Search results for: collaborative learning approach

Authors: Ashwaq Alsulami, Jianhua Shao

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Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.

Keywords: attributed community, attribute detection, community, social network

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Authors: Gabrielle Brand, Christopher Etherton-Beer

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The learning landscape of higher education environment is changing, with an increased focus over the past decade on how educators might begin to cultivate reflective skills in health professions students. In addition, changing professional requirements demand that health professionals are adequately prepared to practice in today’s complex Australian health care systems, including responding to changing demographics of population ageing. To counteract a widespread perception of health professions students’ disinterest in caring for older persons, the authors will report on an exploratory, mixed method research study that used photographs, narrative and small group work to enhance medical and nursing students’ reflective learning experience. An innovative photo-elicitation technique and reflective questioning prompts were used to increase engagement, and challenge students to consider new perspectives (around ageing) by constructing shared storylines in small groups. The qualitative themes revealed how photographs, narratives and small group work created learning spaces for reflection whereby students could safely explore their own personal and professional values, beliefs and perspectives around ageing. By providing the space for reflection, the students reported how they found connection and meaning in their own learning through a process of self-exploration that often challenged their assumptions of both older people and themselves as future health professionals. By integrating cognitive and affective elements into the learning process, this research demonstrates the importance of embedding visual methodologies that enhance reflection and transformative learning. The findings highlight the importance of integrating the arts into predominantly empirically driven health professional curricula and can be used as a catalyst for individual and/or collective reflection which can potentially enhance empathy, insight and understanding of the lived experiences of older patients. Based on these findings, the authors have developed ‘Depth of Field: Exploring Ageing’ an innovative, interprofessional, digital reflective learning resource that uses Prezi Inc. software (storytelling tool that presents ideas on a virtual canvas) to enhance students’ reflective capacity in the higher education environment.

Keywords: narrative, photo-elicitation, reflective learning, qualitative research

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Authors: Nodelman V.

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Despite serious difficulties in the assimilation of the conceptual system of Calculus, software in the educational process is used only occasionally, and even then, mainly for illustration purposes. The following are a few reasons: The non-trivial nature of the studied material, Lack of skills in working with software, Fear of losing time working with software, The variety of the software itself, the corresponding interface, syntax, and the methods of working with the software, The need to find suitable models, and familiarize yourself with working with them, Incomplete compatibility of the found models with the content and teaching methods of the studied material. This paper proposes an active use of the developed non-commercial software VusuMatica, which allows removing these restrictions through Broad support for the studied mathematical material (and not only Calculus). As a result - no need to select the right software, Emphasizing the unity of mathematics, its intrasubject and interdisciplinary relations, User-friendly interface, Absence of special syntax in defining mathematical objects, Ease of building models of the studied material and manipulating them, Unlimited flexibility of models thanks to the ability to redefine objects, which allows exploring objects characteristics, and considering examples and counterexamples of the concepts under study. The construction of models is based on an original approach to the analysis of the structure of the studied concepts. Thanks to the ease of construction, students are able not only to use ready-made models but also to create them on their own and explore the material studied with their help. The presentation includes examples of using VusuMatica in studying the concepts of limit and continuity of a function, its derivative, and integral.

Keywords: counterexamples, limitations and requirements, software, teaching and learning calculus, user-friendly interface and syntax

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Authors: Catherine Mary Abou-Zaid

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This quantitative study discusses the concerns with the self-directed learning (SDL) skills of the undergraduate nursing students in a medical university in Bahrain. The nursing undergraduate student SDL study was conducted taking all 4 years and compiling data collected from the students themselves by survey questionnaire. The aim of the study is to understand and change the attitudes of self-directed learning among the undergraduate students. The SDL of the undergraduate student nurses has been noticed to be lacking and motivation to actually perform without supervision while out-with classrooms are very low. Their use of the resources available on the virtual learning environment and also within the university is not as good as it should be for a university student at this level. They do not use them to their own advantage. They are not prepared for the transition from high school to an academic environment such as a university or college. For some students it is the first time in their academic lives that they have faced sharing a classroom with the opposite sex. For some this is a major issue and we as academics need to be aware of all issues that they come to higher education with. Design Methodology: The design methodology that was chosen was a quantitative design using convenience sampling of the students who would be asked to complete survey questionnaire. This sampling method was chosen because of the time constraint. This was completed by the undergraduate students themselves while in class. The questionnaire was analyzed by the statistical package for social sciences (SPSS), the results interpreted by the researcher and the findings published in the paper. The analyzed data will also be reported on and from this information we as educators will be able to see the student’s weaknesses regarding self-directed learning. The aims and objectives of the research will be used as recommendations for the improvement of resources for the students to improve their SDL skills. Conclusion: The results will be able to give the educators an insight to how we can change the self-directed learning techniques of the students and enable them to embrace the skills and to focus more on being self-directed in their studies rather than having to be put on to a SDL pathway from the educators themselves. This evidence will come from the analysis of the statistical data. It may even change the way in which the students are selected for the nursing programme. These recommendations will be reported to the head of school and also to the nursing faculty.

Keywords: self-directed learning, undergraduate students, transition, statistical package for social sciences (SPSS), higher education

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Jonathan May

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This paper argues that the value of games relates to the sensation of freedom they create, and this in turn results from their nature as voluntary, non-necessary tasks. Attempts to gamify necessary learning tasks are therefore challenged to create this sensation of freedom and so they often fail to create the pleasure and value found in traditional games. It then demonstrates a route to creating this sensation of freedom through the maximization of varied and creative solutions to such problems.

Keywords: gamification, games, philosophy of games, freedom, voluntary action, necessity, motivation, value of games

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Authors: Adedoyin Opeyemi Osokoya

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The study assesses the influence of social media on perceived learning outcome of agricultural science students in tertiary institutions in Oyo state, Nigeria. The four-stage sampling procedure was used to select participants. All students in the seven tertiary institutions that offer agriculture science as a course of study in Oyo State was the population. A university, a college of agriculture and a college of education were sampled, and a department from each was randomly selected. Twenty percent of the students’ population in the respective selected department gave a sample size of 165. Questionnaire was used to collect information on respondents’ personal characteristics and information related to access to social media. Data were analysed using descriptive statistics, chi-square, correlation, and multiple regression at the 0.05 confidence level. Age and household size were 21.13 ± 2.64 years and 6 ± 2.1 persons respectively. All respondents had access to social media, majority (86.1%) owned Android phone, 57.6% and 52.7% use social media for course work and entertainment respectively, while the commonly visited sites were WhatsApp, Facebook, Google, Opera mini. Over half (53.9%) had an unfavourable attitude towards the use of social media for learning; benefits of the use of social media for learning was high (56.4%). Removal of information barrier created by distance (x̄=1.58) was the most derived benefit, while inadequate power supply (x̄=2.36), was the most severe constraints. Age (β=0.23), sex (β=0.37), ownership of Android phone (β=-1.29), attitude (β=0.37), constraints (β =-0.26) and use of social media (β=0.23) were significant predictors of influence on perceived learning outcomes.

Keywords: use of social media, agricultural science students, undergraduates of tertiary institutions, Oyo State of Nigeria

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Arzu Akar Gençer, Mübeccel Gönen

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The objective of the study is to investigate the impact of the projects based on Reggio Emilia Approach, on the creative thinking skills of preschool children. The study is carried out with eighteen 6 years old children in a class of a preschool, and entailed the development of projects based on Reggio Emilia approach with the children, for a period of 3 months. The study employs qualitative model. The children were analyzed with reference to the creative thinking aspects (rationality, originality, flexibility, and applicability) of the projects applied. As the projects based on Reggio Emilia approach arose out of the interests and curiosity of the children, and had their roots in the existing class culture, it is possible to conclude that they have an impact on the creativity of the children with reference to the aspects of creative thinking.

Keywords: Reggio Emilia approach, project, creativity, preschool children

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Mei-Chun Chang

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Due to digital transformation and social media development in recent years, various real-time interactive digital tools have been developed to meet the design demands for virtual reality avatars, which also promote digital content learners' active participation in the creation process. As a result, new social media design tools have the characteristics of intuitive operation with a simplified interface for fast production, from which works can be simply created. This study carried out observations, records, questionnaire surveys, and interviews on the creation and learning of visual avatars made by students of the National Taiwan University of Science and Technology (NTUST) with the VRoid Studio 3D modeling tool so as to explore their learning effectiveness on the design of visual avatars. According to the results of this study, the VRoid Studio 3D character modeling tool has a positive impact on the learners and helps to improve their learning effectiveness. Students with low academic achievements said that they could complete the conceived modeling with their own thinking by using the design tool, which increased their sense of accomplishment. Conclusions are drawn according to the results, and relevant future suggestions are put forward.

Keywords: virtual avatar, character design, social media, vroid studio, creation, digital learning

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Authors: Sarah Lister, Pauline Palmer

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Research findings presented within this paper form part of a larger scale collaboration between academics at Manchester Metropolitan University and a technology company. The overarching aims of this project focus on developing a series of game-based resources to promote the teaching of aspects of mathematics through a second language (L2) in primary schools. This study explores the potential of game-based learning (GBL) as a dynamic way to engage and motivate learners, making learning fun and purposeful. The research examines the capacity of GBL resources to provide a meaningful and purposeful context for CLIL. GBL is a powerful learning environment and acts as an effective vehicle to promote the learning of mathematics through an L2. The fun element of GBL can minimise stress and anxiety associated with mathematics and L2 learning that can create barriers. GBL provides one of the few safe domains where it is acceptable for learners to fail. Games can provide a life-enhancing experience for learners, revolutionizing the routinized ways of learning through fusing learning and play. This study argues that playing games requires learners to think creatively to solve mathematical problems, using the L2 in order to progress, which can be associated with the development of higher-order thinking skills and independent learning. GBL requires learners to engage appropriate cognitive processes with increased speed of processing, sensitivity to environmental inputs, or flexibility in allocating cognitive and perceptual resources. At surface level, GBL resources provide opportunities for learners to learn to do things. Games that fuse subject content and appropriate learning objectives have the potential to make learning academic subjects more learner-centered, promote learner autonomy, easier, more enjoyable, more stimulating and engaging and therefore, more effective. Data includes observations of the children playing the games and follow up group interviews. Given that learning as a cognitive event cannot be directly observed or measured. A Cognitive Discourse Functions (CDF) construct was used to frame the research, to map the development of learners’ conceptual understanding in an L2 context and as a framework to observe the discursive interactions that occur learner to learner and between learner and teacher. Cognitively, the children were required to engage with mathematical content, concepts and language to make decisions quickly, to engage with the gameplay to reason, solve and overcome problems and learn through experimentation. The visual elements of the games supported the learning of new concepts. Children recognised the value of the games to consolidate their mathematical thinking and develop their understanding of new ideas. The games afforded them time to think and reflect. The teachers affirmed that the games provided meaningful opportunities for the learners to practise the language. The findings of this research support the view that using the game-based resources supported children’s grasp of mathematical ideas and their confidence and ability to use the L2. Engaging with the content and language through the games led to deeper learning.

Keywords: CLIL, gaming, language, mathematics

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Peter Ecke

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This research presents insights from a questionnaire study and autobiographic narrative analyses about the language and cultural backgrounds, challenges, interests, and needs, as well as perceptions about bilingualism and language learning of undergraduate students at a Public University in the southwestern United States. Participants were 650 students, enrolled in college-level general education courses, entitled “Becoming multilingual: Learning and maintaining two or more languages” between 2020 and 2024. Data were collected via pre- and post-course questionnaires administered online through the Qualtrix XM platform and complemented with analyses of excerpts from autobiographical narratives that students produced as part of the course assignments. Findings, for example, show that course participants have diverse linguistic backgrounds. The five most frequently reported L1s were English (about 50% of course participants), Spanish, Arabic, Mandarin, and Korean (in that order). The five most frequently reported L2s were English, Spanish, French, ASL, Japanese, German, and Mandarin (in that order). Participants also reported on their L2, L3, L4, and L5 if applicable. Most participants (over 60%) rated themselves bilingual or multilingual whereas 40% considered themselves to be monolingual or foreign language learners. Only about half of the participants reported feeling very or somewhat comfortable with their language skills, but these reports changed somewhat from the pre- to the post-course survey. About half of participants were mostly interested in learning how to effectively learn a foreign language. The other half of participants reported being most curious about learning about themselves as bi/multilinguals, (re)learning a language used in childhood, learning how to bring up a child as a bi/multilingual or learning about people who speak multiple languages (distributed about evenly). Participants’ comments about advantages and disadvantages of being bilingual remained relatively stable but their agreement with common myths about bilingualism and language learning changed from the pre- to the post-course survey. Students’ reflections in the autobiographical narratives and comments in (institutionally administered) anonymous course evaluations provided additional data on students’ concerns about their current language skills and uses as well as their perceptions about learning outcomes and the usefulness of the general education course for their current and future lives. It is hoped that the presented findings and discussion will spark interest among colleagues in offering similar courses as a resource for college students (and possibly other audiences), including those from migrant, indigenous, multilingual, and multicultural communities to contribute to a more harmonious bilingualism and well-being of college students who are or inspire to become bi-or multilingual.

Keywords: autobiographic narratives, general education university course, harmonious bilingualism and well-being, multilingualism, questionnaire study

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Authors: N. Hammid, F. Bouarab-Dahmani, T. Berkane

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Since its appearance, the MOOC (massive open online course) is gaining more and more intention of the educational communities over the world. Apart from the current MOOCs design and purposes, the creators of MOOC focused on the importance of the connection and knowledge exchange between individuals in learning. In this paper, we present a conceptual model for massive open online blended courses where teachers over the world can collaborate and exchange their experience to get a common efficient content designed as a MOOC opened to their students to live a better learning experience. This model is based on disciplines’ concepts capitalization and the detection of the obstacles met by their students when faced with problem situations (exercises, projects, case studies, etc.). This detection is possible by analyzing the frequently of semantic errors committed by the students. The participation of teachers in the design of the course and the attendance by their students can guarantee an efficient and extensive participation (an important number of participants) in the course, the learners’ motivation and the evaluation issues, in the way that the teachers designing the course assess their students. Thus, the teachers review, together with their knowledge, offer a better assessment and efficient connections to their students.

Keywords: massive open online course, MOOC, online learning, e-learning

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Authors: Kafeero Frank

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This study aimed at developing a frame work for open source software adoption in an institution of higher learning in Uganda, with the case of KIU as a study area. There were mainly four research questions based on; individual staff interaction with open source software forum, perceived FOSS characteristics, organizational characteristics and external characteristics as factors that affect open source software adoption. The researcher used causal-correlation research design to study effects of these variables on open source software adoption. A quantitative approach was used in this study with self-administered questionnaire on a purposively and randomly sampled sample of university ICT staff. Resultant data was analyzed using means, correlation coefficients and multivariate multiple regression analysis as statistical tools. The study reveals that individual staff interaction with open source software forum and perceived FOSS characteristics were the primary factors that significantly affect FOSS adoption while organizational and external factors were secondary with no significant effect but significant correlation to open source software adoption. It was concluded that for effective open source software adoption to occur there must be more effort on primary factors with subsequent reinforcement of secondary factors to fulfill the primary factors and adoption of open source software. Lastly recommendations were made in line with conclusions for coming up with Kyambogo University frame work for open source software adoption in institutions of higher learning. Areas of further research recommended include; Stakeholders’ analysis of open source software adoption in Uganda; Challenges and way forward. Evaluation of Kyambogo University frame work for open source software adoption in institutions of higher learning. Framework development for cloud computing adoption in Ugandan universities. Framework for FOSS development in Uganda IT industry

Keywords: open source software., organisational characteristics, external characteristics, cloud computing adoption

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Authors: Ahmad Salman

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Electronic Response Systems such as Kahoot, Poll Everywhere, and Google Classroom are gaining a lot of popularity when surveying audiences in events, meetings, and classroom. The reason is mainly because of the ease of use and the convenience these tools bring since they provide mobile applications with a simple user interface. In this paper, we present a case study on the effectiveness of using Electronic Response Systems on student participation and learning experience in a classroom. We use a polling application for class exercises in two different technology-oriented classes. We evaluate the effectiveness of the usage of the polling applications through statistical analysis of the students performance in these two classes and compare them to the performances of students who took the same classes without using the polling application for class participation. Our results show an increase in the performances of the students who used the Electronic Response System when compared to those who did not by an average of 11%.

Keywords: Interactive Learning, Classroom Technology, Electronic Response Systems, Polling Applications, Learning Evaluation

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Authors: Tracy B.E. Omorogiuwa

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Understanding the role of social work in improving societal well-being cannot be separated from the place of field education, which is an integral aspect of social work education. Field learning provides students with knowledge and opportunities to experience solving issues in the field and giving them a clue of the practice situation. Despite being a crucial component in social work curriculum, field education occupies a large space in learning outcome, given the issues and challenges pertaining to its purpose and significance in the society. The drive of this paper is to provide insight on the specific ways in which field education has been conceived, realized and valued in the society. Emphasis is on the significance of field instruction; the link with classroom learning; and the structure of field experience in social work education. Given documented analysis and experience, this study intends to contribute to the development of social work curriculum, by analyzing the pattern, issues and challenges fronting the social work field education in the University of Benin, Nigeria.

Keywords: challenges, curriculum, field education, social work education

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Authors: Marian Lissett Olaya

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This article examined the factors associated with incorporating peer assessment into English language classes in a public university in Colombia. This is done in the context of writing English class for 4th-semester students. The research instruments consisted of peer assessment questionnaires, student diaries, and interviews. Findings showed that among the factors, motivation, frustration, anxiety, and lack of confidence appeared. Data revealed that peer assessment enables students to write competencies through training, teachers' guidance, and the provision of a collaborative environment.

Keywords: writing skills, peer assessment, formative assessment, language acquisition

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Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: W. Shin, Y. Kim

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This study focuses on disconfirming that wide-area networks can be made mobile, highly-available, and wireless. This methodological test shows that IPv7 and context-free grammar are mismatched. In the cases of robots, a similar tendency is also revealed. Further, we also prove that public-private key pairs could be built embedded, adaptive, and wireless. Finally, we disconfirm that although hash tables can be made distributed, interposable, and autonomous, XML and DNS can interfere to realize this purpose. Our experiments soon proved that exokernelizing our replicated Knesis keyboards was more significant than interrupting them. Our experiments exhibited degraded average sampling rate.

Keywords: collaborative communication, DNS, local-area networks, XML

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Authors: Andrew Adjah Sai, Portia Opoku Boadi

Abstract:

Explaining change is an abstract endeavor. Many management scholars have adopted metaphors to explain change. In this paper, we deal with the drivers of technological change. We use a historical and theoretical approach to review and elaborate on the concepts and context about a specific case. We discuss the limitations of each approach proffered and the implications as a consequence on technological change. We present plurality and multiplicity of perspectives using a socio-technical approach to explain technological change contextually on an organizational level. We show by using our model how technology absorption and diffusion can be accelerated through artefactual institutions to enable social change. The multiplicity of perspectives and plurality of our arguments creates a fine explanation of the e-Estonia case as an example.

Keywords: artefactual institutions, e-Estonia, social change, technological trajectories

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

Abstract:

Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

Abstract:

With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: mobile computing, deep learning apps, sensitive information, static analysis

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Authors: Hao Li, Guy Y. Cornejo Maceda, Yiqing Li, Jianguo Tan, Marek Morzynski, Bernd R. Noack

Abstract:

We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich database of machine learning control (MLC) optimizing a feedback law for a cost function in the plant. The proposed methodology provides (1) insights into the control landscape, which maps control laws to performance, including extrema and ridge-lines, (2) a catalogue of representative flow states and their contribution to cost function for investigated control laws and (3) visualization of the dynamics. Key enablers are classification and feature extraction methods of machine learning. The analysis is successfully applied to the stabilization of a mixing layer with sensor-based feedback driving an upstream actuator. The fluctuation energy is reduced by 26%. The control replaces unforced Kelvin-Helmholtz vortices with subsequent vortex pairing by higher-frequency Kelvin-Helmholtz structures of lower energy. These efforts target a human interpretable, fully automated analysis of MLC identifying qualitatively different actuation regimes, distilling corresponding coherent structures, and developing a digital twin of the plant.

Keywords: machine learning control, mixing layer, feedback control, model-free control

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Authors: Pi-Lan Yang

Abstract:

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: Marie C. Ryan

Abstract:

The current paper seeks to reconsider differentiation in order to establish whether differentiation has succeeded in its benevolent aim to support individual needs through teaching adaptations or whether paradoxically our attention to differentiation has served to exclude and marginalise. This paper does not deny variation in learner needs and accepts that inclusion requires teachers to adapt and modify curricular content; rather it seeks to examine whether differentiation as it is conceptualised and implemented is fit for purpose when it comes to adapting teaching in view of learner differences. The paper will also explore an alternative approach to supporting learner differences through teaching modifications which may offer a safer path to an inclusive educational reality.

Keywords: inclusion, differentiation, special education, universal design for learning

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