Search results for: active learning approach

Authors: Bernardita Tornero

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Cognitive Acceleration in Mathematics Education (CAME) programmes have been used successfully for promoting the development of thinking skills in school students for the last 30 years. Given that the approach has had a tremendous impact on the thinking capabilities of participating students, this study explored the experience of using the programme with prospective primary teachers in Chile. Therefore, this study not only looked at the experience of prospective primary teachers during the CAME course as learners, but also examined how they perceived the approach from their perspective as future teachers, as well as how they could transfer the teaching strategies they observed to their future classrooms. Given the complexity of the phenomenon under study, this research used a mixed methods approach. For this reason, the impact that the CAME course had on prospective teachers’ thinking skills was not only approached by using a test that assessed the participants’ improvements in these skills, but their learning and teaching experiences were also recorded through qualitative research tools (learning journals, interviews and field notes). The main findings indicate that, at the end of the CAME course, prospective teachers not only demonstrated higher thinking levels, but also showed positive attitudinal changes towards teaching and learning in general, and towards mathematics in particular. The participants also had increased confidence in their ability to teach mathematics and to promote thinking skills in their students. In terms of the CAME methodology, prospective teachers not only found it novel and motivating, but also commented that dealing with the thinking skills topic during a university course was both unusual and very important for their professional development. This study also showed that, at the end of the CAME course, prospective teachers felt they had developed strategies that could be used in their classrooms in the future. In this context, the relevance of the study is not only that it described the impact and the positive results of the first experience of using a CAME approach with prospective teachers, but also that some of the conclusions have significant implications for the teaching of thinking skills and the training of primary school teachers.

Keywords: cognitive acceleration, formal reasoning, prospective teachers, initial teacher training

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Authors: Nurul Nadrah

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As a result of the COVID-19 pandemic, a movement control order was implemented, leading to the adoption of online learning as a substitute for conventional classroom instruction. Thus, this study aims to determine the differences in skill performance between online learning and conventional methods among nursing students. We employed a quasi-experimental design with purposive sampling, involving a total of 59 nursing students, and used online learning as the intervention. As a result, the study found there was a significant difference in student skill performance between online learning and conventional methods. As a conclusion, in times of hardship, it is necessary to implement alternative pedagogical approaches, especially in critical fields like nursing, to ensure the uninterrupted progression of educational programs. This study suggests that online learning can be effectively employed as a means of imparting knowledge to nursing students during their training.

Keywords: nursing education, online learning, skill performance, conventional learning method

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Authors: Mariyappan Shanmugam, Bin Yu

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Schottky-barrier solar cells are demonstrated employing 2D-layered MoS2 and WS2 semiconductor nanofilms as photo-active material candidates synthesized by chemical vapor deposition method. Large area MoS2 and WS2 nanofilms are stacked by layer transfer process to achieve thicker photo-active material studied by atomic force microscopy showing a thickness in the range of ~200 nm. Two major vibrational active modes associated with 2D-layered MoS2 and WS2 are studied by Raman spectroscopic technique to estimate the quality of the nanofilms. Schottky-barrier solar cells employed MoS2 and WS2 active materials exhibited photoconversion efficiency of 1.8 % and 1.7 % respectively. Fermi-level pinning at metal/semiconductor interface, electronic transport and possible recombination mechanisms are studied in the Schottky-barrier solar cells.

Keywords: two-dimensional nanosheet, graphene, hexagonal boron nitride, solar cell, Schottky barrier

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Authors: Sumitra Nuanmeesri

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The research focus on study, analyze and design the model of the infrastructure and computer networks for distance education, online learning via new media, e-learning and blended learning. The collected information from study and analyze process that information was evaluated by the index of item objective congruence (IOC) by 9 specialists to design model. The results of evaluate the model with the mean and standard deviation by the sample of 9 specialists value is 3.85. The results showed that the infrastructure and computer networks are designed to be appropriate to a great extent appropriate to a great extent.

Keywords: blended learning, new media, infrastructure and computer network, tele-education, online learning

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Authors: M. Zezou

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This paper focuses on the design of a Massive Open Online Course (MOOC) about Content Language Integrated Learning (CLIL) and more specifically about how teachers can use CLIL as an educational approach incorporating technology in their teaching as well. All the four weeks of the MOOC will be presented and a step-by-step analysis of each lesson will be offered. Additionally, the paper includes detailed lesson plans about CLIL lessons with proposed CLIL activities and games in which technology plays a central part. The MOOC is structured based on certain criteria, in order to ensure success, as well as a positive experience that the learners need to have after completing this MOOC. It addresses to all language teachers who would like to implement CLIL into their teaching. In other words, it presents the methodology that needs to be followed so as to successfully carry out a CLIL lesson and achieve the learning objectives set at the beginning of the course. Firstly, in this paper, it is very important to give the definitions of MOOCs and LMOOCs, as well as to explore the difference between a structure-based MOOC (xMOOC) and a connectivist MOOC (cMOOC) and present the criteria of a successful MOOC. Moreover, the notion of CLIL will be explored, as it is necessary to fully understand this concept before moving on to the design of the MOOC. Onwards, the four weeks of the MOOC will be introduced as well as lesson plans will be presented: The type of the activities, the aims of each activity and the methodology that teachers have to follow. Emphasis will be placed on the role of technology in foreign language learning and on the ways in which we can involve technology in teaching a foreign language. Final remarks will be made and a summary of the main points will be offered at the end.

Keywords: CLIL, cMOOC, lesson plan, LMOOC, MOOC criteria, MOOC, technology, xMOOC

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Authors: Ali Al-Zughaibi

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The purpose of this paper is to present a modeling and control of the quarter car active suspension system with unknown mass, unknown time-delay and road disturbance. The objective of designing the controller by deriving a control law to achieve stability of the system and convergence that can considerably improve the ride comfort and road disturbance handling. Thus is accomplished by using Routh-Herwitz criterion and based on some assumptions. A mathematical proof is given to show the ability of the designed controller to ensure stability and convergence of the active suspension system and dispersion oscillation of system with unknown mass, time-delay and road disturbances. Simulations were also performed for controlling quarter car suspension, where the results obtained from these simulations verify the validity of the proposed design.

Keywords: active suspension system, time-delay, disturbance rejection, dynamic uncertainty

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Authors: Seyyed Hassan Seyyedrezaei

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The advent of computer and ICT thereafter has introduced many irrevocable changes in learning and teaching. There is substantially growing need for the use of IT and ICT in language learning and teaching. In other words, the integration of Information Technology (IT) into online teaching is of vital importance for education and assessment. Considering the fact that the image of education is undergone drastic changes by the advent of technology, education systems and teachers move beyond the walls of traditional classes and methods in order to join with other educational centers to revitalize education. Given the advent of distance learning, online courses and virtual universities, e-assessment has taken a prominent place in effective teaching and meeting the learners' educational needs. The purpose of this paper is twofold: first, scrutinizing e-learning, it discusses how and why e-assessment is becoming widely used by educationalists and administrators worldwide. As a second purpose, a couple of effective strategies for online assessment will be enumerated.

Keywords: e-assessment, e learning, ICT, online assessment

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Authors: Hani Mustafa, Cristina Gonzalez Ruiz, Estelle Bech

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The growing influence of digital technologies has made learning and interaction more accessible, resulting in effective collaboration if properly managed. Technology enabled learning has become an important conduit for learning, including collaborative learning. The use of wiki technology, for example, has opened a new learning platform that enables the integration of social, linguistic, and cognitive processes of language learning. It encourages students to collaborate in the construction, analysis, and understanding of knowledge. But to what extent is the use of wikis effective in promoting collaborative learning among students. In addition, how do students perceive this technology in enhancing their language learning? In this study, students were be given a wiki project to complete collaboratively with their group members. Students had to write collaboratively to produce and present a seven-day travel plan in which they had to describe places to visit and things to do to explore the best historical and cultural aspects of the country. The study involves students learning French, Malay, and Spanish as a foreign language. In completing this wiki project, students will move from passive learning of language to real engagement with classmates, requiring them to collaborate and negotiate effectively with one another. The objective of the study is to ascertain to what extent does wiki technology helped in promoting collaborative learning and improving language skills from students’ perception. It is found that while there was improvement in students language skills, the overall experience was less positive due to unfamiliarity with a new learning tool.

Keywords: collaborative learning, foreign language, wiki, teaching

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Authors: Muhammad Bilal, Adil Ahmed

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This study proposes an approach for electrical induction motor predictive maintenance utilizing machine learning algorithms. On the basis of a study of temperature data obtained from sensors put on the motor, the goal is to predict motor failures. The proposed models are trained to identify whether a motor is defective or not by utilizing machine learning algorithms like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). According to a thorough study of the literature, earlier research has used motor current signature analysis (MCSA) and vibration data to forecast motor failures. The temperature signal methodology, which has clear advantages over the conventional MCSA and vibration analysis methods in terms of cost-effectiveness, is the main subject of this research. The acquired results emphasize the applicability and effectiveness of the temperature-based predictive maintenance strategy by demonstrating the successful categorization of defective motors using the suggested machine learning models.

Keywords: predictive maintenance, electrical induction motors, machine learning, temperature signal methodology, motor failures

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Charles St. Geste, Michael Neumann, Catherine Twohig

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Our primary aim is to examine farmer-centered participatory action learning as a tool to improve agricultural production, build resilience to climate shocks and, more broadly, advance community-driven solutions for sustainable development in rural communities across Haiti. For over six years, sixty plus farmers from Deslandes, Haiti, organized in three traditional work groups called konbits, have designed and tested low-input agroecology techniques as part of the Konbit Vanyan Kapab Pwoje Agroekoloji. The project utilizes a participatory action learning approach, emphasizing social inclusion, building on local knowledge, experiential learning, active farmer participation in trial design and evaluation, and cross-community sharing. Mixed methods were used to evaluate changes in knowledge and adoption of agroecology techniques, confidence in advancing agroecology locally, and innovation among Konbit Vanyan Kapab farmers. While skill and knowledge in application of agroecology techniques varied among individual farmers, a majority of farmers successfully adopted techniques outside of the trial farms. The use of agroecology techniques on trial and individual farms has doubled crop production in many cases. Farm income has also increased, and farmers report less damage to crops and property caused by extreme weather events. Furthermore, participatory action strategies have led to greater local self-determination and greater capacity for sustainable community development. With increased self-confidence and the knowledge and skills acquired from participating in the project, farmers prioritized sharing their successful techniques with other farmers and have developed a farmer-to-farmer training program that incorporates participatory action learning. Using adult education methods, farmers, trained as agroecology educators, are currently providing training in sustainable farming practices to farmers from five villages in three departments across Haiti. Konbit Vanyan Kapab farmers have also begun testing production of value-added food products, including a dried soup mix and tea. Key factors for success include: opportunities for farmers to actively participate in all phases of the project, group diversity, resources for application of agroecology techniques, focus on group processes and overcoming local barriers to inclusive decision-making.

Keywords: agroecology, participatory action learning, rural Haiti, sustainable community development

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Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

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Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Snehal R. Pathak, Sachin S. Munnoli

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Evaluation of dynamic earth pressure on retaining wall is a topic of primary importance. In present paper, dynamic active earth pressure and displacement of flexible cantilever retaining wall has been evaluated analytically using 2-DOF mass-spring-dashpot model by incorporating both wall and backfill properties. The effect of wall flexibility on dynamic active earth pressure and wall displacement are studied and presented in graphical form. The obtained results are then compared with the various conventional methods, experimental analysis and also with PLAXIS analysis. It is observed that the dynamic active earth pressure decreases with increase in the wall flexibility while wall displacement increases linearly with flexibility of the wall. The results obtained by proposed 2-DOF analytical model are found to be more realistic and economical.

Keywords: earth pressure, earthquake, 2-DOF model, Plaxis, retaining walls, wall movement

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Authors: Winnie Wing Mui So, Yu Chen

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This study investigated primary school teachers’ and students’ perceptions of science learning in an e-learning environment. This study used a multiple case study design and involved eight science teachers and their students from four Hong Kong primary schools. The science topics taught included ‘season and weather’ ‘force and movement’, ‘solar and lunar eclipse’ and ‘living things and habitats’. Data were collected through lesson observations, interviews with teachers, and interviews with students. Results revealed some differences between the teachers’ and the students’ perceptions regarding the usefulness of e-learning resources, the organization of student-centred activities, and the impact on engagement and interactions in lessons. The findings have implications for the more effective creation of e-learning environments for science teaching and learning in primary schools.

Keywords: e-learning, science education, teacher' and students' perceptions, primary schools

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Authors: Nezha Badi

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It has been shown in the literature that Algerian university students suffer from low levels of reading comprehension proficiency, which hinder their overall proficiency in English. This low level is mainly related to the methodology of teaching reading which is employed by the teacher in the classroom (a teacher-centered environment), as well as students’ poor sense of self-efficacy to undertake reading comprehension activities. Arguably, what is needed is an approach necessary for enhancing students’ self-beliefs about their abilities to deal with different reading comprehension activities. This can be done by providing them with opportunities to take responsibility for their own learning (learners’ autonomy). As a result of learning autonomy, learners’ beliefs about their abilities to deal with certain language tasks may increase, and hence, their language learning ability. Therefore, this experimental research study attempts to assess the extent to which an integrated approach combining one particular reading approach known as ‘collaborative strategic reading’ (CSR), and teacher’s attributional feedback (on students’ reading performance and strategy use) can improve the reading comprehension skill and the sense of self-efficacy of EFL Algerian university students. It also seeks to examine students’ main reasons for their successful or unsuccessful achievements in reading comprehension activities, and whether students’ attributions for their reading comprehension outcomes can be modified after exposure to the instruction. To obtain the data, different tools including a reading comprehension test, questionnaires, an observation, an interview, and learning logs were used with 105 second year Algerian EFL university students. The sample of the study was divided into three groups; one control group (with no treatment), one experimental group (CSR group) who received a CSR instruction, and a second intervention group (CSR Plus group) who received teacher’s attribution feedback in addition to the CSR intervention. Students in the CSR Plus group received the same experiment as the CSR group using the same tools, except that they were asked to keep learning logs, for which teacher’s feedback on reading performance and strategy use was provided. The results of this study indicate that the CSR and the attributional feedback intervention was effective in improving students’ reading comprehension proficiency and sense of self-efficacy. However, there was not a significant change in students’ adaptive and maladaptive attributions for their success and failure d from the pre-test to the post-test phase. Analysis of the perception questionnaire, the interview, and the learning logs shows that students have positive perceptions about the CSR and the attributional feedback instruction. Based on the findings, this study, therefore, seeks to provide EFL teachers in general and Algerian EFL university teachers in particular with pedagogical implications on how to teach reading comprehension to their students to help them achieve well and feel more self-efficacious in reading comprehension activities, and in English language learning more generally.

Keywords: attributions, attributional feedback, collaborative strategic reading, self-efficacy

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Md. Mizanoor Rahman, Md. Zakir Hossain Talukder, M. Mahruf C. Shohel, Prithvi Shrestha

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In Bangladesh, classroom practice and English Learning (EL) competencies acquired both by the teacher and learner in primary and secondary schools are still very weak. Therefore, English is the most commonly failed examination subject at the school level; in addition, there are severe problems in communicative English by the Bangladeshi nationals– this has been characterized as a constraint to economic development. Job applicants and employees often lack English language skills necessary to work effectively. As a result; both government and its international development partners such as DFID, UNESCO, and CIDA have been very active to uplift the quality of the English language learning and implementing projects with innovative approaches. Recently; the economy has been increasing and in line with this, the technology has been deployed in English learning to improve reading, writing, speaking and listening skills. Young Bangladeshi creative, from a variety of backgrounds including film, animation, photography, and digital media are being trained to develop ideas for English Language Teaching (ELT) media. They are being motivated to develop a wide range of ideas for low cost English learning media products. English Language education policy in Bangladesh supports communicative language teaching practices and accordingly, actors have been influencing curriculum, textbook, deployment of technology and assessment changes supporting communicative ELT. The various projects are also being implemented to reform the curriculum, revise the textbook and adjust the assessment mechanism so that the country can increase in proficiency in communicative English among the population. At present; the numbers of teachers, students and adult learners classified at higher levels of proficiency because of deployment of technology and motivation for learning and using English among school population of Bangladesh. The current paper discusses the various interventions in Bangladesh with appropriate media to improve the competencies of the ELT among population.

Keywords: English learning, technology, education, psychological sciences

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Authors: Anne McCarthy, Anna Kim, Yin Song, Kyoo Jo, Donald Cropek, Sungmin Hong

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Corrosion prevention remains an indispensable concern across a spectrum of industries, demanding inventive and adaptable methodologies to effectively tackle the ever-evolving obstacles presented by corrosive surroundings. This abstract introduces a pioneering approach to corrosion prevention that amalgamates the distinct attributes of magneto-responsive polymers with finely adjustable hydrophobicity inspired by the structure of cicada wings, effectively deterring bacterial proliferation and biofilm formation. The proposed strategy entails the creation of an innovative array of magneto-responsive nanostructures endowed with the capacity to dynamically modulate their hydrophobic characteristics. This dynamic control over hydrophobicity facilitates active repulsion of water and corrosive agents on demand. Additionally, the cyclic motion generated by magnetic activation prevents the biofilms formation and rejection. Thus, the synergistic interplay between magneto-active nanostructures and hydrophobicity manipulation establishes a versatile defensive mechanism against diverse corrosive agents. This study introduces a novel method for corrosion prevention, harnessing the advantages of magneto-active nanostructures and the precision of hydrophobicity adjustment, resulting in water-repellency, effective biofilm removal, and offering a promising solution to handle corrosion-related challenges. We believe that the combined effect will significantly contribute to extending asset lifespan, improving safety, and reducing maintenance costs in the face of corrosion threats.

Keywords: magneto-active material, nanoimprinting, corrosion prevention, hydrophobicity

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Authors: Sagheer Ahmad

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Teaching is a sacred profession whereby students are developed in their mental abilities to cope with the challenges of the remote world. Thinkers have developed plenty of interesting ways to make the learning process quick and absorbing for the students. However, third world countries are still lacking these remote facilities in the institutions, and therefore, teaching is totally dependent upon the skills of the teachers. Skillful teachers use self-devised and stimulating ideas to grab the attention of their students. Most of the time their ideas are based on local grounds with which the students are already familiar. This self-explanatory characteristic is the base of several local ideologies to disseminate scientific knowledge to new generations. Biology is such a subject which largely bases upon hypotheses, and teaching it in an interesting way is needful to create a friendly relationship between teacher and student, and to make a fantastic learning environment. Taxonomic classification if presented as it is, may not be attractive for the secondary school students who just start learning about biology at elementary levels. Presenting this hierarchy by exemplifying Kingdom, Phylum, Class, Order, family, genus and Species as comparatives of our division into continents, countries, cities, towns, villages, homes and finally individuals could be an attention-grabbing approach to make this concept get into bones of students. Similarly, many other interesting approaches have also been adopted to teach students in a fascinating way so that learning science subjects may not be boring for them. Discussing these appealing ways of teaching students can be a valuable stimulus to refine teaching methodologies about science, thereby promoting the concept of friendly learning.

Keywords: biology, innovative approaches, taxonomic classification, teaching

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Authors: Kafi Razzaq Ahmed

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Speaking fluently in a second language requires vocabulary, grammar, and pronunciation skills. Teaching the English language entails teaching pronunciation. In professional literature, there have been a lot of attempts to integrate technology into improving the pronunciation of learners. The technique is also neglected in Kurdish contexts, Salahaddin University – Erbil included. Thus, the main aim of the research is to point out the efficiency of using video materials for both language teachers and learners within and beyond classroom learning and teaching environments to enhance student's pronunciation. To collect practical data, a research project has been designed. In subsequent research, a posttest will be administered after each lesson to 100 first-year students at Salahaddin University-Erbil English departments. All students will be taught the same material using different methods, one based on video materials and the other based on the traditional approach to teaching pronunciation. Finally, the results of both tests will be analyzed (also knowing the attitudes of both the teachers and the students about both lessons) to indicate the impact of using video in the process of teaching and learning pronunciation.

Keywords: video, pronunciation, teaching, learning

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Authors: Ivan Maguire, Ciprian Briciu, Alan Barrett, Dara Kervick, Jens Ducrèe, Fiona Regan

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With the ever-increasing myriad of applications of which microfluidic devices are capable, reliable fluidic actuation development has remained fundamental to the success of these microfluidic platforms. There are a number of approaches which can be taken in order to integrate liquid actuation on microfluidic platforms, which can usually be split into two primary categories; active microvalves and passive microvalves. Active microvalves are microfluidic valves which require a physical parameter change by external, or separate interaction, for actuation to occur. Passive microvalves are microfluidic valves which don’t require external interaction for actuation due to the valve’s natural physical parameters, which can be overcome through sample interaction. The purpose of this paper is to illustrate how further improvements to past microvalve solutions can largely enhance systematic reliability and performance, with both novel active and passive microvalves demonstrated. Covered within this scope will be two alternative and novel microvalve solutions for centrifugal microfluidic platforms; a revamped pneumatic-dissolvable film active microvalve (PAM) strategy and a spray-on Sol-Gel based hydrophobic passive microvalve (HPM) approach. Both the PAM and the HPM mechanisms were demonstrated on a centrifugal microfluidic platform consisting of alternating layers of 1.5 mm poly(methyl methacrylate) (PMMA) (for reagent storage) sheets and ~150 μm pressure sensitive adhesive (PSA) (for microchannel fabrication) sheets. The PAM approach differs from previous SOLUBON™ dissolvable film methods by introducing a more reliable and predictable liquid delivery mechanism to microvalve site, thus significantly reducing premature activation. This approach has also shown excellent synchronicity when performed in a multiplexed form. The HPM method utilises a new spray-on and low curing temperature (70°C) sol-gel material. The resultant double layer coating comprises a PMMA adherent sol-gel as the bottom layer and an ultra hydrophobic silica nano-particles (SNPs) film as the top layer. The optimal coating was integrated to microfluidic channels with varying cross-sectional area for assessing microvalve burst frequencies consistency. It is hoped that these microvalving solutions, which can be easily added to centrifugal microfluidic platforms, will significantly improve automation reliability.

Keywords: centrifugal microfluidics, hydrophobic microvalves, lab-on-a-disc, pneumatic microvalves

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Authors: Ilana Lavy, Rami Rashkovits

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In this study, we describe Information Systems students' learning model which was applied by students in order to cope with professional knowledge gaps in the context of their final project. The students needed to implement a software system according to specifications and design they have made beforehand. They had to select certain technologies and use them. Most of them decided to use programming environments that were learned during their academic studies. The students had to cope with various levels of knowledge gaps. For that matter they used learning strategies that were organized by us as a learning model which includes two phases each suitable for different learning tasks. We analyze the learning model, describing advantages and shortcomings as perceived by the students, and provide excerpts to support our findings.

Keywords: knowledge gaps, independent learner skills, self-regulated learning, final project

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning

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Authors: Shahram Jamali, Samira Hamed

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One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.

Keywords: active queue management, RED, Markov model, random early detection algorithm

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Sonia Regina Lyra

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The desire for unequivocal clarity is understandable, but this can make one forget that things of the soul are experiential processes, or transformations, which should never be designated unilaterally if it is not wanted to transform something that moves, a living thing, into something static. Among the so-called ‘things of the soul’ there are especially spontaneous fantasies, that emerge during the processes, as a result from the use of the active imagination technique, for when fantasy is not forced, violated, or subjugated by an illegitimate, intellectually preconceived idea, then it is a legitimate and authentic product of the unconscious mind. This is how one can gain access to unadulterated information about everything that transcends the conscious mind. However, it is vital to discern between ego and non-ego, because this principle will result in a release of energy and a renewal of life, which will come to have meaning. This study will deal with the active imagination as a knowledge that depends on the individual experience of the therapist because the patient will be taken just to reach where the unconscious of the therapist was assimilated to his own conscience. In this way, the therapist becomes the method itself, being his personality, a fundamental part of an effective factor.

Keywords: active imagination, effective factor, synchronicity, symptom

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

Procedia PDF Downloads 94

Authors: Vinai K. Singh

Abstract:

In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

Procedia PDF Downloads 136

Authors: Carla Aramouny

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Digital fabrication, between advancements in software and machinery, is pushing practice today towards more complexity in design, allowing for unparalleled explorations. It is giving designers the immediate capacity to apply their imagined objects into physical results. Yet at no time have questions of material knowledge become more relevant and crucial, as technological advancements approach a radical re-invention of the design process. As more and more designers look towards tactile crafts for material know-how, an interest in natural behaviors has also emerged trying to embed intelligence from nature into the designed objects. Concerned with enhancing their immediate environment, designers today are pushing the boundaries of design by bringing in natural systems, materiality, and advanced fabrication as essential processes to produce active designs. New Territories, a yearly architecture and design course on digital design and materiality, allows students to explore processes of digital fabrication in intersection with natural systems and hands-on experiments. This paper will highlight the importance of learning from nature and from physical materiality in a digital design process, and how the simultaneous move between the digital and physical realms has become an essential design method. It will detail the work done over the course of three years, on themes of natural systems, crafts, concrete plasticity, and active composite materials. The aim throughout the course is to explore the design of products and active systems, be it modular facades, intelligent cladding, or adaptable seating, by embedding current digital technologies with an understanding of natural systems and a physical know-how of material behavior. From this aim, three main themes of inquiry have emerged through the varied explorations across the three years, each one approaching materiality and digital technologies through a different lens. The first theme involves crossing the study of naturals systems as precedents for intelligent formal assemblies with traditional crafts methods. The students worked on designing performative facade systems, starting from the study of relevant natural systems and a specific craft, and then using parametric modeling to develop their modular facades. The second theme looks at the cross of craft and digital technologies through form-finding techniques and elastic material properties, bringing in flexible formwork into the digital fabrication process. Students explored concrete plasticity and behaviors with natural references, as they worked on the design of an exterior seating installation using lightweight concrete composites and complex casting methods. The third theme brings in bio-composite material properties with additive fabrication and environmental concerns to create performative cladding systems. Students experimented in concrete composites materials, biomaterials and clay 3D printing to produce different cladding and tiling prototypes that actively enhance their immediate environment. This paper thus will detail the work process done by the students under these three themes of inquiry, describing their material experimentation, digital and analog design methodologies, and their final results. It aims to shed light on the persisting importance of material knowledge as it intersects with advanced digital fabrication and the significance of learning from natural systems and biological properties to embed an active performance in today’s design process.

Keywords: digital fabrication, design and craft, materiality, natural systems

Procedia PDF Downloads 128