A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after (...) they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according (...) to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...) framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for (...) this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according (...) to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

This study focused on the adaptability and attitude of Grade 6 students in the Modular Learning Approach. This study aims to create a comprehensive review of the adaptability and attitude of elementary students in the Modular Learning Approach Especially. It seeks to describe and analyze the following: the profile of the respondents in terms of sex and education of the parents; the adaptability of the respondents toward the relationship between the student's profile in terms of their sex and (...) educational attainment of parents; the attitude of the respondent's significant relationship between sex and educational attainment. The researcher used the quantitative survey questionnaires to gather data from 128 Grade 6 students in five Elementary Schools in Bongabon, Nueva Ecija who enrolled during the School Year 2020-2021. The study revealed that the respondent's demographic profile in sex has no significant relationship to their adaptability and attitude in the modular learning approach. Yet the parents' educational attainment of the respondents has a significant relationship to adaptability in terms of learning assistance and support in the modular learning approach. The results further signified that parents' educational attainment is a basis for adaptability in modular learning. -/- . (shrink)

In recent years, the significance of industrial heritage has seemed to become a growing trend in international heritage studies. Concerning their attributed values and the crucial needs for urban development, this branch of cultural heritage has been considered the important grid of cities. This has caused a great acceptance of adaptive reuse practices especially among developing countries which is a smart response to an ongoing debate to reach sustainable development. The flexibility of these buildings and sites seems an important (...) criterion, which can be improved through adaptive reuse practice. Therefore, this research aims to introduce the concept of flexibility in industrial heritage sites, evaluate its criteria among adaptive reuse practices, and make a comprehensive flexibility model for it. Indeed, the final goal is to determine the condition that based on the flexibility model, the adaptive reuse practice would be a proper way of encountering these sites. A historical-interpretation research method, analytical-description techniques, and questionnaire-based interviews are applied in this research. Results indicate that flexibility has genuinely been considered in this practice. Analysing flexibility techniques, this paper suggests a valuable framework to achieve the flexibility of industrial heritage as the presupposition of successful adaptive reuse in these sites. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

Face-to-face learning engagement has been suspended due to the health crisis that affected the whole world. This led to the adaptation of modular distance learning to continue delivering quality education. As the transition continues, its effectiveness has been frequently assessed. The key purpose of this study is to determine the relationship between the level of implementation of modular distance learning and English Writing Proficiency among the senior high school students in Saint Joseph College Maasin City School year (...) 2021-2022. The study employed a descriptive correlational design with 39 purposively chosen respondents. The main tools used in gathering the data were questionnaires, learning modules, and rubrics. Findings revealed that the implementation of modular distance learning (MDL) has an overall very satisfactory rating based on the perception of the students. The student’s perception of the implementation of modular distance learning and their writing proficiency is significantly related. Moreover, it revealed that students had difficulties in finding supplementary learning materials and educational support from home. The researcher recommends a set of guidelines for the successful and effective implementation of modular distance learning as the output of the study. (shrink)

This research is motivated by the need to unravel the progression of learning models, which have been adapting to meet the demands of society in its constant dynamics of fluctuation and transformation. The aim of this work is to systematically examine the evolution of learning models, highlighting the paradigmatic changes that have favored the transition from traditional learning approaches to more innovative and transdisciplinary proposals. To achieve this, a bibliographic analysis is carried out, supported by the hermeneutic (...) method for the contextual interpretation of literature and discourses, in order to unravel the complexities inherent in the evolution of learning models. The results highlight the limiting influence of the simplicity paradigm in traditional educational formation and the need for a shift towards dialogicity and disciplinary collaboration and integration to advance towards complexity. It concludes by arguing in favor of adopting the complexity paradigm, advocating for a transdisciplinary epistemology and an interstructuring approach to learning, which allow for integral human development. Recognizing human complexity in teaching and learning demands a radical transformation of education towards more holistic and transformative practices, essential for building an equitable and just society. (shrink)

The study highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient (...) load. (shrink)

Universal Basic Income (UBI) has been proposed as a potential way in which welfare states could be made more responsive to the ever-shifting evolutionary challenges of institutional adaptation in a dynamic environment. It has been proposed as a tool of “real freedom” (Van Parijs) and as a tool of making the welfare state more efficient. (Friedman) From the point of view of complexity theory and evolutionary economics, I argue that only a welfare state model that is “polycentrically” (Polanyi, Hayek) organized (...) as an evolving network of distributed decision-making can respond to the challenges of complexity in an adaptively efficient manner. UBI has the theoretical possibility to facilitate flexible, bottom-up innovation, since a) it embodies the “rule of law” principles of generality, nondiscrimination, simplicity, and transparency, and b) it grants people widespread freedom to experiment, innovate, and deviate from established practices and norms; but whether it can be made to work as intended depends on a number of variables in its design. I situate my research in the contemporary UBI debate around automation and technological development, which I interpret creatively through the lens of contemporary evolutionary political economy. I advance a syncretic model of institutional design based upon the shared insights of the Santa Fe, Neo-Hayekian, and Neo-Schumpeterian schools of evolutionary economics that, following Adam Thierer and Michael Munger, I call the framework of Permissionless Innovation (PI). Under the PI framework, which is a development of the tradition of evolutionary liberalism (Hume, Smith, Mandeville, Mill, Hayek, Polanyi, Schumpeter, Hodgson), individuals are granted the default right to innovate without having to ask for permission, and the right to basic income, which they can use to support their right to innovate, mutate, and experiment. This framework is a liberal framework to the extent it relies on widespread human freedom as a means of fostering innovation and social learning. And it is an evolutionary framework to the extent that it seeks to facilitate evolutionary socioeconomic processes for the sake of social progress and welfare advancement. I proceed to show, through an analysis of how innovation operates in both the economy and the cultural domain, that UBI can be used as an important cornerstone of an evolutionary liberal model of “ecostructural” governance that treats the socioeconomic order as an ecological garden of spontaneous growths, adaptations, and innovations. I call this the Permissionless Innovation Universal Basic Income (PIUBI) framework. In it, the right to innovate, deviate, and mutate is upheld as the “gold standard” of institutional design whereby individuals are granted widespread freedoms combined with the provision of UBI and other innovation-fostering (carefully bound) services and regulations. In the coming decades, UBI may even enable poor people to take better advantage of Human Enhancement Technologies (HETs) and other experimental innovations. Such a framework is compatible with a fast pace of evolutionary development, but the regulatory model accompanying such a framework must be capable of responding to evolutionary lock-ins, maladaptive innovations, self-destructive behaviour on the part of UBI recipients, and the management of catastrophic and existential risks. In the end, I will have shown that the PIUBI framework can be justified as a plausible default institutional mechanism for responding to radical uncertainty, but it needs to be experimentally adapted to changing circumstances with the help of various situation-specific, polycentric, multi-level, approaches. (shrink)

Due to the COVID-19 outbreak, education was interrupted. To continue offering high-quality education led to a dramatic transition away from face-to-face instruction and to blended learning. However, modular distance learning, as one of the adaptable learning modes, was chosen by most parents. Hence, this study seeks to determine the role of parents in the effectiveness of modular distance learning during the COVID-19 pandemic era, ascertain whether there is a relationship between the parents’ roles and their backgrounds, (...) determine whether there is a relationship between the parents’ backgrounds and the children’s academic performance and determine whether the child’s educational performance was related to the role of the parents. A survey questionnaire was used to acquire information on the parents’ profiles and the role they played or did not play. Mean, frequency count, and Spearman correlation were used to analyze the data. Results showed a relationship between parents’ profiles and roles in their child’s academic achievement. The children’s academic performance in modular distance learning is boosted, mainly if the parents work and earn a higher income. Modular distance learning is effective when parents act as friendly teachers and motivators for their children and are employed in either public or private agencies. -/- . (shrink)

The mind's basic task is to organize adaptive behaviour. I argue that necessary conditions to achieve this are acquiring a 'body-self', a differentiated perception, motor intuition, and motor control. The latter three can be learnied implicitly by crosswise comparing the perceived actual situation, the desired situation, the perceived result and the anticipated result.

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies (...) that provide personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

In IEEE 802.15.4 standard for low-power low-range wireless communications, only one channel is employed for transmission which can result in increased energy consumption, high network delay and poor packet delivery ratio (PDR). In the subsequent IEEE 802.15.4-2015 standard, a Time-slotted Channel Hopping (TSCH) mechanism has been developed which allows for a periodic yet fixed frequency hopping pattern over 16 different channels. Unfortunately, however, most of these channels are susceptible to high-power coexisting Wi-Fi signal interference and to possibly some other ISM-band (...) transmissions. This interference manifests itself in the form of the presence/absence of other devices with either or both static and dynamic channel selection policies. In order to isolate channels with undesirable conditions, blacklisting mechanisms are defined to adapt the channel hopping process. However, the existing solutions which form blacklists unrealistically assume that the statistical model of the external interference remains fixed, and do not vary over time. In this paper, we realistically assume that the impact of external interferes on 802.15.4 may generally follow a non-stationary pattern, and accordingly formulate the adaptive channel hopping problem as a Dynamic Multi-Armed Bernoulli Bandit (Dynamic MABB) process from the machine learning theory. We then propose an online learning algorithm with track-ability properties for computing an adaptive hopping policy. Simulations confirm that when the statistics of the external interference has a switching regime, the proposed solution outperforms the previous schemes in terms of both energy efficiency as well as two important KPIs for TSCH-based networks, i.e., PDR and latency. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

This study focuses on the step-to-step procedure in creating Facebook Social Learning Group (FBSLG) and the perception of students on using FBSLG as learning management tool. Descriptive method was employed in this study participated by two hundred eighty (280) teacher education students in Nueva Ecija University of Science and Technology – College of Education during the academic year 2020-2021 who were purposively selected based on the criteria set by the researcher. Five simple steps on creating FBSLG were discussed (...) by the researcher. Using the adapted survey questionnaire, the data revealed that the students strongly agreed on the four aspects of perception about FBSLG namely: pedagogical affordance, social affordance, technological affordance, and their overall perception on the platform. Some worries of the students on the platform were addressed like the safety of sharing ideas in the FBSLG and some technical difficulties were encountered. It is concluded that the FBSLG is a good platform of conducting classes during this time of pandemic, so the use of this platform to other subjects is highly recommended. (shrink)

This paper examines the contributing issues that enhance students learning through adapting ELT materials and assessing its effectiveness forwarding to take measures. The study used both qualitative and quantitative approaches. The study was conducted a research by developing questionnaire, observation and document analysis. The data were collected from principals, teachers, students and woreda education officers. The data collected were arranged and organized for analysis and interpretation. Based on the analysed data, the study found out that limitations in the implementation (...) of authentic material adaptation and how to use various adaptive techniques. Teachers lack of motive and interest to design and select language materials that fit the curriculum and closely correspond with the aims of the teaching program and the needs of the students. Thus, the study recommended that adapted materials should be carefully evaluated and selected before being used for a language classroom. (shrink)

We work with a large spatialized array of individuals in an environment of drifting food sources and predators. The behavior of each individual is generated by its simple neural net; individuals are capable of making one of two sounds and are capable of responding to sounds from their immediate neighbors by opening their mouths or hiding. An individual whose mouth is open in the presence of food is “fed” and gains points; an individual who fails to hide when a predator (...) is present is “hurt” by losing points. Opening mouths, hiding, and making sounds each exact an energy cost. There is no direct evolutionary gain for acts of cooperation or “successful communication” per se. In such an environment we start with a spatialized array of neural nets with randomized weights. Using standard learning algorithms, our individuals “train up” on the behavior of successful neighbors at regular intervals. Given that simple setup, will a community of neural nets evolve a simple language for signaling the presence of food and predators? With important qualifications, the answer is “yes.” In a simple spatial environment, pursuing individualistic gains and using partial training on successful neighbors, randomized neural nets can learn to communicate. (shrink)

Collaborative learning emphasizes student-to-student interaction and the instructor’s role as a facilitator. Collaborative Online International Learning (COIL) was founded in 2005 by the State University of New York (SUNY) to help schools adapt their single classroom courses to an online, collaborative format and establish strong collaborations with professors with whom they would join classes and co-teach using SUNY COIL conferences and website, as well as pre-established partnerships between the institutions. However, as the globe becomes increasingly interconnected, educational challenges (...) aimed at cultivating intercultural competency become more important (Ceo-DiFrancesco & Bender-Slack, 2016). That is why this study aimed to (1) understand the lived experience of students who went under the COIL program in relation to SDG 12, (2) review the new knowledge students obtained when they took part in the COIL program in relation to SDG 12, and (3) discover the challenges students encountered while participating in the COIL program in relation to SDG 12. The researcher surveyed Filipino and Japanese students who participated in the COIL program concerning SDG12 from St. Patrick School of Quezon City, Kwansei Gakuin University, and Meiwa Senior High School. The study employed qualitative phenomenological research, and pertinent data were obtained through an open-ended survey questionnaire. The study was analyzed using Colaizzi’s descriptive phenomenological method. The results showed that the lived experiences of students who went under the COIL program in relation to SD12 have something to do with all their learnings and enjoyable experiences, intercultural and global interactions, and environmental discussions and action plans related to SDG 12. Moreover, students learned and understood better the negative impact of waste on the environment, environmental awareness and practices, the culture and norms in the Philippines and Japan, and the importance and benefits of collaboration. Lastly, the study also revealed the different challenges students encounter while participating in the COIL program in relation to SDG 12, such as cultural differences, language barriers, technical difficulties, and difficulties in experimentation. Thus, it was recommended to learn to adapt to everyone and adjust according to countries’ different cultures, practice and be proficient with the English language, have backup devices and platforms to use to ease technological problems, and improve implementations of experiments. (shrink)

Distance education has quickly been integrated into the education system anywhere, and the pandemic has intensified the need for it. The Philippines is just one of the countries that grappled with the effects of the unseen enemy; therefore, leaning mainly to home-based learning just cope to the need for learning and continuity. This study aimed to elicit the satisfaction level of Filipino college students both enrolled in private and public higher education in the country. To proceed with the (...) study, the researchers adapted an 18-item questionnaire to find out the satisfaction level of the students in the home-based learning modality implemented in the entire country during the school year 2022 – 2023. Using the satisfaction scale adapted from Nabayra and Tambong (2023), results showed that Filipino students had an overall slight satisfaction with the implemented home-based learning modality. Moreover, the demographics, namely gender, year level, type of institution, and type of home-based learning modality, are not directly associated with students' level of satisfaction, which can only contribute less than 1% of the variance to their satisfaction. (shrink)

In this paper, we bring together research on complex problem solving with that on motivational psychology about goal setting. Complex problems require motivational effort because of their inherent difficulties. Goal Setting Theory has shown with simple tasks that high, specific performance goals lead to better performance outcome than do-your-best goals. However, in complex tasks, learning goals have proven more effective than performance goals. Based on the Zurich Resource Model, so-called motto-goals should activate a person’s resources through positive affect. It (...) was found that motto-goals are effective with unpleasant duties. Therefore, we tested the hypothesis that motto-goals outperform learning and performance goals in the case of complex problems. A total of N = 123 subject participated in the experiment. In dependence of their goal condition, subjects developed a personal motto, learning, or performance goal. This goal was adapted for the computer-simulated complex scenario Tailorshop, where subjects worked as managers in a small fictional company. Other than expected, there was no main effect of goal condition for the management performance. An unexpected gender effect revealed better performance for men than women, pointing to a potential stereotype threat. As hypothesized, motto goals led to higher positive and lower negative affect than the other two goal types. Even though positive affect decreased and negative affect increased in all three groups during Tailorshop completion, participants with motto goals reported the lowest rates of negative affect. Exploratory analyses investigated the role of affect in complex problem solving via mediational analyses and the influence of goal type on perceived goal attainment. (shrink)

After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two (...) possible ways traditional reinforcement learning could be altered to remove this roadblock. (shrink)

In American universities, even Asian Philosophy is still often taught following methods adapted from European universities of the nineteenth century. Whether or not this approach is well-suited to philosophy as it was conceived in that era, it is inadequate if the aim is to develop a deep appreciation of Japanese philosophy. To limit what we consider Japanese philosophy to only what bears a distinct resemblance to academic Western philosophy, and accordingly to approach Japanese philosophy purely theoretically, is to risk missing (...) the greater part. Much of Japanese philosophy is applied philosophy, or in other words, what Pierre Hadot calls a “way of life,” and to appropriate it meaningfully requires practice rather than mere intellectual study alone. Thus, I contend that a proper means for introducing Western students is a more holistic method grounded in practicing traditional arts, such as composing haiku. I argue that the seventeenth century poet Matsuo Bashō can serve as a valuable resource in this process. I conclude with a description of the methods that I use in my efforts at teaching Japanese philosophy to undergraduate university students in South Texas. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

An interesting application for social robots is to act as a presenter, for example as a museum guide. In this paper, we present a fully automated system architecture for building adaptive presentations for embodied agents. The presentation is generated from a knowledge graph, which is also used to track the grounding state of information, based on multimodal feedback from the user. We introduce a novel way to use large-scale language models (GPT-3 in our case) to lexicalise arbitrary knowledge graph (...) triples, greatly simplifying the design of this aspect of the system. We also present an evaluation where 43 participants interacted with the system. The results show that users prefer the adaptive system and consider it more human-like and flexible than a static version of the same system, but only partial results are seen in their learning of the facts presented by the robot. (shrink)

Due to the COVID-19 pandemic, preschool teachers must quickly adjust to online education. During COVID-19, teachers have been forced to embrace technology. This study investigates the lived experiences and challenges of preschool teachers. Employing the Interpretative Phenomenological Analysis, the findings of this study were: It was found that managing parent expectations and dealing with challenging parent behavior were among the sources of stress for preschool teachers. This fear of being judged or criticized by parents could influence their teaching practices and (...) interactions with students, leading to feelings of anxiety or insecurity. Further, preschool teachers faced challenges in implementing blended learning, including technological issues, a lack of support and resources, and difficulties creating engaging online content. However, as teachers gained more experience and knowledge about effectively using online tools and technologies for instruction, these challenges became more manageable. Also limited or unreliable internet connectivity was also a significant challenge faced by teachers and students in the transition to online learning. The digital divide and various technical, bandwidth, and security concerns exacerbated it. Internet connection was a major frustration for her during online classes as it affected her ability to communicate and teach her students effectively. Moreover, preschool teachers have had to adapt to the challenges of blended learning by implementing various strategies and making preparations to guide and support their students effectively. These strategies have included using games and other engaging activities to keep students motivated and engaged, developing online lesson plans, and using online teaching platforms and tools. However, the transition to blended learning has also been a source of stress for preschool teachers. They have had to manage additional workloads and responsibilities and deal with challenges such as supporting students with varying technology access and skills. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS (...) have revolutionized traditional educational methods and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

Organisms that share the capability of storing information about experiences in the past have an actively generated background resource on which they can compare and evaluate more recent experiences in order to quickly or even better react than in previous situations. This is an essential competence for all reaction and adaptation purposes of living organisms. Such memory/learning skills can be found from akaryotes up to unicellular eukaryotes, fungi, animals and plants, although until recently, it had been mentioned only as (...) a capability of higher animals. With the rise of epigenetics, the context-dependent marking of experiences at both the phenotype and the genotype level is an essential perspective to understand memory and learning in all organisms. Both memory and learning depend on a variety of successful communication processes within the whole organism. (shrink)

Due to the unprecedented COVID-19 incident, basic education institutions have faced different challenges in their teaching-learning activities. Particularly conducting assessments remotely during COVID-19 has posed extraordinary challenges for basic education institutions owing to lack of preparation superimposed with the inherent problems of remote assessment. Descriptive-evaluation research was employed since the present study attempts to examines the assessment practices and competences on remote learning assessment of teachers in Graceville National High School. For the study, questionnaires were prepared and data (...) nine (9) senior high school teachers and forty-two (42) junior high school teachers were collected and analyzed. The gathered data from this study were subjected to the descriptive and inferential statistics. The data revealed that the most frequent number of the teacher- respondents in the study was 36 - 40 years old at age, female, Technology Livelihood Education (TLE) major, 6 - 10 years in service in Department of Education, and bachelor's degree holders. More so, the teacher respondents' level of teacher competencies on remote learning assessment was observed high competent. In addition, level of assessment practices in terms of clarity of assessment, time and effort on task, quality of feedback, motivational belief and self-esteem to learners, self-assessment and reflection of learning of the learners, and assessment - content and process in adapting teaching to students needs was mostly practice. However, it was completely practicing the interaction and dialogue about learning progress. The results confirmed that there was a significant difference on the teacher respondents' level of competence on the remote learning assessment when group according to their age, gender, areas of specialization, and training attended. On the other hands, teacher year of teaching and educational attainment revealed it has no significant difference on their level of competence on the remote learning assessment. Looking forward, that age, gender, year of teaching, areas of specialization, educational attainment, and training attended significantly differs to the level of assessment practices of the teachers. It also observed that the teachers experiencing highly challenges encountered on the remote learning assessment. It is recommended to implement the proposed school-based testing reforms and should be monitored and evaluated for the optimum advantage of the school and community. (shrink)

The chaotic situation of today’s VUCA (volatility, uncertainty, complexity, and ambiguity) and TUNA (Turbulent, Uncertain, Novel, Ambiguous) world is bringing more and more active and passive reforms, including positive and negative aspects, that reform business models. Educational institutions are not exceptional. Regarding the nature of educational institutions’ operation in today’s rapidly changing context, school leaders also need to raise concerns similar to those of business managers from other industries: “How do their institutions continuously renovate to adapt to tomorrow’s world?” Thus, (...) based on organisational learning theories, this project aims to establish a framework to evaluate educational leadership’s influence on the organisational knowledge circulation effectiveness within the K-12 education context. The study combines a structured literature review, bibliometrics analysis, and high-order structural equation model analysis on the survey for teachers among Vietnamese public and private K-12 schools. First, this project proposed and validated a toolset to measure educational leadership as a combination of principalship and teacher leadership. Second, it revealed new insights about the impacts of educational leadership, knowledge circulation, and school culture on teachers’ job satisfaction. The project also offers insights into teachers’ professional development activities across various school types and grade levels in Vietnam, which contribute to further continuous renovation of Vietnamese K-12 schools, and diversify the corpus of educational leadership studies. This project’s results extended the applications of organisational learning theories in K-12 education, as well as can be adopted to establish further knowledge management frameworks in other settings such as higher education or vocational education. (shrink)

The rapid adoption of telemedicine has transformed healthcare delivery, especially during the COVID-19 pandemic. This digital shift has enabled medical professionals to offer consultations and manage patients remotely, ensuring continuity of care while reducing exposure risks. However, the integration of telemedicine has presented both opportunities and challenges for doctors, particularly in terms of their work-life balance. This paper explores the digital adaptation of doctors in public and private hospitals concerning telemedicine practices and its impact on their work-life harmony. The study (...) highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load. (shrink)

The COVID-19 Pandemic has undoubtedly affected learners, and as such, adjustments need to be made for successful teaching and learning through online learning. However, in Ghana, the effects of the COVID-19 Pandemic did not spare adult learners who are compelled by educational reforms to upgrade themselves academically using online learning platforms. Using a descriptive design, the study explored the perceptions and experiences of 166 (online data collection) adult learners as they pursue their academic programs through online (...) class='Hi'>learning. An adapted questionnaire on perceptions and online learning experiences developed by Khan, Nabi, Khojah, and Tahir (2021) was used for the data collection. The Confirmatory Factor Analysis technique was used to analyse the data. The study revealed that respondents felt confident while using online learning content but refuted that those technologies permit them to take tests and submit assignments electronically. Furthermore, the study revealed that online learning service was making the learning process very simplified. The revelation means that adult learners possess positive experiences and perceptions about online learning. Therefore, online teaching and learning in higher education institutions should be made a routine activity in Ghana even after the COVID-19 Pandemic because the world is approaching a technological world. (shrink)

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...) Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

This book assembles recent research on memory and learning in plants. Organisms that share a capability to store information about experiences in the past have an actively generated background resource on which they can compare and evaluate coming experiences in order to react faster or even better. This is an essential tool for all adaptation purposes. Such memory/learning skills can be found from bacteria up to fungi, animals and plants, although until recently it had been mentioned only as (...) capabilities of higher animals. With the rise of epigenetics the context dependent marking of experiences on the genetic level is an essential perspective to understand memory and learning in organisms. -/- Plants are highly sensitive organisms that actively compete for environmental resources. They assess their surroundings, estimate how much energy they need for particular goals, and then realize the optimum variant. They take measures to control certain environmental resources. They perceive themselves and can distinguish between ‘self’ and ‘non-self’. They process and evaluate information and then modify their behavior accordingly. -/- The book will guide scientists in further investigations on these skills of plant behavior and on how plants mediate signaling processes between themselves and the environment in memory and learning processes. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

In this chapter I zoom in on a topic in climate ethics that has not previously received academic scrutiny: the intersection between evolutionary ethics and climate change. I argue that in the context of climate discourse, an evolutionary perspective can be illuminating, but may also invite moral corruption and reasoning fallacies. Relating my discussion to the general theme of the book, I argue that academic philosophy is well-positioned to fulfil a specific societal role, which is particularly important in the age (...) of popular science: to raise awareness of the ethical predicaments to which scientific findings can give rise, and to highlight the normative assumptions implicit in the reasoning of science communicators. (shrink)

The results are interpreted in terms of a deficit in initial rule learning and subsequent generalization of these rules to new stimuli. Negative feedback is adequately processed at a neural level but this information is not used to improve behaviour on subsequent trials. As learning is degraded, the process of error detection at the moment of the actual response is diminished. Therefore, the current study demonstrates that disturbed error-monitoring processes play a central role in the often reported (...) class='Hi'>learning deficits in individuals with PP. (shrink)

The investigation of the degree of amotivation and subsequent intervention towards the motivation of undergraduate vocational and technical education (VTE) students has not received the same amount of attention as other disciplines. Despite the negative impact of a lack of volitional drive on students' practical skills learning, there is scarcity of literature on amotivation among VTE undergraduate students. This study aimed to demonstrate the effectiveness of problem-based learning (PBL) in transitioning undergraduate students from a state of being amotivated (...) to motivated. The study utilized a quasi-experimental research design and selected 168 students in their penultimate and final years majoring in agricultural education, business education, computer education, home economics, and industrial technical education who met the study's inclusion criteria. The intervention consisted of eight weeks of treatment and four weeks of follow-up meetings. An existing academic motivation scale was adapted for use, and data was collected through a self-report questionnaire. Data analysis was conducted using independent sample t-tests, and paired sample t-tests. The findings revealed that the level of amotivation among the students was high, and PBL had a significant impact on transitioning VTE students from being amotivated to motivated in the treatment group compared to those in the control group. Furthermore, the positive gains were maintained by the treatment group at follow-up. The study suggests that a PBL program can effectively address students' lack of volitional drive to engage in academic activities among undergraduate students majoring in agricultural education, business education, computer education, home economics, and industrial technical in Nigeria. (shrink)

: Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such (...) as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

Information and Communication Technologies, ICTs, has now for decades being increasingly taken into use for higher education, enabling distance learning, e-learning and online learning, mainly in parallel to mainstream educational practise. The concept Blended learning (BL) aims at the integration of ICTs with these existing educational practices. The term is frequently used, but there is no agreed-upon definition. The general aim of this dissertation is to identify new possible perspectives on ICTs and access to higher education, (...) for negotiating the dichotomy between campus-based and ICT-enabled education. The access options of BL are in focus for this dissertation, although BL is generally seen as a campus phenomenon, and shares a place perspective. The main research questions in the dissertation are 1) how BL can be understood in the context of increased access to education, moreover, (2) how time can be work as a more constructive perspective for designing ICTs in education, compared to place. The dissertation comprises five articles. The first is conceptual and concentrates on place and time in blended learning, and forms a time-based model and perspective, drawing on the tension between synchronous and asynchronous modalities instead of a place-based center-periphery model. The following article examines the differences between North American and European use of the term BL, in education and research, and finds that BL is not much used by European researchers, although the term is frequently used in educational environments. Two design and intervention studies, articles 3 and 4, make experiments using the BL time-based model. In article 3, a group of untraditional learners at a learning centre in Arvidsjaur attends a synchronous co-located study circle group and participates in an asynchronous and global Massive Open Online Course (MOOC). In article 4, nine students in a preparatory year for entering engineering studies volunteer and participate in a pilot distance course experiment, where prevention of procrastination is a high priority. For this, agile framework theory, constructivist learning theory and the time-based model are used in design and analysis. The last article (5) reconnects learning to place by discussing and adapting Triple- and Quadruple Helix theory for regional development in the knowledge society to four regional European cases. At the end of the synthesis, an outline of the access affordances with the time-based model is given, drawing on Adam’s timescape theory. The discussion of ICT integration into education is made drawing on Floridi’s Philosophy of Information, which provides many tools to view discourses of ICTs in education critically, and also envisions the concept of e-ducation in the infosphere, where other blend issues appear connected to weak artificial intelligence and the pervasive power of ICTs. (shrink)

This paper explores the application of AI-driven HR analytics in shaping workforce agility, focusing on how real-time data collection, analysis, and modeling foster an adaptable workforce. It highlights the role of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing talent deployment. Additionally, the paper discusses how AI enhances strategic decision-making by providing precise metrics and insights into employee behavior, productivity, and satisfaction. The integration of AI into HR systems ultimately shifts HR from a traditionally reactive to a (...) highly proactive, data-driven function. (shrink)

Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches (...) in our previous research on an out-of-distribution attack. In this research, we dived deeper to understand why they works better for other domain dataset but with poor performance on available benchmark malware dataset like Malimg, Malevis, Sorel, and Avast CTU malware dataset. We explored the both the embedding and vector spaces in datasets and compare them with that from other research domain, and find a surprising wide variation between the embedding and vector spaces in malware datasets. We assert that current state-of-the-art machine and deep learning models does not address the wide variation of embedding and vector spaces which are peculiar to malware dataset, hence their poor performance on out-of-distribution attack classification, and so concluded that addressing this variation in embedding and vector spaces will bring about substantial increase in detection of previously unseen out-of-distribution attack. (shrink)

The primary purpose of the study was to determine the level of parents’ engagement in the modular learning of elementary school students. The researchers used survey questionnaires as an instrument, utilized the descriptive type of research and total population sampling in the selection of the respondents, the entire parents of Grades 4-6 was studied. The respondents of this study are the 30 parents which are categorized based on their educational attainment and family monthly income. The researchers used the adapted (...) research questionnaire from the study of Gumapac, et al., (2022) which based on the three indicators: sustaining motivation, coaching and tutoring and monitoring student’s compliance. The result of the study showed that there is no significance difference in the parents’ level of engagement in modular learning when categorized according to educational attainment and family monthly income. Parents are encouraged to continue to support and motivate their children in various aspects and encourage their children to continue in schooling despite on the family income or educational attainment of their parents. Furthermore, the future researchers should conduct the same study in different schools, particularly in Island Garden City of Samal. (shrink)

How can we improve business ethics education for the twenty first century? This study evaluates the effectiveness of a visual case exercise in the form of a 3D immersive game given to undergraduate students at two UK Universities as part of a mandatory business ethics module. We propose that due to evolving learning styles, the immersive nature of interactive games lends itself as a vehicle to make the learning of ethics more ‘concrete’ and ‘personal’ and therefore more engaging. (...) To achieve this, we designed and built an immersive 3D simulation game in the style of a visual case. The effectiveness of the game was evaluated using a mixed methods approach measuring recognised and adapted constructs from the technology acceptance model. Results demonstrate that students found the game beneficial to their learning of ethics with the development of knowledge and skills applicable to the real world and that they engaged with the process due to game elements. Findings demonstrate the potential for the development of simulated games to teach ethics at all levels and modes of delivery and the contribution of this type of visual case model as a pedagogic method. (shrink)

This article attempts to show why an experimental hypothesis is plausible and merits testing; in brief, the hypothesis is that autism begins with a failure in early learning and that changing the environment of early learning would dramatically change its incidence. Strong statistical evidence supporting this hypothesis has been published by Waldman et al., but to date this evidence has largely been ignored, perhaps because it challenges prevalent beliefs about autism. This article also suggests that the current epidemic (...) of autism is serious enough, and intellectually mysterious enough, to merit attention from a wider community of brain scientists: New ideas are needed. A confirmation of this article’s hypothesis would have interesting implications for brain science. (shrink)

Home-based biology experiments are activities that utilize household materials that have been adapted for the remote learning environment and are aligned to standard learning competencies. Recognizingthe households and kitchens as extensions of laboratories, HBEs can be used to deliver authentic learning experiences for the students at home. Furthermore, there are several attributes of HBEs that should be considered before the implementation of the activity. These attributes are, it is ethical and safe to perform, it produces tangible products, (...) encourages students to reflect, promotes collaboration, materials are easy-to-find and affordable and lastly, home-based biology experiments are modifiable. Also, HBE encourages students to work independently under the supervision of their parents and teachers. Parents and guardians, as collaborators, should ensure students'safety, monitor their child’s progress, provide a safe environment and conducive work area, and should report all cases of accidents and problems to the subject teacher. In conclusion, home-based biology experiments are one of the emerging teaching tools for remote authentic learning in the new normal. Moreover, HBEs could also be used to address the problem of the lack of hands-on activity in remote learning. Further studies should be conducted on the effectiveness of HBEs in different topics in biology. (shrink)

For several years, scholars have (for good reason) been largely preoccupied with worries about the use of artificial intelligence and machine learning (AI/ML) tools to make decisions about us. Only recently has significant attention turned to a potentially more alarming problem: the use of AI/ML to influence our decision-making. The contexts in which we make decisions—what behavioral economists call our choice architectures—are increasingly technologically-laden. Which is to say: algorithms increasingly determine, in a wide variety of contexts, both the sets (...) of options we choose from and the way those options are framed. Moreover, artificial intelligence and machine learning (AI/ML) makes it possible for those options and their framings—the choice architectures—to be tailored to the individual chooser. They are constructed based on information collected about our individual preferences, interests, aspirations, and vulnerabilities, with the goal of influencing our decisions. At the same time, because we are habituated to these technologies we pay them little notice. They are, as philosophers of technology put it, transparent to us—effectively invisible. I argue that this invisible layer of technological mediation, which structures and influences our decision-making, renders us deeply susceptible to manipulation. Absent a guarantee that these technologies are not being used to manipulate and exploit, individuals will have little reason to trust them. (shrink)