This study evaluated the effectiveness of four game-based activities (GBAs) in teaching ecosystems to Grade 7 Biology students. Involving 69 students (34 control, 35 experimental), the quasi-experimental study used a mixed-methods approach. The researcher utilized a static-group comparison design for the quantitative phase and a thematic analysis for the qualitative phase. Quantitative analysis revealed significant improvements in the experimental group's social skills (p<0.05; Cohen’s d = 0.63) and conceptual understanding (p<0.05; Cohen’s d = 0.86). Descriptive statistical analysis also suggests that (...) GBAs can foster positive attitudes toward using GBAs and learning. Qualitative data from journal entries and teacher-observer interviews highlighted enhanced communication, collaboration, confidence, and participation among students using GBAs. The study also recognized the interconnection between conceptual understanding, social skills, and attitude toward learning, not just as pedagogical tools but also as practical tools in improving students’ affective domain, creating a safe environment beneficial for the learning process in general. (shrink)

Two great problems of learning confront humanity: learning about the nature of the universe and about ourselves and other living things as a part of the universe, and learning how to become civilized or enlightened. The first problem was solved, in essence, in the 17th century, with the creation of modern science. But the second problem has not yet been solved. Solving the first problem without also solving the second puts us in a situation of great danger. (...) All our current global problems have arisen as a result. What we need to do, in response to this unprecedented crisis, is learn from our solution to the first problem how to solve the second one. This was the basic idea of the 18th century Enlightenment. Unfortunately, in carrying out this programme, the Enlightenment made three blunders, and it is this defective version of the Enlightenment programme, inherited from the past, that is still built into the institutional/intellectual structure of academic inquiry in the 21st century. In order to solve the second great problem of learning we need to correct the three blunders of the traditional Enlightenment. This involves changing the nature of social inquiry, so that social science becomes social methodology or social philosophy, concerned to help us build into social life the progress-achieving methods of aim-oriented rationality, arrived at by generalizing the progress-achieving methods of science. It also involves, more generally, bringing about a revolution in the nature of academic inquiry as a whole, so that it takes up its proper task of helping humanity learn how to become wiser by increasingly cooperatively rational means. The scientific task of improving knowledge and understanding of nature becomes a part of the broader task of improving global wisdom. The outcome would be what we so urgently need: a kind of inquiry rationally designed and devoted to helping us make progress towards a genuinely civilized world. We would succeed in doing what the Enlightenment tried but failed to do: learn from scientific progress how to go about making social progress towards as good a world as possible. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

Two great problems of learning confront humanity: learning about the nature of the universe and our place in it, and learning how to become civilized. The first problem was solved, in essence, in the 17th century, with the creation of modern science. But the second problem has not yet been solved. Solving the first problem without also solving the second puts us in a situation of great danger. All our current global problems have arisen as a result. (...) What we need to do, in response to this unprecedented crisis, is learn from our solution to the first problem how to solve the second. This was the basic idea of the 18th century Enlightenment. Unfortunately, in carrying out this programme, the Enlightenment made three blunders, and it is this defective version of the Enlightenment programme that we have institutionalized in 20th century academic inquiry. In order to solve the second great problem of learning we need to correct the three blunders of the traditional Enlightenment. This involves changing the nature of social inquiry, so that social science becomes social methodology or social philosophy, concerned to help us build into social life the progress-achieving methods of aim-oriented rationality, arrived at by generalizing the progress-achieving methods of science. It also involves, more generally, bringing about a revolution in the nature of academic inquiry as a whole, so that it takes up its proper task of helping humanity learn how to become wiser by increasingly cooperatively rational means. The scientific task of improving knowledge and understanding of nature becomes a part of the broader task of improving global wisdom. (shrink)

Concept maps are found to be useful in eliciting knowledge, meaningful learning, evaluation of understanding and in studying the nature of changes taking place during cognitive development, particularly in the classroom. Several experts have claimed the effectiveness of this tool for learning science. We agree with the claim, but the effectiveness will improve only if we gradually introduce a certain amount of discipline in constructing the maps. The discipline is warranted, we argue, because science thrives to be an (...) unambiguous and rigorously structured body of knowledge. Since learning science may be seen as a process where a novice is expected to be transformed into an expert, we use the context of learning science for making the proposal. Further, we identify certain anomalies in the evaluation of concept maps, and suggest that the evaluation should be based on semantics of the linking words (relation types) and not on graphical criteria alone. (shrink)

Preparing students for the real challenges in life is one of the most important goals in education. Constructivism is an approach that uses real-life experiences to construct knowledge. Problem-Based Learning (PBL), for almost five decades now, has been the most innovative constructivist pedagogy used worldwide. However, with the rising popularity, there is a need to revisit empirical studies regarding PBL to serve as a guide and basis for designing new studies, making institutional policies, and evaluating educational curricula. This need (...) has led the researchers to do a meta-analysis to analyse the effectiveness of PBL on secondary students’ achievement in different scientific disciplines. Following the set of inclusion and exclusion criteria, 11 studies in Eurasia, Africa, and America conducted from 2016 to 2020 have qualified for this study. Six of which focused on JHS (n = 1047) and five on SHS (n = 375). Studies were obtained from various meta-search engines including Google, ERIC, and JSTOR. Further, the researchers used Harzing’s Publish and Perish software to exhaust the search process. Sample size, mean, and standard deviation were analysed using the Comprehensive MetaAnalysis version 3 to determine the effect sizes (Hedge’s g) and the results of moderator analysis, forest plot, funnel plot, and Begg-Mazumdar test. Findings have shown that PBL, as an approach to teaching science, had a large and positive effect (ES = .871) on the achievement of secondary students. However, grade levels and various scientific disciplines did not influence students’ learning achievement. The conduct of more studies on the different factors affecting PBL implementation and specific effects of PBL on various student domains is recommended to facilitate comparative educational research in the future. (shrink)

Can we learn to create a better world? Yes, if we first create traditions and institutions of learning rationally devoted to that end. At present universities all over the world are dominated by the idea that the basic aim of academic inquiry is to acquire knowledge. Such a conception of inquiry, judged from the standpoint of helping us learn wisdom and civilization, is grotesquely and damagingly irrational. We need to change our approach to academic enterprise if we are to (...) create a kind of inquiry rationally devoted to helping us become more civilized. (shrink)

This study developed and validated E-learning Self-directed Interactive Modules (E-SelfIMo) for Earth Science. The study employed Research and Development method, using the Borg and Gall development procedure, in creating eight e-modules using Kotobee software, evaluating them by experts and students, and determining their effectiveness in terms of students' conceptual understanding. Experts agreed that E-SelfIMo met the DepEd standards for non-printed learning materials, and students attested to their high validity in content, format, and usefulness. Pretest and posttest results for (...) the effectiveness of E-SelfIMo revealed that students exposed to the E-SelfIMo have progressed from “Near Mastery” to “Near Full Mastery” in the chosen Earth Science topics, showing more significant improvement in their conceptual understanding than the control group. This study concludes that E-SelfIMo supports interactive, active, and self-directed learning thereby, improving students’ motivation, engagement, interest, and performance. The E-SelfIMo could be used with technology integration as a teaching and learning tool. -/- . (shrink)

This study aimed to examine the challenges encountered by science teachers in implementing modular distance learning and the coping strategies they employed to address these challenges. Using a mixed-method research approach, data were collected through a survey of thirty-eight Junior High School science teachers in Mandaon District in Masbate, Philippines. Findings revealed that the implementation of modular distance learning presented various challenges, including technical problems, distribution and retrieval difficulties, student utilization issues, and unreliable assessment results. To cope with (...) these challenges, teachers employed various strategies, including attending ICT training, asking for technical assistance, developing income-generating projects, and utilizing differentiated instruction and targeted assessments. The study also highlights the need for more parental involvement, adequate funding for schools, clear instructions and guidance to students, and proper monitoring of student performance. Overall, the study suggests that with effective implementation and support, modular distance learning can be an effective mode of instruction, particularly during times of crisis. (shrink)

The study sought to investigate the effects of using cooperative learning on female teacher trainees of the Colleges of Education in learning some selected topics in Integrated Science. The investigation also sought to determine whether the Cooperative Learning Approach enhances the attitude and motivation of the trainees towards learning of Integrated Science. The study was carried out at the St. Monica’s College of Education in the Mampong Municipality of the Ashanti Region. In all, 80 teacher trainees (...) consisting of 40 each from control and experimental groups were purposively sampled to participate in the study. The teacher trainees in the experimental group were exposed to the Cooperative Learning Approach and the trainees in the control group were lectured during the period of the study. The results of the study showed that the cooperative learning strategy was very relevant and beneficial in helping the teacher trainees of the St. Monica’s College of Education to improve on their performance in, and attitudes towards, the teaching and learning of Integrated Science. It was therefore recommended among other things that the science teachers should endeavour to integrate cooperative learning into their routine methods of instruction in the teaching and learning of Integrated Science. (shrink)

The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students enrolled in computer science (...) curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...) of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

Children acquire complex concepts like DOG earlier than simple concepts like BROWN, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Werning 2010). This is the Complex- First Paradox. We present a novel solution to the Complex-First Paradox. Our solution builds on a generalization of Xu and Tenenbaum’s (2007) Bayesian model of word learning. By focusing on a rational theory of concept learning, (...) we show that it is easier to infer the meaning of complex concepts than that of simple concepts. (shrink)

Aims: This paper assessed the motivation and perception of Grade 12 public school students in learning social science during the pandemic. It also investigated the difference in their motivation and perception. -/- Study Design: Descriptive-comparative design. -/- Place and Duration of Study: School Division of a Component City in Northern Negros Occidental, between January 2021 to July 2022. -/- Methodology: The study utilized the descriptive-comparative design. The study was assessed by 436 stratified randomly sampled students. The assessments were gathered (...) using the modified motivation and perception questionnaires. In analyzing the data, mean, standard deviation, Mann Whitney, and Kruskal Wallis were employed. -/- Results: Generally, the motivation (M=3.83, SD= 0.67) shows an agreeable result. The extrinsic goal orientation (M=4.15, SD= 0.88) is rated highest with an agreeable result, and social engagement (M=3.46, SD= 0.92) is the lowest with a neutral result. Meanwhile, they have agreeable perceptions (M=3.69, SD= 0.65) with perceived value (M=3.79, SD= 0.79) as highest and perceived teachers' attitude (M=3.46, SD= 0.89) as lowest. Moreover, there was no difference in their motivation when group according to sex [U=21954.5, p=0.721] and track [U=22104.0, p=0.875]. While a significant difference in academic performance [χ2(4)=53.069, p=0.000]. In terms of perception, it found no difference when grouped according to sex [U=22309.0, p=0.771] and track [U=22163.0, p=0.912]. While a significant difference in academic performance [χ2(4)=32.042, p=0.000]. -/- Conclusion: The study implies the need to address students' motivation and perception issues to ensure their quality learning of social science. (shrink)

The shift from conventional, face-to-face classroom teaching to distance education is a complex process that brings various challenges. To better understand the impact of this transition, the researchers examined the perceptions of secondary science teachers (n = 42) and students (n = 137). Specifically, the study focused on evaluating learner-centered, action-oriented, and transformative learning – referred to as LCAOT learning – in science distance education. The researchers developed a 26-item, 4-point Likert scale questionnaire that was distributed online to (...) the target respondents. Additionally, the researchers interviewed teachers and students and analyzed various documents, such as self-learning modules and learners’ activity sheets, to triangulate the survey data. The findings revealed that the principles of LCAOT learning were apparent in science distance education and exemplified through tools such as the Know, Want to Know, and Learned charts and personal journals. The study also revealed that teachers and students faced challenges during the transition to distance education, including inadequate equipment and poor internet connectivity. However, they responded to these challenges by using various means of communication, collaborating with peers, and exploring new roles and identities. The researchers recommend using the developed instrument and continuing to evaluate the effectiveness of teaching strategies employed in distance education in science, as well as further studies on the impact of LCAOT learning on students’ academic achievement. (shrink)

We argue that the core social being system is unlike other core systems in that it participates in frequent, widespread learning. As a result, the social being system is less constant throughout the lifespan and less informationally encapsulated than other core systems. This learning supports the development of the precursors of bias, but also provides avenues for preempting it.

Introduction: The Faculty of Medicine, University of Moratuwa, established during the COVID-19 pandemic, was compelled to conduct the teaching activities online for the first intake of students until their first bar examination. Online learning is known to be linked to several health issues. This study aims to explore the academic performance and perceived health effects related to online learning in the Faculty of Medicine, Moratuwa. Methods: A descriptive cross-sectional study was conducted among all 104 first-intake students using an (...) anonymous online self-administered questionnaire to explore health effects and academic performance after ethical clearance. Results: Out of 104 students, 82 students responded (response rate=78.85%). Majority participated from home using their own laptop (74.4%) and personal Wi-Fi (69.5%) and spent 5-10 hours daily for online academic activities (72%). Majority complained of related physical (86.6%), and psychological (81.7%) health issues: eye problems (69.5%), headache (47.6%), backache (43.9%), stress (46.3%) and concentration difficulties (45.1%). The first bar examination was cleared by 84.1% with the majority (52.4%) obtaining classes. There was a statistically significant positive association (p< .05) between physical and psychological health concerns with the average screen-time spent on academic activities, but not with the academic performance. There was also no statistically significant association between screen-time and academic performance. Conclusions: Majority of students using online learning had related physical and psychological health issues which were positively associated with the screen-time though not with academic performance. Designing online activities with less screen time may be considered as a strategy to minimise health effects related to online learning. (shrink)

Two great problems of learning confront humanity: learning about the universe, and learning how to live wisely. The first problem was solved with the creation of modern science, but the second problem has not been solved. This combination puts humanity into a situation of unprecedented danger. In order to solve the second problem we need to learn from our solution to the first problem. This requires that we bring about a revolution in the overall aims and methods (...) of academic inquiry, so that it takes up its proper task of promoting wisdom. (shrink)

In this paper I defend a teleological explanation of normativity, i. e., I argue that what an organism is supposed to do is determined by its etiological function. In particular, I present a teleological account of the normativity that arises in learning processes, and I defend it from some objections.

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

Learning to love the reviewer -/- Issue: 43(4) November 2017. Viewpoint Page 83 -/- Quan Hoang Vuong Western University Hanoi, Centre for Interdisciplinary Social Research, Hanoi, Vietnam.

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding (...) misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do (...) something? And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...) a few AI models to characterize cancers, however none have sufficiently tended to this misdiagnosis issue. Likewise, comparative investigations that have proposed models for assessment of such cancers generally don't think about the heterogeneity and the size of the information. Thusly, we propose an AI based approach which joins another strategy of pre handling the information for highlights change, resampling methods to dispense with the predisposition and the deviation of precariousness and performing classifier tests in light of the and Deep learning Algorithm as Artificial brain organization. -/- Tumors (STT). (shrink)

The changing conditions for the accumulation and transmission of knowledge in the age of multimedia networks make it inevitable that old philosophical problems become formulated in a new light. Above all, the problem of the unity of knowledge is once again a topical issue. The situation-dependent acquisition of knowledge that is made possible by mobile learning transcends the boundaries of traditional disciplines, linking the domains of text, diagram, and picture. Database integration and multimedia search become central problems in the (...) epistemology of the 21st century, while handheld devices are emerging as vital technologies for supporting collaborative learning. -- Kristóf Nyíri has published widely on Wittgenstein, Austrian intellectual history, and the philosophy of communication. He directs the interdisciplinary research program COMMUNICATIONS IN THE 21ST CENTURY, conducted jointly by the Institute for Philosophical Research of the Hungarian Academy of Sciences and Westel Mobile Telecommunications, Budapest. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with (...) the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from (...) data that is unstructured or unlabeled. Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a (...) new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments. (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)

In recent work, Stephen John (2018, 2019) has deepened the social epistemological perspective on expert testimony by arguing that science communication often operates at the institutional level, and that at that level sincerity, transparency, and honesty are not necessarily epistemic virtues. In this paper I consider his arguments in the context of science journalism, a key constituent of the science communication ecosystem. I argue that this context reveals both the weakness of his arguments and a need for further analysis of (...) how non-experts learn from experts. (shrink)

The English language is immensely rich in terms of vocabulary. When learning vocabulary, successful students employ specific strategies. Several studies have been conducted to explore the vocabulary learning strategies (VLS) English language learners employ. However, there is a lack of empirical research on the topic in Afghanistan. Therefore, the aims of this study were to 1) explore the VLS undergraduate English learners employ; 2) examine the correlation between VLS and gender; 3) examine the correlation between VLS and students’ (...) year of studies; and 4) examine the correlation between VLS and students’ English proficiency level. The study used a descriptive-quantitative research design. The study discovered that metacognitive and memory strategies were the most frequently used, while social strategies were the least frequently used. Moreover, the correlation results revealed that metacognitive strategies were more prevalent among males, while memory strategies were more popular among females. (shrink)

In this paper I argue that to understand minded agency – the capacity we typically find instantiated in instances of human behaviour that could sensibly be questioned by asking “What did you do?” – one needs to understand childhood, i.e. the trajectory of learning to act. I discuss two different types of trajectory, both of which seem to take place during childhood and both of which might be considered crucial to learning to act: a growth of bodily control (...) (GBC) and a growth in taking responsibility (GTR). The discussion of GTR takes up about half of the entire paper. In the final two sections I argue that GTR is the most promising trajectory in terms of which to understand a child’s process of learning to act. (shrink)

This book gives an account of work that I have done over a period of decades that sets out to solve two fundamental problems of philosophy: the mind-body problem and the problem of induction. Remarkably, these revolutionary contributions to philosophy turn out to have dramatic implications for a wide range of issues outside philosophy itself, most notably for the capacity of humanity to resolve current grave global problems and make progress towards a better, wiser world. A key element of the (...) proposed solution to the first problem is that physics is about only a highly specialized aspect of all that there is – the causally efficacious aspect. Once this is understood, it ceases to be a mystery that natural science says nothing about the experiential aspect of reality, the colours we perceive, the inner experiences we are aware of. That natural science is silent about the experiential aspect of reality is no reason whatsoever to hold that the experiential does not objectively exist. A key element of the proposed solution to the second problem is that physics, in persistently accepting unified theories only, thereby makes a substantial metaphysical assumption about the universe: it is such that a unified pattern of physical law runs through all phenomena. We need a new conception, and kind, of physics that acknowledges, and actively seeks to improve, metaphysical presuppositions inherent in the methods of physics. The problematic aims and methods of physics need to be improved as physics proceeds. These are the ideas that have fruitful implications, I set out to show, for a wide range of issues: for philosophy itself, for physics, for natural science more generally, for the social sciences, for education, for the academic enterprise as a whole and, most important of all, for the capacity of humanity to learn how to solve the grave global problems that menace our future, and thus make progress to a better, wiser world. It is not just science that has problematic aims; in life too our aims, whether personal, social or institutional, are all too often profoundly problematic, and in urgent need of improvement. We need a new kind of academic enterprise which helps humanity put aims-and-methods improving meta-methods into practice in personal and social life, so that we may come to do better at achieving what is of value in life, and make progress towards a saner, wiser world. This body of work of mine has met with critical acclaim. Despite that, astonishingly, it has been ignored by mainstream philosophy. In the book I discuss the recent work of over 100 philosophers on the mind-body problem and the metaphysics of science, and show that my earlier, highly relevant work on these issues is universally ignored, the quality of subsequent work suffering as a result. My hope, in publishing this book, is that my fellow philosophers will come to appreciate the intellectual value of my proposed solutions to the mind-body problem and the problem of induction, and will, as a result, join with me in attempting to convince our fellow academics that we need to bring about an intellectual/institutional revolution in academic inquiry so that it takes up its proper task of helping humanity learn how to solve problems of living, including global problems, and make progress towards as good, as wise and enlightened a world as possible. (shrink)

This work investigates one of the most widespread yet elusive ideas about our experience of art: the idea that there is something cognitively valuable in engaging with great artworks, or, in other words, that we learn from them. This claim and the age-old controversy that surrounds it are reconsidered in light of the psychological and neuroscientific literature on learning, in one of the first systematic efforts to bridge the gap between philosophical and scientific inquiries on the topic. The work (...) has five chapters. Chapter 1 lays down its conceptual bases: it explains what learning is taken to be in the current philosophical debate and it points out how Bayesian cognitive science (particularly in its predictive processing formulations) might be well-suited to capture the kind of learning involved in our engagement with the arts. The following chapters test this latter hypothesis with respect to particular art forms, namely literature and literary language (Chapter 2), narrative (Chapter 3), and visual art, music and motor activities (Chapter 4). The fine-grained discussions conducted in each of these areas will enable us to see that the relationship between art and learning is indeed fundamental and pervasive. The final chapter (Chapter 5) examines the consequences of this fact for our understanding of the role of art in our epistemic practices, its ultimate usefulness and value, and its place in the interdisciplinary study of the human mind. The upshot is a novel and wide-ranging picture, both philosophically informed and empirically sound, that bypasses many of the problems and dead ends of the current philosophical debate on the topic and captures the deep sense in which art and learning are interrelated. (shrink)

A shared narrative in the literature on the evolution of cooperation maintains that social _learning_ evolves early to allow for the transmission of cumulative culture. Social _norms_, whilst present at the outset, only rise to prominence later on, mainly to stabilise cooperation against the threat of defection. In contrast, I argue that once we consider insights from social epistemology, an expansion of this narrative presents itself: An interesting kind of social norm — an epistemic coordination norm — was operative in (...) early and important instances of specialised social learning. I show how there’s a need for such norms in two key social learning strategies and explain how this need is constituted. In assessor-teaching (e.g. Castro et al., 2019b, 2021), epistemic coordination norms allow agents to coordinate around the _content_ of social learning, i.e., what is to be known and how this is to be done. These norms also allow agents to coordinate around the form of cultural learning in what’s sometimes called strategic social learning (Laland, 2004; Hoppitt & Laland, 2013; Heyes, 2018, Chap. 5) and elsewhere. Broadly speaking, this concerns how cultural learning is organised within the social group. The upshot is that the evolution of social learning and social norms are intertwined in important and underappreciated ways from early on. The above matters as it informs our views about the evolution of social norms more generally. Truly _social_ norms emerged to coordinate a plurality of complex behaviours and interactions, amongst them specialised social learning. I substantiate this view by contrasting it with Jonathan Birch’s views on the evolution of norms. What results is a general but cohesive narrative on the early evolution of social norms. (shrink)

According to cognitive science of religion (CSR) people naturally veer toward beliefs that are quite divergent from Anselmian monotheism or Christian theism. Some authors have taken this view as a starting point for a debunking argument against religion, while others have tried to vindicate Christian theism by appeal to the noetic effects of sin or the Fall. In this paper, we ask what theologians can learn from CSR about the nature of the divine, by looking at the CSR literature and (...) what it identifies as commonalities across religions. We use a pluralist, non-confessional approach to outline properties of the divine. We connect our approach to Hick’s religious pluralism, Ramakrishna’s realization of God through multiple spiritual paths, and Gellman’s inexhaustible plenitude. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms (...) that allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

This paper details how ghost hunting, as a set of learning activities, can be used to enhance critical thinking and philosophy of science classes. We describe in some detail our own work with ghost hunting, and reflect on both intended and unintended consequences of this pedagogical choice. This choice was partly motivated by students’ lack of familiarity with science and philosophic questions about it. We offer reflections on our three different implementations of the ghost hunting activities. In addition, we (...) discuss the practical nuances of implementing these activities, as well the relation of ghost hunting to our course content, including informal fallacies and some models for scientific inference. We conclude that employing ghost hunting along-side traditional activities and content of critical thinking and philosophy of science offers a number of benefits, including being fun, increasing student attendance, enhancing student learning, and providing a platform for campus wide dialogues about philosophy. (shrink)

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)

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is used (...) to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

Looking out of Information Science (IS) it´s a dangerous attempt to compare this relative new science direct with Philosophy. Here you find a first circumspective trial of an investigation of the traditionally named “queen of science”, Philosophy, two thousand years old and - direct opposite - the only a half century old Information Science. For me it is till now not yet clear how to do this in a serious scientific manner. I worked in Applied Informatics for 30 years and (...) make Information Science since about 15 years. Here I dare to publish for first time the results. SOKRATES (469 – 399 b.Chr.), PLATON (428/27- 348/47 b.Chr.) und ARISTOTELES (384 - 322 b.Chr.) as inventors of our traditional occidental Philosophy, have founded the search of the sense of our Human Life, Thinking and Acting as an own science. They set the Joy of Life on top of their way of thinking. PLATON has separated this special new thinking from the „Sophists“ who had a very good public image too at his time. But they were thinking more about common business facts and knowledge only. Today we would call them manufacturer, qualified skilled workers or even bachelors of special sciences. Philosophy has (since over 20 centuries) till today first of all the smart and high duty to serve Religion and Ethics as mental, spirit- and language-grounded science-base. In other direction it was used to overthink our whole surrounding nature theoretically and completely by our best Human Mind. It´s our traditional science on our mental highest level. All sciences can be related by Philosophy. That´s possible by our human ability to Learn, Think, Understand and finally Know any interesting new fact. Where and how do we have now to integrate this new own science Information Science? We search consciously term-oriented and make an abstract science-theoretical comparison to find answers and definitions. (shrink)

So instead of trying to ignore the metrics’ importance, this article introduces an increasingly powerful tool for a publishing academic to track one’s academic impact. Of course, you will soon see that the method is centered on the citation, but there is also more than the citation count alone. The tool is called Dimensions.

In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their (...) effect on the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models. (shrink)

A nation’s economy runs on the knowledge and skills of its people.Quality assurance mechanisms for higher education institutions must take cognizance of the graduates' acquisition of skills to become productive and contributory for societal development. The study is a quantitative survey assessing the attainment of the immediate program learning outcomes of the graduating Bachelor of Science in Information Technology of one campus of a public higher education institution in the Philippines. It also assessed the introspection and level of satisfaction (...) of the graduates in their studies. Findings disclosed that the graduates have a high level of attainment of the program's learning outcomes and a high level of satisfaction. The high school where the respondents graduated is the single variable that defined the difference in attainment of program intended learning outcomes. As to the graduates' introspections, themost significant percentage of them learned the program from family, relatives, and friends. Reasons for pursuing the degree program are because of parents’ influence, prestige of IT profession. Meanwhile, the university's reputation, affordable tuition fees, and proximity to home were the factors affecting the enrolment in the BSIT program. The findings will be the basis for strategic program interventions of the program. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...) precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance (...) of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

Constructive realism (CR) is an attempt to overcome the difficulties associated with naïve realism and radical constructivism. There are different versions for CR. In this paper, I defend a particular version of CR. Complexity of reality, on the one hand, and the impact of human mind, language, and culture, on the other, leads to the inevitable contribution of constructs in knowledge development. According to the CR, even if mental, linguistic and cultural side of constructs could not be avoided in principle, (...) a certain proportion of reality-contact can be considered and defended for the constructs. In this sense, human mind and culture has a constitutive role to play in knowledge acquiring instead of being an innocent means in the process of acquiring knowledge. On the other hand, the role played by the reality in this process cannot be ignored because, according to the CR, knowledge consists after all of knowing 'something' in the real world. On the whole, taking into account both the constitutive role of mind and culture, on the one hand, and the inevitable role of the reality, on the other, shows the difference between the CR and pure realism, pure constructivism, and those versions of CR that do not take the constitutive role of mind into account. There are requirements for the CR in science education. First of all, there must be a constant caution for making sure that our scientific theories have caught the proportion of reality-contact. This point shows the difference between the CR and pure constructivism. This caution should be present in our science teaching as well. Secondly, we should encourage our students to develop imaginative alternative constructs when they are learning sciences. This shows the difference between the CR with naïve realists who ignore the importance of students' imaginations in suggesting alternative constructs. Notwithstanding, while we encourage the students to develop their imaginative constructs, there should be considered a limitation for their over-justification about their constructs. In this way, we as teachers should urge them to take counter-evidence most seriously into account. This is a point in which constructive realists are distinguished from pragmatists too. Pragmatists do not give up their theories in confrontation with counter-evidence but rather look for almost limitless changes in their constructs in order for making the counterevidence compatible with their theories, whereas constructive realists will be ready to take counterevidence more serious and correct their constructs accordingly. (shrink)