Search results for: spatial learning

Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

Procedia PDF Downloads 156

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Matthias Grünke

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In this single-case study, we evaluated the effects of a self-graphing intervention to help students improve their reading fluency. Our participant was a 10-year-old girl with a suspected learning disability in reading. We applied an ABAB reversal design to test the efficacy of our approach. The dependent measure was the number of correctly read words from a children’s book within five minutes. Our participant recorded her daily performance using a simple line diagram. Results indicate that her reading rate improved simultaneously with the intervention and dropped as soon as the treatment was suspended. The findings give reasons for optimism that our simple strategy can be a very effective tool in supporting students with learning disabilities to boost their reading fluency.

Keywords: single-case study, learning disabilities, elementary education, reading problems, reading fluency

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Authors: Enrique Barra, Aldo Gordillo, Juan Quemada

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This paper presents the Virtual Science Hub platform. It is an open source platform that combines a social network, an e-learning authoring tool, a video conference service and a learning object repository for science teaching enrichment. These four main functionalities fit very well together. The platform was released in April 2012 and since then it has not stopped growing. Finally we present the results of the surveys conducted and the statistics gathered to validate this approach.

Keywords: e-learning, platform, authoring tool, science teaching, educational sciences

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Authors: Xuewen Chen, Aizhen Liang, Xiaoping Zhang

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To better understand the spatial variability of soil penetration resistance (SPR) and soil water content (SWC) induced by different tillage and crop rotation in a Mollisol of Northeast China, the soil was sampled from the tillage experiment which was established in Dehui County, Jilin Province, Northeast China, in 2001. Effect of no-tillage (NT), moldboard plow (MP) and ridge tillage (RT) under corn-soybean rotation (C-S) and continuous corn (C-C) system on SPR and SWC were compared with horizontal and vertical variations. The results showed that SPR and SWC spatially varied across the ridge. SPR in the rows was higher than inter-rows, especially in topsoil (2.5-15 cm) of NT and RT plots. SPR of MP changed in the trend with the curve-shaped ridge. In contrast to MP, NT, and RT resulted in average increment of 166.3% and 152.3% at a depth of 2.5-17.5 cm in the row positions, respectively. The mean SPR in topsoil in the rows means soil compaction is not the main factor limiting plant growth and crop yield. SPR in the row of RT soil was lower than NT at a depth of 2.5-12.5 cm. The SWC in NT and RT soil was highest in the inter-rows and least in the rows or shoulders, respectively. However, the lateral variation trend of MP was opposite to NT. From the profile view of SWC, MP was greater than NT and RT in 0-20 cm of the rows. SWC in RT soil was higher than NT in the row of 0-20 cm. Crop rotation did not have a marked impact on SPR and SWC. In addition to the tillage practices, the factor which affects SPR greatly was depth but not position. These two factors have significant effects on SWC. These results indicated that the adoption of RT was a more suitable conservation tillage practices than NT in the black soil of Northeast China.

Keywords: row, soil penetration resistance, spatial variability, tillage practice

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Authors: Ya-Mei Chang

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This research is intended to apply spatio-temporal point process methods to the dengue fever data in Tainan. The spatio-temporal intensity function of the dataset is assumed to be separable. The kernel estimation is a widely used approach to estimate intensity functions. The intensity function is very helpful to study the relation of the spatio-temporal point process and some covariates. The covariate effects might be nonlinear. An nonparametric smoothing estimator is used to detect the nonlinearity of the covariate effects. A fitted parametric model could describe the influence of the covariates to the dengue fever. The correlation between the data points is detected by the K-function. The result of this research could provide useful information to help the government or the stakeholders making decisions.

Keywords: dengue fever, spatial point process, kernel estimation, covariate effect

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Authors: Suriyani Ariffin, Nor Aziah Alias, Khairil Iskandar Othman, Haslinda Yusoff

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An Integrated Cumulative Grade Point Average (iCGPA) is a mechanism and strategy to ensure the curriculum of an academic programme is constructively aligned to the expected learning outcomes and student performance based on the attainment of those learning outcomes that is reported objectively in a spider web. Much effort and time has been spent to develop a viable mechanism and trains academics to utilize the platform for reporting. The question is: How well do learners conceive the idea of their achievement via iCGPA and whether quality learner attributes have been nurtured through the iCGPA mechanism? This paper presents the architecture of an integrated CGPA mechanism purported to address a holistic evaluation from the evaluation of courses learning outcomes to aligned programme learning outcomes attainment. The paper then discusses the students’ understanding of the mechanism and evaluation of their achievement from the generated spider web. A set of questionnaires were distributed to a group of students with iCGPA reporting and frequency analysis was used to compare the perspectives of students on their performance. In addition, the questionnaire also explored how they conceive the idea of an integrated, holistic reporting and how it generates their motivation to improve. The iCGPA group was found to be receptive to what they have achieved throughout their study period. They agreed that the achievement level generated from their spider web allows them to develop intervention and enhance the programme learning outcomes before they graduate.

Keywords: learning outcomes attainment, iCGPA, programme learning outcomes, spider web, iCGPA reporting skills

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Authors: Guanghua Zhang, Fubao Wang, Weijun Duan

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Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output.

Keywords: convolution neural network, discriminator, generator, unsupervised learning

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Resham Badri

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Cities today are unable to justify equitable distribution of theirsocio- economic and infrastructural benefits to the marginalized urban poor, and the emergence of a pressing pandemic like COVID-19 has amplified its impact. Lack of identity, vulnerability, and inaccessibility contribute to exclusion. Owing to systemic gaps in institutional processes, urban development policiesfail to represent and cater to the urban poor. This paper aims to be a roadmap for the Indian Government to understand the significance of the designation of a navigable spatial identity to slum dwellers in the form of a digital address, which can form the fundamental basis of identification to enable accessibility to not only basic servicesbut also other utilities. Capitalizing on such a granular and technology backed approach shall allow to target and reach out to the urban poor strategically andaid effective urban governance. This paper adopts a three-pronged approach;(i) Policy analysis- understanding gaps in existing urban policies of India, such as the Pradhan Mantri Awas Yojana, Swachh Bharat Mission, and Adhaar Card policy, (ii) Program Evaluation- analyzing a case study, where slum dwellers in Kolhapur city in India have been provided with navigable addresses using Google Plus Codes and have gained access to basic services, vaccinations, and other emergency deliveries in COVID-19 times, (iii) Policy recommendation. This designation of a navigable spatial identity has tremendous potential to form the foundation on which policies can further base their data collection and service delivery processes to not only provide basic services but also other infrastructural and social welfare initiatives. Hence, a massive window of opportunity lies in addressing the unaddressed to elevate their living standards and respond to their basic needs.

Keywords: policy analysis, urban poor, navigable spatial identity, accessibility

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Authors: Kumiko Inagaki

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In recent, years, Japanese universities have begun to face a dilemma: more than half of all high school graduates go on to attend an institution of higher learning, overwhelming Japanese universities accustomed to small student bodies. These universities have been forced to embrace qualitative changes to accommodate the increased number and diversity of students who enter their establishments, students who differ in their motivations for learning, their levels of eagerness to learn, and their perspectives on the future. One of these changes is an increase in awareness among Japanese educators of the importance of active learning, which deepens students’ understanding of course material through a range of activities, including writing, speaking, thinking, and presenting, in addition to conventional “passive learning” methods such as listening to a one-way lecture. 　The purpose of this study is to examine the effectiveness of the teaching method adapted to improve active learning. A teaching method designed to promote active learning was implemented in a social science course at one of the most popular universities in Japan. A questionnaire using a five-point response format was given to students in 2,305 courses throughout the university to evaluate the effectiveness of the method based on the following measures: ① the ratio of students who were motivated to attend the classes, ② the rate at which students learned new information, and ③ the teaching method adopted in the classes. The results of this study show that the percentage of students who attended the active learning course eagerly, and the rate of new knowledge acquired through the course, both exceeded the average for the university, the department, and the subject area of social science. In addition, there are strong correlations between teaching method and student motivation and between teaching method and knowledge acquisition rate. These results indicate that the active learning teaching method was effectively implemented and that it may improve student eagerness to attend class and motivation to learn.

Keywords: active learning, Japanese university, teaching method, university education

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Authors: Zhang Cong, Cai Runlin, Jia Fengjiao

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The evolution of the urban agglomeration spatial structure requires strong support of the inter-city traffic system. And the inter-city traffic system can not only meet the demand of the urban agglomeration transportation but also guide the economic development. To correctly understand the relationship between inter-city traffic planning and urban agglomeration can help the urban agglomeration coordinated developing with the inter-city traffic system. The Yangtze River Delta is one of the most representative urban agglomerations in China with strong economic vitality, high city levels, diversified urban space form, and improved transport infrastructure. With the promotion of industrial division in the Yangtze River Delta and the regional travel facilitation brought by inter-city traffic, the urban agglomeration is characterized by highly increasing of inter-city transportation demand, the urbanization of regional traffic, adjacent regional transportation links breaking administrative boundaries, the networked channels and so on. Therefore, the development of inter-city traffic system presents new trends and challenges. This paper studies the interactions between inter-city traffic system and regional economic growth, regional factor flow, and regional spatial structure evolution in the Yangtze River Delta from two dimensions of time and space. On this basis, the adaptability of inter-city traffic development mode and urban agglomeration space structure is analyzed. First of all, the coordination between urban agglomeration planning and inter-city traffic planning is judged from the planning level. Secondly, the coordination between inter-city traffic elements and industries and population distributions is judged from the perspective of space. Finally, the coordination of the cross-regional planning and construction of inter-city traffic system is judged. The conclusions can provide an empirical reference for intercity traffic planning in Yangtze River Delta region and other urban agglomerations, and it is also of great significance to optimize the allocation of urban agglomerations and the overall operational efficiency.

Keywords: evolution, interaction, inter-city traffic system, spatial structure

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Authors: Erhan Eroğlu

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This paper presents a new method called Mentor and Mentee Based Learning. This new method is becoming more and more common especially at workplaces. This study is significant as it clearly underlines how it works well. Education has always aimed at equipping people with the necessary knowledge and information. For many decades it went on teachers’ talk and chalk methods. In the second half of the nineteenth century educators felt the need for some changes in delivery systems. Some new terms like self- discovery, learner engagement, student centered learning, hands on learning have become more and more popular for such a long time. However, some educators believe that there is much room for better learning methods in many fields as they think the learners still cannot fulfill their potential capacities. Thus, new systems and methods are still being developed and applied at education centers and work places. One of the latest methods is assigning some mentors for the newly recruited employees and training them within a mentor and mentee program which allows both parties to see their strengths and weaknesses and the areas which can be improved. This paper aims at finding out the perceptions of the mentors and mentees on the programs they are offered at their workplaces and suggests some betterment alternatives. The study has been conducted via a qualitative method whereby some interviews have been done with both mentors and mentees separately and together. Results show that it is a great way to train inexperienced one and also to refresh the older ones. Some points to be improved have also been underlined. The paper shows that education is not a one way path to follow.

Keywords: learning, mentor, mentee, training

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Jaeseo Lim, Jooyong Park

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Although there is ample evidence that student involvement enhances learning, college education is still mainly centered on lectures. However, in recent years, the effectiveness of discussions and the use of collective intelligence have attracted considerable attention. This study intends to examine the empirical effects of discussions on learning outcomes in various conditions. Eighty eight college students participated in the study and were randomly assigned to three groups. Group 1 was told to review material after a lecture, as in a traditional lecture-centered class. Students were given time to review the material for themselves after watching the lecture in a video clip. Group 2 participated in a discussion in groups of three or four after watching the lecture. Group 3 participated in a discussion after studying on their own. Unlike the previous two groups, students in Group 3 did not watch the lecture. The participants in the three groups were tested after studying. The test questions consisted of memorization problems, comprehension problems, and application problems. The results showed that the groups where students participated in discussions had significantly higher test scores. Moreover, the group where students studied on their own did better than that where students watched a lecture. Thus discussions are shown to be effective for enhancing learning. In particular, discussions seem to play a role in preparing students to solve application problems. This is a preliminary study and other age groups and various academic subjects need to be examined in order to generalize these findings. We also plan to investigate what kind of support is needed to facilitate discussions.

Keywords: discussions, education, learning, lecture, test

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Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Michael Stewart

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Mutation testing is a type of software testing proposed in the 1970s where program statements are deliberately changed to introduce simple errors so that test cases can be validated to determine if they can detect the errors. Test cases are executed against the mutant code to determine if one fails, detects the error and ensures the program is correct. One major issue with this type of testing was it became intensive computationally to generate and test all possible mutations for complex programs. This paper used reinforcement learning and parallel processing within the context of mutation testing for the selection of mutation operators and test cases that reduced the computational cost of testing and improved test suite effectiveness. Experiments were conducted using sample programs to determine how well the reinforcement learning-based algorithm performed with one live mutation, multiple live mutations and no live mutations. The experiments, measured by mutation score, were used to update the algorithm and improved accuracy for predictions. The performance was then evaluated on multiple processor computers. With reinforcement learning, the mutation operators utilized were reduced by 50 – 100%.

Keywords: automated-testing, machine learning, mutation testing, parallel processing, reinforcement learning, software engineering, software testing

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Authors: Lise P. Labéjof, Krisnayne S. Ribeiro, Nicolle P. dos Santos

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An unrecorded experiment of use of the smartphone as a tool for practical classes of histology is presented in this article. Behavior, learning of the students of three science courses at the University were analyzed and compared as well as the mode of teaching of this discipline and the appreciation of the students, using either digital photographs taken by phone or drawings for record microscopic observations, analyze and interpret histological sections of human or animal tissues.

Keywords: cell phone, digital micrographies, learning of sciences, teaching practices

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Authors: Ramorola Mmankoko Ziphorah

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Videoconference technology represents a recent experiment of technology integration into teaching and learning in South Africa. Increasingly, videoconference technology is commonly used as a substitute for the traditional face-to-face approaches to teaching and learning in helping tutors to reshape and change their teaching practices. Interestingly, though, some studies point out that videoconference technology is commonly used for knowledge dissemination by tutors and not so much for the actual teaching of course content in Open and Distance Learning context. Though videoconference technology has become one of the dominating technologies available among Open and Distance Learning institutions, it is not clear that it has been used as effectively to bridge the learning distance in time, geography, and economy. While tutors are prepared theoretically, in most tutor preparation programs, on the use of videoconference technology, there are still no practical guidelines on how they should go about integrating this technology into their course teaching. Therefore, there is an urgent need to focus on tutor development, specifically on their capacities and skills to use videoconference technology. The assumption is that if tutors become competent in the use of the videoconference technology for course teaching, then their use in Open and Distance Learning environment will become more commonplace. This is the imperative of the 4th Industrial Revolution (4IR) on education generally. Against the current vacuum in the practice of using videoconference technology for course teaching, the current study proposes a qualitative phenomenological approach to investigate the efficacy of videoconferencing as an approach to student learning. Using interviews and observation data from ten participants in Open and Distance Learning institution, the author discusses how dialogue and structure interacted to provide the participating tutors with a rich set of opportunities to deliver course content. The findings to this study highlight various challenges experienced by tutors when using videoconference technology. The study suggests tutor development programs on their capacity and skills and on how to integrate this technology with various teaching strategies in order to enhance student learning. The author argues that it is not merely the existence of the structure, namely the videoconference technology, that provides the opportunity for effective teaching, but that is the interactions, namely, the dialogue amongst tutors and learners that make videoconference technology an attractive vehicle for challenging and changing tutors practice.

Keywords: open distance learning, transactional distance, tutor, videoconference

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Authors: S. Chee Choy, Daljeet Singh Sedhu

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This study examines the relationships between how and why students learned and academic achievement for 2646 university students from various faculties. The LALQ, a self-report measure of student approaches to learning was administered and academic achievement data were obtained from student CGPA. The results showed significant differences in the approach to learning of male and female students. How and why students learned can influence their achievement and efficacy as well. High and low achievers have different learning behaviours. High female achievers were more likely to learn for a better future and be persistent in it. Meanwhile high male achievers were more likely to seek approval from their peers and be more confident about graduating on time from their university. The implications of individual differences and limitations of the study are discussed.

Keywords: student learning, learner awareness, student achievement, LALQ

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Authors: Francimar Alves, Marcos Castro, Marllus Lustosa

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In the context of the teaching of computer programming, the choice of tool to use is very important in the initiation and continuity of learning a programming language. The literature tools do not always provide usability and pedagogical dynamism clearly and accurately for effective learning. This hypothesis implies fall in productivity and difficulty of learning a particular programming language by students. The integrated development environments (IDEs) Dev-C ++ and Code :: Blocks are widely used in introductory courses for undergraduate courses in Computer Science for learning C and C ++ languages. However, after several years of discontinuity maintaining the source code of Dev-C ++ tool, the continued use of the same in the teaching and learning process of the students of these institutions has led to difficulties, mainly due to the lack of update by the official developers, which resulted in a sequence of problems in using it on educational settings. Much of the users, dissatisfied with the IDE Dev-C ++, migrated to Code :: Blocks platform targeting the more dynamic range in the learning process of the C and C ++ languages. Nevertheless, there is still the need to create a tool that can provide the resources of most IDE's software development literature, however, more interactive, simple, accurate and efficient. This motivation led to the creation of Falcon C ++ tool, IDE that brings with features that turn it into an educational platform, which focuses primarily on increasing student learning index in the early disciplines of programming and algorithms that use the languages ​​C and C ++ . As a working methodology, a field research to prove the truth of the proposed tool was used. The test results and interviews with entry-level students and intermediate in a postsecondary institution gave basis for the composition of this work, demonstrating a positive impact on the use of the tool in teaching programming, showing that the use of Falcon C ++ software is beneficial in the teaching process of the C and C ++ programming languages.

Keywords: ide, education, learning, development, language

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Authors: Lynette Nickleberry Stewart

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The present presentation seeks to explore distinction across disciplines in the appropriateness of accelerated courses and suggestions for implementing accelerated courses in various disciplines. Grounded in a review of research on accelerated learning (AL), this presentation will discuss the intradisciplinary appropriateness of accelerated courses for various topics and student types, and make suggestions for implementing augmented courses. Meant to inform an emerging ‘handbook’ of accelerated course development, facilitators will lead participants in a discussion of personal challenges and triumphs in their attempts at accelerated course design.

Keywords: adult learning, abbreviated courses, accelerated learning, course design

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Authors: Bahman Gorjian

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This study investigated the effects of closed-captioning on vocabulary learning and listening comprehension of English-language movies. Captioning is thus an effective language-learning tool for persons learning English as a second language. Because students may learn a foreign language "passively," utilizing subtitles on television could make learning English enjoyable for them. Closed captioning is an electrical technique that converts spoken words from a television program's audio into written text that mimics subtitles in another language. The findings of this study showed the importance of using closed-captioning software when learning a foreign language. As a result, these must be considered when teaching EFL/ESL. The influence of watching movies with closed captions on vocabulary and hearing is compared in this study. This goal can be reached by employing a closed-captioned movie as a teaching tool in the classroom. This research was critical because it demonstrates the advantages of closed-captioning programs in EFL classrooms for both teachers and students. The study's findings assisted teachers in better understanding how to employ closed captioning as a teaching tool in the classroom. The effects will be seen as even more significant for language learners who use the method.

Keywords: closed-captions, listening, comprehension, vcabulary

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Authors: Dominique Arrouays, Songchao Chen, Anne C. Richer-De-Forges

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Soils are at the crossing of many issues such as food and water security, sustainable energy, climate change mitigation and adaptation, biodiversity protection, human health and well-being. They deliver many ecosystem services that are essential to life on Earth. Therefore, there is a growing demand for soil information on a national and global scale. Unfortunately, many countries do not have detailed soil maps, and, when existing, these maps are generally based on more or less complex and often non-harmonized soil classifications. An estimate of their uncertainty is also often missing. Thus, there are not easy to understand and often not properly used by end-users. Therefore, there is an urgent need to provide end-users with spatially exhaustive grids of essential soil properties, together with an estimate of their uncertainty. One way to achieve this is digital soil mapping (DSM). The concept of DSM relies on the hypothesis that soils and their properties are not randomly distributed, but that they depend on the main soil-forming factors that are climate, organisms, relief, parent material, time (age), and position in space. All these forming factors can be approximated using several exhaustive spatial products such as climatic grids, remote sensing products or vegetation maps, digital elevation models, geological or lithological maps, spatial coordinates of soil information, etc. Thus, DSM generally relies on models calibrated with existing observed soil data (point observations or maps) and so-called “ancillary co-variates” that come from other available spatial products. Then the model is generalized on grids where soil parameters are unknown in order to predict them, and the prediction performances are validated using various methods. With the growing demand for soil information at a national and global scale and the increase of available spatial co-variates national and continental DSM initiatives are continuously increasing. This short review illustrates the main national and continental advances in Europe, the diversity of the approaches and the databases that are used, the validation techniques and the main scientific and other issues. Examples from several countries illustrate the variety of products that were delivered during the last ten years. The scientific production on this topic is continuously increasing and new models and approaches are developed at an incredible speed. Most of the digital soil mapping (DSM) products rely mainly on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs or for existing conventional maps. However, some scientific issues remain to be solved and also political and legal ones related, for instance, to data sharing and to different laws in different countries. Other issues related to communication to end-users and education, especially on the use of uncertainty. Overall, the progress is very important and the willingness of institutes and countries to join their efforts is increasing. Harmonization issues are still remaining, mainly due to differences in classifications or in laboratory standards between countries. However numerous initiatives are ongoing at the EU level and also at the global level. All these progress are scientifically stimulating and also promissing to provide tools to improve and monitor soil quality in countries, EU and at the global level.

Keywords: digital soil mapping, global soil mapping, national and European initiatives, global soil mapping products, mini-review

Procedia PDF Downloads 184

Authors: Lee Cahaner, Nissim Leon

Abstract:

The segregation of populations is one of the main axes in the research of urban geography, which refers to the spatial and functional relationships between settlements. In Israel, this phenomenon has its unique expression in the spatial processes concerning the ultra-orthodox population. This population holds a set of interactions within itself as well as with the non-orthodox surrounding population because of historical and contemporary motivations on its which strength depends on its homogeneousness and separation. Its demographic growth rate and the internal social processes that the ultra-orthodox society undergoes create a new image of the ultra-orthodox concentration and its location in the Israeli space. The goals of the present study have also been defined with the express intention of filling the scholarly vacuum noted above: firstly, to discuss the development of the Israeli ultra-Orthodox sector’s hierarchical and spatial structure as of 2015, in light of the principles and mechanisms that guide it and vis-à-vis the general population’s hierarchical locality system; secondly, to map Israel’s ultra-Orthodox population, with attention to its physical boundaries, its subdivisions (Hassidic, Lithuanian, Sephardic) and the geographical and demographic processes that have characterized it in recent years; and thirdly, to shed light on the interactions between ultra-Orthodox localities via several different parameters, e.g. migration, education, transportation, employment, consumerism and community services. In order to understand the changes in ultra-Orthodox geographic distribution and the social processes that these changes have generated, a number of research activities were conducted during the course of this study− notably, gathering and assembling material from earlier academic studies, newspaper advertisements, state and private archives; in-depth interviews with major figures in the ultra-Orthodox community and others who come into contact with it; tours of the core areas of ultra-Orthodox settlement; and gathering quantitative and qualitative data from the statistical reports of governmental and other bodies. In addition, a multi-participant (2400-respondent) quantitative survey was conducted among residents of the new ultra-Orthodox cities, designed to elucidate the attributes and spatial attitudes of the residents− as a means of tracing and understanding this new settlement pattern within ultra-Orthodox space. A major portion of the quantitative and qualitative material was processed to form a system of maps that visually describe the distribution of Israel’s ultra-Orthodox population.

Keywords: migration, new cities, segregation, ultra-orthodox

Procedia PDF Downloads 403

Authors: Fidelia A. Orji, Julita Vassileva

Abstract:

This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

Procedia PDF Downloads 128

Authors: Tsai-Pei Liu, Zhi-Rou Zheng, Tzu-Tzu Wen

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Innovative logistics system simulation teaching is to extract the characteristics of the system through simulation methodology. The system has randomness and interaction problems in the execution time. Therefore, the simulation model can usually deal with more complex logistics process problems, giving students different learning modes. Students have more autonomy in learning time and learning progress. System simulation has become a new educational tool, but it still needs to accept many tests to use it in the teaching field. Although many business management departments in Taiwan have started to promote, this kind of simulation system teaching is still not popular, and the prerequisite for popularization is to be supported by students. This research uses an extension of Integration Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the acceptance of students in universities of science and technology to use system simulation as a learning tool. At the same time, it is hoped that this innovation can explore the effectiveness of the logistics system simulation after the introduction of teaching. The results indicated the significant influence of performance expectancy, social influence and learning value on students’ intention towards confirmed the influence of facilitating conditions and behavioral intention. The extended UTAUT2 framework helps in understanding students’ perceived value in the innovative logistics system teaching context.

Keywords: UTAUT2, logistics system simulation, learning value, Taiwan

Procedia PDF Downloads 115

Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

Abstract:

As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

Procedia PDF Downloads 111