion protein was confirmed by SDS PAGE and western blot with expected size of 62.5 kDa. Functional display of xyn was examined by activity assay. Cell surface displayed xyn exhibited the highest activity at 37 °c, 0.3 mM IPTG. As a summary, TibA displaying system has the potential for further industrial applications. Moreover, this is the first report of the display of xylanase using TibA on the surface of E. coli.

Keywords: biocatalysis, cell surface display, Escherichia coli, TibA autotransporter

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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: Uvais Qidwai, Mohamed Shakir

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This paper presents an initial study related to the use of robotic toys as teaching and therapeutic aid tools for teachers and care-givers as well as parents of children with various levels of autism spectrum disorder (ASD). Some of the most common features related to the behavior of a child with ASD are his/her social isolation, living in their own world, not being physically active, and not willing to learn new things. While the teachers, parents, and all other related care-givers do their best to improve the condition of these kids, it is usually quite an uphill task. However, one remarkable observation that has been reported by several teachers dealing with ASD children is the fact that the same children do get attracted to toys with lights and sounds. Hence, this project targets the development/modifications of such existing toys into appropriate behavior training tools which the care-givers can use as they would desire. Initially, the remote control is in hand of the trainer, but after some time, the child is entrusted with the control of the robotic toy to test for the level of interest. It has been found during the course of this study that children with quite low learning activity got extremely interested in the robot and even advanced to controlling the robot with the Electromyography (EMG). It has been observed that the children did show some hesitation in the beginning 5 minutes of the very first sessions of such interaction but were very comfortable afterwards which has been considered as a very strong indicator of the potential of this technique in teaching and rehabilitation of children with ASD or similar brain disorders.

Keywords: Autism Spectrum Disorder (ASD), robotic toys, IR control, electromyography, LabVIEW based remote control

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Authors: L. K. Nivedha, V. Maruthapandian, R. Kothandaraman

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Rechargeable zinc-air batteries (ZAB) are gaining significant research attention owing to their high energy density and copious zinc resources worldwide. However, the unsolved obstacles such as dendrites, passivation, depth of discharge and the lack of an efficient cathode catalyst restrict their practical application1. By and large, non-noble transition metal-based catalysts are well-reputed materials for catalysing oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) with greater stability in alkaline medium2. Herein, we report the synthesis and application of Mn₃O4-NiFeLDH/Carbon composite as a cathode catalyst for rechargeable ZAB. The synergetic effects of the mixed transition metals (Mn/Ni/Fe) have aided in catalysing ORR and OER in alkaline electrolyte with a shallow potential gap of 0.7 V. The composite, by its distinctive physicochemical characteristics, shows an excellent OER activity with a current density of 1.5 mA cm⁻² at a potential of 1.6 V and a superior ORR activity with an onset potential of 0.8 V when compared with their counterparts. Nevertheless, the catalyst prefers a two-electron pathway for the electrochemical reduction of oxygen which results in a limiting current density of 2.5 mA cm⁻². The bifunctional activity of the Mn₃O₄-NiFeLDH/Carbon composite was utilized in developing rechargeable ZAB. The fully fabricated ZAB delivers an open circuit voltage of 1.4 V, a peak power density of 70 mW cm⁻², and a specific capacity of 800 mAh g⁻¹ at a current density of 20 mA cm⁻² with an average discharge voltage of 1 V and the cell is operable upto 50 mA cm-2. Rechargeable ZAB demonstrated over 110 h at 10 mA cm⁻². Further, the cause for the diminished charge-discharge performance experienced beyond the 100th cycle was investigated, and carbon corrosion was testified using Infrared spectroscopy.

Keywords: rechargeable zinc-air battery, oxygen evolution reaction, bifunctional catalyst, alkaline medium

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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: 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: Khaled Alshammari

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A facile approach to synthesizing highly active and stable Au/NiO catalysts for the hydrogenation of nitro-aromatics is reported. Preformed gold nanoparticles have been immobilized onto NiO using a colloidal method. In this article, the reduction of 4-nitrophenol with NaBH4 has been used as a model reaction to investigate the catalytic activity of synthesized Au/NiO catalysts. In addition, we report a systematic study of the reduction kinetics and the influence of specific reaction parameters such as (i) temperature, (ii) stirring rate, (iii) sodium borohydride concentration and (iv) substrate/metal molar ratio. The reaction has been performed at a substrate/metal molar ratio of 7.4, a ratio significantly higher than previously reported. The reusability of the catalyst has been examined, with little to no decrease in activity observed over 5 catalytic cycles. Systematic variation of Au loading reveals the successful synthesis of low-cost and efficient Au/NiO catalysts at very low Au content and using high substrate/metal molar ratios.

Keywords: nonochemistry, catalyst, nanoparticles supported, characterization of materials, colloidal nanoparticles

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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

Procedia PDF Downloads 128

Authors: H. Hanine, H. H'ssaini, M. Ibno Alaoui, A. Nablousi, H. Zahir, S. Ennahli, H. Latrache, H. Zine Abidine

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Biochemical and pomological variability among 14 cultivars of Prunus dulcis planted in a germoplasm collection site in Morocco were evaluated. Almond samples from six local and eight foreign cultivars (France, Italy, Spain, and USA) were characterized. Biochemical and pomological data revealed significant genetic variability among the 14 cultivars; local cultivars exhibited higher total polyphenol content. Oil content ranged from 35 to 57% among cultivars; both Texas and Toundout genotypes recorded the highest oil content. Total protein concentration from select cultivars ranged from 50 mg/g in Ferraduel to 105 mg/g in Rizlane1 cultivars. Antioxidant activity of almond samples was examined by a DPPH (1,1-diphenyl-2-picrylhydrazyl) radical-scavenging assay; the antioxidant activity varied significantly within the cultivars, with IC50 (the half-maximal inhibitory concentration) values ranging from 2.25 to 20 mg/ml. Autochthonous cultivars originated from the Oujda region exhibited higher tegument total polyphenol and amino acid content compared to others. The genotype Rizlane2 recorded the highest flavonoid content. Pomological traits revealed a large variability within the almond germplasms. The hierarchical clustering analysis of all the data regarding pomological traits distinguished two groups with some particular genotypes as distinct cultivars, and groups of cultivars as polyclone varieties. These results strongly exhibit a potential use of Moroccan-originated almonds as potential clones for future selection due to their nutritional values and pomological traits compared to well-established cultivars.

Keywords: antioxidant activity, DDPH, Moroccan almonds, Prunus dulcis

Procedia PDF Downloads 222

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 117

Authors: Eylem Tas

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This empirical study aims to explore the development of students' entrepreneurial skills through problem-based learning within the context of university-industry collaborations (UICs) in curriculum co-design and co-delivery (CDD). The research question guiding this study is: "How do problem-based learning and university-industry collaborations influence the development of students' entrepreneurial skills in the context of curriculum co-design and co-delivery?” To address this question, the study was conducted in a big city in Germany and involved interviews with stakeholders from various industries, including the private sector, government agencies (govt), and non-governmental organizations (NGOs). These stakeholders had established collaborative partnerships with the targeted university for projects encompassing entrepreneurial development aspects in CDD. The study sought to gain insights into the intricacies and subtleties of UIC dynamics and their impact on fostering entrepreneurial skills. Qualitative content analysis, based on Mayring's guidelines, was employed to analyze the interview transcriptions. Through an iterative process of manual coding, 442 codes were generated, resulting in two main sections: "the role of problem-based learning and UIC in fostering entrepreneurship" and "challenges and requirements of problem-based learning within UIC for systematical entrepreneurship development.” The chosen experimental approach of semi-structured interviews was justified by its capacity to provide in-depth perspectives and rich data from stakeholders with firsthand experience in UICs in CDD. By enlisting participants with diverse backgrounds, industries, and company sizes, the study ensured a comprehensive and heterogeneous sample, enhancing the credibility of the findings. The first section of the analysis delved into problem-based learning and entrepreneurial self-confidence to gain a deeper understanding of UIC dynamics from an industry standpoint. It explored factors influencing problem-based learning, alignment of students' learning styles and preferences with the experiential learning approach, specific activities and strategies, and the role of mentorship from industry professionals in fostering entrepreneurial self-confidence. The second section focused on various interactions within UICs, including communication, knowledge exchange, and collaboration. It identified key elements, patterns, and dynamics of interaction, highlighting challenges and limitations. Additionally, the section emphasized success stories and notable outcomes related to UICs' positive impact on students' entrepreneurial journeys. Overall, this research contributes valuable insights into the dynamics of UICs and their role in fostering students' entrepreneurial skills. UICs face challenges in communication and establishing a common language. Transparency, adaptability, and regular communication are vital for successful collaboration. Realistic expectation management and clearly defined frameworks are crucial. Responsible data handling requires data assurance and confidentiality agreements, emphasizing the importance of trust-based relationships when dealing with data sharing and handling issues. The identified key factors and challenges provide a foundation for universities and industrial partners to develop more effective UIC strategies for enhancing students' entrepreneurial capabilities and preparing them for success in today's digital age labor market. The study underscores the significance of collaborative learning and transparent communication in UICs for entrepreneurial development in CDD.

Keywords: collaborative learning, curriculum co-design and co-delivery, entrepreneurial skills, problem-based learning, university-industry collaborations

Procedia PDF Downloads 45

Authors: M. Masar, P. Janota, J. Sedlak, M. Machovsky, I. Kuritka

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Zinc oxide (ZnO) nano crystals assembled porous architecture was prepared by thermal decomposition of zinc oxalate precursor at various temperatures ranging from 400-900°C. The effect of calcining temperature on structure and morphology was examined by scanning electron microscopy (SEM), X-ray diffractometry, thermogravimetry, and BET adsorption analysis. The porous nano crystalline ZnO morphology was developed due to the release of volatile precursor products, while the overall shape of ZnO micro crystals was retained as a legacy of the precursor. The average crystallite size increased with increasing temperature of calcination from approximately 21 nm to 79 nm, while the specific surface area decreased from 30 to 1.7 m2g-1. The photo catalytic performance of prepared ZnO powders was evaluated by degradation of methyl violet 2B, a model compound. The significantly highest photo catalytic activity was achieved with powder calcined at 500°C. This may be attributed to the sufficiently well-developed crystalline arrangement, while the specific surface area is still high enough.

Keywords: ZnO, porous structure, photodegradation, methyl violet

Procedia PDF Downloads 396

Authors: Peter Akayuure, Michael Johnson Nabie

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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 352

Authors: W. Mazari, K. Boucherit, Z. Boucherit-Otmani, M. N. Rahmoun, M. Benabdallah

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The hospital or any other establishment of care can be considered as an ecosystem where the patient comes into contact with a frightening microbial universe and a risk to contract infection that is referred to as nosocomial or health care-associated. In these last years, the incidence of these infections has risen sharply. Several microorganisms are the cause of these nosocomial infections and the emergence of resistance of the microbial strains against antibiotics creates a danger to public health. The search for new antimicrobial agents to overcome this problem has produced interesting compounds through chemical synthesis, which plays a very important role in the research and discovery of new drugs. It is in this framework that our study was conducted at our laboratory and it involves evaluating the antibacterial activity of thirteen 2-pyridone derivatives synthesized by two methods, the diffusion disc method and the dilution method against eight Gram negative bacterial strains. The results seem interesting especially for two products that have shown the best activities against Escherichia coli ATCC 25922 and Enterobacter cloacae ATCC 13047 with CMI of 512µg/ml.

Keywords: heterocyclic derivatives, chemical synthesis, antimicrobial activity, biotechnology

Procedia PDF Downloads 345