Search results for: learning assessment

Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Zahra Alikhani Koopaei

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In the fifth lesson of the third-grade mathematics book, students are introduced to the concept of multiplication. However, some students showed a lack of interest in learning this topic. To address this, a simple electronic multiplication table was designed with the aim of making the concept of multiplication entertaining and engaging for students. It provides them with moments of excitement during the learning process. To achieve this goal, a device was created that produced a bell sound when two wire ends were connected. Each wire end was connected to a specific number in the multiplication table, and the other end was linked to the corresponding answer. Consequently, if the answer is correct, the bell will ring. This study employs interactive and engaging methods to teach mathematics, particularly to students who have previously shown little interest in the subject. By integrating game-based learning and critical thinking, we observed an increase in understanding and interest in learning multiplication compared to before using this method. This further motivated the students. As a result, the intelligent multiplication table was successfully designed. Students, under the instructor's supervision, could easily construct the device during the lesson. Through the implementation of these operations, the concept of multiplication was firmly established in the students' minds. Engaging multiple intelligences in each student enhances a more stable and improved understanding of the concept of multiplication.

Keywords: intelligent multiplication table, design, construction, education, increased interest, students

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Authors: Suraj Gururaj, Sumantha Udupa U.

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Browsing the Web has emerged as the de facto activity performed on the Internet. Although browsing gets tracked, the manageability aspect of Web browsing history is very poor. In this paper, we have a workable solution implemented by using machine learning and natural language processing techniques for efficient manageability of user’s browsing history. The significance of adding such a capability to a Web browser is that it ensures efficient and quick information retrieval from browsing history, which currently is very challenging. Our solution guarantees that any important websites visited in the past can be easily accessible because of the intelligent and automatic classification. In a nutshell, our solution-based paper provides an implementation as a browser extension by intelligently classifying the browsing history into most relevant category automatically without any user’s intervention. This guarantees no information is lost and increases productivity by saving time spent revisiting websites that were of much importance.

Keywords: adhoc retrieval, Chrome extension, supervised learning, tile, Web personalization

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Authors: A. Alshabeb

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Vocabulary is one of the elements that links the four skills of reading, writing, speaking, and listening and is very critical in learning a foreign language. This study aims to determine how Saudi Arabian EFL students learn English vocabulary via Twitter. The study adopts a mixed sequential research design in collecting and analysing data. The results of the study provide several recommendations for vocabulary learning. Moreover, the study can help teachers to consider the possibilities of using Twitter further, and perhaps to develop new approaches to vocabulary teaching and to support students in their use of social media.

Keywords: social media, twitter, vocabulary, web 2

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Walter M. Millanzi, Stephen M. Kibusi

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Background: Currently, there has been a progressive shortage not only to the number but also the quality of medical practitioners for the most of nursing. Despite that, those who are present exhibit unethical and illegal practices, under standard care and malpractices. The concern is raised in the ways they are prepared, or there might be something missing in nursing curricula or how it is delivered. There is a need for transforming or testing new teaching modalities to enhance competent health workforces. Objective: to investigate the Effect of Facilitation in a Problem-based Environment (FPBE) on metacognition, self-directed learning and learning motivation to undergraduate nurse student in Tanzanian higher learning institutions. Methods: quasi-experimental study (quantitative research approach). A purposive sampling technique was employed to select institutions and achieving a sample size of 401 participants (interventional = 134 and control = 267). Self-administered semi-structured questionnaire; was the main data collection methods and the Statistical Package for Service Solution (v. 20) software program was used for data entry, data analysis, and presentations. Results: The pre-post test results between groups indicated noticeably significant change on metacognition in an intervention (M = 1.52, SD = 0.501) against the control (M = 1.40, SD = 0.490), t (399) = 2.398, p < 0.05). SDL in an intervention (M = 1.52, SD = 0.501) against the control (M = 1.40, SD = 0.490), t (399) = 2.398, p < 0.05. Motivation to learn in an intervention (M = 62.67, SD = 14.14) and the control (n = 267, M = 57.75), t (399) = 2.907, p < 0.01). A FPBE teaching pedagogy, was observed to be effective on the metacognition (AOR = 1.603, p < 0.05), SDL (OR = 1.729, p < 0.05) and Intrinsic motivation in learning (AOR = 1.720, p < 0.05) against conventional teaching pedagogy. Needless, was less likely to enhance Extrinsic motivation (AOR = 0.676, p > 0.05) and Amotivation (AOR = 0.538, p > 0.05). Conclusion and recommendation: FPBE teaching pedagogy, can improve student’s metacognition, self-directed learning and intrinsic motivation to learn among nurse students. Nursing curricula developers should incorporate it to produce 21st century competent and qualified nurses.

Keywords: facilitation, metacognition, motivation, self-directed

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Authors: Angela Meyer

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The operation and maintenance cost for wind parks make up a major fraction of the park’s overall lifetime cost. To minimize the cost and risk involved, an optimal operation and maintenance strategy requires continuous monitoring and analysis. In order to facilitate this, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behavior in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. The presented decision support system and method are applicable for any turbine type and manufacturer providing telemetry data of the turbine operating state. We demonstrate the successful detection and diagnosis of anomalous operating states in a case study at a German onshore wind park comprised of Vestas V112 turbines.

Keywords: anomaly detection, decision support, machine learning, monitoring, performance optimization, wind turbines

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Authors: Kuek Teik Sheng, Leaw Zee Guan, Lim Wah Kien, Ting Tin Tin

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Educational technology such as YouTube and Kahoot have arisen as an alternative to effective learning among higher education institutions. There are many researches done in carrying out experiments to test different educational technologies and received positive feedback from students. Yet, similar study is hardly found in Malaysia especially study that includes the latest educational technologies. As a developing country, it is crucial to ensure that these emerging technologies are assisting students in learning process before it is widely adopted in institutions. This paper conducted a study to explore the perception of higher education institution students on the current educational technologies in Malaysia which include online educational games, online videos/course, social media, presentation tools and resource management tool. Some of these technologies have not been looked into its potential in effective learning process. An online survey using questionnaire is conducted among a target of 300 university/college. In the survey, the result shows that majority of the target students in Malaysia agree that the current educational technologies help them in learning, understanding and manage their studies. It is necessary to discover students’ perceptions on the educational technologies in order to provide guidelines for the educators/institutions in selecting appropriate technology to conduct the lecture/tutorial efficiently and effectively.

Keywords: education, educational technology, Facebook, PowerPoint, YouTube

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Authors: Nina N. Serdar, Jelena R. Pejović

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This paper presents seismic risk assessment of two bridge structure, based on the probabilistic performance-based seismic assessment methodology. Both investigated bridges are tree span continuous RC curved bridges with the difference in column shapes. First bridge (type A) has a wall-type pier and second (type B) has a two-column bent with circular columns. Bridges are designed according to European standards: EN 1991-2, EN1992-1-1 and EN 1998-2. Aim of the performed analysis is to compare seismic behavior of these two structures and to detect the influence of column shapes on the seismic response. Seismic risk assessment is carried out by obtaining demand fragility curves. Non-linear model was constructed and time-history analysis was performed using thirty five pairs of horizontal ground motions selected to match site specific hazard. In performance based analysis, peak column drift ratio (CDR) was selected as engineering demand parameter (EDP). For seismic intensity measure (IM) spectral displacement was selected. Demand fragility curves that give probability of exceedance of certain value for chosen EDP were constructed and based on them conclusions were made.

Keywords: RC curved bridge, demand fragility curve, wall type column, nonlinear time-history analysis, circular column

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Authors: R. Bensaada, M. Almansba, M. Ould Ouali, R. Ferhoum, N. E. Hannachi

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The aim of this work is to simulate the ductile fracture of SEN specimens in aluminium alloy. The assessment of fracture toughness is performed with the calculation of Jc (the critical value of J-Integral) through the resistance curves. The study is done using finite element code calculation ABAQUSTM including an elastic plastic with damage model of material’s behaviour. The procedure involves specimens of four different thicknesses and four ligament sizes for every thickness. The material of study is an aluminium alloy 6061-T6 for which the necessary parameters to complete the study are given. We found the same results for the same specimen’s thickness and for different ligament sizes when the fracture criterion is evaluated.

Keywords: j-integral, critical-j, damage, fracture toughness

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Authors: Noxolo Mafu

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This study presents an overview of strategic planning in South African higher education institutions by tracing its trends and mystique in order to identify its impact. Over the democratic decades, strategic planning has become integral to institutional survival. It has been used as a potent tool by several institutions to catch up and surpass counterparts. While planning has always been part of higher education, strategic planning should be considered different. Strategic planning is primarily about development and maintenance of a strategic fitting between an institution and its dynamic opportunities. This presupposes existence of sets of stages that institutions pursue of which, can be regarded for assessment of the impact of strategic planning in an institution. The network theory serves guides the study in demystifying apparent organisational networks in strategic planning processes.

Keywords: network theory, strategy, planning, strategic planning, assessment, impact

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Authors: Carmen W. H. Chan, Helen Y. L. Chan, Kai Chow Choi, Ka Ming Chow, Cecilia W. M. Kwan, Nancy H. Y. Ng, Jackie Robinson

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Introduction: In Hong Kong, a significant number of deaths occur in acute care wards, which requires nurses in these settings to provide end-of-life care and lead ACP implementation. However, nurses in these settings, in fact, have very low-level involvement in ACP discussions because of limited training in ACP conversations. Objective: This study aims to assess the impact of a multi-media experiential ACP (MEACP) training program, which is guided by the experiential learning model and theory of planned behaviour, on nurses' knowledge and confidence in assisting patients with ACP. Methodology: The study utilizes a cluster randomized controlled trial with a 12-week follow-up. Eligible nurses working in acute care hospital wards are randomly assigned at the ward level, in a 1:1 ratio, to either the control group (no ACP education) or the intervention group (4-week MEACP training program). The training programme includes training through a webpage and mobile application, as well as a face-to-face training workshop with enhanced lectures and role play, which is based on the Theory of Planned Behavior and Kolb's Experiential Learning Model. Questionnaires were distributed to assess nurses' knowledge (a 10-item true/false questionnaire) and level of confidence (five-point Likert scale) in ACP at baseline (T0), four weeks after the baseline assessment (T1), and 12 weeks after T1 (T2). In this interim report, data analysis was mainly descriptive in nature. Result: The interim report focuses on the preliminary results of 165 nurses at T0 (Control: 74, Intervention: 91) over a 5-month period, 69 nurses from the control group who completed the 4-week follow-up and 65 nurses from the intervention group who completed the 4-week MEACP training program at T1. The preliminary attrition rate is 6.8% and 28.6% for the control and intervention groups, respectively, as some nurses did not complete the whole set of online modules. At baseline, the two groups were generally homogeneous in terms of their years of nursing practice, weekly working hours, working title, and level of education, as well as ACP knowledge and confidence levels. The proportion of nurses who answered all ten knowledge questions correctly increased from 13.8% (T0) to 66.2% (T1) for the intervention group and from 13% (T0) to 20.3% (T1) for the control group. The nurses in the intervention group answered an average of 7.57 and 9.43 questions correctly at T0 and T1, respectively. They showed a greater improvement in the knowledge assessment at T1 with respect to T0 when compared with their counterparts in the control group (mean difference of change score, Δ=1.22). They also exhibited a greater gain in level of confidence at T1 compared to their colleagues in the control group (Δ=0.91). T2 data is yet available. Conclusion: The prevalence of nurses engaging in ACP and their level of knowledge about ACP in Hong Kong is low. The MEACP training program can enrich nurses by providing them with more knowledge about ACP and increasing their confidence in conducting ACP.

Keywords: advance directive, advance care planning, confidence, knowledge, multi-media experiential, randomised control trial

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Authors: Htay Htay Yi, Zin May Aye

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Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: false negative rate, intrusion detection system, machine learning methods, performance

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Authors: Cory A. Logston

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It is imperative researchers continue to explore every transformative strategy to increase empathy and awareness of racial bias. Racism is a social and political concept that uses stereotypical ideology to highlight racial inequities. Everyone has biases they may not be aware of toward disparate out-groups. There is some form of racism in every profession; doctors, lawyers, and teachers are not immune. There have been numerous successful and unsuccessful strategies to motivate and transform an individual’s unconscious biased attitudes. One method designed to induce a transformative experience and identify implicit bias is virtual reality (VR). VR is a technology designed to transport the user to a three-dimensional environment. In a virtual reality simulation, the viewer is immersed in a realistic interactive video taking on the perspective of a Black man. The viewer as the character experiences discrimination in various life circumstances growing up as a child into adulthood. For instance, the prejudice felt in school, as an adolescent encountering the police and false accusations in the workplace. Current research suggests that an immersive VR simulation can enhance self-awareness and become a transformative learning experience. This study uses virtual reality immersion and transformative learning theory to create empathy and identify any unintentional racial bias. Participants, White teachers, will experience a VR immersion to create awareness and identify implicit biases regarding Black students. The desired outcome provides a springboard to reconceptualize their own implicit bias. Virtual reality is gaining traction in the research world and promises to be an effective tool in the transformative learning process.

Keywords: empathy, implicit bias, transformative learning, virtual reality

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Authors: Te Kīpa Kēpa B. Morgan, Oliver W. Mcmillan, Dylan N. Taute, Tumanako N. Fa'aui

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Epistemologies are ways of knowing. Indigenous Peoples are aware that they do not perceive and experience the world in the same way as others. So it is important when empowering Indigenous epistemologies, such as that of the New Zealand Māori, to also be able to represent a scientific understanding within the same analysis. A geothermal development assessment tool has been developed by adapting the Mauri Model Decision Making Framework. Mauri is a metric that is capable of representing the change in the life-supporting capacity of things and collections of things. The Mauri Model is a method of grouping mauri indicators as dimension averages in order to allow holistic assessment and also to conduct sensitivity analyses for the effect of worldview bias. R-shiny is the coding platform used for this Vision Mātauranga research which has created an expert decision support tool (DST) that combines a stakeholder assessment of worldview bias with an impact assessment of mauri-based indicators to determine the sustainability of proposed geothermal development. The initial intention was to develop guidelines for quantifying mātauranga Māori impacts related to geothermal resources. To do this, three typical scenarios were considered: a resource owner wishing to assess the potential for new geothermal development; another party wishing to assess the environmental and cultural impacts of the proposed development; an assessment that focuses on the holistic sustainability of the resource, including its surface features. Indicator sets and measurement thresholds were developed that are considered necessary considerations for each assessment context and these have been grouped to represent four mauri dimensions that mirror the four well-being criteria used for resource management in Aotearoa, New Zealand. Two case studies have been conducted to test the DST suitability for quantifying mātauranga Māori and other biophysical factors related to a geothermal system. This involved estimating mauri0meter values for physical features such as temperature, flow rate, frequency, colour, and developing indicators to also quantify qualitative observations about the geothermal system made by Māori. A retrospective analysis has then been conducted to verify different understandings of the geothermal system. The case studies found that the expert DST is useful for geothermal development assessment, especially where hapū (indigenous sub-tribal grouping) are conflicted regarding the benefits and disadvantages of their’ and others’ geothermal developments. These results have been supplemented with evaluations for the cumulative impacts of geothermal developments experienced by different parties using integration techniques applied to the time history curve of the expert DST worldview bias weighted plotted against the mauri0meter score. Cumulative impacts represent the change in resilience or potential of geothermal systems, which directly assists with the holistic interpretation of change from an Indigenous Peoples’ perspective.

Keywords: decision support tool, holistic geothermal assessment, indigenous knowledge, mauri model decision-making framework

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Authors: Kim Bower

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Evaluating and researching motivation in language learning is a complex and multi-faceted activity. Various models for investigating learner motivation have been proposed in the literature, but no one model supplies a complex and coherent model for investigating a range of motivational characteristics. Here, such a methodological framework, which includes exemplification of sources of evidence and potential methods of investigation, is proposed. The process model for the investigation of motivation within language learning settings proposed is based on a complex dynamic systems perspective that takes account of cognition and affects. It focuses on three overarching aspects of motivation: the learning environment, learner engagement and learner identities. Within these categories subsets are defined: the learning environment incorporates teacher, course and group specific aspects of motivation; learner engagement addresses the principal characteristics of learners' perceived value of activities, their attitudes towards language learning, their perceptions of their learning and engagement in learning tasks; and within learner identities, principal characteristics of self-concept and mastery of the language are explored. Exemplifications of potential sources of evidence in the model reflect the multiple influences within and between learner and environmental factors and the possible changes in both that may emerge over time. The model was initially developed as a framework for investigating different models of Content and Language Integrated Learning (CLIL) in contrasting contexts in secondary schools in England. The study, from which examples are drawn to exemplify the model, aimed to address the following three research questions: (1) in what ways does CLIL impact on learner motivation? (2) what are the main elements of CLIL that enhance motivation? and (3) to what extent might these be transferable to other contexts? This new model has been tried and tested in three locations in England and reported as case studies. Following an initial visit to each institution to discuss the qualitative research, instruments were developed according to the proposed model. A questionnaire was drawn up and completed by one group prior to a 3-day data collection visit to each institution, during which interviews were held with academic leaders, the head of the department, the CLIL teacher(s), and two learner focus groups of six-eight learners. Interviews were recorded and transcribed verbatim. 2-4 naturalistic observations of lessons were undertaken in each setting, as appropriate to the context, to provide colour and thereby a richer picture. Findings were subjected to an interpretive analysis by the themes derived from the process model and are reported elsewhere. The model proved to be an effective and coherent framework for planning the research, instrument design, data collection and interpretive analysis of data in these three contrasting settings, in which different models of language learning were in place. It is hoped that the proposed model, reported here together with exemplification and commentary, will enable teachers and researchers in a wide range of language learning contexts to investigate learner motivation in a systematic and in-depth manner.

Keywords: investigate, language-learning, learner motivation model, dynamic systems perspective

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Authors: Alonge Isaac Olusola

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Taking a cue from countries such as China and Japan, there is no doubt that the teaching and learning of Mother Tongue ( MT) or Language of Immediate Environment (LIE) is a potential source of development in every country. The engine of economic, scientific, technological and political advancement would be more functional when the language of instruction for teaching and learning in schools is in the child’s mother tongue. The purpose of this paper therefore, is to delve into the genesis of the official recognition given to the teaching and learning of Nigerian languages at national level with special focus on Yoruba language. Yoruba language and other Nigerian languages were placed on a national pedestal by a Nigerian Educational Minister, Late Professor Babatunde Fafunwa, who served under the government of General Ibrahim Babangida (1985 – 1993). Through his laudable effort, the teaching and learning of Nigerian languages in schools all over the nation was incorporated officially in the national policy of education. Among all the Nigerian languages, Hausa, Igbo and Yoruba were given foremost priorities because of the large population of their speakers. Since the Fafunwa era, Yoruba language has become a national subject taught in primary, secondary and tertiary institutions in Nigeria. However, like every new policy, its implementation has suffered several forms of criticisms and impediments from governments, policy makers, curriculum developers, school administrators, teachers and learners. This paper has been able to arrive at certain findings through oral interviews, questionnaires and evaluation of pupils/students enrolment and performances in Yoruba language with special focus on the South-west and North central regions of Nigeria. From the research carried out, some factors have been found to be responsible for the successful implementation or otherwise of Yoruba language instruction policy in some schools, colleges and higher institutions in Nigeria. In conclusion, the paper made recommendations on how the National Policy of Education would be implemented to enhance the teaching and learning of Yoruba language in all Nigerian schools.

Keywords: language of immediate environment, mother tongue, national policy of education, yoruba language

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Authors: Farzaneh Badinlou, Reza Kormi-Nouri, Monika Knopf, Kamal Kharazi

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Research into long-term memory has demonstrated that the richness of the knowledge base cues in memory tasks improves retrieval process, which in turn influences learning and memory performance. The present research investigated the idea that adding cues connected to knowledge can affect memory performance in the context of action memory in children. In action memory studies, participants are instructed to learn a series of verb–object phrases as verbal learning and experience-based learning (learning by doing and learning by observation). It is well established that executing action phrases is a more memorable way to learn than verbally repeating the phrases, a finding called enactment effect. In the present study, a total of 410 students from four grade groups—2nd, 4th, 6th, and 8th—participated in this study. During the study, participants listened to verbal action phrases (VTs), performed the phrases (SPTs: subject-performed tasks), and observed the experimenter perform the phrases (EPTs: experimenter-performed tasks). During the test phase, cued recall test was administered. Semantic relational cues (i.e., well-integrated vs. poorly integrated items) were manipulated in the present study. In that, the participants were presented two lists of action phrases with high semantic integration between verb and noun, e.g., “write with the pen” and with low semantic integration between verb and noun, e.g., “pick up the glass”. Results revealed that experience-based learning had a better results than verbal learning for both well-integrated and poorly integrated items, though manipulations of semantic relational cues can moderate the enactment effect. In addition, children of different grade groups outperformed for well- than poorly integrated items, in flavour of older children. The results were discussed in relation to the effect of knowledge-based information in facilitating retrieval process in children.

Keywords: action memory, enactment effect, knowledge-based cues, school-aged children, semantic relational cues

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Authors: Wen Chen

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To investigate the interleaving effect of subject matter (mathematics vs. history) and perceptual modality (visual vs. auditory materials) on student’s attention and learning outcomes, the present study collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data, and learning outcomes from micro-lectures. Eighty-one 7th grade students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). The results showed that although interleaved conditions may show advantages in certain indices, the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning classes.

Keywords: cognitive load, interleaving effect, micro-lectures, sustained attention

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Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

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Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: A. Flora, A. Di Lascio, D. Cardone, G. Gesualdi, G. Perrone

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This paper considers the seismic assessment and retrofit of a pilotis-type RC frame building, which was designed for gravity loads only, prior to the introduction of seismic design provisions. Pilotis-type RC frame buildings, featuring an uniform infill throughout the height and an open ground floor, were, and still are, quite popular all over the world, as they offer large open areas very suitable for retail space at the ground floor. These architectural advantages, however, are of detriment to the building seismic behavior, as they can determine a soft-storey collapse mechanism. Extensive numerical analyses are carried out to quantify and benchmark the performance of the selected building, both in terms of overall collapse capacity and expected losses. Alternative retrofit strategies are then examined, including: (i) steel jacketing of RC columns and beam-column joints, (ii) steel bracing and (iv) seismic isolation. The Expected Annual Loss (EAL) of the selected case-study building, pre- and post-rehabilitation, is evaluated, following a probabilistic approach. The breakeven time of each solution is computed, comparing the initial cost of the retrofit intervention with expected benefit in terms of EAL reduction.

Keywords: expected annual loss, reinforced concrete buildings, seismic loss assessment, seismic retrofit

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Authors: Vanja Skoric

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The paper examines whether the impacts of artificial intelligence (AI) can be meaningfully addressed through the rights-based approach to AI design, investigating in particular how the inclusive, participatory process of assessing the AI impact would make this viable. There is a significant gap between envisioning rights-based AI systems and their practical application. Plausibly, internalizing human rights approach within AI design process might be achieved through identifying and assessing implications of AI features human rights, especially considering the case of vulnerable individuals and communities. However, there is no clarity or consensus on how such an instrument should be operationalised to usefully identify the impact, mitigate harms and meaningfully ensure relevant stakeholders’ participation. In practice, ensuring the meaningful inclusion of those individuals, groups, or entire communities who are affected by the use of the AI system is a prerequisite for a process seeking to assess human rights impacts and risks. Engagement in the entire process of the impact assessment should enable those affected and interested to access information and better understand the technology, product, or service and resulting impacts, but also to learn about their rights and the respective obligations and responsibilities of developers and deployers to protect and/or respect these rights. This paper will provide an overview of the study and practice of the participatory design process for AI, including inclusive impact assessment, its main elements, propose a framework, and discuss the lessons learned from the existing theory. In addition, it will explore pathways for enhancing and promoting individual and group rights through such engagement by discussing when, how, and whom to include, at which stage of the process, and what are the pre-requisites for meaningful and engaging. The overall aim is to ensure using the technology that works for the benefit of society, individuals, and particular (historically marginalised) groups.

Keywords: rights-based design, AI impact assessment, inclusion, harm mitigation

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Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

Procedia PDF Downloads 112

Authors: Zuhairah Abdul Hadi, Mohd Nazir bin Md. Zabit, Zuriadah Ismail

Abstract:

Metacognitive has been empirically evidenced to be one important element influencing learning outcomes. Expert learners engage in metacognition by monitoring and controlling their thinking, and listing, considering and selecting the best strategies to achieve desired goals. Studies also found that good critical thinkers engage in more metacognition and people tend to activate more metacognition when solving complex problems. This study extends past studies by performing a qualitative analysis to understand metacognitive patterns among two high and two low performing groups by carefully examining video and audio records taken during Problem-based learning activities. High performing groups are groups with majority members scored well in Watson Glaser II Critical Thinking Appraisal (WGCTA II) and academic achievement tests. Low performing groups are groups with majority members fail to perform in the two tests. Audio records are transcribed and analyzed using schemas adopted from past studies. Metacognitive statements are analyzed using three stages model and patterns of metacognitive are described by contexts, components, and levels for each high and low performing groups.

Keywords: academic achievement, critical thinking, metacognitive, problem-based learning

Procedia PDF Downloads 287

Authors: Chalermpol Tapsai

Abstract:

The objectives of this research were to develop and find the efficiency of integrated online lessons by investigating the usage of online lessons, the relationship between learners’ background knowledge, and the achievement after learning with online lessons. The sample group in this study consisted of 97 students randomly selected from 121 students registering in 1/2012 at Trimitwittayaram Learning Center. The sample technique employed stratified sample technique of 4 groups according to their proficiency, i.e. high, moderate, low, and non-knowledge. The research instrument included online lessons in integration model on the topic of Java Programming, test after each lesson, the achievement test at the end of the course, and the questionnaires to find learners’ satisfaction. The results showed that the efficiency of online lessons was 90.20/89.18 with the achievement of after learning with the lessons higher than that before the lessons at the statistically significant level of 0.05. Moreover, the background knowledge of the learners on the programming showed the positive relationship with the achievement learning at the statistically significant level at 0.05. Learners with high background knowledge employed less exercises and samples than those with lower background knowledge. While learners with different background in the group of moderate and low did not show the significant difference in employing samples and exercises.

Keywords: integration model, online lessons, learners’ background knowledge, efficiency

Procedia PDF Downloads 360

Authors: Abdullah Tahir

Abstract:

The omission of regular medications in surgical patients poses a significant challenge in healthcare settings and is associated with increased morbidity during hospital stays. Human factors such as high workload, poor communication, and emotional stress are known to contribute to these omissions, particularly evident in the surgical assessment unit (SAU) due to its high patient burden and long wait times. This study aimed to quantify and address the issue by implementing targeted interventions to enhance compliance in prescribing regular medications for surgical patients at Stoke Mandeville Hospital, United Kingdom. Data were collected on 14 spontaneous days between April and May 2023, and the frequency of prescription omissions was recorded using a tally chart. Subsequently, informative posters were introduced in the SAU, and presentations were given to the surgical team to emphasize the importance of compliance in this area. The interventions were assessed using a second data collection cycle, again over 14 spontaneous days in May 2023. Results demonstrated an improvement from 40% (60 out of 150) to 74% (93 out of 126) of patients having regular medications prescribed at the point of clerking. These findings highlight the efficacy of frequent prompts and awareness-raising interventions in increasing workforce compliance and addressing the issue of prescription omissions in the SAU.

Keywords: prescription omissions, quality improvement, regular medication, surgical assessment unit

Procedia PDF Downloads 79

Authors: Qasim Khan, Furkan Ahmad, Asfar A. Khan, M. Saad Alam, Faiz Ahmad

Abstract:

In this paper, partial discharge analysis is performed in cavities artificially created in insulation. The setup is according with Cigre-II Method. Circular Samples created from Perspex Sheet with different configuration with changing number of cavities. Assessment of insulation health can be performed by Partial Discharge measurement as this has been found to be important means of condition monitoring. The experiments are done using MPD 540, which is a modern partial discharge measurement system. By analyzing the PD activity obtained for various voids/cavities, it is observed that the PD voltages show variation for cavity’s diameter, depth even for its ratios. This can be employed for scrutiny of insulation system.

Keywords: partial discharges, condition monitoring, insulation defects, degradation and corrosion, PMMA

Procedia PDF Downloads 518

Authors: Engy S. El-Kayal, Mohamed M. S. Arafa

Abstract:

There are various causes of ankle pain including traumatic and non-traumatic causes. Various imaging techniques are available for assessment of AP. MRI is considered to be the imaging modality of choice for ankle joint evaluation with an advantage of its high spatial resolution, multiplanar capability, hence its ability to visualize small complex anatomical structures around the ankle. However, the high costs and the relatively limited availability of MRI systems, as well as the relatively long duration of the examination all are considered disadvantages of MRI examination. Therefore there is a need for a more rapid and less expensive examination modality with good diagnostic accuracy to fulfill this gap. HRU has become increasingly important in the assessment of ankle disorders, with advantages of being fast, reliable, of low cost and readily available. US can visualize detailed anatomical structures and assess tendinous and ligamentous integrity. The aim of this study was to compare the diagnostic accuracy of HRU with MRI in the assessment of patients with AP. We included forty patients complaining of AP. All patients were subjected to real-time HRU and MRI of the affected ankle. Results of both techniques were compared to surgical and arthroscopic findings. All patients were examined according to a defined protocol that includes imaging the tendon tears or tendinitis, muscle tears, masses, or fluid collection, ligament sprain or tears, inflammation or fluid effusion within the joint or bursa, bone and cartilage lesions, erosions and osteophytes. Analysis of the results showed that the mean age of patients was 38 years. The study comprised of 24 women (60%) and 16 men (40%). The accuracy of HRU in detecting causes of AP was 85%, while the accuracy of MRI in the detection of causes of AP was 87.5%. In conclusions: HRU and MRI are two complementary tools of investigation with the former will be used as a primary tool of investigation and the latter will be used to confirm the diagnosis and the extent of the lesion especially when surgical interference is planned.

Keywords: ankle pain (AP), high-resolution ultrasound (HRU), magnetic resonance imaging (MRI) ultrasonography (US)

Procedia PDF Downloads 193