Search results for: incremental learning

Authors: Loreta Axhami

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Media literacy in the digital age emerges not only as a set of skills to generate true knowledge and information but also as a pedagogy methodology, as a kind of educational philosophy. In addition to such innovations as information integration and communication technologies, media infrastructures, and web usage in the educational system, media literacy enables the change in the learning methods, pedagogy, teaching programs, and school curriculum itself. In this framework, this study focuses on ICT's impact on teaching and learning methods and the degree they are reflected in the Albanian education system. The study is based on a combination of quantitative and qualitative methods of scientific research. Referring to the study findings, it results that student’s limited access to the internet in school, focus on the hardcopy textbooks and the role of the teacher as the only or main source of knowledge and information are some of the main factors contributing to the implementation of authoritarian pedagogical methods in the Albanian education system. In these circumstances, the implementation of media literacy is recommended as an apt educational process for the 21st century, which requires a reconceptualization of textbooks as well as the application of modern teaching and learning methods by integrating information and communication technologies.

Keywords: authoritarian pedagogic model, education system, ICT, media literacy

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Authors: Thifhuriwi Emmanuel Madzunye, Patricia Harpur, Ephias Ruhode

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A review of the pertinent literature identified a gap concerning the hindrances and opportunities accompanying the implementation of real-time online learning systems (RTOLs) in rural areas. Whilst RTOLs present a possible solution to teaching and learning issues in rural areas, little is known about the implementation of digital strategies among schools in isolated communities. This study explores associated guidelines that have the potential to inform decision-making where Internet-based education could improve educational opportunities. A systematic literature review has the potential to consolidate and focus on disparate literature served to collect interlinked data from specific sources in a structured manner. During qualitative data analysis (QDA) of selected publications via the application of a QDA tool - ATLAS.ti, the following overarching themes emerged: digital divide, educational strategy, human factors, and support. Furthermore, findings from data collection and literature review suggest that signiant factors include a lack of digital knowledge, infrastructure shortcomings such as a lack of computers, poor internet connectivity, and handicapped real-time online may limit students’ progress. The study recommends that timeous consideration should be given to the influence of the digital divide. Additionally, the evolution of educational strategy that adopts digital approaches, a focus on training of role-players and stakeholders concerning human factors, and the seeking of governmental funding and support are essential to the implementation and success of RTOLs.

Keywords: communication, digital divide, digital skills, distance, educational strategy, government, ICT, infrastructures, learners, limpopo, lukalo, network, online learning systems, political-unrest, real-time, real-time online learning, real-time online learning system, pass-rate, resources, rural area, school, support, teachers, teaching and learning and training

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Authors: Rui Mao, Heming Ji, Xiaoyu Wang

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Keystroke dynamics are widely used in identity recognition. It has the advantage that the individual typing rhythm is difficult to imitate. It also supports continuous authentication through the keyboard without extra devices. The existing keystroke dynamics authentication methods based on machine learning have a drawback in supporting relatively complex scenarios with massive data. There are drawbacks to both feature extraction and model optimization in these methods. To overcome the above weakness, an authentication model of keystroke dynamics based on deep learning is proposed. The model uses feature vectors formed by keystroke content and keystroke time. It ensures efficient continuous authentication by cooperating attention mechanisms with the combination of CNN and Bi-LSTM. The model has been tested with Open Data Buffalo dataset, and the result shows that the FRR is 3.09%, FAR is 3.03%, and EER is 4.23%. This proves that the model is efficient and accurate on continuous authentication.

Keywords: keystroke dynamics, identity authentication, deep learning, CNN, LSTM

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Authors: Luis F. Caballero-castro, Dirsa Feliciano, Daniela Novoa, Orlando Arroyo, Jesús D. Villalba-morales

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Colombia faces a critical challenge in seismic resilience due to the prevalence of informal housing, which constitutes approximately 70% of residential structures. More than 10 million Colombians (20% of the population), live in homes susceptible to collapse in the event of an earthquake. This, combined with the fact that 83% of the population is in intermediate and high seismic hazard areas, has brought serious consequences to the country. These consequences became evident during the 1999 Armenia earthquake, which affected nearly 100,000 properties and represented economic losses equivalent to 1.88% of that year's Gross Domestic Product (GDP). Despite previous efforts to reinforce informal housing through methods like externally reinforced masonry walls, alternatives related to seismic protection systems (SPDs), such as Buckling-Restrained Braces (BRB), have not yet been explored in the country. BRBs are reinforcement elements capable of withstanding both compression and tension, making them effective in enhancing the lateral stiffness of structures. In this study, the use of low-cost and easily installable BRBs for the retrofit of informal housing in Colombia was evaluated, considering the economic limitations of the communities. For this purpose, a case study was selected involving an informally constructed dwelling in the country, from which field information on its structural characteristics and construction materials was collected. Based on the gathered information, nonlinear models with and without BRBs were created, and their seismic performance was analyzed and compared through incremental static (pushover) and nonlinear dynamic analyses. In the first analysis, the capacity curve was identified, showcasing the sequence of failure events occurring from initial yielding to structural collapse. In the second case, the model underwent nonlinear dynamic analyses using a set of seismic records consistent with the country's seismic hazard. Based on the results, fragility curves were calculated to evaluate the probability of failure of the informal housings before and after the intervention with BRBs, providing essential information about their effectiveness in reducing seismic vulnerability. The results indicate that low-cost BRBs can significantly increase the capacity of informal housing to withstand earthquakes. The dynamic analysis revealed that retrofit structures experienced lower displacements and deformations, enhancing the safety of residents and the seismic performance of informally constructed houses. In other words, the use of low-cost BRBs in the retrofit of informal housing in Colombia is a promising strategy for improving structural safety in seismic-prone areas. This study emphasizes the importance of seeking affordable and practical solutions to address seismic risk in vulnerable communities in earthquake-prone regions in Colombia and serves as a model for addressing similar challenges of informal housing worldwide.

Keywords: buckling-restrained braces, fragility curves, informal housing, incremental dynamic analysis, seismic retrofit

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: F. Demir

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Innovation is highly critical for every company, especially for technology-based organizations looking to sustain their competitive advantage. However, this is not an easy task. Regardless of the size of the enterprise, market and location, all organizations face numerous challenges. Even though huge barriers to innovation exist in different countries, firm- and industry-specific challenges can be distinguished. This paper examines innovation strategies and obstacles to innovation in research and technology organizations (RTO) of Turkey. From the most important to the least, nine different challenges are ranked according the results of this survey. The findings reveal that to take the lead in innovation, financial constraint is the biggest challenge, which is consistent with the related literature. It ranked number one in this study. Beyond that, based on a sample of 40 RTOs, regional challenges such as underdeveloped regional innovation ecosystem plays a significant role in hampering innovation. Most of the organizations (55%) embrace an incremental approach to innovation, while only few pursue radical shifts. About 40% of the RTOs focus on product innovation, and 27.5% of them concentrate on technological innovation, while a very limited number aim for operational excellence and customer engagement as the focus of their strategic innovation efforts.

Keywords: innovation strategies, innovation challenges, emerging economies, research and technology organizations

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Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

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Authors: Adeyemi Adeyinka, Yemisi Ilesanmi

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This study investigated parental involvement and motivation as predictors of students’ learning outcomes in value concepts in Yoruba language in Ibadan, Nigeria. Value concepts in Yoruba language aimed at teaching moral lessons and transmitting Yoruba culture. However, feelers from schools and the society reported students’ poor achievement in examinations and negative attitude to the subject. Previous interventions focused on teaching strategies with little consideration for student-related factors. The study was anchored on psychosocial learning theory. The respondents were senior secondary II students with mean age of 15.50 ± 2.25 from 20 public schools in Ibadan, Oyo-State. In all, 1000 students were selected (486 males and 514 females) through proportionate to sample size technique. Instruments used were Students’ Motivation (r=0.79), Parental Involvement (r=0.87), and Attitude to Yoruba Value Concepts (r=0.94) scales and Yoruba Value Concepts Achievement Test (r=0.86). Data were analyzed using descriptive statistics, Pearson product moment correlation and Multiple regressions at 0.05 level of significance. Findings revealed a significant relationship between parental involvement (r=0.54) and students’ achievement in and attitude to (r=0.229) value concepts in Yoruba. The composite contribution of parental involvement and motivation to students’ achievement and attitude was significant, contributing 20.3% and 5.1% respectively. The relative contributions of parental involvement to students’ achievement (β = 0.073; t = 1.551) and attitude (β = 0.228; t = 7.313) to value concepts in Yoruba were significant. Parental involvement was the independent variable that strongly predicts students’ achievement in and attitude to Yoruba value concepts. Parents should inculcate indigenous knowledge in their children and support its learning at school.

Keywords: parental involvement, motivation, predictors, learning outcomes, value concepts in Yoruba

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Ahmet Erdoğan, Şahin Kesici, Mustafa Baloğlu

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Information technologies have lead to changes in the areas of communication, learning, and teaching. Besides offering many opportunities to the learners, these technologies have changed the teaching methods and beliefs of teachers. What the Technological Pedagogical Content Knowledge (TPACK) means to the teachers is considerably important to integrate technology successfully into teaching processes. It is necessary to understand how to plan and apply teacher training programs in order to balance students’ pedagogical and technological knowledge. Because of many inefficient teacher training programs, teachers have difficulties in relating technology, pedagogy and content knowledge each other. While providing an efficient training supported with technology, understanding the three main components (technology, pedagogy and content knowledge) and their relationship are very crucial. The purpose of this study is to determine whether motivational beliefs and self-regulated learning strategies are significant predictors of mathematics teacher candidates' TPACK perceptions. A hundred seventy five Turkish mathematics teachers candidates responded to the Motivated Strategies for Learning Questionnaire (MSLQ) and the Technological Pedagogical And Content Knowledge (TPACK) Scale. Of the group, 129 (73.7%) were women and 46 (26.3%) were men. Participants' ages ranged from 20 to 31 years with a mean of 23.04 years (SD = 2.001). In this study, a multiple linear regression analysis was used. In multiple linear regression analysis, the relationship between the predictor variables, mathematics teacher candidates' motivational beliefs, and self-regulated learning strategies, and the dependent variable, TPACK perceptions, were tested. It was determined that self-efficacy for learning and performance and intrinsic goal orientation are significant predictors of mathematics teacher candidates' TPACK perceptions. Additionally, mathematics teacher candidates' critical thinking, metacognitive self-regulation, organisation, time and study environment management, and help-seeking were found to be significant predictors for their TPACK perceptions.

Keywords: candidate mathematics teachers, motivational beliefs, self-regulated learning strategies, technological and pedagogical knowledge, content knowledge

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Authors: M. Greeshma, V. Ashvini, P. Jayarekha

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Project based learning (PBL) is a student-driven educational framework and offers the student an opportunity for in-depth investigations of courses. This paper presents the need of PBL in engineering education for the student to graduate with a capacity to design and implement complex problems. The implementation strategy of PBL and its related challenges are presented. The case study that energizes the engineering curriculum with a relevance to the real-world of technology along with its benefits to the students is also included.

Keywords: PBL, engineering education, curriculum, implement complex

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Authors: Yesuselvi Manickam, Tan Soon Chin

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Internship is a supervised and structured learning experience related to one’s field of study or career goal. Internship allows students to obtain work experience and the opportunity to apply skills learned during university. Internship is a valuable learning experience for students; however, literature on employer assessment is scarce on Malaysian student’s internship experience. This study focuses on employer’s perspective on student’s performances during their three months of internship. The results are based on the descriptive analysis of 45 sets of question gathered from the on-site supervisors of the interns. The survey of 45 on-site supervisor’s feedback was collected through postal mail. It was found that, interns have not met their on-site supervisor’s expectations in many areas. The significance of this study is employer’s assessment on the internship shall be used as feedback to improve on ways how to prepare students for their internship and employments in future.

Keywords: employers perspective, internship, structured learning, student’s performances

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Authors: Jane Killough

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A podcast series was developed to support learning amongst first-year undergraduate mental health nursing students. Many first-year students do not have any clinical experience and find it difficult to engage with theory, which can present as cumbersome. Further, it can be challenging to relate abstract concepts to everyday mental health practice. Mental health professionals and service users from practice were interviewed on a range of core topics that are key to year one learning. The podcasts were made available, and students could access these recordings at their convenience to fit in with busy daily routines. The aim was to enable meaningful learning by providing access to those who have lived experience and who can, in effect, bring to life the theory being taught in university and essentially bridge the theory and practice gap while fostering working relationships between practice and academics. The student experience will be evaluated using a logic model.

Keywords: education, mental health nursing students, podcast, practice, undergraduate

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Authors: Porntipa Taksin, Jutamas Wongchan, Amornrat Karamee

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The researchers’ experience of student affairs in 2011-2013, we found that few undergraduate nursing students become student association members who participated in co-curricular activities, they have limited skill of self-directed-learning and leadership. We developed “A Leadership Promoting Program” using Self-Directed Learning concept. The program included six activities: 1) Breaking the ice, Decoding time, Creative SMO, Know me-Understand you, Positive thinking, and Creative dialogue, which include four aspects of these activities: decision-making, implementation, benefits, and evaluation. The one-group, pretest-posttest quasi-experimental research was designed to examine the effects of the program on participation in co-curricular activities. Thirty five students participated in the program. All were members of the board of undergraduate nursing student association of Boromarajonani College of Nursing, Chonburi. All subjects completed the questionnaire about participation in the activities at beginning and at the end of the program. Data were analyzed using descriptive statistics and dependent t-test. The results showed that the posttest scores of all four aspects mean were significantly higher than the pretest scores (t=3.30, p<.01). Three aspects had high mean scores, Benefits (Mean = 3.24, S.D. = 0.83), Decision-making (Mean = 3.21, S.D. = 0.59), and Implementation (Mean=3.06, S.D.=0.52). However, scores on evaluation falls in moderate scale (Mean = 2.68, S.D. = 1.13). Therefore, the Leadership Promoting Program based on Self-Directed Learning Approach could be a method to improve students’ participation in co-curricular activities and leadership.

Keywords: participation in co-curricular activities, undergraduate nursing students, leadership promoting program, self-directed learning

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Authors: Fiasal Assiri, Joanna Wincenciak, David Morrison-Love

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With the rapid spread of the COVID-19 pandemic, the Saudi government suspended students from going to school to combat the outbreak. As e-learning was not applied at all in schools, online teaching and learning have been revived in Saudi Arabia by providing a new platform called ‘Madrasati.’ Several studies have used the Decomposed Theory of Planned Behaviour (DTPB)to examineindividuals’ intention behavior in many fields. However, there is a lack of studies investigating the determinants of teachers’ continued intention touseMadrasati platform. The purpose of this paper is to present a conceptual model in light of DTPB. To enhance the predictability of the model, the study incorporates other variables, including learning content quality and interactivity as sub-factors under the perceived usefulness, students and government influences under the subjective norms, and technical support and prior e-learning experience under the perceived behavioral control. The model will be further validated using a mixed methods approach. Such findings would help administrators and stakeholders to understand teachers’ needs and develop new methods that might encourage teachers to continue using Madrasati effectively in their teaching.

Keywords: madrasati, decomposed theory of planned behaviour, continuance intention, attitude, subjective norms, perceived behavioural control

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Authors: Elham Sharifineyestani, Mohammad Farshchin

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Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

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Authors: Raluca Ionela Maxim

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Designing a gender responsive Science, Technology, Engineering and Mathematics (STEM) mobile game based learning solution (mGBL) is a challenge in terms of content, gamification level and equal engagement of girls and boys. The goal of this case study was to research and create a high-fidelity prototype design of a mobile game that contains role-models as avatars that guide and expose girls and boys to STEM learning content. For this research purpose it was applied the methodology of design sprint with five-phase process that combines design thinking principles. The technique of this methodology comprises smart interviews with STEM experts, mind-map creation, sketching, prototyping and usability testing of the interactive prototype of the gender responsive STEM mGBL. The results have shown that the effect of the avatar/role model had a positive impact. Therefore, by exposing students (boys and girls) to STEM role models in an mGBL tool is helpful for the decreasing of the gender inequalities in STEM fields.

Keywords: design thinking, design sprint, gender-responsive STEM education, mobile game based learning, role-models

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: K. V. F. Fatokun, P. A. Eniayeju

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This study concerns the effects of concept mapping-guided discovery integrated teaching approach on the learning style and achievement of chemistry students. The sample comprised 162 senior secondary school (SS 2) students drawn from two science schools in Nasarawa State which have equivalent mean scores of 9.68 and 9.49 in their pre-test. Five instruments were developed and validated while the sixth was purely adopted by the investigator for the study, Four null hypotheses were tested at α = 0.05 level of significance. Chi square analysis showed that there is a significant shift in students’ learning style from accommodating and diverging to converging and assimilating when exposed to concept mapping- guided discovery approach. Also t-test and ANOVA that those in experimental group achieve and retain content learnt better. Results of the Scheffe’s test for multiple comparisons showed that boys in the experimental group performed better than girls. It is therefore concluded that the concept mapping-guided discovery integrated approach should be used in secondary schools to successfully teach electrochemistry. It is strongly recommended that chemistry teachers should be encouraged to adopt this method for teaching difficult concepts.

Keywords: integrated teaching approach, concept mapping-guided discovery, achievement, retention, learning styles and interest

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Authors: Netanel Stern

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Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

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Authors: Seyedomid Fatemi

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The indoor and outdoor climate of an architectural or closed space determines opportunities that children have for different types of learning. Bringing green areas into the classroom might give freedom to teachers to incorporate learning activities that activate different senses such as sight or touch. Green spaces and design in the classroom can help children practice different concepts they learn in subjects such as science. The practical aspect of having green areas or designs in the classroom can also facilitate high-level and long-lasting learning. Green spaces, particularly in the play areas or around schools, improve creativity and give experimental opportunities for children and enjoyment the beautiful and green nature, which impacts cognitive development in the in long-term. Furthermore, previous studies indicated that green settings generally may help attention concentration and self-discipline. Bringing green areas into classrooms or outdoors for children can look different depending on resources. It could look like green spaces, indoor or outdoor, or have representation or samples of green space. From the design perspective, bringing green areas into school might mean incorporating these spaces when designing different areas in school or attending to colors and shapes to reflect green spaces.

Keywords: green areas, cognitive development, children cognitive, class design

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Authors: M. Kolotylo, H. Zheng, R. Parente, R. Dahiya

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Large number of organizations, frequently motivated by budget and cost cuts, outsource their Information Technology (IT) positions every year. Although the objective of reduction in financial obligations is often not accomplished, many buyer companies still manage to benefit from outsourcing projects. Knowledge Transfer (KT), being one of the major processes that take place during IT outsourcing partnership, may exert a strong impact on the performance of the parties involved, particularly that of the buyer. Research, however, lacks strong conceptual basis for the possible benefits that KT from supplier may bring to the buyer; and for the mechanisms that may be adopted by the buyer to maximize such benefit. This paper aims to fill this gap by proposing a conceptual framework of organizational learning and development of dynamic capabilities enabled by KT from the supplier to the buyer. The study examines buyer-supplier relationships in the context of IT outsourcing transactions, and theorizes how KT from the supplier to the buyer helps the performance of the buyer. It warrants that more research is carried out in order to explicate and provide evidence regarding the role that KT plays in strategic improvements for the buyer. The paper proposes to take up a two-fold approach to the research: conceptual development that utilizes logical argumentation and interpretive historical research, as well as a qualitative case study which aims to capture and understand the complex processes involved. Thus, the study provides a comprehensive visualization of the dynamics of the conditions under which participation in IT outsourcing partnership might be of benefit to the buyer company. The framework demonstrates the mechanisms involved in buyer’s achievement of strategic alignment through organizational learning enabled by KT from the supplier. It highlights that organizational learning involves a balance between exploitation of assets and exploration of new possibilities, and further notes that the dynamic capabilities mediate the effect of organizational learning on firm performance. The paper explicates in what ways managers can leverage outsourcing projects to execute strategy, which would enable their organization achieve better performance. The study concludes that organizational learning enables the firm to develop IT capabilities of strategic planning, IT integration, and IT relationships in the outsourcing context, and that IT capabilities developed through the organizational learning would help the firm in achieving strategic alignment.

Keywords: dynamic capabilities, it outsourcing, knowledge transfer, organizational learning, strategic alignment

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Authors: Meital Amzalag, Sharon Hardof-Jaffe

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In the last few decades, children and youth have been immersed in digital technologies. Indeed, recent studies explored the implication of technology use in their leisure and learning activities. Educators face an essential need to utilize technology and implement them into the curriculum. To do that, educators need to understand how young people use digital technology. This study aims to explore K12 students' digital lives from their point of view, to reveal their digital activities, age and gender differences with respect to digital activities, and to present the students' attitudes towards technologies in learning. The study approach is quantitative and includes354 students ages 6-16 from three schools in Israel. The online questionnaire was based on self-reports and consists of four parts: Digital activities: leisure time activities (such as social networks, gaming types), search activities (information types and platforms), and digital application use (e.g., calendar, notes); Digital skills (requisite digital platform skills such as evaluation and creativity); Social and emotional aspects of digital use (conducting digital activities alone and with friends, feelings, and emotions during digital use such as happiness, bullying); Digital attitudes towards digital integration in learning. An academic ethics board approved the study. The main findings reveal the most popular K12digital activities: Navigating social network sites, watching TV, playing mobile games, seeking information on the internet, and playing computer games. In addition, the findings reveal age differences in digital activities, such as significant differences in the use of social network sites. Moreover, the finding raises gender differences as girls use more social network sites and boys use more digital games, which are characterized by high complexity and challenges. Additionally, we found positive attitudes towards technology integration in school. Students perceive technology as enhancing creativity, promoting active learning, encouraging self-learning, and helping students with learning difficulties. The presentation will provide an up-to-date, accurate picture of the use of various digital technologies by k12 students. In addition, it will discuss the learning potentials of such use and how to implement digital technologies in the curriculum. Acknowledgments: This study is a part of a broader study about K-12 digital life in Israel and is supported by Mofet-the Israel Institute for Teachers'Development.

Keywords: technology and learning, K-12, digital life, gender differences

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Authors: Paula Paulino, Alzira Matias, Ana Margarida Veiga Simão

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Self-regulated learning (SRL) directs students in analyzing proposed tasks, setting goals and designing plans to achieve those goals. The literature has suggested a metacognitive strategy for goal attainment known as Mental Contrasting with Implementation Intentions (MCII). This strategy involves Mental Contrasting (MC), in which a significant goal and an obstacle are identified, and Implementation Intentions (II), in which an "if... then…" plan is conceived and operationalized to overcome that obstacle. The present study proposes to assess the MCII process and whether it promotes students’ commitment towards learning goals during school tasks in sciences subjects. In this investigation, we intended to study the MCII strategy in a systemic context of the classroom. Fifty-six students from middle school and secondary education attending a public school in Lisbon (Portugal) participated in the study. The MCII strategy was explicitly taught in a procedure that included metacognitive modeling, guided practice and autonomous practice of strategy. A mental contrast between a goal they wanted to achieve and a possible obstacle to achieving that desire was instructed, and then the formulation of plans in order to overcome the obstacle identified previously. The preliminary results suggest that the MCII metacognitive strategy, applied to the school context, leads to more sophisticated reflections, the promotion of learning goals and the elaboration of more complex and specific self-regulated plans. Further, students achieve better results on school tests and worksheets after strategy practice. This study presents important implications since the MCII has been related to improved outcomes and increased attendance. Additionally, MCII seems to be an innovative process that captures students’ efforts to learn and enhances self-efficacy beliefs during learning tasks.

Keywords: implementation intentions, learning goals, mental contrasting, metacognitive strategy, self-regulated learning

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Authors: Lizhi Ma, Chengcheng Hu, Fuxian Wong

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Partial reduce (P-Reduce) has set a state-of-the-art performance on distributed machine learning in the heterogeneous environment over the All-Reduce architecture. The dynamic P-Reduce based on the exponential moving average (EMA) approach predicts all the intermediate model parameters, which raises unreliability. It is noticed that the approximation trick leads the wrong way to obtaining model parameters in all the nodes. In this paper, SAP-Reduce is proposed, which is a variant of the All-Reduce distributed training model with staleness-aware dynamic P-Reduce. SAP-Reduce directly utilizes the EMA-like algorithm to generate the normalized weights. To demonstrate the effectiveness of the algorithm, the experiments are set based on a number of deep learning models, comparing the single-step training acceleration ratio and convergence time. It is found that SAP-Reduce simplifying dynamic P-Reduce outperforms the intermediate approximation one. The empirical results show SAP-Reduce is 1.3× −2.1× faster than existing baselines.

Keywords: collective communication, decentralized distributed training, machine learning, P-Reduce

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Authors: Ekeh Greg

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Learning has been part of human existence from time immemorial. The aim of every learning is to know the truth. In education, it is desirable that true knowledge is imparted and imbibed. For this to be achieved, there is need for honesty, in this context, academic honesty among students, especially in e-learning. This is an ethical issue since honesty bothers on human conduct. However, research findings have shown that academic honesty has remained a big challenge to online learners, especially among the university students. This is worrisome since the university education is the final education system and a gateway to life in the wider society after schooling. If they are practicing honesty in their academic life, it is likely that they will practice honesty in the in the society, thereby bringing positive contributions to the society wherever they find themselves. With this in mind, the significance of this study becomes obvious. On grounds of this significance, this paper focuses on strategies that are adjudged certain to enhance the practice of honesty in e-learning so as to enable learners to be well equipped to contribute to the society through honest ways. The aim of the paper is to contribute to the efforts of instilling the consciousness and practice of honesty in the minds and hearts of learners. This will, in turn, promote effective teaching and learning, academic high standard, competence and self-confidence in university education. Philosophical methods of conceptual analysis, clarification, description and prescription are adopted for the study. Philosophical perspective is chosen so as to ground the paper on the basis of rationality rather than emotional sentiments and biases emanating from cultural, religious and ethnic differences and orientations. Such sentiments and biases can becloud objective reasoning and sound judgment. A review of related literature is also carried out. The findings show that academic honesty in e-learning is a cherished value, but it is bedeviled by some challenges, such as care-free attitude on the part of students and absence of monitoring. The findings also show that despite the challenges facing academic honesty, strategies such as self-discipline, determination, hard work, imbibing ethical and philosophical principles, among others, can certainly enhance the practice of honesty in e-learning among university students. The paper, therefore, concludes that these constitute strategies for enhancing academic honesty among students. Consequently, it is suggested that instructors, school counsellors and other stakeholders should endeavour to see that students are helped to imbibe these strategies and put them into practice. Students themselves are enjoined to cherish honesty in their academic pursuit and avoid short-cuts. Short-cuts can only lead to mediocrity and incompetence on the part of the learners, which may have long adverse consequences, both on themselves and others.

Keywords: academic, ethical, philosophical, strategies

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Authors: Xu Jie

Abstract:

As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decision-making problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security) by modeling the multi-job collaborative cryptographic service scheduling problem as a multi-objective optimized job flow scheduling problem and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real-time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: cloud computing, cryptography on-demand service, reinforcement learning, workflow scheduling

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Menelaos N. Katsantonis, Athanasios Manikas, Alexandros Chatzis, Stavros Doropoulos, Anastasios Avramis, Ioannis Mavridis

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Museums enhance their potential by adopting new technologies and techniques to appeal to more visitors and engage them in creative and joyful activities. In this study, the Haptic Cycle is presented, a cycle of museum activities proposed for the development of museum learning approaches with optimized effectiveness and engagement. Haptic Cycle envisages the improvement of the museum’s services by offering a wide range of activities. Haptic Cycle activities make the museum’s exhibitions more approachable by bringing them closer to the visitors. Visitors can interact with the museum’s artifacts and explore them haptically and sonically. Haptic Cycle proposes constructivist learning activities in which visitors actively construct their knowledge by exploring the artifacts, experimenting with them and realizing their importance. Based on the Haptic Cycle, we developed the HapticSOUND system, an innovative virtual reality system that includes an advanced user interface that employs gesture-based technology. HapticSOUND’s interface utilizes the leap motion gesture recognition controller and a 3D-printed traditional Cretan lute, utilized by visitors to perform various activities such as exploring the lute and playing notes and songs.

Keywords: haptic cycle, HapticSOUND, museum learning, gesture-based, leap motion

Procedia PDF Downloads 92