Search results for: Deep learning based segmentation

Authors: Lorenzo Gontrani, Marilena Carbone, Domenica Tommasa Donia, Elvira Maria Bauer, Pietro Tagliatesta

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One of the fields where DES shows remarkable added values is the synthesis Of inorganic materials, in particular nanoparticles. In this field, the higher- ent and highly-tunable nano-homogeneities of DES structure give origin to a marked templating effect, a precious role that has led to the recent bloom of a vast number of studies exploiting these new synthesis media to prepare Nanomaterials and composite structures of various kinds. In this contribution, the most recent developments in the field will be reviewed, and some ex-citing examples of novel metal oxide nanoparticles syntheses using non-toxic type-IV Deep Eutectic Solvents will be described. The prepared materials possess nanometric dimensions and show flower-like shapes. The use of the pre- pared nanoparticles as fluorescent materials for the detection of various contaminants is under development.

Keywords: metal deep eutectic solvents, nanoparticles, inorganic synthesis, type IV DES, lamellar

Procedia PDF Downloads 130

Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

Procedia PDF Downloads 77

Authors: Hao Yuan, Yongjian Shen, Xiangjun He, Yuheng Li, Zhouzhou Zhang, Pengyu Zhang, Minkang Cai

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Remote sensing data can provide support for decision-making in disaster assessment or disaster relief. The traditional processing methods of sensitive targets in remote sensing mapping are mainly based on manual retrieval and image editing tools, which are inefficient. Methods based on deep learning for sensitive target hiding are faster and more flexible. But these methods have disadvantages in training time and cost of calculation. This paper proposed a target hiding model Self Attention (SA) Deepfill, which used self-attention modules to replace part of gated convolution layers in image inpainting. By this operation, the calculation amount of the model becomes smaller, and the performance is improved. And this paper adds free-form masks to the model’s training to enhance the model’s universal. The experiment on an open remote sensing dataset proved the efficiency of our method. Moreover, through experimental comparison, the proposed method can train for a longer time without over-fitting. Finally, compared with the existing methods, the proposed model has lower computational weight and better performance.

Keywords: remote sensing mapping, image inpainting, self-attention mechanism, target hiding

Procedia PDF Downloads 127

Authors: Sijia Guo

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The development of new technologies and the falling cost of high-speed Internet access have made it easier for institutes and language teachers to opt different ways to communicate with students at distance. The emergence of web-conferencing applications, which integrate text, chat, audio / video and graphic facilities, offers great opportunities for language learning to through the multimodal environment. This paper reports on data elicited from a Ph.D. study of using web-conferencing in the teaching of first-year Chinese class in order to promote learners’ collaborative learning. Firstly, a comparison of four desktop videoconferencing (DVC) tools was conducted to determine the pedagogical value of the videoconferencing tool-Blackboard Collaborate. Secondly, the evaluation of 14 campus-based Chinese learners who conducted five one-hour online sessions via the multimodal environment reveals the users’ choice of modes and their learning preference. The findings show that the tasks designed for the web-conferencing environment contributed to the learners’ collaborative learning and second language acquisition.

Keywords: computer-mediated communication (CMC), CALL evaluation, TBLT, web-conferencing, online Chinese teaching

Procedia PDF Downloads 304

Authors: Amira Potter

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Since three years, ESPRIT has adopted project-based learning across its curricula. The philosophy behind this reform is to prepare its future engineers to become more operational once they integrate the workplace. It allows them to learn all the required skills expected from them by their future employers. This learner-centered method helps the students take responsibility for their own learning, solve real-world problems and carry out muli-faceted projects. Therefore, the teacher who used to be considered as the detainer of the knowledge has become more of a facilitator and a coach, encouraging their students’ learning process. This innovative way to English teaching has enabled the students to learn the English language differently. The target language is learnt cooperatively through group work, presentations, debating and many other communicative activities. The speaking skill in English language remains by far the most challenging skill for Tunisian students with an educational background based on Arabic as a first language and French as a second language. The student’s initial resistance to speak English in front of their classmates and the way they end up performing their work, shows the real progress they managed to achieve through PBL approach. The article will focus on the positive impact PBL has had on oral fluency among Esprit engineering students and how it has been achieved. It will also describe how speaking skill is taught and assessed at ESPRIT.

Keywords: cooperative, engineer, innovative, project-based learning

Procedia PDF Downloads 316

Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

Procedia PDF Downloads 114

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

Procedia PDF Downloads 197

Authors: Ahmed Amin Mousa, M. Abd El-Salam

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This study discusses a simple solution for the problem of shortage in learning resources for kindergarten teachers. Occasionally, kindergarten teachers cannot access proper resources by usual search methods as libraries or search engines. Furthermore, these methods require a long time and efforts for preparing. The study is expected to facilitate accessing learning resources. Moreover, it suggests a potential direction for using QR code inside the classroom. The present work proposes that QR code can be used for digitizing kindergarten curriculums and accessing various learning resources. It investigates using QR code for saving information related to the concepts which kindergarten teachers use in the current educational situation. The researchers have established a guide for kindergarten teachers based on the Egyptian official curriculum. The guide provides different learning resources for each scientific and mathematical concept in the curriculum, and each learning resource is represented as a QR code image that contains its URL. Therefore, kindergarten teachers can use smartphone applications for reading QR codes and displaying the related learning resources for students immediately. The guide has been provided to a group of 108 teachers for using inside their classrooms. The results showed that the teachers approved the guide, and gave a good response.

Keywords: kindergarten, child, learning resources, QR code, smart phone, mobile

Procedia PDF Downloads 285

Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

Procedia PDF Downloads 172

Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

Procedia PDF Downloads 163

Authors: Paola Utreras, Yazmina Olmos, Loreto Sanhueza

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This work has the purpose of study and incorporate Problem Based Learning (PBL) for engineering students, through the analysis of several thermal images of dwellings located in different geographical points of the Region de los Ríos, Chile. The students analyze how heat is transferred in and out of the houses and how is the relation between heat transfer and climatic conditions that affect each zone. As a result of this activity students are able to acquire significant learning in the unit of heat and temperature, and manage to reverse previous conceptual errors related with energy, temperature and heat. In addition, student are able to generate prototype solutions to increase thermal efficiency using low cost materials. Students make public their results in a report using scientific writing standards and in a science fair open to the entire university community. The methodology used to measure previous Conceptual Errors has been applying diagnostic tests with everyday questions that involve concepts of heat, temperature, work and energy, before the unit. After the unit the same evaluation is done in order that themselves are able to evidence the evolution in the construction of knowledge. As a result, we found that in the initial test, 90% of the students showed deficiencies in the concepts previously mentioned, and in the subsequent test 47% showed deficiencies, these percent ages differ between students who carry out the course for the first time and those who have performed this course previously in a traditional way. The methodology used to measure Significant Learning has been by comparing results in subsequent courses of thermodynamics among students who have received problem based learning and those who have received traditional training. We have observe that learning becomes meaningful when applied to the daily lives of students promoting internalization of knowledge and understanding through critical thinking.

Keywords: engineering students, heat flow, problem-based learning, thermal images

Procedia PDF Downloads 229

Authors: Nathan Labiosa, Aryan Kohli

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This study introduces a hybrid machine learning model for classifying proteins, developed to address the complexities of protein sequence and structural analysis. Utilizing an architecture that combines a lightweight transformer with a concurrent neural network, the hybrid model leverages both sequential and intrinsic physical properties of proteins. Trained on a comprehensive dataset from the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the model demonstrates a classification accuracy of 95%, outperforming existing methods by at least 15%. The high accuracy achieved demonstrates the potential of this approach to innovate protein classification, facilitating advancements in drug discovery and the development of personalized medicine. By enabling precise protein function prediction, the hybrid model allows for specialized strategies in therapeutic targeting and the exploration of protein dynamics in biological systems. Future work will focus on enhancing the model’s generalizability across diverse datasets and exploring the integration of more machine learning techniques to refine predictive capabilities further. The implications of this research offer potential breakthroughs in biomedical research and the broader field of protein engineering.

Keywords: amino acids, deep learning, enzymes, neural networks, protein classification, proteins, transformers

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Authors: C. Vogeler

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Change processes such as individualization and digitalization have an impact on higher education. Graduates are expected to cooperate in creative work processes in their professional life. During their studies, they need to be prepared accordingly. This includes modern learning scenarios that integrate the benefits of digital media. Therefore, design thinking and blended learning have been combined in the project-based seminar conception introduced here. The presented seminar conception has been realized and evaluated with students of information sciences since September 2017. Within the seminar, the students learn to work on a project. They apply the methods in a problem-based learning scenario. Task of the case study is to arrange a conference on the topic gaming in libraries. In order to collaborative develop creative possibilities of realization within the group of students the design thinking method has been chosen. Design thinking is a method, used to create user-centric, problem-solving and need-driven innovation through creative collaboration in multidisciplinary teams. Central characteristics are the openness of this approach to work results and the visualization of ideas. This approach is now also accepted in the field of higher education. Especially in problem-based learning scenarios, the method offers clearly defined process steps for creative ideas and their realization. The creative process can be supported by digital media, such as search engines and tools for the documentation of brainstorming, creation of mind maps, project management etc. Because the students have to do two-thirds of the workload in their private study, design thinking has been combined with a blended learning approach. This supports students’ preparation and follow-up of the joint work in workshops (flipped classroom scenario) as well as the communication and collaboration during the entire project work phase. For this purpose, learning materials are provided on a Moodle-based learning platform as well as various tools that supported the design thinking process as described above. In this paper, the seminar conception with a combination of design thinking and blended learning is described and the potentials and limitations of the chosen strategy for the development of a course with a multimedia approach in higher education are reflected.

Keywords: blended learning, design thinking, digital media tools and methods, flipped classroom

Procedia PDF Downloads 193

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

Procedia PDF Downloads 474

Authors: Samar Alharbi, Yota Dimitriadi

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With the exponential growth of information and communication technologies in higher education, new online teaching strategies have become increasingly important for student engagement and learning. In particular, some institutions depend solely on asynchronous e-learning to provide courses for their students. The major challenge facing these institutions is how to improve the quality of teaching and learning in their asynchronous tools. One of the most important methods that can help e-learner to enhance their social learning and social presence in asynchronous learning setting is immediacy. This study explores tutors perceptions of their instructional immediacy practices as part of their communication actions in online learning environments. It was used a mixed-methods design under the umbrella of pragmatic philosophical assumption. The participants included tutors at an educational institution in a Saudi university. The participants were selected with a purposive sampling approach and chose an institution that offered fully online courses to students. The findings of the quantitative data show the importance of teachers’ immediacy practices in an online text-based learning environment. The qualitative data contained three main themes: the tutors’ encouragement of student interaction; their promotion of class participation; and their addressing of the needs of the students. The findings from these mixed methods can provide teachers with insights into instructional designs and strategies that they can adopt in order to use e-immediacy in effective ways, thus improving their students’ online learning experiences.

Keywords: asynchronous e-learning, higher education, immediacy, tutor

Procedia PDF Downloads 197

Authors: Nabbengo Minovia

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Teaching and education science encompasses a broad spectrum of research and practices aimed at understanding and improving the processes of teaching and learning. This abstract explores key themes within this field, including pedagogical methodologies, educational psychology, curriculum development, and the integration of technology in education. It highlights the importance of evidence-based practices in enhancing student outcomes and fostering lifelong learning. The abstract also discusses current trends such as personalized learning, inclusive education, and the role of educators as facilitators of knowledge and critical thinking. By examining these aspects, this abstract aims to contribute to the ongoing dialogue on effective educational strategies and their impact on shaping future generations.

Keywords: employability through skilling, excellence as a way to self-esteem, science as an art, skills gained through learning

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Authors: Adamu Zubairu Mohammed, Fransis Oforum Arimoro, Salihu Maikudi Ibrahim, Y. I. Auta, T. I. Arowosegbe, Y. Abdullahi

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The present study was conducted to evaluate the effect of depth on the distribution of zooplankton and some physicochemical parameters in Tungan Kawo Lake (Wushishi dam). Water and zooplankton samples were collected from the surface, 3.0 meters deep and 6.0 meters deep, for a period of 24 hours for six months. Standard procedures were adopted for the determination of physicochemical parameters. Results have shown significant differences in the pH, DO, BOD Hardness, Na, and Mg. A total of 1764 zooplankton were recorded, comprising 35 species, with cladocera having 18 species (58%), 14 species of copepoda (41%), 3 species of diptera (1.0%). Results show that more of the zooplankton were recorded in the 3.0 meters-deep region compared to the two other depts and a significant difference was observed in the distribution of Ceriodaphnia dubia, Daphnia laevis, and Leptodiaptomus coloradensis. Though the most abundant zooplankton was recorded in the 3.0 meters deep, Leptodiaptomus coloradesnsis, which was observed in the 6.0 meters deep as the most individual observed, this was followed by Daphnia laevis. Canonical correspondence analysis between physicochemical parameters and the zooplankton indicated a good relationship in the Lake. Ceriodaphnia dubia was found to have a good association with oxygen, sodium, and potassium, while Daphnia laevis and Leptodiaptomus coloradensis are in good relationship with magnesium and phosphorus. It was generally observed that this depth does not have much influence on the distribution of zooplankton in Wushishi Lake.

Keywords: zooplankton, standard procedures, canonical correspondence analysis, Wushishi, canonical, physicochemical parameter

Procedia PDF Downloads 86

Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Budoor Al Abid

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Educational systems are increasingly provided as open online services, providing guidance and support for individual learners. To adapt the learning systems, a proper evaluation must be made. This paper builds the evaluation model Fuzzy C Means Adaptive System (FCMAS) based on data mining techniques to assess the difficulty of the questions. The following steps are implemented; first using a dataset from an online international learning system called (slepemapy.cz) the dataset contains over 1300000 records with 9 features for students, questions and answers information with feedback evaluation. Next, a normalization process as preprocessing step was applied. Then FCM clustering algorithms are used to adaptive the difficulty of the questions. The result is three cluster labeled data depending on the higher Wight (easy, Intermediate, difficult). The FCM algorithm gives a label to all the questions one by one. Then Random Forest (RF) Classifier model is constructed on the clustered dataset uses 70% of the dataset for training and 30% for testing; the result of the model is a 99.9% accuracy rate. This approach improves the Adaptive E-learning system because it depends on the student behavior and gives accurate results in the evaluation process more than the evaluation system that depends on feedback only.

Keywords: machine learning, adaptive, fuzzy logic, data mining

Procedia PDF Downloads 192

Authors: Ibode Osa Felix

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The use of e-Learning is presently intensified in the academic world following the outbreak of the Covid-19 pandemic in early 2020. Hitherto, e-learning had made its debut in teaching and learning many years ago when it emerged as an aspect of Computer Based Teaching, but never before has its patronage become so important and popular as currently obtains. Previous studies revealed that there is an ongoing debate among researchers on the efficacy of the E-learning mode of instruction over the traditional teaching method. Therefore, the study examined the effect of E-learning and Conventional Mode of Instruction on Students Achievement in the English Language. The study is a quasi-experimental study in which 230 students, from three public secondary schools, were selected through a simple random sampling technique. Three instruments were developed, namely, E-learning Instructional Guide (ELIG), Conventional Method of Instructional Guide (CMIG), and English Language Achievement Test (ELAT). The result revealed that students taught through the conventional method had better results than students taught online. The result also shows that girls taught with the conventional method of teaching performed better than boys in the English Language. The study, therefore, recommended that effort should be made by the educational authorities in Nigeria to provide internet facilities to enhance practices among learners and provide electricity to power e-learning equipment in the secondary schools. This will boost e-learning practices among teachers and students and consequently overtake conventional method of teaching in due course.

Keywords: e-learning, conventional method of teaching, achievement in english, electricity

Procedia PDF Downloads 169

Authors: Takwa Jahan

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Music can be used in ESL classroom to create a learning environment. As literature abounds with positive statements, music can be used as a vehicle for second language acquisition. Music can be applied as an instrument to help second language learners to acquire vocabulary, grammar, spelling and other four skills and to expand cultural knowledge. Vocabulary learning is perceived boring by learners. As listening to music and singing songs are enjoyable to students, it can be used effectively to acquire vocabulary in second language. This paper reports a study to find out how music exhilarates vocabulary acquisition as the learners stay relaxed and thus learning becomes more enjoyable. For conducting my research two groups of fifty students- music and non-music group were formed. Data were collected through class observation, test, questionnaires, and interview. The finding shows that music group acquired much amount of vocabulary than the non-music group. They enjoyed vocabulary learning activities based on listening songs.

Keywords: effective instrument, ESL classroom, music, relax environment, vocabulary learning

Procedia PDF Downloads 365

Authors: Alvaro Figueira, Bruno Cabral

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Nowadays, students are used to be assessed through an online platform. Educators have stepped up from a period in which they endured the transition from paper to digital. The use of a diversified set of question types that range from quizzes to open questions is currently common in most university courses. In many courses, today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload of modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be a lot of feedback from these activities to student’s, usually it is restricted to the assessments of online well-defined tasks. In this article, we propose an automatic system that informs students of abnormal deviations of a 'correct' learning path in the course. Our approach is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. In the major learning management systems (LMS) currently available, the interaction between the students and the system itself is registered in log files in the form of registers that mark beginning of actions performed by the user. Our proposed system uses that logged information to derive new one: the time each student spends on each activity, the time and order of the resources used by the student and, finally, the online resource usage pattern. Then, using the grades assigned to the students in previous years, we built a learning dataset that is used to feed a machine learning meta classifier. The produced classification model is then used to predict the grades a learning path is heading to, in the current year. Not only this approach serves the teacher, but also the student to receive automatic feedback on her current situation, having past years as a perspective. Our system can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS.

Keywords: data mining, e-learning, grade prediction, machine learning, student learning path

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Authors: Eirini Busack

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Since the outbreak of the Covid-19 pandemic and the sudden transfer to online teaching, teachers have struggled to reconstruct their teaching and learning materials to adapt them to the new reality of online teaching and learning. Consequently, the pupils’ learning was disrupted during this orientation phase. Due to the above situation, teachers from all fields concluded that it is vital that their pupils should be able to continue their learning even without the teacher being physically present. Various websites and applications have been in use since then in hope that pupils will still enjoy a qualitative education; unfortunately, this was often not the case. To address this issue, it was therefore decided to focus the research on the development of digital learning paths. The fundamentals of these learning paths include the implementation of scenario-based learning (digital storytelling), the integration of media-didactic theory to make it pedagogically appropriate for learners, alongside instructional design knowledge and the drive to promote autonomous learners. This particular research is being conducted within the frame of the research project “Sustainable integration of subject didactic digital teaching-learning concepts” (InDiKo, 2020-2023), which is currently conducted at the University of Education Karlsruhe and investigates how pre-service teachers can acquire the necessary interdisciplinary and subject-specific media-didactic competencies to provide their future learners with digitally enhanced learning opportunities, and how these competencies can be developed continuously and sustainably. As English is one of the subjects involved in this project, the English Department prepared a seminar for the pre-service secondary teachers: “Media-didactic competence development: Developing learning paths & Digital Storytelling for English grammar teaching.” During this seminar, the pre-service teachers plan and design a Moodle-based differentiated lesson sequence on an English grammar topic that is to be tested by secondary school pupils. The focus of the present research is to assess the secondary school pupils’ expectations from an English grammar-focused digital learning path created by pre-service English teachers. The nine digital learning paths that are to be distributed to 25 pupils were produced over the winter and the current summer semester as the artifact of the seminar. Finally, the data to be quantitatively analysed and interpreted derive from the online questionnaires that the secondary school pupils fill in so as to reveal their expectations on what they perceive as a stimulating and thus effective grammar-focused digital learning path.

Keywords: digital storytelling, learning paths, media-didactics, autonomous learning

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Authors: Ahlem Abid, Farah Jemili

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Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

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Authors: Natarajan Hema, Tamilarasan Karunakaran

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Technology is remodeling the process of teaching and learning. An inflection point is faced where technological interventions are rewiring learning process in formal classrooms. Employment depends on dynamic learning capability. Transforming the functionalities of teaching-learning-assessment through innovation is needed to modify the roles of teacher to enabler and learner to the dynamic learner. This makeover is vital for English language teaching where English is acquired as a skill, exercised as ability and get stabilized as a competence. This reshaping could be achieved through providing autonomy to participants of learning. This paper explores parameters and components aiding such a transformation. The differentiated responsibilities and other critical learning support systems are projected as viable options. New age teaching practices are studied for feasibilities to aid transformation and being put forth an inter-operable teaching-learning system for a learner-centric ELT classrooms. LOTUS model developed by the authors is also studied for its inclusiveness to promote skill acquisition.

Keywords: ELT methodology, communicative competence, skill acquisition , new age teaching

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Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

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Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

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Authors: Ricky Saputra, Rashmi Walvekar, Mohammad Khalid

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Landfills, water contamination, and toxic gas emission are a few impacts faced by the environment due to the increasing number of αof waste rubber tires (WRT). In spite of such concerning issue, only minimal efforts are taken to reclaim or recycle these wastes as their products are generally not-profitable for companies. Unlike the typical reclamation process, devulcanization is a method to selectively cleave sulfidic bonds within vulcanizates to avoid polymeric scissions that compromise elastomer’s mechanical and tensile properties. The process also produces devulcanizates that are re-processable similar to virgin rubber. Often, a devulcanizing agent is needed. In the current study, novel and sustainable ammonium chloride-based ternary deep eutectic solvents (TDES), with a different number of α-hydrogens, were utilised to devulcanize ground rubber tire (GRT) as an effort to implement green chemistry to tackle such issue. 40-mesh GRT were soaked for 1 day with different TDESs and sonicated at 37-80 kHz for 60-120 mins and heated at 100-140oC for 30-90 mins. Devulcanizates were then filtered, dried, and evaluated based on the percentage of by means of Flory-Rehner calculation and swelling index. The result shows that an increasing number of α-Hs increases the degree of devulcanization, and the value achieved was around eighty-percent, thirty percent higher than the typical industrial-autoclave method. Resulting bondages of devulcanizates were also analysed by Fourier transform infrared spectrometer (FTIR), Horikx fitting, and thermogravimetric analyser (TGA). The earlier two confirms only sulfidic scissions were experienced by GRT through the treatment, while the latter proves the absence or negligibility of carbon-chains scission.

Keywords: ammonium, sustainable, deep eutectic solvent, α-hydrogen, waste rubber tire

Procedia PDF Downloads 124

Authors: Sandra Baßler, Peter Dünow, Mathias Marquardt

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A Fourier series based learning control (FSBLC) algorithm for tracking trajectories of mechanical systems with unknown nonlinearities is presented. Two processes are introduced to which the FSBLC with PD controller is applied. One is a simplified service robot capable of climbing stairs due to special wheels and the other is a propeller driven pendulum with nearly the same requirements on control. Additionally to the investigation of learning the feed forward for the desired trajectories some considerations on the implementation of such an algorithm on low cost microcontroller hardware are made. Simulations of the service robot as well as practical experiments on the pendulum show the capability of the used FSBLC algorithm to perform the task of improving control behavior for repetitive task of such mechanical systems.

Keywords: climbing stairs, FSBLC, ILC, service robot

Procedia PDF Downloads 310

Authors: Pia Kastl

Abstract:

By combining face-to-face sessions with digital selflearning units, the learning process can be enhanced and learning success improved. Potentials of blended learning are the flexibility and possibility to get in touch with lecturers and fellow students face-toface. It also offers the opportunity to individualize and self-regulate the learning process. Aim of this article is to analyse how different learning environments affect students’ learning behavior and how digital tools can be used effectively. The analysis also considers the extent to which the field of study affects the students’ preferences. Semi-structured interviews were conducted with students from different disciplines at two German universities (N= 60). The questions addressed satisfaction and perception of online, faceto-face and blended learning courses. In addition, suggestions for improving learning experience and the use of digital tools in the different learning environments were surveyed. The results show that being present on campus has a positive impact on learning success and online teaching facilitates flexible learning. Blended learning can combine the respective benefits, although one challenge is to keep the time investment within reasonable limits. The use of digital tools differs depending on the subject. Medical students are willing to use digital tools to improve their learning success and voluntarily invest more time. Students of the humanities and social sciences, on the other hand, are reluctant to invest additional time. They do not see extra study material as an additional benefit their learning success. This study illustrates how these heterogenous demands on learning environments can be met. In addition, potential for improvement will be identified in order to foster both learning process and learning success. Learning environments can be meaningfully enriched with digital elements to address student diversity in higher education.

Keywords: blended learning, higher education, diversity, learning styles

Procedia PDF Downloads 65

Authors: Nadira Zaidi

Abstract:

The aim of this study is to gain direct feedback of interviewees in a simulated interview process. Reflection based on qualitative data analysis has been utilized through the Gibbs Reflective Cycle, with 30 students as respondents at the Undergraduate level. The respondents reflected on the positive and negative aspects of this active learning process in order to increase their performance in actual job interviews. Results indicate that students engaged in the process successfully imbibed the feedback that they received from the interviewers and also identified the areas that needed improvement.

Keywords: experiential learning, positive and negative impact, reflection, simulated

Procedia PDF Downloads 139