Search results for: transnational learning

Authors: Matthias Haenisch, Katharina Hermann, Marc Godau, Verena Weidner

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Over the past two decades, international music education has been characterized by a growing interest in informal learning for formal contexts and a "compositional turn" that has moved from closed to open forms of composing. This change occurs under social and technological conditions that permeate 21st-century musical practices. This forms the background of Musical Communities in the (Post)Digital Age (MusCoDA), a four-year joint research project of the University of Erfurt (UE) and the University of Education Karlsruhe (PHK), funded by the German Federal Ministry of Education and Research (BMBF). Both explore songwriting processes as an example of collective creativity in (post)digital communities, one in formal and the other in informal learning contexts. Collective songwriting will be studied from a network perspective, that will allow us to view boundaries between both online and offline as well as formal and informal or hybrid contexts as permeable and to reconstruct musical learning practices. By comparing these songwriting processes, possibilities for a pedagogical-didactic interweaving of different educational worlds are highlighted. Therefore, the subproject of the University of Erfurt investigates school music lessons with the help of interviews, videography, and network maps by analyzing new digital pedagogical and didactic possibilities. In the first step, the international literature on songwriting in the music classroom was examined for design development. The analysis focused on the question of which methods and practices are circulating in the current literature. Results from this stage of the project form the basis for the first instructional design that will help teachers in planning regular music classes and subsequently reconstruct musical learning practices under these conditions. In analyzing the literature, we noticed certain structural methods and concepts that recur, such as the Building Blocks method and the pre-structuring of the songwriting process. From these findings, we developed a deck of cards that both captures the current state of research and serves as a method for design development. With this deck of cards, both teachers and students themselves can plan their individual songwriting lessons by independently selecting and arranging topic, structure, and action cards. In terms of science communication, music educators' interactions with the card game provide us with essential insights for developing the first design. The overall goal of MusCoDA is to develop an empirical model of collective musical creativity and learning and an instructional design for teaching music in the postdigital age.

Keywords: card game, collective songwriting, community of practice, network, postdigital

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Authors: Amani Hamdan

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A post-workshop of higher-order thinking skills (HOTS), for faculty from diverse academic disciplines, was conducted and the researcher surveyed the participants’ intentions and plans to include HOTS as a goal, as learning and teaching task in their practices. Follow-up interviews with a random sample of participants were used to determine if they fulfilled their intentions three 3 months after the workshop. The degree of planned and enacted HOTS then was analyzed against the post-workshop HOT ability and knowledge. This is one topic that has not been adequately explored in faculty professional development literature where measuring the effect of learning on their ability to use what they learned. This qualitative method study explored a group of male and female faculty members (n=85) enrolled in HOTS 2 day workshop. The results showed that 89% of faculty members although were mostly enthused to apply what they learned after a 3 months period they were caught up with routine presentations and lecturing.

Keywords: higher education, faculty development, Saudi Arabia, higher order thinking skills

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Authors: Sara Blanc, José V. Benlloch-Dualde, Laura Grindei, Ana C. Torres, Angélica Monteiro

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This paper presents a study that leads a new experience by introducing digital learning applied to a case study focused on primary and secondary school garden-based education. The approach represents an example of interaction among different education and research agents at different countries and levels, such as universities, public and private research, and schools, to get involved in the implementation of education for sustainable development that will make students become more sensible to natural environment, more responsible for their consumption, more aware about waste reduction and recycling, more conscious of the sustainable use of natural resources and, at the same time, more ‘digitally competent’. The experience was designed attending to the European digital education context and OECD directives in transversal skills education. The paper presents the methodology carried out in the study as well as outcomes obtained from experience.

Keywords: school gardens, primary education, secondary education, science technology and innovation in education, digital learning, sustainable development goals, university, knowledge transference

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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Authors: Vijaya K. Narne, Gerard Loquet, Tobias Piechowiak, Dorte Hammershoi, Jesper H. Schmidt

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BEAR, which stands for better hearing rehabilitation is a large-scale project in Denmark designed and executed by three national universities, three hospitals, and the hearing aid industry with the aim to improve hearing aid fitting. A total of 1963 hearing impaired people were included and were segmented into subgroups based on hearing-loss, demographics, audiological and questionnaires data (i.e., the speech, spatial and qualities of hearing scale [SSQ-12] and the International Outcome Inventory for Hearing-Aids [IOI-HA]). With the aim to provide a better hearing-aid fit to individual patients, we applied modern machine learning techniques with traditional audiograms rule-based systems. Results show that age, speech discrimination scores, and audiogram configurations were evolved as important parameters in characterizing sub-population from the data-set. The attempt to characterize sub-population reveal a clearer picture about the individual hearing difficulties encountered and the benefits derived from more individualized hearing aids.

Keywords: hearing loss, audiological data, machine learning, hearing aids

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Authors: O. V. Vyazovska

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Behavioral efficiency is one of the main principles to be successful in nature. Accuracy of visual discrimination is determined by the attention, learning experience, and memory. In the experimental condition, pigeons’ responses to visual stimuli presented on the screen of the monitor are behaviorally manifested by pecking or not pecking the stimulus, by the number of pecking, reaction time, etc. The higher the probability of rewarding is, the more likely pigeons will respond to the stimulus. We trained 8 pigeons (Columba livia) on a stagewise go/no-go visual discrimination task.16 visual stimuli were created from all possible combinations of four binary dimensions: brightness (dark/bright), size (large/small), line orientation (vertical/horizontal), and shape (circle/square). In the first stage, we presented S+ and 4 S-stimuli: the first that differed in all 4-dimensional values from S+, the second with brightness dimension sharing with S+, the third sharing brightness and orientation with S+, the fourth sharing brightness, orientation and size. Then all 16 stimuli were added. Pigeons rejected correctly 6-8 of 11 new added S-stimuli at the beginning of the second stage. The results revealed that pigeons’ behavior at the beginning of the second stage was controlled by probabilities of rewarding for 4 dimensions learned in the first stage. More or fewer mistakes with dimension discrimination at the beginning of the second stage depended on the number S- stimuli sharing the dimension with S+ in the first stage. A significant inverse correlation between the number of S- stimuli sharing dimension values with S+ in the first stage and the dimensional learning rate at the beginning of the second stage was found. Pigeons were more confident in discrimination of shape and size dimensions. They made mistakes at the beginning of the second stage, which were not associated with these dimensions. Thus, the received results help elucidate the principles of dimensional stimulus control during learning compound multidimensional visual stimuli.

Keywords: visual go/no go discrimination, selective attention, dimensional stimulus control, pigeon

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Authors: Jing Zhao, Yongqing Bai, Qiaofang Shi, Huaihao Zhang

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Advances in software technology enable computational chemistry to be commonly applied in various research fields, especially in pedagogy. Thus, in order to expand and improve experimental instructions of computational chemistry for undergraduates, we designed an introductory experiment—research on acrylamide molecular structure and physicochemical properties. Initially, students construct molecular models of acrylamide and polyacrylamide in Gaussian and Materials Studio software respectively. Then, the infrared spectral data, atomic charge and molecular orbitals of acrylamide as well as solvation effect of polyacrylamide are calculated to predict their physicochemical performance. At last, rheological experiments are used to validate these predictions. Through the combination of molecular simulation (performed on Gaussian, Materials Studio) with experimental verification (rheology experiment), learners have deeply comprehended the chemical nature of acrylamide and polyacrylamide, achieving good learning outcomes.

Keywords: upper-division undergraduate, computer-based learning, laboratory instruction, molecular modeling

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Authors: Richard Mababu Mukiur

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Leadership is an important drive that plays a key role in the success and development of organizations, particularly in the current context of digital transformation, highly competitivity and globalization. Leaders are persons that hold a dominant and privileged position within an organization, field, or sector of activities and are able to manage, motivate and exercise a high degree of influence over other in order to achieve the institutional goals. They achieve commitment and engagement of others to embrace change, and to make good decisions. Leadership studies in higher education institutions have examined how effective leaders hold their organizations, and also to find approaches which fit best in the organizations context for its better management, transformation and improvement. Moreover, recent studies have highlighted the impact of leadership styles on organizational performance and innovation capacities, since some styles give better results than others. Effective leadership is part of learning process that take place through day-to-day tasks, responsibilities, and experiences that influence the organizational performance, innovation and engagement of employees. The adoption of appropriate leadership styles can improve organization results and encourage learning process, team skills and performance, and employees' motivation and engagement. In the case of case of Information Technology sector, leadership styles are particularly crucial since this sector is leading relevant changes and transformations in the knowledge society. In this context, the main objective of this study is to analyze managers leadership styles with their relation to organizational performance and innovation that may be mediated by learning organization process and demographic variables. Therefore, it was hypothesized that the transformational and transactional leadership will be the main style adopted in Information Technology sector and will influence organizational performance and innovation capacity. A sample of 540 participants from Information technology sector has been determined in order to achieve the objective of this study. The Multifactor Leadership Questionnaire was administered as the principal instrument, Scale of innovation and Learning Organization Questionnaire. Correlations and multiple regression analysis have been used as the main techniques of data analysis. The findings indicate that leadership styles have a relevant impact on organizational performance and innovation capacity. The transformational and transactional leadership are predominant styles in Information technology sector. The effective leadership style tend to be characterized by the capacity of generating and sharing knowledge that improve organization performance and innovation capacity. Managers are adopting and adapting their leadership styles that respond to the new organizational, social and cultural challenges and realities of contemporary society. Managers who encourage innovation, foster learning process, share experience are useful to the organization since they contribute to its development and transformation. Learning process capacity and demographic variables (age, gender, and job tenure) mediate the relationship between leadership styles, innovation capacity and organizational performance. The transformational and transactional leadership tend to enhance the organizational performance due to their significant impact on team-building, employees' engagement and satisfaction. Some practical implications and future lines of research have been proposed.

Keywords: leadership styles, tranformational leadership, organisational performance, organisational innovation

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Authors: Bahareh Golchin, Nooshin Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: Hsiaoping Wu

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With the wide use of Internet, learners are exposed to the wider world. This exposure permits learners to discover new information and combine a variety of media in order to reach in-depth and broader understanding of their literacy and the world. Many paper-based teaching, learning and assessment modalities can be transferred to a digital platform. This study examines the use of e-portfolios for ESL (English as a second language) pre-service teacher. The data were collected by reviewing 100 E-portfolio from 2013 to 2015 in order to synthesize meaningful information about e-portfolios for ESL pre-service teachers. Participants were generalists, bilingual and ESL pre-service teachers. The studies were coded into two main categories: learning gains, including assessment, and technical skills. The findings showed that using e-portfolios enhanced and developed ESL pre-service teachers’ teaching and assessment skills. Also, the E-portfolio also developed the pre-service teachers’ technical stills to prepare a comprehensible portfolio to present who they are. Finally, the study and presentation suggested e-portfolios for ecological issues and educational purposes.

Keywords: assessment, e-portfolio, pre-service teacher, reflection

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Authors: Hope Amba Neji

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The study adopted a quasi-experimental design. Two schools were used for the experimental study, while one school was used for the control. The experimental groups were subjected to treatment for four weeks with chemistry museum artifacts and a visit as made to the museum so that learners would have real-life learning experiences with museum resources, while the control group was taught with the conventional method. The instrument for the study was a 20-item Chemistry Memory Test (CMT) and a 10-item Chemistry Interest Questionnaire (CIQ). The reliability was ascertained using (KR-20) and alpha reliability coefficient, which yielded a reliability coefficient of .83 and .81, respectively. Data obtained was analyzed using Analysis of Covariance (ANCOVA) and Analysis of variance (ANOVA) at 0.05 level of significance. Findings revealed that museum artifacts have a significant effect on students’ memory enhancement and interest in chemistry. It was recommended chemistry learning should be enhanced, motivating and real with museum artifacts, which significantly aid memory enhancement and interest in chemistry.

Keywords: museum artifacts, memory, chemistry, atitude

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson

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Artificial Intelligence (AI) in the Engineering, Procurement, and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Nevertheless, AI has become an attractive technology for organizations looking to automate repetitive tasks to reduce manual work. Meanwhile, the current AI market has started offering various solutions and services. The contribution of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to a specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Keywords: artificial intelligence, machine learning, deep learning, innovation, engineering, procurement and construction industry, AI in the EPC industry

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Authors: Michael Wendland

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This paper presents a framework to teach a methodical design approach by the help of using a mixture of game-based learning, design challenges and competitions as forms of direct assessment. In today’s world, developing products is more complex than ever. Conflicting goals of product cost and quality with limited time as well as post-pandemic part shortages increase the difficulty. Common design approaches for mechatronic products mitigate some of these effects by helping the users with their methodical framework. Due to the inherent complexity of these products, the number of involved resources and the comprehensive design processes, students very rarely have enough time or motivation to experience a complete approach in one semester course. But, for students to be successful in the industrial world, it is crucial to know these methodical frameworks and to gain first-hand experience. Therefore, it is necessary to teach these design approaches in a real-world setting and keep the motivation high as well as learning to manage upcoming problems. This is achieved by using a game-based approach and a set of design challenges that are given to the students. In order to mimic industrial collaboration, they work in teams of up to six participants and are given the main development target to design a remote-controlled robot that can manipulate a specified object. By setting this clear goal without a given solution path, a constricted time-frame and limited maximal cost, the students are subjected to similar boundary conditions as in the real world. They must follow the methodical approach steps by specifying requirements, conceptualizing their ideas, drafting, designing, manufacturing and building a prototype using rapid prototyping. At the end of the course, the prototypes will be entered into a contest against the other teams. The complete design process is accompanied by theoretical input via lectures which is immediately transferred by the students to their own design problem in practical sessions. To increase motivation in these sessions, a playful learning approach has been chosen, i.e. designing the first concepts is supported by using lego construction kits. After each challenge, mandatory online quizzes help to deepen the acquired knowledge of the students and badges are awarded to those who complete a quiz, resulting in higher motivation and a level-up on a fictional leaderboard. The final contest is held in presence and involves all teams with their functional prototypes that now need to contest against each other. Prices for the best mechanical design, the most innovative approach and for the winner of the robotic contest are awarded. Each robot design gets evaluated with regards to the specified requirements and partial grades are derived from the results. This paper concludes with a critical review of the proposed framework, the game-based approach for the designed prototypes, the reality of the boundary conditions, the problems that occurred during the design and manufacturing process, the experiences and feedback of the students and the effectiveness of their collaboration as well as a discussion of the potential transfer to other educational areas.

Keywords: design challenges, game-based learning, playful learning, methodical framework, mechatronics, student assessment, constructive alignment

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Authors: Patrick Mapulanga, Chisomo Petros Ganya

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Purpose: This study aimed to investigate the integration of Simulation-Based Education (SBE) into nursing and midwifery curricula in resource-constrained countries using Malawi as a case study. The purpose of this study is to assess the extent to which SBE is mentioned in curricula and explore the associated content, assessment criteria, and guidelines. Methodology: The research methodology involved a desk study of nursing and midwifery curricula in Malawi. A comprehensive review was conducted to identify references to SBE by examining documents such as official curriculum guides, syllabi, and educational policies. The focus is on understanding the prevalence of SBE without delving into the specific content or assessment details. Findings: The findings revealed that SBE is indeed mentioned in the nursing and midwifery curricula in Malawi; however, there is a notable absence of detailed content and assessment criteria. While acknowledgement of SBE is a positive step, the lack of specific guidelines poses a challenge to its effective implementation and assessment within the educational framework. Conclusion: The study concludes that although the recognition of SBE in Malawian nursing and midwifery curricula signifies a potential openness to innovative learning strategies, the absence of detailed content and assessment criteria raises concerns about the practical application of SBE. Addressing this gap is crucial for harnessing the full transformative potential of SBE in resource-constrained environments. Areas for Further Research: Future research endeavours should focus on a more in-depth exploration of the content and assessment criteria related to SBE in nursing and midwifery curricula. Investigating faculty perspectives and students’ experiences with SBE could provide valuable insights into the challenges and opportunities associated with its implementation. Study Limitations and Implications: The study's limitations include reliance on desk-based analysis, which limits the depth of understanding regarding SBE implementation. Despite this constraint, the implications of the findings underscore the need for curriculum developers, educators, and policymakers to collaboratively address the gaps in SBE integration and ensure a comprehensive and effective learning experience for nursing and midwifery students in resource-constrained countries.

Keywords: simulation based education, transformative learning, nursing and midwifery, curricula, Malawi

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Authors: Matteo Calabrese, Martin Cimmino, Dimos Kapetis, Martina Manfrin, Donato Concilio, Giuseppe Toscano, Giovanni Ciandrini, Giancarlo Paccapeli, Gianluca Giarratana, Marco Siciliano, Andrea Forlani, Alberto Carrotta

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In this paper we describe a machine learning methodology for Predictive Maintenance (PdM) applied on woodworking industrial machines. PdM is a prominent strategy consisting of all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the challenges with PdM approach is to design and develop of an embedded smart system to enable the health status of the machine. The proposed approach allows screening simultaneously multiple connected machines, thus providing real-time monitoring that can be adopted with maintenance management. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime of woodworking machines. The effectiveness of the methodology is demonstrated by testing an independent sample of additional woodworking machines without presenting machine down event.

Keywords: predictive maintenance, machine learning, connected machines, artificial intelligence

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Authors: Rena Upitis, Philip C. Abrami, Karen Boese

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The success of one-on-one music study relies heavily on the ability of the teacher to provide sufficient direction to students during weekly lessons so that they can successfully practice from one lesson to the next. Traditionally, these instructions are given in a paper notebook, where the teacher makes notes for the students after describing a task or demonstrating a technique. The ability of students to make sense of these notes varies according to their understanding of the teacher’s directions, their motivation to practice, their memory of the lesson, and their abilities to self-regulate. At best, the notes enable the student to progress successfully. At worst, the student is left rudderless until the next lesson takes place. Digital notebooks have the potential to provide a more interactive and effective bridge between music lessons than traditional pen-and-paper notebooks. One such digital notebook, Cadenza, was designed to streamline and improve teachers’ instruction, to enhance student practicing, and to provide the means for teachers and students to communicate between lessons. For example, Cadenza contains a video annotator, where teachers can offer real-time guidance on uploaded student performances. Using the checklist feature, teachers and students negotiate the frequency and type of practice during the lesson, which the student can then access during subsequent practice sessions. Following the tenets of self-regulated learning, goal setting and reflection are also featured. Accordingly, the present paper addressed the following research questions: (1) How does the use of the Cadenza digital music notebook engage students and their teachers?, (2) Which features of Cadenza are most successful?, (3) Which features could be improved?, and (4) Is student learning and motivation enhanced with the use of the Cadenza digital music notebook? The paper describes the results 10 months of field-testing of Cadenza, structured around the four research questions outlined. Six teachers and 65 students took part in the study. Data were collected through video-recorded lesson observations, digital screen captures, surveys, and interviews. Standard qualitative protocols for coding results and identifying themes were employed to analyze the results. The results consistently indicated that teachers and students embraced the digital platform offered by Cadenza. The practice log and timer, the real-time annotation tool, the checklists, the lesson summaries, and the commenting features were found to be the most valuable functions, by students and teachers alike. Teachers also reported that students progressed more quickly with Cadenza, and received higher results in examinations than those students who were not using Cadenza. Teachers identified modifications to Cadenza that would make it an even more powerful way to support student learning. These modifications, once implemented, will move the tool well past its traditional notebook uses to new ways of motivating students to practise between lessons and to communicate with teachers about their learning. Improvements to the tool called for by the teachers included the ability to duplicate archived lessons, allowing for split screen viewing, and adding goal setting to the teacher window. In the concluding section, proposed modifications and their implications for self-regulated learning are discussed.

Keywords: digital music technologies, electronic notebooks, self-regulated learning, studio music instruction

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Authors: Jarek Krajewski, David Daxberger

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We propose a stress detection method based on an RGB camera using heart rate detection, also known as Photoplethysmography Imaging (PPGI). This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. A stationary lab setting with simulated video conferences is chosen using constant light conditions and a sampling rate of 30 fps. The ground truth measurement of heart rate is conducted with a common PPG system. The proposed approach for pulse peak detection is based on a machine learning-based approach, applying brute force feature extraction for the prediction of heart rate pulses. The statistical analysis showed good agreement (correlation r = .79, p<0.05) between the reference heart rate system and the proposed method. Based on these findings, the proposed method could provide a reliable, low-cost, and contactless way of measuring HR parameters in daily-life environments.

Keywords: heart rate, PPGI, machine learning, brute force feature extraction

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Authors: K. Tejasri, K. Suvarna Vani, S. Prathyusha, S. Ramya

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Proteins are chained sequences of amino acids which are brought together by the peptide bonds. Many varying formations of the chains are possible due to multiple combinations of amino acids and rotation in numerous positions along the chain. Protein structure prediction is one of the crucial goals worked towards by the members of bioinformatics and theoretical chemistry backgrounds. Among the four different structure levels in proteins, we emphasize mainly the secondary level structure. Generally, the secondary protein basically comprises alpha-helix and beta-sheets. Multi-class classification problem of data with disparity is truly a challenge to overcome and has to be addressed for the beta strands. Imbalanced data distribution constitutes a couple of the classes of data having very limited training samples collated with other classes. The secondary structure data is extracted from the protein primary sequence, and the beta-strands are predicted using suitable machine learning algorithms.

Keywords: proteins, secondary structure elements, beta-sheets, beta-strands, alpha-helices, machine learning algorithms

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Authors: Niloufar Yadgari

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GANs are a potent form of deep learning models that have found success in various fields. They are part of the larger group of generative techniques, which aim to produce authentic data using a probabilistic model that learns distributions from actual samples. In clinical settings, GANs have demonstrated improved abilities in capturing spatially intricate, nonlinear, and possibly subtle disease impacts in contrast to conventional generative techniques. This review critically evaluates the current research on how GANs are being used in imaging studies of different neurological conditions like Alzheimer's disease, brain tumors, aging of the brain, and multiple sclerosis. We offer a clear explanation of different GAN techniques for each use case in neuroimaging and delve into the key hurdles, unanswered queries, and potential advancements in utilizing GANs in this field. Our goal is to connect advanced deep learning techniques with neurology studies, showcasing how GANs can assist in clinical decision-making and enhance our comprehension of the structural and functional aspects of brain disorders.

Keywords: GAN, pathology, generative adversarial network, neuro imaging

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Authors: Andrew Kipp

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Student absenteeism is harmful to both the school and the absentee student. One approach to improving student absenteeism is targeting contextual factors within the students’ learning environment. However, contemporary literature has not taken an ecological agency approach to understanding student absenteeism. Ecological agency is a theoretical framework that magnifies the interplay between the environment and the actions of people within the environment. To elaborate, the person’s personal history and aspirations and the environmental conditions provide potential outlets or restrictions to their intended action. The framework provides the unique perspective of understanding absentee students’ decision-making through the affordances and constraints found in their learning environment. To that effect, the study was guided by the question, “Why do absentee students decide to engage in absenteeism in a suburban Kansas school?” A case study methodology was used to answer the research question. Four suburban, Kansas high school absentee students in the 2020-2021 school year were selected for the study. The fall 2020 semester was in a remote learning setting, and the spring 2021 semester was in an in-person learning setting. The study captured their decision-making with respect to school attendance throughsemi-structured interviews, prolonged observations, drawings, and concept maps. The data was analyzed through thematic analysis. The findings revealed that peer socialization opportunities, methods of instruction, shifts in cultural beliefs due to COVID-19, manifestations of anxiety and lack of space to escape their anxiety, social media bullying, and the inability to receive academic tutoring motivated the participants’ daily decision to either attend or miss school. The findings provided a basis to improve several institutional and classroom practices. These practices included more student-led instruction and less teacher-led instruction in both in-person and remote learning environments, promoting socialization through classroom collaboration and clubs based on emerging student interests, reducing instances of bullying through prosocial education, safe spaces for students to escape the classroom to manage their anxiety, and more opportunities for one-on-one tutoring to improve grades. The study provides an example of using the ecological agency approach to better understand the personal and environmental factors that lead to absenteeism. The study also informs educational policies and classroom practices to better promote student attendance. Further research should investigate other school contexts using the ecological agency theoretical framework to better understand the influence of the school environment on student absenteeism.

Keywords: student absenteeism, ecological agency, classroom practices, educational policy, student decision-making

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Authors: Vishnu Goyal, Basant Agarwal

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Sentiment analysis research provides methods for identifying the people’s opinion written in blogs, reviews, social networking websites etc. Sentiment analysis is to understand what opinion people have about any given entity, object or thing. Sentiment analysis research can be broadly categorised into three types of approaches i.e. semantic orientation, machine learning and lexicon based approaches. Feature selection methods improve the performance of the machine learning algorithms by eliminating the irrelevant features. Information gain feature selection method has been considered best method for sentiment analysis; however, it has the drawback of selection of threshold. Therefore, in this paper, we propose a hybrid feature selection methods comprising of information gain and proposed feature selection method. Initially, features are selected using Information Gain (IG) and further more noisy features are eliminated using the proposed feature selection method. Experimental results show the efficiency of the proposed feature selection methods.

Keywords: feature selection, sentiment analysis, hybrid feature selection

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Authors: Ahlam Elkilani, Bayan Elsheikh Ali, Rasha Abu Romman, Amjed Al-mousa, Belal Sababha

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The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.

Keywords: green home, resident aware, resident profile, activity learning, machine learning

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Authors: Joko Nurkamto

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A lesson plan is a part of the planning phase in a learning and teaching system framing the scenario of pedagogical activities in the classroom. It informs a decision on what to teach and how to landscape classroom interaction. Regardless of these benefits, the writer has witnessed the fact that lesson plans are viewed merely as a teaching document. Therefore, this paper will explore teachers’ beliefs and practices in designing lesson plans. It focuses primarily on how both teachers and students negotiate lesson plans in which the students are deemed to be the agents of instructional innovations. Additionally, the paper will talk about how such lesson plans are enacted. To investigate these issues, document analysis, in-depth interviews, participant classroom observation, and focus group discussion will be deployed as data collection methods in this explorative case study. The benefits of the paper are to show different roles of lesson plans and to discover different ways to design and enact such plans from a socio-interactional perspective.

Keywords: instructional innovation, learning and teaching system, lesson plan, pedagogical activities, teachers' beliefs and practices

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Authors: Jingwen Ou

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Writing for academic purposes in a second or foreign language poses a significant challenge for non-native speakers, particularly at the tertiary level, where English academic writing for L2 students is often hindered by difficulties in academic discourse, including vocabulary, academic register, and organization. The past two decades have witnessed a rising popularity in the application of the data-driven learning (DDL) approach in EAP writing instruction. In light of such a trend, this study aims to enhance the integration of DDL into English for academic purposes (EAP) writing classrooms by investigating the perception of Chinese college students regarding the use of corpus tools for improving EAP writing. Additionally, the research explores their corpus consultation behaviors during training to provide insights into corpus-assisted EAP instruction for DDL practitioners. Given the uprising popularity of DDL, this research aims to investigate Chinese university students’ use of corpus tools with three main foci: 1) the influence of corpus tools on learning behaviours, 2) the influence of corpus tools on students’ academic writing performance outcomes, and 3) students’ perceptions and potential perceptional changes towards the use of such tools. Three corpus tools, CQPWeb, Sketch Engine, and LancsBox X, are selected for investigation due to the scarcity of empirical research on patterns of learners’ engagement with a combination of multiple corpora. The research adopts a pre-test / post-test design for the evaluation of students’ academic writing performance before and after the intervention. Twenty participants will be divided into two groups: an intervention and a non-intervention group. Three corpus training workshops will be delivered at the beginning, middle, and end of a semester. An online survey and three separate focus group interviews are designed to investigate students’ perceptions of the use of corpus tools for improving academic writing skills, particularly the rhetorical functions in different essay sections. Insights from students’ consultation sessions indicated difficulties with DDL practice, including insufficiency of time to complete all tasks, struggle with technical set-up, unfamiliarity with the DDL approach and difficulty with some advanced corpus functions. Findings from the main study aim to provide pedagogical insights and training resources for EAP practitioners and learners.

Keywords: corpus linguistics, data-driven learning, English for academic purposes, tertiary education in China

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Authors: Omid Noroozi, Martin Mulder

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The motivational potential of educational games is undeniable especially for teaching topics and skills that are difficult to deal with in traditional educational situations such as argumentation competence. Willingness to argue has an association with student epistemic beliefs, which can influence whether, and the way in which students engage in argumentative discourse activities and critical discussion. The goal of this study was to explore how undergraduate students engage with argumentative discourse activities which have been designed to intensify debate, and whether epistemic beliefs are significant to the outcomes. A pre-test, post-test design was used with students who were assigned to groups of four. They were asked to argue a controversial topic with the aim of exploring various perspectives, and the 'pros and cons' on the topic of 'Genetically Modified Organisms (GMOs)'. The results show that the game facilitated argumentative discourse and a willingness to argue and challenged peers, regardless of students’ epistemic beliefs. Furthermore, the game was evaluated positively in terms of students’ motivation and satisfaction with the learning experience.

Keywords: argumentation, attitudinal change, epistemic beliefs, dialogue, digital game objectives and theoretical

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Authors: Angela Mathias Kavishe

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Sexual harassment directed at women is reported in many societies, including in Tanzania. The advent of ICT technology, especially in universities, seems to aggravate the situation by extending harassment to cyberspace in various forms, including cyberstalking. Evidence shows that online violence is more dangerous than physical one due to the ability to access multiple private information, attack many victims, mask the perpetrator's identity, suspend the threat for a long time and spread over time and space. The study aimed to measure the magnitude of cyber harassment in Tanzanian higher learning institutions and to assess institutional sensitivity to ICT-mediated gender-based violence. It was carried out in 4 higher learning institutions in Tanzania: Mwalimu Nyerere Memorial Academy and Institute of Finance Management in Dar es Salaam and SAUT, and the University of Dodoma, where a survey questionnaire was distributed to 400 students and 40 key informants were interviewed. It was found that in each institution, the majority of female students experienced online harassment on social media perpetrated by ex-partners, male students, and university male teaching staff. The perpetrators compelled the female students to post nude pictures, have sexual relations with them, or utilize the posted private photographs to force female students to practice online or offline sexual relations. These threats seem to emanate from social-cultural beliefs about the subordinate position of women in society and that women's bodies are perceived as sex objects. It is therefore concluded that cyberspace provides an alternative space for perpetrators to exercise violence towards women.

Keywords: cyberstalking, embodiment, gender-based violence, internet

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Authors: David Crighton, Kelly Smith

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The ability to adopt creative pedagogical approaches is increasingly vital in today’s educational landscape. This study examines the institutional barriers that hinder educators, in the UK, from embracing such innovation, focusing specifically on the experiences and perspectives of educational leaders. Current literature primarily focuses on the challenges that academics and teachers encounter, particularly highlighting how management culture and audit processes negatively affect their ability to be creative in classrooms and lecture theatres. However, this focus leaves a gap in understanding management perspectives, which is crucial for providing a more holistic insight into the challenges encountered in educational settings. To explore this gap, we are conducting semi-structured interviews with senior leaders across various educational contexts, including universities, schools, and further education colleges. This qualitative methodology, combined with thematic analysis, aims to uncover the managerial, financial, and administrative pressures these leaders face in fostering creativity in teaching and supporting professional learning opportunities. Preliminary insights indicate that educational leaders face significant barriers, such as institutional policies, resource limitations, and external performance indicators. These challenges create a restrictive environment that stifles educators' creativity and innovation. Addressing these barriers is essential for empowering staff to adopt more creative pedagogical approaches, ultimately enhancing student engagement and learning outcomes. By alleviating these constraints, educational leaders can cultivate a culture that fosters creativity and flexibility in the classroom. These insights will inform practical recommendations to support institutional change and enhance professional learning opportunities, contributing to a more dynamic educational environment. In conclusion, this study offers a timely exploration of how leadership can influence the pedagogical landscape in a rapidly evolving educational context. The research seeks to highlight the crucial role that educational leaders play in shaping a culture of creativity and adaptability, ensuring that institutions are better equipped to respond to the challenges of contemporary education.

Keywords: educational leadership, professional learning, creative pedagogy, marketisation

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Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

Procedia PDF Downloads 76