Search results for: perceptual learning
3380 Online Augmented Reality Mathematics Application
Authors: Farhaz Amyn Rajabali, Collins Odour
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Mathematics has been there for over 4000 years and has been one of the very first educational topics explored by human civilization. Throughout the years, it has become a complex study and has derived so many other subjects. With advancements in ICT, most of the computation in mathematics is done using powerful computers. In many different countries, the children in primary and secondary schools face difficulties in learning mathematics, and this has many reasons behind it, one being the students don’t engage much with the mathematical concepts hence failing to understand them deeply. The objective of this system is to help the students understand this mathematical concept interactively, which in return will encourage the love for learning and increase thorough understanding of many concepts. Research was conducted among a group of samples and about 50% of respondents replied that they had never used an augmented reality application before. This means that the chances for this system to be accepted in the market are high due to its innovative idea. Around 60% of people did recommend the use of this system to learn mathematics. The study also showed several challenges in an educational system, including but not limited to lack of resources which was chosen by 30% of respondents, the challenge to read from textbooks (34.6%) and how hard it is to visualize concepts (46.2%). The survey question asked what benefits the users see using augmented reality to learn mathematics. The responses that were picked the most were increased student engagement and using real-world examples to understand concepts, both being 65.4% and followed by easy access to learning material at 61.5%, and increased knowledge retention at 50%. This shows that there are plenty of issues with an education system that can be addressed by software applications; now that the newer generation is so enthusiastic about electronic devices, it can actually be used to deliver good knowledge and skills to the upcoming students and mitigate most of the challenges faced currently. The study concludes that the implementation of the system is a best practice for the educational system especially leveraging a new technology that has the ability to attract the attention of many young students and use it to deliver information. It will also give rise to awareness of new technology and on multiple ways it can be implemented. Addressing the educational sector in developing countries using information technology is an imperative task since these kids studying now is the future of the country and will use what they learn and understand during their childhood will help them to make decisions about their lives in the future which will not only affect them personally but also affect the whole society in general.Keywords: AR, mathematics, system development, augmented reality
Procedia PDF Downloads 813379 Loan Supply and Asset Price Volatility: An Experimental Study
Authors: Gabriele Iannotta
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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment
Procedia PDF Downloads 1183378 Exploring the Neural Mechanisms of Communication and Cooperation in Children and Adults
Authors: Sara Mosteller, Larissa K. Samuelson, Sobanawartiny Wijeakumar, John P. Spencer
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This study was designed to examine how humans are able to teach and learn semantic information as well as cooperate in order to jointly achieve sophisticated goals. Specifically, we are measuring individual differences in how these abilities develop from foundational building blocks in early childhood. The current study adopts a paradigm for novel noun learning developed by Samuelson, Smith, Perry, and Spencer (2011) to a hyperscanning paradigm [Cui, Bryant and Reiss, 2012]. This project measures coordinated brain activity between a parent and child using simultaneous functional near infrared spectroscopy (fNIRS) in pairs of 2.5, 3.5 and 4.5-year-old children and their parents. We are also separately testing pairs of adult friends. Children and parents, or adult friends, are seated across from one another at a table. The parent (in the developmental study) then teaches their child the names of novel toys. An experimenter then tests the child by presenting the objects in pairs and asking the child to retrieve one object by name. Children are asked to choose from both pairs of familiar objects and pairs of novel objects. In order to explore individual differences in cooperation with the same participants, each dyad plays a cooperative game of Jenga, in which their joint score is based on how many blocks they can remove from the tower as a team. A preliminary analysis of the noun-learning task showed that, when presented with 6 word-object mappings, children learned an average of 3 new words (50%) and that the number of objects learned by each child ranged from 2-4. Adults initially learned all of the new words but were variable in their later retention of the mappings, which ranged from 50-100%. We are currently examining differences in cooperative behavior during the Jenga playing game, including time spent discussing each move before it is made. Ongoing analyses are examining the social dynamics that might underlie the differences between words that were successfully learned and unlearned words for each dyad, as well as the developmental differences observed in the study. Additionally, the Jenga game is being used to better understand individual and developmental differences in social coordination during a cooperative task. At a behavioral level, the analysis maps periods of joint visual attention between participants during the word learning and the Jenga game, using head-mounted eye trackers to assess each participant’s first-person viewpoint during the session. We are also analyzing the coherence in brain activity between participants during novel word-learning and Jenga playing. The first hypothesis is that visual joint attention during the session will be positively correlated with both the number of words learned and with the number of blocks moved during Jenga before the tower falls. The next hypothesis is that successful communication of new words and success in the game will each be positively correlated with synchronized brain activity between the parent and child/the adult friends in cortical regions underlying social cognition, semantic processing, and visual processing. This study probes both the neural and behavioral mechanisms of learning and cooperation in a naturalistic, interactive and developmental context.Keywords: communication, cooperation, development, interaction, neuroscience
Procedia PDF Downloads 2473377 Random Forest Classification for Population Segmentation
Authors: Regina Chua
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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling
Procedia PDF Downloads 883376 Emerging Technologies for Learning: In Need of a Pro-Active Educational Strategy
Authors: Pieter De Vries, Renate Klaassen, Maria Ioannides
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This paper is about an explorative research into the use of emerging technologies for teaching and learning in higher engineering education. The assumption is that these technologies and applications, which are not yet widely adopted, will help to improve education and as such actively work on the ability to better deal with the mismatch of skills bothering our industries. Technologies such as 3D printing, the Internet of Things, Virtual Reality, and others, are in a dynamic state of development which makes it difficult to grasp the value for education. Also, the instruments in current educational research seem not appropriate to assess the value of such technologies. This explorative research aims to foster an approach to better deal with this new complexity. The need to find out is urgent, because these technologies will be dominantly present in the near future in all aspects of life, including education. The methodology used in this research comprised an inventory of emerging technologies and tools that potentially give way to innovation and are used or about to be used in technical universities. The inventory was based on both a literature review and a review of reports and web resources like blogs and others and included a series of interviews with stakeholders in engineering education and at representative industries. In addition, a number of small experiments were executed with the aim to analyze the requirements for the use of in this case Virtual Reality and the Internet of Things to better understanding the opportunities and limitations in the day-today learning environment. The major findings indicate that it is rather difficult to decide about the value of these technologies for education due to the dynamic state of change and therefor unpredictability and the lack of a coherent policy at the institutions. Most decisions are being made by teachers on an individual basis, who in their micro-environment are not equipped to select, test and ultimately decide about the use of these technologies. Most experiences are being made in the industry knowing that the skills to handle these technologies are in high demand. The industry though is worried about the inclination and the capability of education to help bridge the skills gap related to the emergence of new technologies. Due to the complexity, the diversity, the speed of development and the decay, education is challenged to develop an approach that can make these technologies work in an integrated fashion. For education to fully profit from the opportunities, these technologies offer it is eminent to develop a pro-active strategy and a sustainable approach to frame the emerging technologies development.Keywords: emerging technologies, internet of things, pro-active strategy, virtual reality
Procedia PDF Downloads 1833375 Characterizing Content Language Integrated Learning (CLIL) Teaching in an EFL Primary School: A Case Study
Authors: Alfia Sari
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The implementation of the Content Language Integrated Learning (CLIL) approach in Indonesia has shown positive impacts in several educational institutions. Several studies have proven the benefits of implementing the CLIL approach, including the development of students’ language and content subject knowledge. Interestingly, one primary school in Surabaya, Indonesia, has been successfully implementing the CLIL approach. The students achieved high content and language subject scores, and the school was accredited A. A study on how the CLIL approach was practiced is important to investigate how teachers implemented it and how students benefited from it. Therefore, this present study attempted to investigate the implementation of the CLIL approach in this school to characterize good practices that can be implemented in other schools. A case study was conducted to observe its implementation in the third-grade classes (English, Science, and Math) by using the Protocol for Language Arts Teaching Observation (PLATO). The findings indicated that the CLIL teaching in this school accommodated the content and language well (scores 3-4). The content and language were clearly integrated, and the teachers successfully carried out the subjects in English. Teachers offered students opportunities to listen, speak, read, and write using the target language. This study described some characteristics of CLIL teaching in primary school that can be used as examples for future CLIL teachers to integrate the content and language in their teaching practices.Keywords: CLIL, ELT, young learners, case study
Procedia PDF Downloads 373374 Utilizing Radio as a Resource Alternative for Disseminating Information to University Students in Ibadan, Nigeria: A Study of Lead City FM and Diamond FM Radio Stations
Authors: Olufemi Sunday Onabajo
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Radio according to communication scholars is a veritable instrument of mass education. However, its full potentials in boosting higher education have not been realized because of the commercial nature of radio stations in Nigeria. The licensing of campus radio for disseminating information on university curricular is aimed at reinforcing information shared during face to face teaching. This study anchored on Agenda Setting and Technology determinism theories seeks to find out the extent to which university students in Lead City University and University of Ibadan, Nigeria have keyed-in to the philosophy of their campus radio – Lead City FM and Diamond FM in making information dissemination in their domiciled universities less cumbersome. The study employs both qualitative and quantitative methods though the use of depth interview for ten (10) academic staff and five (5) radio personnel of both radio stations; and a questionnaire addressed to 200 students of both institutions using the systematic random sampling technique. The data collected was analyzed using simple percentage and chi-square one tail test, and it was discovered that students of both universities and their radio personnel are yet to realize the potentials of campus radio as a resource alternative to effective learning, and recommends the coming together of all stakeholders to articulate the way forward.Keywords: disseminating information, effective learning, resource alternative, utilizing radio
Procedia PDF Downloads 2893373 Fostering Non-Traditional Student Success in an Online Music Appreciation Course
Authors: Linda Fellag, Arlene Caney
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E-learning has earned an essential place in academia because it promotes learner autonomy, student engagement, and technological aptitude, and allows for flexible learning. However, despite advantages, educators have been slower to embrace e-learning for ESL and other non-traditional students for fear that such students will not succeed without the direct faculty contact and academic support of face-to-face classrooms. This study aims to determine if a non-traditional student-friendly online course can produce student retention and performance rates that compare favorably with those of students in standard online sections of the same course aimed at traditional college-level students. One Music faculty member is currently collaborating with an English instructor to redesign an online college-level Music Appreciation course for non-traditional college students. At Community College of Philadelphia, Introduction to Music Appreciation was recently designated as one of the few college-level courses that advanced ESL, and developmental English students can take while completing their language studies. Beginning in Fall 2017, the course will be critical for international students who must maintain full-time student status under visa requirements. In its current online format, however, Music Appreciation is designed for traditional college students, and faculty who teach these sections have been reluctant to revise the course to address the needs of non-traditional students. Interestingly, presenters maintain that the online platform is the ideal place to develop language and college readiness skills in at-risk students while maintaining the course's curricular integrity. The two faculty presenters describe how curriculum rather than technology drives the redesign of the digitized music course, and self-study materials, guided assignments, and periodic assessments promote independent learning and comprehension of material. The 'scaffolded' modules allow ESL and developmental English students to build on prior knowledge, preview key vocabulary, discuss content, and complete graded tasks that demonstrate comprehension. Activities and assignments, in turn, enhance college success by allowing students to practice academic reading strategies, writing, speaking, and student-faculty and peer-peer communication and collaboration. The course components facilitate a comparison of student performance and retention in sections of the redesigned and existing online sections of Music Appreciation as well as in previous sections with at-risk students. Indirect, qualitative measures include student attitudinal surveys and evaluations. Direct, quantitative measures include withdrawal rates, tests of disciplinary knowledge, and final grades. The study will compare the outcomes of three cohorts in the two versions of the online course: ESL students, at-risk developmental students, and college-level students. These data will also be compared with retention and student outcomes data of the three cohorts in f2f Music Appreciation, which permitted non-traditional student enrollment from 1998-2005. During this eight-year period, the presenter addressed the problems of at-risk students by adding language and college success support, which resulted in strong retention and outcomes. The presenters contend that the redesigned course will produce favorable outcomes among all three cohorts because it contains components which proved successful with at-risk learners in f2f sections of the course. Results of their study will be published in 2019 after the redesigned online course has met for two semesters.Keywords: college readiness, e-learning, music appreciation, online courses
Procedia PDF Downloads 1693372 The Transformative Landscape of the University of the Western Cape’s Elearning Center: Institutionalizing ELearning
Authors: Paul Dankers, Juliet Stoltenkamp, Carolynne Kies
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In May 2005, the University of the Western Cape (UWC) established an eLearning Division (ED) that, over the past 18 years, accelerated into the institutionalization of an efficient eLearning Centre. The initial objective of the ED was to incessantly align itself with emerging technologies caused by digital transformation, which progressively impacted Higher Education Institutions (HEIs) globally. In this paper, we present how the UWC eLearning Division (ED) first evolved into the eLearning Development and Support Unit (EDUS), currently called the ‘Centre for Innovative Education and Communication Technologies (CIECT). CIECT was strategically separated from the Department of Information and Communication Services (ICS) in 2009 and repositioned as an independent structure at UWC. Using a comparative research method, we highlight the transformative eLearning landscape at UWC by doing a detailed account of the shift in practices. Our research method will determine the initial vision and outcomes of institutionalizing an eLearning division. The study aims to compare across space or time the eLearning division’s rate of growth. By comparing the progressive growth of the UWCs eLearning division over the years, we will be able to document the successes and achievements of the eLearning division precisely. This study’s outcomes will act as a reference for novel research subjects on formalising eLearning. More research that delves into the effectiveness of having an eLearning division at HEIs in support of students’ teaching and learning is needed.Keywords: eLearning, institutionalization, teaching and learning, transformation
Procedia PDF Downloads 303371 Virtual Science Laboratory (ViSLab): The Effects of Visual Signalling Principles towards Students with Different Spatial Ability
Authors: Ai Chin Wong, Wan Ahmad Jaafar Wan Yahaya, Balakrishnan Muniandy
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This study aims to explore the impact of Virtual Reality (VR) using visual signaling principles in learning about the science laboratory safety guide; this study involves students with different spatial ability. There are two types of science laboratory safety lessons, which are Virtual Reality with Signaling (VRS) and Virtual Reality Non Signaling (VRNS). This research has adopted a 2 x 2 quasi-experimental factorial design. There are two types of variables involved in this research. The two modes of courseware form the independent variables with the spatial ability as the moderator variable. The dependent variable is the students’ performance. This study sample consisted of 141 students. Descriptive and inferential statistics were conducted to analyze the collected data. The major effects and the interaction effects of the independent variables on the independent variable were explored using the Analyses of Covariance (ANCOVA). Based on the findings of this research, the results exhibited low spatial ability students in VRS outperformed their counterparts in VRNS. However, there was no significant difference in students with high spatial ability using VRS and VRNS. Effective learning in students with different spatial ability can be boosted by implementing the Virtual Reality with Signaling (VRS) in the design as well as the development of Virtual Science Laboratory (ViSLab).Keywords: spatial ability, science laboratory safety, visual signaling principles, virtual reality
Procedia PDF Downloads 2453370 Aromatic Medicinal Plant Classification Using Deep Learning
Authors: Tsega Asresa Mengistu, Getahun Tigistu
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Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network
Procedia PDF Downloads 4203369 Introduction of Integrated Image Deep Learning Solution and How It Brought Laboratorial Level Heart Rate and Blood Oxygen Results to Everyone
Authors: Zhuang Hou, Xiaolei Cao
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The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contactless devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contactless mini program HealthyPai’s heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) ± standard deviation was 1.4827 ± 1.7452 in the lab, 6.9231 ± 5.6426 in the real-life setting. In the SpO2 study of 24 samples, the MAE ± standard deviation of the measurement was 1.0375 ± 0.7745. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework, can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.Keywords: remote photoplethysmography, heart rate, oxygen saturation, contactless measurement, mini program
Procedia PDF Downloads 1293368 Explaining Irregularity in Music by Entropy and Information Content
Authors: Lorena Mihelac, Janez Povh
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In 2017, we conducted a research study using data consisting of 160 musical excerpts from different musical styles, to analyze the impact of entropy of the harmony on the acceptability of music. In measuring the entropy of harmony, we were interested in unigrams (individual chords in the harmonic progression) and bigrams (the connection of two adjacent chords). In this study, it has been found that 53 musical excerpts out from 160 were evaluated by participants as very complex, although the entropy of the harmonic progression (unigrams and bigrams) was calculated as low. We have explained this by particularities of chord progression, which impact the listener's feeling of complexity and acceptability. We have evaluated the same data twice with new participants in 2018 and with the same participants for the third time in 2019. These three evaluations have shown that the same 53 musical excerpts, found to be difficult and complex in the study conducted in 2017, are exhibiting a high feeling of complexity again. It was proposed that the content of these musical excerpts, defined as “irregular,” is not meeting the listener's expectancy and the basic perceptual principles, creating a higher feeling of difficulty and complexity. As the “irregularities” in these 53 musical excerpts seem to be perceived by the participants without being aware of it, affecting the pleasantness and the feeling of complexity, they have been defined as “subliminal irregularities” and the 53 musical excerpts as “irregular.” In our recent study (2019) of the same data (used in previous research works), we have proposed a new measure of the complexity of harmony, “regularity,” based on the irregularities in the harmonic progression and other plausible particularities in the musical structure found in previous studies. We have in this study also proposed a list of 10 different particularities for which we were assuming that they are impacting the participant’s perception of complexity in harmony. These ten particularities have been tested in this paper, by extending the analysis in our 53 irregular musical excerpts from harmony to melody. In the examining of melody, we have used the computational model “Information Dynamics of Music” (IDyOM) and two information-theoretic measures: entropy - the uncertainty of the prediction before the next event is heard, and information content - the unexpectedness of an event in a sequence. In order to describe the features of melody in these musical examples, we have used four different viewpoints: pitch, interval, duration, scale degree. The results have shown that the texture of melody (e.g., multiple voices, homorhythmic structure) and structure of melody (e.g., huge interval leaps, syncopated rhythm, implied harmony in compound melodies) in these musical excerpts are impacting the participant’s perception of complexity. High information content values were found in compound melodies in which implied harmonies seem to have suggested additional harmonies, affecting the participant’s perception of the chord progression in harmony by creating a sense of an ambiguous musical structure.Keywords: entropy and information content, harmony, subliminal (ir)regularity, IDyOM
Procedia PDF Downloads 1213367 Using Computer Vision to Detect and Localize Fractures in Wrist X-ray Images
Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez
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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning
Procedia PDF Downloads 673366 Semantic Differences between Bug Labeling of Different Repositories via Machine Learning
Authors: Pooja Khanal, Huaming Zhang
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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.Keywords: bug classification, bug labels, GitHub issues, semantic differences
Procedia PDF Downloads 1903365 Students With Special Educational Needs in Regular Classrooms and their Peer Effects on Learning Achievement
Authors: José María Renteria, Vania Salas
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This study explores the impact of inclusive education on the educational outcomes of students without Special Educational Needs (non-SEN) in Peru, utilizing official Ministry of Education data and implementing cross-sectional regression analyses. Inclusive education is a complex issue that, without appropriate adaptations and comprehensive understanding, can present substantial challenges to the educational community. While prior research from developed nations offers diverse perspectives on the effects of inclusive education on non-SEN students, limited evidence exists regarding its impact in developing countries. Our study addresses this gap by examining inclusive education in Peru and its effects on non-SEN students, thereby contributing to the existing literature. the findings reveal that, on average, the presence of SEN students in regular classrooms does not significantly affect their non-SEN counterparts. However, we uncover heterogeneous effects contingent on the specific type of SEN and students’ academic placement. These results emphasize the importance of targeted resources, specialized teachers, and parental involvement in facilitating successful inclusive education, particularly for specific SEN types and students positioned at the lower end of the academic achievement spectrum. In summary, this study underscores the need for tailored strategies and additional resources to foster the success of inclusive education and calls for further research in this field to expand our understanding and enhance educational policy.Keywords: inclusive education, special educational needs, learning achievement, Peru, Basic education
Procedia PDF Downloads 693364 Locally Crafted Sustainability: A Scoping Review for Nesting Social-Ecological and Socio-Technical Systems Towards Action Research in Agriculture
Authors: Marcia Figueira
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Context: Positivist transformations in agriculture were responsible for top-down – often coercive – mechanisms of uniformed modernization that weathered local diversities and agency. New development pathways need to now shift according to comprehensive integrations of knowledge - scientific, indigenous, and local, and to be sustained on political interventions, bottom-up change, and social learning if climate goals are to be met – both in mitigation and adaptation. Objectives The objectives of this research are to understand how social-ecological and socio-technical systems characterisation can be nested to bridge scientific research/knowledge into a local context and knowledge system; and, with it, stem sustainable innovation. Methods To do so, we conducted a scoping review to explore theoretical and empirical works linked to Ostrom’s Social-Ecological Systems framework and Geels’ multi-level perspective of socio-technical systems transformations in the context of agriculture. Results As a result, we were able to identify key variables and connections to 1- understand the rules in use and the community attributes influencing resource management; and 2- how they are and have been shaped and shaping systems innovations. Conclusion Based on these results, we discuss how to leverage action research for mutual learning toward a replicable but highly place-based agriculture transformation frame.Keywords: agriculture systems innovations, social-ecological systems, socio-technical systems, action research
Procedia PDF Downloads 883363 Machine Learning in Gravity Models: An Application to International Recycling Trade Flow
Authors: Shan Zhang, Peter Suechting
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Predicting trade patterns is critical to decision-making in public and private domains, especially in the current context of trade disputes among major economies. In the past, U.S. recycling has relied heavily on strong demand for recyclable materials overseas. However, starting in 2017, a series of new recycling policies (bans and higher inspection standards) was enacted by multiple countries that were the primary importers of recyclables from the U.S. prior to that point. As the global trade flow of recycling shifts, some new importers, mostly developing countries in South and Southeast Asia, have been overwhelmed by the sheer quantities of scrap materials they have received. As the leading exporter of recyclable materials, the U.S. now has a pressing need to build its recycling industry domestically. With respect to the global trade in scrap materials used for recycling, the interest in this paper is (1) predicting how the export of recyclable materials from the U.S. might vary over time, and (2) predicting how international trade flows for recyclables might change in the future. Focusing on three major recyclable materials with a history of trade, this study uses data-driven and machine learning (ML) algorithms---supervised (shrinkage and tree methods) and unsupervised (neural network method)---to decipher the international trade pattern of recycling. Forecasting the potential trade values of recyclables in the future could help importing countries, to which those materials will shift next, to prepare related trade policies. Such policies can assist policymakers in minimizing negative environmental externalities and in finding the optimal amount of recyclables needed by each country. Such forecasts can also help exporting countries, like the U.S understand the importance of healthy domestic recycling industry. The preliminary result suggests that gravity models---in addition to particular selection macroeconomic predictor variables--are appropriate predictors of the total export value of recyclables. With the inclusion of variables measuring aspects of the political conditions (trade tariffs and bans), predictions show that recyclable materials are shifting from more policy-restricted countries to less policy-restricted countries in international recycling trade. Those countries also tend to have high manufacturing activities as a percentage of their GDP.Keywords: environmental economics, machine learning, recycling, international trade
Procedia PDF Downloads 1613362 Assessing the Impacts of Folktales (Story Telling) On the Moral Advancement of Children Yoruba Communities in Ute-Owo, Nigeria
Authors: Felicia Titilayo Olanrewaju
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Folktales are a subclass of folklores which are verbally told and passed down from one generation to another, from the elderly ones to their children, usually at moonlight. These tales are heavily laden with moral lessons of what should be done and what not within the society. Though these are oftentimes heavily embellished yet are related to guide, guard, train, and dishing out moral attributes and mores worthwhile for ethical progression of the young minds within our traditional settings. With the rapid advancement of technological know-how, the existence of most of these moral-inclined stories becomes questionable; hence this study appraised the influences of these traditional storytellings have in the upgrading of moral learning of ethical behavioral traits acceptable among the Yoruba people. Oral interviews couples with recording gadgets were used to collate both sample parents' and children’s responses within a particular community in Owo (ute) local government area of Owo Ondo State, Nigeria. Findings reveal that diverse tales told at moonlight periods have an untold impact on the speedy growth of the children intellectually than the modern happenings around them. These telltale stories become powerful aids in learning goodly traits and eschewing bad manners. It is recommended that folk stories be told within the household among the family after hard labour in the evenings as this would help develop human relationships and brings about a strong sense of community bindings.Keywords: folktales, folklores, impact, advancement, ethical progression
Procedia PDF Downloads 1723361 Competition as an Appropriate Instructional Practice in the Physical Education Environment: Reflective Experiences
Authors: David Barney, Francis Pleban, Muna Muday
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The purpose of this study was to explore gender differences of former physical education students related to reflective experiences of competition in physical education learning environment. In the school environment, students are positioned in competitive situations, including in the physical education context. Therefore it is important to prepare future physical educators to address the role of competition in physical education. Participants for this study were 304 college-aged students and young adults (M = 1.53, SD = .500), from a private university and local community located in the western United States. When comparing gender, significant differences (p < .05) were reported for four (questions 5, 7, 12, and 14) of the nine scaling questions. Follow-up quantitative findings reported that males (41%) more than females (27%) witnessed fights in physical education environment during competitive games. Qualitative findings reported fighting were along the lines of verbal confrontation. Female participants tended to experience being excluded from games, when compared to male participants. Both male and female participants (total population; 95%, males; 98%; and females 92%) were in favor of including competition in physical education for students. Findings suggest that physical education teachers and physical education teacher education programs have a responsibility to develop gender neutral learning experiences that help students better appreciate the role competition plays, both in and out of the physical education classroom.Keywords: competition, physical education, physical education teacher education, gender
Procedia PDF Downloads 4883360 Advancing the Analysis of Physical Activity Behaviour in Diverse, Rapidly Evolving Populations: Using Unsupervised Machine Learning to Segment and Cluster Accelerometer Data
Authors: Christopher Thornton, Niina Kolehmainen, Kianoush Nazarpour
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Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations.Keywords: physical activity, machine learning, under 5s, disability, accelerometer
Procedia PDF Downloads 2003359 Artificial Intelligence in Patient Involvement: A Comprehensive Review
Authors: Igor A. Bessmertny, Bidru C. Enkomaryam
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Active involving patients and communities in health decisions can improve both people’s health and the healthcare system. Adopting artificial intelligence can lead to more accurate and complete patient record management. This review aims to identify the current state of researches conducted using artificial intelligence techniques to improve patient engagement and wellbeing, medical domains used in patient engagement context, and lastly, to assess opportunities and challenges for patient engagement in the wellness process. A search of peer-reviewed publications, reviews, conceptual analyses, white papers, author’s manuscripts and theses was undertaken. English language literature published in 2013– 2022 period and publications, report and guidelines of World Health Organization (WHO) were also assessed. About 281 papers were retrieved. Duplicate papers in the databases were removed. After application of the inclusion and exclusion criteria, 41 papers were included to the analysis. Patient counseling in preventing adverse drug events, in doctor-patient risk communication, surgical, drug development, mental healthcare, hypertension & diabetes, metabolic syndrome and non-communicable chronic diseases are implementation areas in healthcare where patient engagement can be implemented using artificial intelligence, particularly machine learning and deep learning techniques and tools. The five groups of factors that potentially affecting patient engagement in safety are related to: patient, health conditions, health care professionals, tasks and health care setting. Active involvement of patients and families can help accelerate the implementation of healthcare safety initiatives. In sub-Saharan Africa, using digital technologies like artificial intelligence in patient engagement context is low due to poor level of technological development and deployment. The opportunities and challenges available to implement patient engagement strategies vary greatly from country to country and from region to region. Thus, further investigation will be focused on methods and tools using the potential of artificial intelligence to support more simplified care that might be improve communication with patients and train health care professionals.Keywords: artificial intelligence, patient engagement, machine learning, patient involvement
Procedia PDF Downloads 723358 Metal Ship and Robotic Car: A Hands-On Activity to Develop Scientific and Engineering Skills for High School Students
Authors: Jutharat Sunprasert, Ekapong Hirunsirisawat, Narongrit Waraporn, Somporn Peansukmanee
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Metal Ship and Robotic Car is one of the hands-on activities in the course, the Fundamental of Engineering that can be divided into three parts. The first part, the metal ships, was made by using engineering drawings, physics and mathematics knowledge. The second part is where the students learned how to construct a robotic car and control it using computer programming. In the last part, the students had to combine the workings of these two objects in the final testing. This aim of study was to investigate the effectiveness of hands-on activity by integrating Science, Technology, Engineering and Mathematics (STEM) concepts to develop scientific and engineering skills. The results showed that the majority of students felt this hands-on activity lead to an increased confidence level in the integration of STEM. Moreover, 48% of all students engaged well with the STEM concepts. Students could obtain the knowledge of STEM through hands-on activities with the topics science and mathematics, engineering drawing, engineering workshop and computer programming; most students agree and strongly agree with this learning process. This indicated that the hands-on activity: “Metal Ship and Robotic Car” is a useful tool to integrate each aspect of STEM. Furthermore, hands-on activities positively influence a student’s interest which leads to increased learning achievement and also in developing scientific and engineering skills.Keywords: hands-on activity, STEM education, computer programming, metal work
Procedia PDF Downloads 4573357 Enhancing Emotional Regulation in Autistic Students with Intellectual Disabilities through Visual Dialogue: An Action Research Study
Authors: Tahmina Huq
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This paper presents the findings of an action research study that aimed to investigate the efficacy of a visual dialogue strategy in assisting autistic students with intellectual disabilities in managing their immediate emotions and improving their academic achievements. The research sought to explore the effectiveness of teaching self-regulation techniques as an alternative to traditional approaches involving segregation. The study identified visual dialogue as a valuable tool for promoting self-regulation in this specific student population. Action research was chosen as the methodology due to its suitability for immediate implementation of the findings in the classroom. Autistic students with intellectual disabilities often face challenges in controlling their emotions, which can disrupt their learning and academic progress. Conventional methods of intervention, such as isolation and psychologist-assisted approaches, may result in missed classes and hindered academic development. This study introduces the utilization of visual dialogue between students and teachers as an effective self-regulation strategy, addressing the limitations of traditional approaches. Action research was employed as the methodology for this study, allowing for the direct application of the findings in the classroom. The study observed two 15-year-old autistic students with intellectual disabilities who exhibited difficulties in emotional regulation and displayed aggressive behaviors. The research question focused on the effectiveness of visual dialogue in managing the emotions of these students and its impact on their learning outcomes. Data collection methods included personal observations, log sheets, personal reflections, and visual documentation. The study revealed that the implementation of visual dialogue as a self-regulation strategy enabled the students to regulate their emotions within a short timeframe (10 to 30 minutes). Through visual dialogue, they were able to express their feelings and needs in socially appropriate ways. This finding underscores the significance of visual dialogue as a tool for promoting emotional regulation and facilitating active participation in classroom activities. As a result, the students' learning outcomes and social interactions were positively impacted. The findings of this study hold significant implications for educators working with autistic students with intellectual disabilities. The use of visual dialogue as a self-regulation strategy can enhance emotional regulation skills and improve overall academic progress. The action research approach outlined in this paper provides practical guidance for educators in effectively implementing self-regulation strategies within classroom settings. In conclusion, the study demonstrates that visual dialogue is an effective strategy for enhancing emotional regulation in autistic students with intellectual disabilities. By employing visual communication, students can successfully regulate their emotions and actively engage in classroom activities, leading to improved learning outcomes and social interactions. This paper underscores the importance of implementing self-regulation strategies in educational settings to cater to the unique needs of autistic students.Keywords: action research, self-regulation, autism, visual communication
Procedia PDF Downloads 563356 Using Machine Learning to Classify Human Fetal Health and Analyze Feature Importance
Authors: Yash Bingi, Yiqiao Yin
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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations
Procedia PDF Downloads 1393355 Effect of Three Instructional Strategies on Pre-service Teachers’ Learning Outcomes in Practical Chemistry in Niger State, Nigeria
Authors: Akpokiere Ugbede Roseline
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Chemistry is an activity oriented subject in which many students achievement over the years are not encouraging. Among the reasons found to be responsible for student’s poor performance in chemistry are ineffective teaching strategies. This study, therefore, sought to determine the effect of guided inquiry, guided inquiry with demonstration, and demonstration with conventional approach on pre-service teachers’ cognitive attainment and practical skills acquisition on stoichiometry and chemical reactions in practical chemistry, Two research questions and hypotheses were each answered and tested respectively. The study was a quasi-experimental research involving 50 students in each of the experimental groups and 50 students in the control group. Out of the five instruments used for the study, three were on stimulus and two on response (Test of Cognitive Attainment and Test of Practical Skills in Chemistry) instruments administered, and dataobtained were analyzed with t-test and Analysis of Variance. Findings revealed, among others, that there was a significant effect of treatments on students' cognitive attainment and on practical skills acquisition. Students exposed to guided inquiry (with/without demonstration) strategies achieved better than those exposed to demonstration with conventional strategy. It is therefore recommended, among others, that Lecturers in Colleges of Education should utilize the guided inquiry strategy for teaching concepts in chemistry.Keywords: instructional strategy, practical chemistry, learning outcomes, pre-service teachers
Procedia PDF Downloads 993354 Small Scale Mobile Robot Auto-Parking Using Deep Learning, Image Processing, and Kinematics-Based Target Prediction
Authors: Mingxin Li, Liya Ni
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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning
Procedia PDF Downloads 1293353 Theoretical Lens Driven Strategies for Emotional Wellbeing of Parents and Children in COVID-19 Era
Authors: Anamika Devi
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Based on Vygotsky’s cultural, historical theory and Hedegaard’s concept of transition, this study aims to investigate to propose strategies to maintain digital wellbeing of children and parents during and post COVID pandemic. Due COVID 19 pandemic, children and families have been facing new challenges and sudden changes in their everyday life. While children are juggling to adjust themselves in new circumstance of onsite and online learning settings, parents are juggling with their work-life balance. A number of papers have identified that the COVID-19 pandemic has affected the lives of many families around the world in many ways, for example, the stress level of many parents increased, families faced financial difficulties, uncertainty impacted on long term effects on their emotional and social wellbeing. After searching and doing an intensive literature review from 2020 and 2021, this study has found some scholarly articles provided solution or strategies of reducing stress levels of parents and children in this unprecedented time. However, most of them are not underpinned by proper theoretical lens to ensure they validity and success. Therefore, this study has proposed strategies that are underpinned by theoretical lens to ensure their impact on children’s and parents' emotional wellbeing during and post COVID-19 era. The strategies will highlight on activities for positive coping strategies to the best use of family values and digital technologies.Keywords: onsite and online learning, strategies, emotional wellbeing, tips, and strategies, COVID19
Procedia PDF Downloads 1633352 Development of a Turbulent Boundary Layer Wall-pressure Fluctuations Power Spectrum Model Using a Stepwise Regression Algorithm
Authors: Zachary Huffman, Joana Rocha
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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations
Procedia PDF Downloads 1323351 Multimodal Content: Fostering Students’ Language and Communication Competences
Authors: Victoria L. Malakhova
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The research is devoted to multimodal content and its effectiveness in developing students’ linguistic and intercultural communicative competences as an indefeasible constituent of their future professional activity. Description of multimodal content both as a linguistic and didactic phenomenon makes the study relevant. The objective of the article is the analysis of creolized texts and the effect they have on fostering higher education students’ skills and their productivity. The main methods used are linguistic text analysis, qualitative and quantitative methods, deduction, generalization. The author studies texts with full and partial creolization, their features and role in composing multimodal textual space. The main verbal and non-verbal markers and paralinguistic means that enhance the linguo-pragmatic potential of creolized texts are covered. To reveal the efficiency of multimodal content application in English teaching, the author conducts an experiment among both undergraduate students and teachers. This allows specifying main functions of creolized texts in the process of language learning, detecting ways of enhancing students’ competences, and increasing their motivation. The described stages of using creolized texts can serve as an algorithm for work with multimodal content in teaching English as a foreign language. The findings contribute to improving the efficiency of the academic process.Keywords: creolized text, English language learning, higher education, language and communication competences, multimodal content
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