Search results for: learning science

Authors: Maria Luisa Boninelli

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This study wants to explore the effects of a mediated intervention program in a primary school. The participants where 120 students aged 8-9, half of them Italian and half immigrants of first or second generation. The activities consisted on the cognitive enhancement of the participants through Feuerstein’s Instrumental Enrichment, (IE) and on an activity centred on body awareness and mediated learning experience. Given that there are limited studied on learners in remedial schools, the current study intented to hypothesized that participants exposed to mediation would yiel a significant improvement in cognitive functioning. Hypothesis One proposed that, following the intervention, improved Q1vata scores of the participants would occur in each of the groups. Hypothesis two postulated that participants within the Mediated Learning Experience would perform significantly better than those group of control. For the intervention a group of 60 participants constituted a group of Mediation sample and were exposed to Mediated Learning Experience through Enrichment Programm. Similiary the other 60 were control group. Both the groups have students with special needs and were exposed to the same learning goals. A pre-experimental research design, in particular a one-group pretest-posttest approach was adopted. All the participants in this study underwent pretest and post test phases whereby they completed measures according to the standard instructions. During the pretest phase, all the participants were simultaneously exposed to Q1vata test for logical and linguistic evaluation skill. During the mediation intervention, significant improvement was demonstrated with the group of mediation. This supports Feuerstein's Theory that initial poor performance was a result of a lack of mediated learning experience rather than inherent difference or deficiencies. Furthermore the use of an appropriate mediated learning enabled the participants to function adequately.

Keywords: cognitive structural modifiability, learning to learn, mediated learning experience, Reuven Feuerstein, special needs

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Authors: Belkhadir Radouane

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In this study, author tried to explore the beliefs of some Moroccan teachers working in the delegations of Safi and Youcefia about the usefulness of first and second languages in learning the third language. More specifically, author attempted to see the extent to which these teachers believe that a first and second language can serve students in learning a third one. The first language in this context is Arabic, the second is French, and the third is English. The teachers’ beliefs were gathered through a questionnaire that was addressed via Google Forms. Then, the results were analyzed using the same application. It was found that teachers are positive about the usefulness of the first and second language in learning the third one, but most of them rarely use in a conscious way activities that serve this purpose.

Keywords: Bilinguilism, teachers beliefs, English as ESL, Morocco

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Authors: Sebastianus Menggo

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The aim of communicative language teaching is to enable learners to communicate in the target language. Each learner is required to perform the micro and macro components in each utterance produced. Utterances produced must be in line with the understanding of competence and performance of each speaker. These are inter-depended. Competence and performance are obliged to be appeared proportionally in creating the utterances. The representative of competence and performance reflects the linguistics identity of a speaker in providing sentences in each certain language community. Each lexicon spoken may lead that interlocutor in comprehending the intentions utterances given. However proportional performance of both components in an utterance needed to be further elaborated. Finding appropriate gap between competence and performance components in a communicative competence must be supported positive response given by the learners.The learners’ inability to keep communicative competence proportionally is caused by inside and outside factors. The inside factors are certain lacks such as lack of self-confidence and lack of motivation which could make students feel ashamed to produce utterances, scared to make mistakes, and have no enough confidence. Knowing learner’s English learning motivation is an urgent variable to be considered in creating conducive atmosphere classroom which will raise the learners to do more toward the achievement of communicative competence. Meanwhile, the outside factor is related with the teacher. The teacher should be able to recognize the students’ problem in creating conducive atmosphere in the classroom that will raise the students’ ability to be an English speaker qualified. Moreover, the aim of this research is to know and describe the English learning motivation affecting students’ communicative competence of 48 students of XI grade of science program at catholic senior of Saint Ignasius Loyola Labuan Bajo, West Flores, Indonesia. Correlation design with purposive procedure applied in this research. Data were collected through questionnaire, interview, and students’ speaking achievement document. Result shows the description of motivation significantly affecting students’ communicative competence.

Keywords: communicative, competence, English, learning, motivation

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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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: Vaida Jurgile

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Intercultural learning is one of the approaches taken to understand the cultural diversity of the modern world and to accept changes in cultural identity and otherness and the expression of tolerance. During intercultural learning, students develop their abilities to interact and communicate with their group members. These abilities help to understand social and cultural differences, to form one’s identity, and to give meaning to intercultural learning. Intercultural education recognizes that a true understanding of differences and similarities of another culture is necessary in order to lay the foundations for working together with others, which contributes to the promotion of intercultural dialogue, appreciation of diversity, and cultural exchange. Therefore, it is important to examine the concept of intercultural learning, revealed through students’ learning experiences and understanding of how this learning takes place and what significance this phenomenon has in higher education. At a scientific level, intercultural learning should be explored in order to uncover the influence of cultural identity, i.e., intercultural learning should be seen in a local context. This experience would provide an opportunity to learn from various everyday intercultural learning situations. Intercultural learning can be not only a form of learning but also a tool for building understanding between people of different cultures. The research object of the study is the process of intercultural learning. The aim of the dissertation is to develop a grounded theory of the process of learning in an intercultural study environment, revealing students’ learning experiences. The research strategy chosen in this study is a constructivist grounded theory (GT). GT is an inductive method that seeks to form a theory by applying the systematic collection, synthesis, analysis, and conceptualization of data. The targeted data collection was based on the analysis of data provided by previous research participants, which revealed the need for further research participants. During the research, only students with at least half a year of study experience, i.e., who have completed at least one semester of intercultural studies, were purposefully selected for the research. To select students, snowballing sampling was used. 18 interviews were conducted with students representing 3 different fields of sciences (social sciences, humanities, and technology sciences). In the process of intercultural learning, language expresses and embodies cultural reality and a person’s cultural identity. It is through language that individual experiences are expressed, and the world in which Others exist is perceived. The increased emphasis is placed on the fact that language conveys certain “signs’ of communication and perception with cultural value, enabling the students to identify the Self and the Other. Language becomes an important tool in the process of intercultural communication because it is only through language that learners can communicate, exchange information, and understand each other. Thus, in the process of intercultural learning, language either promotes interpersonal relationships with foreign students or leads to mutual rejection.

Keywords: intercultural learning, grounded theory, students, other

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Authors: David S. Rosenstein

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The author believes that modern spoken languages should be equally difficult (or easy) to learn, since all normal children learning their native languages do so at approximately the same rate and with the same competence, progressing from easy to more complex grammar and syntax in the same way. Why then, do some languages seem more difficult than others? Perhaps people are referring to the written language, where it may be true that mastering Chinese requires more time than French, which in turn requires more time than Spanish. But this may be marginal, since Chinese and French children quickly catch up to their Spanish peers in reading comprehension. Rather, the real differences in difficulty derive from two sources: hardened L1 language habits trying to cope with contrasting L2 habits; and unfamiliarity with unique L2 characteristics causing faulty expectations. It would seem that effective L2 teaching and learning must take these two sources of difficulty into consideration. The author feels that the latter (faulty expectations) causes the greatest difficulty, making effective teaching and learning somewhat different for each given foreign language. Examples from Chinese and other languages are presented.

Keywords: learning different languages, language learning difficulties, faulty language expectations

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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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

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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: Ruwan Gunawardane

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Second language teaching and learning is a complex process in which various factors are identified as having a negative impact on the competency in English among undergraduates of Sri Lanka. One such issue is the lack of intrinsic motivation among them to learn English despite the fact that they all know the importance of English. This study attempted to ascertain how the intrinsic motivation of undergraduates to learn English can be improved through reading out of curiosity. Humans are curious by nature, and cognitive psychology says that curiosity facilitates learning, memory, and motivation. The researcher carried out this study during the closure of universities due to the outbreak of the coronavirus through ‘Online Reading Café’, an online reading programme introduced by himself. He invited 1166 students of the Faculty of Science, University of Ruhuna, to read 50 articles taken from CNN and the BBC and posted at least two to three articles on the LMS of the faculty almost every day over a period of 23 days. The themes of the articles were based on the universe, exploration of planets, scientific experiments, evolution, etc., and the students were encouraged to collect as many words, phrases, and sentence structures as possible while reading and to form meaningful sentences using them. The data obtained through the students’ feedback was qualitatively analyzed. It was found that these undergraduates were interested in reading something out of curiosity, due to which intrinsic motivation is enhanced, and it facilitates competence in L2.

Keywords: English, competence, reading, curiosity

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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: Abdulwali H. Aldahmash, Naem M. Alamri

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The purpose of this study was to analyze Saudi Arabian science and mathematics teachers’ attitudes toward integrating STEM in teaching before and after they participated in a professional development workshop focused on STEM integration in a specific middle school science and mathematics unit. The participants were 48 Saudi Arabian science and mathematics teachers who participated in a three-day workshop held in Riyadh, Saudi Arabia. The research method was a pretest-posttest group design. The primary data source was the instrument for teachers' attitudes toward teaching integrated STEM. The results indicate that Saudi Arabian science and mathematics teachers’ perceptions of difficulties decreased due to their participation in the professional development workshop on integrated STEM. Meanwhile, the teachers' self-efficacy improved following their participation in the STEM professional development (PD) workshop. However, no perceived effect was found for the teachers' perceptions of the relevance of or their anxiety about or enjoyment of integrated STEM teaching due to their participation in the three-day PD workshop.

Keywords: STEM integration, attitude toward STEM, STEM workshop, professional development

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Authors: A. Olusegun Egunjobi, Samson Sunday Adekunte

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This study investigated the teachers’ and learners’ ICT-readiness assessment for agricultural science instruction in secondary schools in Ogun State, Nigeria. However, the sample population of 6 and 120 agricultural science teachers and learners were randomly selected respectively from 3 public and 3 private senior secondary schools in Ado-Odo/Ota Local Government Area of Ogun State, Nigeria. Descriptive survey design of ex post-facto type was adopted for the study. Two structured questionnaires tagged Teachers’ and Learners’ Questionnaires on ICT-Readiness for Agricultural Science Instruction TQICTRASI and LQICTRASI respectively were used for data collection. The two questionnaires were subjected to Cronbach alpha coefficient with the results 0.86 and 0.82 respectively. Five research hypotheses were tested at 0.05 level of significance. Findings revealed that teachers in private senior secondary school (SSS) were more ready and prepared than their counterparts in public SSS with the result t-value = 4.25 greater than t-critical = 2.77, df = 4 at p<0.05. Also, learners in private SSS were more prepared and ready for the utilisation of ICT-facilities for agricultural science instruction with the result t-value = 3.51 greater than t-critical = 1.98, df = 118 at p<0.05. However, male and female learners in both private and public SSS were equally prepared and ready for the ICT-facilities utilisation for agricultural science instruction, thus, there were no significant differences in their ICT-readiness. Therefore, the study proffered that, both male and female teachers and learners should be more ICT-compliant and always ready to upgrade their skills and knowledge in ICT-facilities, utilisation for agricultural science instruction and even for other school subjects particularly in Ogun State and in generally in Nigeria.

Keywords: ICT-readiness, teachers’ and learners’ assessment, private and public senior secondary schools, agricultural science instruction

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Authors: Fouad Mrad, Viviane Zaccour

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The accelerated developments in the political, economic, environmental, security, health, and social folders are exhausting planners across the world, especially in Arab countries. The impact of the tension is multifaceted and has resulted in conflicts, wars, migration, and human insecurity. The potential cross-cutting role that science, innovation and technology can play in supporting Arab societies to address these pressing challenges is a serious, unique chance for the people of the region. This opportunity is based on the existing capacity of educated youth and inaccessible talents in the local universities and research centers. It has been accepted that Arab countries have achieved major advancements in the economy, education and social wellbeing since the 70s of the 20th Century. Mainly direct outcome of the oil and other natural resources. The UN Secretary-General, during the Education Summit in Sep 2022, stressed that “Learning continues to underplay skills, including problem-solving, critical thinking and empathy.” Stars of Science by Qatar Foundation was launched in 2009 and has been sustained through 2023. Consistent mission from the start: To mobilize a new generation of Pan-Arab innovators and problem solvers by encouraging youth participation and interest in Science, Technology and Entrepreneurship throughout the Arab world via the program and its social media activities. To make science accessible and attractive to mass audiences by de-mystifying the process of innovation. Harnessing best practices within reality TV to show that science, engineering, and innovation are important in everyday life and can be fun.” Thousands of Participants learned unforgettable lessons; winners changed their lives forever as they learned and earned seed capital; they became drivers of change in their countries and families; millions of viewers were exposed to an innovative experimental process, and culturally, several relevant national institutions adopted the SOS track in their national initiatives. The program exhibited experientially youth self-efficacy as the most distinct core property of human agency, which is an individual's belief in his or her capacity to execute behaviors necessary to produce specific performance attainments. In addition, the program proved that innovations are performed by networks of people with different sets of technological, useful knowledge, skills and competencies introduced by socially shared technological knowledge as a main determinant of economic activities in any economy.

Keywords: science, invention, innovation, Qatar foundation, QSTP, prototyping

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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: Cristian Matti, Valerie Fowles, Eva Enyedi, Piotr Pogorzelski

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The system mapping process can be defined as a knowledge service where a team of facilitators, experts and practitioners facilitate a guided conversation, enable the exchange of information and support an iterative curation process. System mapping processes rely on science-based tools to introduce and simplify a variety of components and concepts of socio-technical systems through metaphors while facilitating an interactive dialogue process to enable the design of co-created maps. System maps work then as “artifacts” to provide information and focus the conversation into specific areas around the defined challenge and related decision-making process. Knowledge management facilitates the curation of that data gathered during the system mapping sessions through practices of documentation and subsequent knowledge co-production for which common practices from data science are applied to identify new patterns, hidden insights, recurrent loops and unexpected elements. This study presents empirical evidence on the application of these techniques to explore mechanisms by which visual tools provide guiding principles to portray system components, key variables and types of data through the lens of climate change. In addition, data science facilitates the structuring of elements that allow the analysis of layers of information through affinity and clustering analysis and, therefore, develop simple indicators for supporting the decision-making process. This paper addresses methodological and empirical elements on the horizontal learning process that integrate system mapping through visual tools, interpretation, cognitive transformation and analysis. The process is designed to introduce practitioners to simple iterative and inclusive processes that create actionable knowledge and enable a shared understanding of the system in which they are embedded.

Keywords: indicators, knowledge management, system mapping, visual tools

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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: Manuela Nayantara Jeyaraj

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Having moved into the 21^st century, it is way past being arguable that innovative technology needs to be incorporated into conventional classroom teaching. Though the Western world has found presumable success in achieving this, it is still a concept under battle in developing countries such as Sri Lanka. Reaching the acme of implementing interactive virtual learning within classrooms is a struggling idealistic fascination within the island. In order to overcome this problem, this study is set to reveal facts that limit the implementation of virtual, interactive learning within the school classrooms and provide hacks that could prove the augmented use of the Virtual World to enhance teaching and learning experiences. As each classroom moves along with the usage of technology to fulfill its functionalities, a few intense hacks provided will build the administrative onuses on a virtual system. These hacks may divulge barriers based on social conventions, financial boundaries, digital literacy, intellectual capacity of the staff, and highlight the impediments in introducing students to an interactive virtual learning environment and thereby provide the necessary actions or changes to be made to succeed and march along in creating an intellectual society built on virtual learning and lifestyle. This digital learning environment will be composed of multimedia presentations, trivia and pop quizzes conducted on a GUI, assessments conducted via a virtual system, records maintained on a database, etc. The ultimate objective of this study could enhance every child's basic learning environment; hence, diminishing the digital divide that exists in certain communities.

Keywords: digital divide, digital learning, digitization, Sri Lanka, teaching methodologies

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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

Abstract:

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

Abstract:

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: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

Abstract:

Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Ramorola Mmankoko Ziphorah

Abstract:

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

Abstract:

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

Procedia PDF Downloads 333