Search results for: mobile game based learning

Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Pi-Lan Yang

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It is controversial which type of gloss condition (i.e., gloss language or gloss position) is more effective in second or foreign language (L2) vocabulary learning. The present study compared the performance on learning ten English words in the conditions of L2 English reading with no glosses and with glosses of Chinese equivalents/translations and L2 English definitions at the side of a page and at an attached sheet for low-intermediate Chinese-speaking learners of English, who were technology university students in Taiwan. It is found first that the performances on the immediate posttest and the delayed posttest were overall better in the gloss condition than those in the no-gloss condition. Next, it is found that the glosses of Chinese translations were more effective and sustainable than those of L2 English definitions. Finally, the effects of L2 English glosses at the side of a page were observed to be less sustainable than those at an attached sheet. In addition, an opinion questionnaire used also showed a preference for the glosses of Chinese translations in L2 English reading. These results would be discussed in terms of automated lexical access, sentence processing mechanisms, and the trade-off nature of storage and processing functions in working memory system, proposed by the capacity theory of language comprehension.

Keywords: glosses of Chinese equivalents/translations, glosses of L2 English definitions, L2 vocabulary learning, L2 English reading

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Authors: Aya Salama

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Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

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Authors: Shamshuddin K., Nagaraj Vannal, Diwakar Kulkarni

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This paper presents details of teaching and learning pedagogical techniques attempted for the undergraduate engineering program to improve the concentration span of students in a classroom. The details of activities such as valid statement, quiz competition, classroom paper, group work and product marketing to make the students remain active for the entire class duration and to improve presentation skills are presented. These activities shown tremendous improvement in student’s performance in academics, also in asking questions, concept understanding and interaction with the course instructor. With these pedagogical activities we are able to achieve Program outcome elements and ABET Program outcomes such as d, i, g and h which are difficult to achieve through the conventional teaching methods.

Keywords: activities, pedagogy, interactive learning, valid statement, quiz competition, classroom papers, group work, product marketing

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Eunice H. Li

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This e-Poster presents how the ‘digital backpack’ was introduced to primary school children in Ivory Coast. The idea of a ‘digital backpack’ was initiated by Mr. Thierry N’Doufou in 2012, who later designed and presented to the rest of the world in September 2014. The motivation behind the backpack was to relieve children of the heavy-weight they carry in their school backpacks. Another motivation was to promote Ivory Coast as a country where all children are brought into the digital era. Thierry N’Doufou regards education as the means by which his nation and the entire African Continent can be developed as a prosperous territory. The ‘digital backpack’ contains the entire curriculum for each class and favours a constructivist approach to learning. The children’s notes and exercises are also included in the pack. Additionally, teachers and parents are able to monitor remotely children’s activities while they are working with the ‘backpack’. Teachers are also able to issue homework, assess student’s progress and manage the student’s coursework. This means that teachers should always think the most appropriate pedagogies that can be used to help children to learn. Furthermore, teachers, parents and fellow students are able to have conversations and discussions by using web portals. It is also possible to access more apps if children would like to have additional learning activities. From the presentation in the e-Poster, it seems reasonable to conclude that the ‘digital backpack’ has potential to reach other-level of education. In this way, all will be able to benefit from this new invention.

Keywords: pedagogy, curriculum, constructivism, social constructivism, distance learning environment, ubiquitous learning environment

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Authors: Shatha Ahmad Alharthi

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Prior research suggests that reduced social interaction can negatively affect well-being and impair mental health (e.g., depression and anxiety), resulting in lower academic performance. The COVID-19 pandemic has significantly limited social interaction among Saudi Arabian school children since the government closed schools and implemented lockdown restrictions to reduce the spread of the disease. These restrictions have resulted in prolonged remote learning for middle school students with unknown consequences for perceived academic performance, mental health, and well-being. This research project explores how middle school Saudi students’ current remote learning practices affect their mental health (e.g., depression and anxiety) and well-being during the lockdown. Furthermore, the study will examine the association between social interaction, mental health, and well-being pertaining to students’ perceptions of their academic achievement. Research findings could lead to a better understanding of the role of lockdown on depression, anxiety, well-being and perceived academic performance. Research findings may also inform policy-makers or practitioners (e.g., teachers and school leaders) about the importance of facilitating increased social interactions in remote learning situations and help to identify important factors to consider when seeking to re-integrate students into a face-to-face classroom setting. Potential implications for future educational research include exploring remote learning interventions targeted at bolstering students’ mental health and academic achievement during periods of remote learning.

Keywords: depression, anxiety, academic performance, social interaction

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Authors: Yacoub Aljaffery

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As much as we think negatively about social media such as TV and smart phones, there are many positive benefits our society can get from it. Cartoons, for example, are made specifically for children. However, in this paper, we will prove how cartoon videos can have a positive impact on adults, especially college students. Since cartoons are meant to be a good learning tool for children, as well as adults, we will show our audience how they can use cartoon in teaching critical thinking and other language skills.

Keywords: social media, TV, teaching, learning, cartoon movies

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Shui Lien Chen, Chen-Yin Kuo

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U-computing use has brings many new services of commerce, which could provide a new experience for customer. Location Based Services (LBS) is one of U-computing service. With increase of the smartphone and mobile internet users, there are many small and medium-sized enterprises (SMEs) take LBS in marketing strategy in Taiwan. For example, they would provide Facebook check-in to get a benefit (e.g. discount, free dessert and coupon) to attract customers purchasing. Therefore, this study is to understand which factors would affect SMEs adoption of u-computing and the performances after adopt U-computing. This study collected 187 useful data that were analyzed by SmartPLS 2.0 software. The results of this study are as follows. First, entrepreneurial orientation and market orientation positively affects innovation. Second, business resources and innovation positively affect u-computing use. Finally, U-computing positively affects both business value and customer value.

Keywords: entrepreneurial orientation, market orientation, innovation, business resources, u-computing use, LBS

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Authors: Cristina Maria Ribeiro Martins Pereira Caridade, Ana Rita Ferreira Morais

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The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with healthcare facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool developed in MATLAB using a graphical user interface for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing you to view original images and fusion images, compare processed and original images, adjust parameters, and save images. The tool proposed in an innovative teaching and learning environment consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques and necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies.

Keywords: image fusion, image processing, teaching-learning simulation tool, biomedical engineering education

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Authors: Chaiwat Waree

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The purposes of the research were to develop online lessons to strengthen the learning process of Master's degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University; to achieve the efficiency criteria of 80/80; and to study the satisfaction of students who use online lessons to strengthen the learning process of Master’s degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University. The sample consisted of 40 University students studying in semester 1, academic year 2012. The sample was determined by Purposive Sampling. Selected students were from the class which the researcher was the homeroom tutor. The tutor was responsible for the teaching of learning process. Tools used in the study were online lessons, 60-point performance test, and evaluation test of satisfaction of students on online lessons. Data analysis yielded the following results; 83.66/88.29 efficiency of online lessons measured against the criteria; the comparison of performance before and after taking online lessons using t-test yielded 29.67. The statistical significance was at 0.05; the average satisfaction level of forty students on online lessons was 4.46 with standard deviation of 0.68.

Keywords: online, lessons, curriculum, instruction

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Authors: Zoltán Höhling, Sarah-Lu Oberschelp, Niklas Gilsdorf, Michael Wirsching, Andrea Kuhnert

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A majority of medical students at the University of Freiburg reported stress-related psychosomatic symptoms which are often associated with their studies. International research supports these findings, as medical students worldwide seem to be at special risk for mental health problems. In some countries and institutions, psychologically based interventions that assist medical students in coping with their stressors have been implemented. It turned out that anonymity is an important aspect here. Many students fear a potential damage of reputation when being associated with mental health problems, which may be due to a high level of competitiveness in classes. Therefore, we launched an online-platform where medical students could anonymously seek help and exchange their experiences with fellow students and experts. Medical students of all semesters have access to it through the university’s learning management system (called “ILIAS”). The informative part of the platform consists of exemplary videos showing medical students (actors) who act out scenes that demonstrate the antecedents of stress-related psychosomatic disorders. These videos are linked to different expert comments, describing the exhibited symptoms in an understandable and normalizing way. The (inter-)active part of the platform consists of self-help tools (such as meditation exercises or general tips for stress-coping) and an anonymous interactive forum where students can describe their stress-related problems and seek guidance from experts and/or share their experiences with fellow students. Besides creating an immediate proposal to help affected students, we expect that competitiveness between students might be diminished and bondage improved through mutual support between them. In the initial phase after the platform’s launch, it was accessed by a considerable number of medical students. On a closer look it appeared that platform sections like general information on psychosomatic-symptoms and self-treatment tools were accessed far more often than the online-forum during the first months after the platform launch. Although initial acceptance of the platform was relatively high, students showed a rather passive way of using our platform. While user statistics showed a clear demand for information on stress-related psychosomatic symptoms and its possible remedies, active engagement in the interactive online-forum was rare. We are currently advertising the platform intensively and trying to point out the assured anonymity of the platform and its interactive forum. Our plans, to assure students their anonymity through the use of an e-learning facility and promote active engagement in the online forum, did not (yet) turn out as expected. The reasons behind this may be manifold and based on either e-learning related issues or issues related to students’ individual needs. Students might, for example, question the assured anonymity due to a lack of trust in the technological functioning university’s learning management system. However, one may also conclude that reluctance to discuss stress-related psychosomatic symptoms with peer medical students may not be solely based on anonymity concerns, but could be rooted in more complex issues such as general mistrust between students.

Keywords: e-tutoring, stress-coping, student support, online forum

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Authors: Dahiru Bawa Muhammad

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Mathematics is accorded a prime position in basic education curriculum because it is envisaged to be an important tool in preparing children for life after school as well as equipping them with skills needed for secondary and higher education. As a result of this, the subject is made compulsory from primary through secondary school and candidates are expected to offer it and pass before fulfilling the requirement for higher education. Against this backdrop, this paper overviewed the basic education programme, context of teaching and learning mathematics at basic education level in Katsina State of Nigeria, relevance of the subject to different fields of human endeavours, challenges threatening the utility of the subject as a tool for the achievement of the goals of basic education programme and concluded by recommending how teaching and learning of mathematics can be improved for even development of citizens within nation states and enhanced/mutual sustainable development of nations in the global village.

Keywords: basic education, junior secondary school education, mathematical centre

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Authors: Arun Prasad Jaganathan

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In today's rapidly evolving technological landscape, the need for efficient and proactive maintenance management solutions has become increasingly evident across various industries. Traditional approaches often suffer from drawbacks such as reactive strategies, leading to potential downtime, increased costs, and decreased operational efficiency. In response to these challenges, this paper proposes an AI-enabled approach to object-based maintenance management aimed at enhancing reliability and efficiency. The paper contributes to the growing body of research on AI-driven maintenance management systems, highlighting the transformative impact of intelligent technologies on enhancing object reliability and operational efficiency.

Keywords: AI, machine learning, predictive maintenance, object-based maintenance, expert team scheduling

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Azusa Katsumata

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Several universities in Japan have introduced activities aimed at the mutual enlightenment of a diversity of people in career education. However, several programs emphasize on delivering results, and on practicing the prepared materials as planned. Few programs focus on unexpected failures and setbacks. This way of learning is important in career education so that classmates can help each other, overcome difficulties, draw out each other’s strengths, and learn from them. Seijo University in Tokyo offered excursion focusing Various Problems of Society, as second year career education course, Students will learn about contraception, infertility, homeless people, LGBT, and they will discuss based on the excursion. This paper aims to study the ‘learning platform’ created by a series of processes such as the excursion, the discussion, and the presentation. In this course, students looked back on their lives and imagined the future in concrete terms, performing tasks in groups. The students came across a range of values through lectures and conversations, thereby developing feelings of self-efficacy. We conducted a questionnaire to measure the development of career in class. From the results of the questionnaire, we can see, in the example of this class, that students respected diversity and understood the importance of uncertainty and discontinuity. Whereas the students developed career awareness, they actually did not come across that scene and would do so only in the future when it became necessary. In this class, students consciously considered social problems, but did not develop the practical skills necessary to deal with these. This is appropriate for one of project, but we need to consider how this can be incorporated into future courses. University constitutes only a single period in life-long career formation. Thus, further research may be indicated to determine whether the positive effects of career education at university continue to contribute to individual careers going forward.

Keywords: career education of university, excursion, learning platform, problems of society

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Authors: B. El Fadil, R. Najar

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STEM education is promoted by many scholars and curricula around the world, but it is not yet well established in the province of Quebec in Canada. In addition, effective instructional STEM activities and design methods are required to ensure that students and teachers' needs are being met. One potential method is the Engineering Design Process (EDP), a methodology that emphasizes the importance of creativity and collaboration in problem-solving strategies. This article reports on a case study that focused on using the EDP to develop instructional materials by means of making a technological artifact to teach mathematical variables and functions at the secondary level. The five iterative stages of the EDP (design, make, test, infer, and iterate) were integrated into the development of the course materials. Data was collected from different sources: pre- and post-questionnaires, as well as a working document dealing with pupils' understanding based on designing, making, testing, and simulating. Twenty-four grade seven (13 years old) students in Northern Quebec participated in the study. The findings of this study indicate that STEM activities have a positive impact not only on students' engagement in classroom activities but also on learning new mathematical concepts. Furthermore, STEM-focused activities have a significant effect on problem-solving skills development in an interdisciplinary approach. Based on the study's results, we can conclude, inter alia, that teachers should integrate STEM activities into their teaching practices to increase learning outcomes and attach more importance to STEM-focused activities to develop students' reflective thinking and hands-on skills.

Keywords: engineering design process, motivation, stem, integration, variables, functions

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Eija Raatikainen

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The aim of the study is to examine psychological safety in the context of change in working life and continuous learning in social- and educational organizations. The participants in the study are social workers and vocational teachers working as employees and supervisors in the capital region of Finland (public and private sectors). Research data has been collected during 2022-2023 using the qualitative method called empathy-based stories (MEBS). Research participants were asked to write short stories about situations related to their work and work community. As researchers, we created and varied the framework narratives (MEBS) in line with the aim of the study and theoretical background. The data were analyzed with content analysis. According to the results, the barriers and prerequisites for psychological safety at work could be located in four different working culture dimensions. The work culture dimensions were named as follows: 1) a work culture focusing on interaction and emotional culture between colleagues, 2) communal work culture, 3) a work culture that enables learning, and 4) a work culture focused on structures and operating models. All these have detailed elements of barriers and prerequisites of psychological safety at work. The results derived from the enlivening methods can be utilized when working with the work community and have discussed psychological safety at work. Also, the method itself (MEBS) can prevent open discussion and reflection on psychological safety at work because of the sensitivity of the topic. Method aloud to imagine, not just talk and share your experiences directly. Additionally, the results of the study can offer one tool or framework while developing phycological safety at work.

Keywords: psychological safety, empathy, empathy-based stories, working life

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Authors: Felicia K. Oluwalola

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This paper examines the relevance of technology on education. It identified the concept of technology on education, bringing real-world learning to the classroom situation, examples of where technology can be used. This study established the fact that technology facilitates students learning compared with traditional method of teaching. It was recommended that the teachers should use technology to supplement, not replace, other instructional modes. It should be used in conjunction with hands-on labs and activities that also address the concepts targeted by the technology. Also, technology should be students centered and not teachers centered.

Keywords: computer, simulation, classroom teaching, education

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Authors: Elisabeth Unterfrauner, Christian Voigt

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Since the foundation of the first Fab Lab by the Massachusetts Institute of Technology about 17 years ago, the Maker movement has spread globally with the foundation of maker spaces and Fab Labs worldwide. In these workshops, citizens have access to digital fabrication technologies such as 3D printers and laser cutters to develop and test their own ideas and prototypes, which makes it an attractive place for start-up companies. Know-How is shared not only in the physical space but also online in diverse communities. According to the Horizon report, the Maker movement, however, will also have an impact on educational settings in the following years. The European project ‘DOIT - Entrepreneurial skills for young social innovators in an open digital world’ has incorporated key elements of making to develop an early entrepreneurial education program for children between the age of six and 16. The Maker pedagogy builds on constructive learning approaches, learning by doing principles, learning in collaborative and interdisciplinary teams and learning through trial and error where mistakes are acknowledged as learning opportunities. The DOIT program consists of seven consecutive elements. It starts with a motivation phase where students get motivated by envisioning the scope of their possibilities. The second step is about Co-design: Students are asked to collect and select potential ideas for innovations. In the Co-creation phase students gather in teams and develop first prototypes of their ideas. In the iteration phase, the prototype is continuously improved and in the next step, in the reflection phase, feedback on the prototypes is exchanged between the teams. In the last two steps, scaling and reaching out, the robustness of the prototype is tested with a bigger group of users outside of the educational setting and finally students will share their projects with a wider public. The DOIT program involves 1,000 children in two pilot phases at 11 pilot sites in ten different European countries. The comprehensive evaluation design is based on a mixed method approach with a theoretical backbone on Lackeus’ model of entrepreneurship education, which distinguishes between entrepreneurial attitudes, entrepreneurial skills and entrepreneurial knowledge. A pre-post-test with quantitative measures as well as qualitative data from interviews with facilitators, students and workshop protocols will reveal the effectiveness of the program. The evaluation results will be presented at the conference.

Keywords: early entrepreneurial education, Fab Lab, maker education, Maker movement

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

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Authors: Ken Munro

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Cruise ships are possibly the most complex collection of systems it is possible to find in one physical, moving location. Propulsion, navigation, power generation and more, combined with a hotel, restaurant, casino, theatre etc, with safety and fire control systems to boot. That complexity creates huge challenges with keeping OT and IT systems apart. Ships engines are often remotely managed, network segregation is often defeated through troubleshooting when at sea. This session will refer to multiple entertaining and informative tales of taking control of ships, including accessing a ships Azipods via a game simulator for passengers. Fortunately, genuine attacks against vessels are very rare, but the effects and impacts to world trade are becoming increasingly obvious.

Keywords: maritime security, cybersecurity, OT, IT, networks

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Authors: Qi Wang

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Accurate pronunciation is the first condition of communication. Compared with the alphabetic languages, Chinese is more difficult for the foreigners to study as a second language, due to the tonal language with the meaningful characters as the written system, especially speaking. This research first presents the statistics of the typical errors of the pronunciations, based on the data of our two- year program of graduate students, which shown 90% of their speaking with strong foreign accents and no obvious change of the pitches, even if they could speak Chinese fluently. Second part, analyzed the caused reasons in the learning and teaching processes. Third part, this result of this research, based the theory of Chinese prosodic words, shown that the earlier the students get trained on prosodics at the beginning and suprasegmentals at intermediate and advanced levels, the better effects for them to communicate in Chinese as a second language.

Keywords: second language, prosodic word, foot, suprasegmental

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Authors: Nilgun Karatas, Bihter Sakin

Abstract:

In this study, the relationships between the use of language learning strategies and English language exit exam success were investigated in the university EFL learners’ context. The study was conducted at Fatih University Prep School. To collect data 3 classes from the A1 module in English language classes completed a questionnaire known as the English Language Learning Strategy Inventory or ELLSI. The data for the present study were collected from the preparatory class students who are studying English as a second language at the School of Foreign Languages. The students were placed into four different levels of English, namely A1, A2, B1, and B2 level of English competency according to European Union Language Proficiency Standard, by means of their English placement test results. The Placement test was conveyed at the beginning of the spring semester in 2014-2015.The ELLSI consists of 30 strategy items which students are asked to rate from 1 (low frequency) to 5 (high frequency) according to how often they use them. The questionnaire and exit exam results were entered onto SPSS and analyzed for mean frequencies and statistical differences. Spearman and Pearson correlation were used in a detailed way. There were no statistically significant results between the frequency of strategy use and exit exam results. However, most questions correlate at a significant level with some of the questions.

Keywords: individual differences, language learning strategies, Fatih University, English language

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Authors: Yuanjin Liu

Abstract:

Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model.

Keywords: ruin probability, retirement withdrawal strategies, predictive models, optimal model

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

Abstract:

Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Kulsoom S. Bughio, Naeem K. Janjua, Gordana Dermody, Leslie F. Sikos, Shamsul Islam

Abstract:

The advancement in information and communication technologies (ICT) and wireless sensor networks have played a pivotal role in the design and development of real-time healthcare solutions, mainly targeting the elderly living in health-assistive smart homes. Such smart homes are equipped with sensor technologies to detect and record activities of daily living (ADL). This survey reviews and evaluates existing approaches and techniques based on real-time sensor-based modeling and reasoning in single-user and multi-user environments. It classifies the approaches into three main categories: learning-based, knowledge-based, and hybrid, and evaluates how they handle temporal relations, granularity, and uncertainty. The survey also highlights open challenges across various disciplines (including computer and information sciences and health sciences) to encourage interdisciplinary research for the detection and recognition of ADLs and discusses future directions.

Keywords: daily living activities, smart homes, single-user environment, multi-user environment

Procedia PDF Downloads 123