Search results for: learning and teaching methods

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: Ofelia A. Damag

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The study was focused on the development and assessment of the illustrated language activities of the 1996 Edition of the Portage Guide to Early Education. It determined the extent of appropriateness, applicability, time efficiency and aesthetics of the illustrated language activities to be used as instructional material not only by teachers, but parents and caregivers as well. The eclectic research design was applied in this study using qualitative and quantitative methods. To determine the applicability and time efficiency of the study, a try out was done. Since the eclectic research design was used, it made use of a researcher-made survey questionnaire and focus group discussion. Analysis of the data was done through weighted mean and ANOVA. The respondents of the study were representatives of Special Education (SPED) teachers, caregivers and parents of a special-needs child, particularly with difficulties in learning basic language skills. The results of the study show that a large number of respondents are SPED teachers and caregivers and are mostly college graduates. Many of them have earned units towards Master’s studies. Moreover, a majority of the respondents have not attended seminars or in-service training in early intervention for them to be more competent in the area of specialization. It is concluded that the illustrated language activities under review in this study are appropriate, applicable, time efficient and aesthetic for use as a tool in teaching. The recommendations are focused on the advocacy for SPED teachers, caregivers and parents of special-needs children to be more consistent in the implementation of the new instructional materials as an aid in an intervention program.

Keywords: illustrated language activities, inclusion, portage guide to early education, special educational needs

Procedia PDF Downloads 159

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: Sofia Serra-Dawa

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Adult relationships accrue on a variety of past social experiences, intentions, and emotions that might predispose and influence the approach to and construction of subsequent relationships. The Adult Attachment Theory (AAT) proposes four types of adult attachment, where attachment is built over two dimensions of anxiety and avoidance: secure, anxious-preoccupied, dismissive-avoidant, and fearful-avoidant. The AAT has been studied in multiple settings such as personal and therapeutic relationships, educational settings, sexual orientation, health, and religion. In music scholarship, the AAT has been used to frame class learning of student singers and study the relational behavior between voice teachers and students. Building on this study, the present inquiry studies how attachment types might characterize learning relationships of music students (in the Western Conservatory tradition), and whether particular instrumental experiences might correlate to given attachment styles. Given certain behavioral cohesive features of established traditions of instrumental playing and performance modes, it is hypothesized that student musicians will display specific characteristics correlated to instrumental traditions, demonstrating clear tendency of attachment style, which in turn has implications on subsequent professional interactions. This study is informed by the methodological framework of Adult Attachment Scale-R (Collins and Read, 1990), which was particularly chosen given its non-invasive questions and classificatory validation. It is further hypothesized that the analytical comparison of musicians’ profiles has the potential to serve as the baseline for other comparative behavioral observation studies [this component is expected to be verified and completed well before the conference meeting]. This research may have implications for practitioners concerned with matching and improving musical teaching and learning relationships and in (professional and amateur) long-term musical settings.

Keywords: adult attachment, music education, musicians attachment profile, musicians relationships

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Sajedah Al Yaari, Adham Al Yaari, Ayman Al Yaari, Montaha Al Yaari, Ayah Al Yaari, Fatehi Eissa

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Dysgraphia is a neurological disorder of written expression that impairs writing ability and fine motor skills, resulting primarily in problems relating not only to handwriting but also to writing coherence and cohesion. We investigate the properties of smart writing technology to highlight some unique features of the effects they cause on the academic performance of pupils with dysgraphia. In Amis, dysgraphics undergo writing problems to express their ideas due to ordinary writing aids, as the default strategy. The Amis data suggests a possible connection between available writing aids and pupils’ writing improvement; therefore, texts’ expression and comprehension. A group of thirteen dysgraphic pupils were placed in a regular classroom of primary school, with twenty-one pupils being recruited in the study as a control group. To ensure validity, reliability and accountability to the research, both groups studied writing courses for two semesters, of which the first was equipped with smart writing aids while the second took place in an ordinary classroom. Two pre-tests were undertaken at the beginning of the first two semesters, and two post-tests were administered at the end of both semesters. Tests examined pupils’ ability to write coherent, cohesive and expressive texts. The dysgraphic group received the treatment of a writing course in the first semester in classes with smart technology and produced significantly greater increases in writing expression than in an ordinary classroom, and their performance was better than that of the control group in the second semester. The current study concludes that using smart teaching aids is a ‘MUST’, both for teaching and learning dysgraphia. Furthermore, it is demonstrated that for young dysgraphia, expressive tasks are more challenging than coherent and cohesive tasks. The study, therefore, supports the literature suggesting a role for smart educational aids in writing and that smart writing techniques may be an efficient addition to regular educational practices, notably in special educational institutions and speech-language therapeutic facilities. However, further research is needed to prompt the adults with dysgraphia more often than is done to the older adults without dysgraphia in order to get them to finish the other productive and/or written skills tasks.

Keywords: smart technology, writing aids, pupils with dysgraphia, hands’ movement

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Authors: Majed A. Alsalem

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Universal Design for Learning (UDL) is common theme in education across the US and an influential model and framework that enables students in general and particularly students who are deaf and hard of hearing (DHH) to access the general education curriculum. UDL helps teachers determine how information will be presented to students and how to keep students engaged. Moreover, UDL helps students to express their understanding and knowledge to others. UDL relies on technology to promote students' interaction with content and their communication of knowledge. This study included 120 DHH students who received daily instruction based on UDL principles. This study presents the results of the study and discusses its implications for the integration of UDL in day-to-day practice as well as in the country's education policy. UDL is a Western concept that began and grew in the US, and it has just begun to transfer to other countries such as Saudi Arabia. It will be very important to researchers, practitioners, and educators to see how UDL is being implemented in a new place with a different culture. UDL is a framework that is built to provide multiple means of engagement, representation, and action and expression that should be part of curricula and lessons for all students. The purpose of this study is to investigate the variables associated with the implementation of UDL in Saudi Arabian schools and identify the barriers that could prevent the implementation of UDL. Therefore, this study used a mixed methods design that use both quantitative and qualitative methods. More insights will be gained by including both quantitative and qualitative rather than using a single method. By having methods that different concepts and approaches, the databases will be enriched. This study uses levels of collecting date through two stages in order to insure that the data comes from multiple ways to mitigate validity threats and establishing trustworthiness in the findings. The rationale and significance of this study is that it will be the first known research that targets UDL in Saudi Arabia. Furthermore, it will deal with UDL in depth to set the path for further studies in the Middle East. From a perspective of content, this study considers teachers’ implementation knowledge, skills, and concerns of implementation. This study deals with effective instructional designs that have not been presented in any conferences, workshops, teacher preparation and professional development programs in Saudi Arabia. Specifically, Saudi Arabian schools are challenged to design inclusive schools and practices as well as to support all students’ academic skills development. The total participants in stage one were 336 teachers of DHH students. The results of the intervention indicated significant differences among teachers before and after taking the training sessions associated with their understanding and level of concern. Teachers have indicated interest in knowing more about UDL and adopting it into their practices; they reported that UDL has benefits that will enhance their performance for supporting student learning.

Keywords: deaf and hard of hearing, professional development, Saudi Arabia, universal design for learning

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Authors: Abdolreza Memari

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In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

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Authors: Eftychia Damaskou

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This article intends to place the question of the adequate approach for teaching multilingualism within the public education. Linguistic education, as it is defined by the Common European Framework of Reference for the Languages, is no longer the proficiency in one or two languages. It’s about the development of a linguistic repertoire, where all linguistic skills find their place. In fact, the linguistic theories that frame the development of plurilingual competence point out the affective and intercultural aspect of such a process, insisting on an awareness of linguistic diversification, rather than an acquisition of communicative competence in many languages. In this spirit, our article attempts to go beyond a mere plurilingual awareness, present a research based on an experience in class, within 115 pupils, aiming at the development of plurilingual competence in five unknown foreign languages. This experience was held through a teaching unit personally conceived and applied, and consisted of a series of 6 activities based on a cross-linguistic content approach. The data analysis proves to be very interesting, as it reveals the development of plurilingual competences, as well as positive attitudes towards less common languages by the majority of our sample.

Keywords: multilingual awareness, multilingual teaching material, plurilingual competence

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Siham Boulaknadel

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Every culture has its share of a rich history of storytelling in oral, visual, and textual form. The Amazigh language, as many languages, has its own which has entertained and informed across centuries and cultures, and its instructional potential continues to serve teachers. According to many researchers, listening to stories draws attention to the sounds of language and helps children develop sensitivity to the way language works. Stories including repetitive phrases, unique words, and enticing description encourage students to join in actively to repeat, chant, sing, or even retell the story. This kind of practice is important to language learners’ oral language development, which is believed to correlate completely with student’s academic success. Today, with the advent of multimedia, digital storytelling for instance can be a practical and powerful learning tool. It has the potential in transforming traditional learning into a world of unlimited imaginary environment. This paper reports on a research project on development of multimedia Storytelling Website using traditional Amazigh oral narratives called “tell me a story”. It is a didactic tool created for the learning of good moral values in an interactive multimedia environment combining on-screen text, graphics and audio in an enticing environment and enabling the positive values of stories to be projected. This Website developed in this study is based on various pedagogical approaches and learning theories deemed suitable for children age 8 to 9 year-old. The design and development of Website was based on a well-researched conceptual framework enabling users to: (1) re-play and share the stories in schools or at home, and (2) access the Website anytime and anywhere. Furthermore, the system stores the students work and activities over the system, allowing parents or teachers to monitor students’ works, and provide online feedback. The Website contains following main feature modules: Storytelling incorporates a variety of media such as audio, text and graphics in presenting the stories. It introduces the children to various kinds of traditional Amazigh oral narratives. The focus of this module is to project the positive values and images of stories using digital storytelling technique. Besides development good moral sense in children using projected positive images and moral values, it also allows children to practice their comprehending and listening skills. Reading module is developed based on multimedia material approach which offers the potential for addressing the challenges of reading instruction. This module is able to stimulate children and develop reading practice indirectly due to the tutoring strategies of scaffolding, self-explanation and hyperlinks offered in this module. Word Enhancement assists the children in understanding the story and appreciating the good moral values more efficiently. The difficult words or vocabularies are attached to present the explanation, which makes the children understand the vocabulary better. In conclusion, we believe that the interactive multimedia storytelling reveals an interesting and exciting tool for learning Amazigh. We plan to address some learning issues, in particularly the uses of activities to test and evaluate the children on their overall understanding of story and words presented in the learning modules.

Keywords: Amazigh language, e-learning, storytelling, language teaching

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Bernice Badal

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The South African English curriculum emphasizes the adoption of the Communicative Approach (CA) using Communicative Language Teaching (CLT) methodologies to develop English as a second language (ESL) learners’ communicative competence in contexts such as township schools in South Africa. However, studies indicate that the adoption of the approach largely remains a rhetoric. Poor English language proficiency among learners and poor student performance, which continues from the secondary to the tertiary phase, is widely attributed to a lack of English language proficiency in South Africa. Consequently, this qualitative study, using a mix of classroom observations and interviews, sought to investigate teacher knowledge of Communicative Competence and the methods and strategies ESL teachers used to develop their learners’ communicative competence. The success of learners’ ability to develop communicative competence in contexts such as township schools in South Africa is inseparable from materials, tasks, teacher knowledge and how they implement the approach in the classrooms. Accordingly, teacher knowledge of the theory and practical implications of the CLT approach is imperative for the negotiation of meaning and appropriate use of language in context in resource-impoverished areas like the township. Using a mix of interviews and observations as data sources, this qualitative study examined teachers’ definitions and knowledge of Communicative competence with a focus on how it influenced their classroom practices. The findings revealed that teachers were not familiar with the notion of communicative competence, the communication process, and the underpinnings of CLT. Teachers’ narratives indicated an awareness that there should be interactions and communication in the classroom, but a lack of theoretical understanding of the types of communication necessary scuttled their initiatives. Thus, conceptual deficiency influences teachers’ practices as they engage in classroom activities in a superficial manner or focus on stipulated learner activities prescribed by the CAPS document. This study, therefore, concluded that partial or limited conceptual and coherent understandings with ‘teacher-proof’ stipulations for classroom practice do not inspire teacher efficacy and mastery of prescribed approaches; thus, more efforts should be made by the Department of Basic Education to strengthen the existing Professional Development workshops to support teachers in improving their understandings and application of CLT for the development of Communicative competence in their learners. The findings of the study contribute to the field of teacher knowledge acquisition, teacher beliefs and practices and professional development in the context of second language teaching and learning with a recommendation that frameworks for the development of communicative competence with wider applicability in resource-poor environments be developed to support teacher understanding and application in classrooms.

Keywords: communicative competence, CLT, conceptual understanding of reforms, professional development

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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: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Jawaher Bakheet Almudarra

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An Educational Supervisor assists teachers to develop their competence and skills in teaching, solving educational problems, and to improve the teaching methods to suit the educational process. They evaluate their teachers and write reports based on their assessments. In 1957, the Saudi Ministry of Education instituted Educational Supervision to facilitate effective management of schools, however, there have been concerns that the Educational Supervision has not been effective in executing its mandate. Studies depicted that Educational supervision has not been effective because it has been marred by poor and autocratic leadership practices such as stringent inspection, commanding and judging. Therefore, there is need to consider some of the ways in which school outcomes can be enhanced through the improvement of Educational supervision practices. Emotional intelligence is a relatively new concept that can be integrated into the Saudi education system that is yet to be examined in-depth and embraced particularly in the realm of educational leadership. Its recognition and adoption may improve leadership practices among Educational supervisors. This study employed a qualitative interpretive approach that will focus on decoding, describing and interpreting the connection between emotional intelligence and leadership. The study also took into account the social constructions that include consciousness, language and shared meanings. The data collection took place in the Office of Educational Supervisors in Riyadh and involved 4 Educational supervisors and 20 teachers from both genders- male and female. The data collection process encompasses three methods namely; qualitative emotional intelligence self-assessment questionnaires, reflective semi-structured interviews, and open workshops. The questionnaires would explore whether the Educational supervisors understand the meaning of emotional intelligence and its significance in enhancing the quality of education system in Saudi Arabia. Subsequently, reflective semi-structured interviews were carried out with the Educational supervisors to explore the connection between their leadership styles and the way they conceptualise their emotionality. The open workshops will include discussions on emotional aspects of Educational supervisors’ practices and how Educational supervisors make use of the emotional intelligence discourse in their leadership and supervisory relationships.

Keywords: directors of educational supervision, emotional intelligence, educational leadership, education management

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Authors: Lisa Chandler, Alistair Ward

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A significant component of design pedagogy is the need to foster design thinking in various contexts and to support students in understanding links between educational exercises and their potential application in professional design practice. It is also important that educators provide opportunities for students to engage with new technologies and encourage them to imagine applying their design skills for a range of outcomes. Problem solving is central to design so it is also essential that students understand that there can be multiple solutions to a design brief, and are supported in undertaking creative experimentation to generate imaginative outcomes. This paper presents a case study examining some innovative approaches to addressing these elements of design pedagogy. It investigates the effectiveness of the Immerse Lab, a three wall projection room at the University of the Sunshine Coast, Australia, as a learning context for design practice, for generating ideas and for supporting learning involving the comparative display of design outcomes. The project required first year design students to create a simple graphic design derived from an ordinary object and to incorporate specific design criteria. Utilizing custom-designed software, the students’ solutions were projected together onto the Immerse walls to create a large-scale, immersive grid of images, which was used to compare and contrast various responses to the same problem. The software also enabled individual student designs to be transformed, multiplied and enlarged in multiple ways and prompted discussions around the applicability of the designs in real world contexts. Teams of students interacted with their projected designs, brainstorming imaginative applications for their outcomes. Analysis of 77 anonymous student surveys revealed that the majority of students found: learning in the Immerse Lab to be beneficial; comparative review more effective than in standard tutorial rooms; that the activity generated new ideas; it encouraged students to think differently about their designs; it inspired students to develop their existing designs or create new ones. The project demonstrates that curricula involving immersive spaces can be effective in supporting engaging and relevant design pedagogy and might be utilized in other disciplinary areas.

Keywords: design pedagogy, immersive education, technology-enhanced learning, visualization

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Kari Björn

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Introduction of games has expanded the application area of computer-aided learning tools to wide variety of age groups of learners. Serious games engage the learners into a real-world -type of simulation and potentially enrich the learning experience. Institutional background of a Bachelor’s level engineering program in Information and Communication Technology is introduced, with detailed focus on one of its majors, Health Technology. As part of a Customer Oriented Software Application thematic semester, one particular course of “eHealth Business and Solutions” is described and reflected in a gamified framework. Learning a consistent view into vast literature of business management, strategies, marketing and finance in a very limited time enforces selection of topics relevant to the industry. Health Technology is a novel and growing industry with a growing sector in consumer wearable devices and homecare applications. The business sector is attracting new entrepreneurs and impatient investor funds. From engineering education point of view the sector is driven by miniaturizing electronics, sensors and wireless applications. However, the market is highly consumer-driven and usability, safety and data integrity requirements are extremely high. When the same technology is used in analysis or treatment of patients, very strict regulatory measures are enforced. The paper introduces a course structure using gamification as a tool to learn the most essential in a new market: customer value proposition design, followed by a market entry game. Students analyze the existing market size and pricing structure of eHealth web-service market and enter the market as a steering group of their company, competing against the legacy players and with each other. The market is growing but has its rules of demand and supply balance. New products can be developed with an R&D-investment, and targeted to market with unique quality- and price-combinations. Product cost structure can be improved by investing to enhanced production capacity. Investments can be funded optionally by foreign capital. Students make management decisions and face the dynamics of the market competition in form of income statement and balance sheet after each decision cycle. The focus of the learning outcome is to understand customer value creation to be the source of cash flow. The benefit of gamification is to enrich the learning experience on structure and meaning of financial statements. The paper describes the gamification approach and discusses outcomes after two course implementations. Along the case description of learning challenges, some unexpected misconceptions are noted. Improvements of the game or the semi-gamified teaching pedagogy are discussed. The case description serves as an additional support to new game coordinator, as well as helps to improve the method. Overall, the gamified approach has helped to engage engineering student to business studies in an energizing way.

Keywords: engineering education, integrated curriculum, learning experience, learning outcomes

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Authors: Maria Zinnia Bardas Hoffmann

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Contrastive Analysis (CA) is the systematic comparison between two languages. It stems from the notion that errors are caused by interference of the L1 system in the acquisition process of an L2. CA represents a useful tool to understand the nature of learning and acquisition. Also, this particular method promises a path to un-derstand the nature of underlying cognitive processes, even when other factors such as intrinsic motivation and teaching strategies were found to best explain student’s problems in acquisition. CA study is justified not only from the need to get a deeper understanding of the nature of SLA, but as an invaluable source to provide clues, at a cognitive level, for those general processes involved in rule formation and abstract thought. It is relevant for cross disciplinary studies and the fields of Computational Thought, Natural Language processing, Applied Linguistics, Cognitive Linguistics and Math Theory. That being said, this paper intends to address here as well its own set of constraints and limitations. Finally, this paper: (a) aims at identifying some of the difficulties students may find in their learning process due to the nature of their specific variety of L1, Rio de la Plata Spanish (RPS), (b) represents an attempt to discuss the necessity for specific models to approach CA.

Keywords: second language acquisition, applied linguistics, contrastive analysis, applied contrastive analysis English language department, meta-linguistic rules, cross-linguistics studies, computational thought, natural language processing

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Authors: Fati̇h Apaydin

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Education technology is an area which constantly changes and creates innovations. As an inevitable part of the changing circumstances, the societies who have a tendency to the improvements keep up with these innovations by using the methods and strategies which have been designed for education technology. At this point, education technology has taken the responsibility to help the individuals improve themselves and teach the effective teaching methods by filling the airs in theoretical information, information security and the practice. The technology which comes to the core of our lives by raising the importance of it day by day and it enforced its position in computer- based environments. As a result, ‘being ready for technological innovations, improvement on computer-based talent, information, ability and attitude’ doctrines have to be given. However, it is today quite hard to deal with the security and reinforcement of this information. The information which is got illegally gives harm to society from every aspect, especially education. This study includes how and to what extent to use these innovative appliances such as computers and the factor of information security of these appliances in computer-based education. As the use of computer is constantly becoming prevalent in our country, both education and computer will never become out of date, so how computer-based education affects our lives and the study of information security for this type of education are important topics.

Keywords: computer, information security, education, technology, development

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Michael Lekan-Kehinde, Abimbola Asojo, Bonnie Sanborn

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Good acoustic performance has been identified as one of the critical Indoor Environmental Quality (IEQ) factors for student learning and development by the National Research Council. Childhood presents the opportunity for children to develop lifelong skills that will support them throughout their adult lives. Acoustic performance of a space has been identified as a factor that can impact language acquisition, concentration, information retention, and general comfort within the environment. Increasingly, students learn by communication between both teachers and fellow students, making speaking and listening crucial. Neurodiversity - while initially coined to describe individuals with autism spectrum disorder (ASD) - widely describes anyone with a different brain process. As the understanding from cognitive and neurosciences increases, the number of people identified as neurodiversity is nearly 30% of the population. This research looks at guidelines and standard for spaces with good acoustical quality and relates it with the experiences of students with autism spectrum disorder (ASD), their parents, teachers, and educators through a mixed methods approach, including selected case studies interviews, and mixed surveys. The information obtained from these sources is used to determine if selected materials, especially properties relating to sound absorption and reverberation reduction, are equally useful in small, medium sized, and large learning spaces and methodologically approaching. The results describe the potential impact of acoustics on Neurodiverse students, considering factors that determine the complexity of sound in relation to the auditory processing capabilities of ASD students. In conclusion, this research extends the knowledge of how materials selection influences the better development of acoustical environments for autism students.

Keywords: acoustics, autism spectrum disorder (ASD), children, education, learning, learning spaces, materials, neurodiversity, sound

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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: Olga Castiblanco, Diego Vizcaíno

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Changing the traditional way of using experimentation in science teaching is quite a challenge. This research results talk about the characterization of physics experiments, not because of the topic it deals with, nor depending on the material used in the assemblies, but related to the possibilities it offers to enrich interaction in the classroom and thereby contribute to the development of scientific thinking skills. It is an action-research of type intervention in the classroom, with four courses of Physics Teaching undergraduate students from a public university in Bogotá. This process allows characterizing typologies such as discrepant, homemade, illustrative, research, recreational, crucial, mental, and virtual experiments. Students' production and researchers' reports on each class were the most relevant data. Content analysis techniques let to categorize the information and obtain results on the richness that each typology of experiment offers when interacting in the classroom. Results show changes in the comprehension of new teachers' role, far from being the possessor and transmitter of the truth. Besides, they understand strategies to engage students effectively since the class advances extending ideas, reflections, debates, and questions, either towards themselves, their classmates, or the teacher.

Keywords: physics teacher training, non-traditional experimentation, contextualized education, didactics of physics

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