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)

Shanks and St. John (1994a) suggest that From the viewpoint of a computer scientist who tries to construct learning systems, that claim seems rather implausible. In this commentary I wish to suggest why, in the hopes of shedding light on the relationship between consciousness and learning.

In this chapter, I will discuss employing exemplars in moral and character education promoting virtue development with the involvement of embodiment. Virtue ethicists propose two phases of virtue development: early virtue habituation and later phronesis cultivation. I will overview prior research on the mechanism of habituation at the biological and neural levels to examine why embodiment is fundamental during the first phase, virtue habituation. Then, I will review recent philosophical and psychological studies about the nature of phronesis, i.e., practical wisdom, (...) to consider how educational activities cultivate it. Based on these preliminary examinations of the course of virtue development, I will examine the previous literature about the role and importance of using exemplars in moral and character education involving concrete and interactive activities with the exemplars involving embodiment during the learning process. Finally, I will draw upon the Self-determination theory about supporting students’ need for autonomy, competence, and relatedness to consider a way to improve the effectiveness of exemplar-applied education along with embodiment. After examining the relevant theoretical frameworks mentioned above, I will discuss several practical guidelines for using exemplars involving character embodiment for virtue development. (shrink)

In this essay, Amy Voss Farris and Pratim Sengupta argue that a democratic approach to children's computing education in a science class must focus on the aesthetics of children's experience. In Democracy and Education, Dewey links “democracy” with a distinctive understanding of “experience.” For Dewey, the value of educational experiences lies in “the unity or integrity of experience.” In Art as Experience, Dewey presents aesthetic experience as the fundamental form of human experience that undergirds all other forms of experiences (...) and that can bring together multiple forms of experiences, locating this form of experience in the work of artists. Particularly relevant to the focus of this essay, computational literacy, Dewey calls the process through which a person transforms a material into an expressive medium an aesthetic experience. Farris and Sengupta argue that the kind of experience that is appropriate for a democratic education in the context of children's computational science is essentially aesthetic in nature. Given that aesthetics has received relatively little attention in STEM education research, the authors' purpose here is to highlight the power of Deweyan aesthetic experience in making computational thinking available and attractive to all children, including those who are disinterested in computing, and especially those who are likely to be discounted by virtue of location, gender, or race. (shrink)

While calls to integrate ethics into computer science education go back decades, recent high-profile ethical failures related to computing technology by large technology companies, governments, and academic institutions have accelerated the adoption of computer ethics education at all levels of instruction. Discussions of how to integrate ethics into existing computer science programmes often focus on the structure of the intervention—embedded modules or dedicated courses, humanists or computer scientists as ethics instructors—or on the specific content (...) to be included—lists of case studies and essential topics to cover. While proponents of computer ethics education often emphasize the importance of closely connecting ethical and technical content in these initiatives, most do not reflect in depth on the variety of ways in which the disciplines can be combined. In this paper, I deploy a framework from cross- disciplinary studies that categorizes academic projects that work across disciplines as multidisciplinary, interdisciplinary, or transdisciplinary, depending on the degree of integration. When applied to computer ethics education, this framework is orthogonal to the structure and content of the initiative, as I illustrate using examples of dedicated ethics courses and embedded modules. It therefore highlights additional features of cross-disciplinary teaching that need to be considered when planning a computer ethics programme. I argue that computer ethics education should aim to be at least interdisciplinary—multidisciplinary initiatives are less aligned with the pedagogical aims of computer ethics—and that computer ethics educators should experiment with fully transdisciplinary education that could transform computer science as a whole for the better. (shrink)

Under the wave of globalization, the ties between countries are getting closer and closer. Based on the differences in the languages of different countries, the importance of English as a universal language is becoming more and more prominent. In the past, English teaching was mainly taught by teachers and students. This mode of English learning is more of theoretical teaching, which has little effect on improving English ability. In the era of intelligence, with the upgrading of technology and the (...) renewal of ideas, interactive English teaching is more accepted by people and becomes a new solution for English teaching. Studying English teaching from the perspective of social cognition, this paper proposes to realize human-computer interaction with the help of advanced science and technology, create an interactive teaching environment, and realize interactive learning in an English environment. This paper designed a control experiment of the experimental group and the control group to verify the impact of human-computer interaction teaching on students’ English performance and investigated students’ performance and teachers’ feelings and their related opinions on human-computer interaction learning. The experimental results of this paper show that human-computer interactive English teaching can help to enhance students’ interest in English learning and improve English expression significantly. (shrink)

Context: Although there are rich descriptive accounts of skill acquisition in the literature, there are no satisfactory explanatory models of the cognitive processes involved. Problem: The aim of the paper is to explain some key phenomena frequently observed in the acquisition of motor skills: the loss of conscious access to knowledge of the structure of a skill and the awareness that an error has been made prior to the receipt of knowledge of results. Method: In the 1970s, the first author (...) implemented a computer program model of the cognitive processes involved in learning and skill acquisition, based on a series of empirical investigations. Recently, with assistance from the second author, the model has been reviewed, updated and re-implemented. Result: The model provides a constructivist account of skill acquisition and associated phenomena. Implications: The model adds to the understanding of motor skill acquisition and will be of interest to researchers and practitioners in physical therapy and sports science. It is also provides a constructivist cognitive architecture that can be fruitfully contrasted with non-constructivist cognitive architectures well-known in cognitive science. (shrink)

Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.

Emerging systems using artificial intelligence (AI) including the complexities of deep learning leading to decision-making outcomes pose challenge, risk alongside opportunities to revolutionize business sectors and thus, human life. Building AI that impact on critical decision-making must be entwined with ethical questioning from the initial conception of design. As academics educating future technologists, we must lead on embedding the importance of ethical thinking for equitable designed systems. Currently, it is unclear across UK Higher Education how widely ethics is taught (...) across programmes of study within computer science disciplines, their importance within the curriculum plus the confidence of academics for its delivery. This paper carries out a survey analysis to identify gaps, measures of engagement, expertise and knowledgebase of topics through both quantitative and qualitative questioning. The survey was distributed to 15 member universities of the Council for Professors and Heads of Computing (CPHC) randomly selected across the UK for which 32 academics engaged in the study. Results show ethics was noted as being important although one-third reported it was not widely embedded across the curriculum. Just less than half of participants schedule the teaching of ethics regularly either across one or several modules. Interestingly, 91% of respondents stated that they have not received any instruction or training and that 62% felt that they require more training on the subject matter. This paper is inspired by wider research from European studies however, this study provides a specific novel focus on a broad range of UK universities, providing comparative analysis and unique findings from the UK perspective. The significance of these findings suggest academics perspectives vary with specific training needs. This demonstrates that this early-stage research requires further exploration of the wider computer science curriculum, sharing of good practice and collaborative working. (shrink)

Emerging systems using artificial intelligence (AI) including the complexities of deep learning leading to decision-making outcomes pose challenge, risk alongside opportunities to revolutionize business sectors and thus, human life. Building AI that impact on critical decision-making must be entwined with ethical questioning from the initial conception of design. As academics educating future technologists, we must lead on embedding the importance of ethical thinking for equitable designed systems. Currently, it is unclear across UK Higher Education how widely ethics is taught (...) across programmes of study within computer science disciplines, their importance within the curriculum plus the confidence of academics for its delivery. This paper carries out a survey analysis to identify gaps, measures of engagement, expertise and knowledgebase of topics through both quantitative and qualitative questioning. The survey was distributed to 15 member universities of the Council for Professors and Heads of Computing (CPHC) randomly selected across the UK for which 32 academics engaged in the study. Results show ethics was noted as being important although one-third reported it was not widely embedded across the curriculum. Just less than half of participants schedule the teaching of ethics regularly either across one or several modules. Interestingly, 91% of respondents stated that they have not received any instruction or training and that 62% felt that they require more training on the subject matter. This paper is inspired by wider research from European studies however, this study provides a specific novel focus on a broad range of UK universities, providing comparative analysis and unique findings from the UK perspective. The significance of these findings suggest academics perspectives vary with specific training needs. This demonstrates that this early-stage research requires further exploration of the wider computer science curriculum, sharing of good practice and collaborative working. (shrink)

Because evolution in natural systems happens so slowly, it is dif- ficult to design inquiry-based labs where students can experiment and observe evolution in the way they can when studying other phenomena. New research in evolutionary computation and artificial life provides a solution to this problem. This paper describes a new A-Life software environment – Avida-ED – in which undergraduate students can test evolutionary hypotheses directly using digital organisms that evolve on their own through the very mechanisms that Darwin discovered.

Given the recent call to strengthen collaboration between researchers and relevant practitioners, we consider participatory design as a way to advance Cognitive Science. Building on examples from the Learning Sciences and Human−Computer Interaction, we (a) explore what, why, who, when, and where researchers can collaborate with community members in Cognitive Science research; (b) examine the ways in which participatory‐design research can benefit the field; and (c) share ideas to incorporate participatory design into existing basic and applied (...) research programs. Through this article, we hope to spark deeper discussions on how cognitive scientists can collaborate with community members to benefit both research and practice. (shrink)

Second-language learners rarely arrive at native proficiency in a number of linguistic domains, including morphological and syntactic processing. Previous approaches to understanding the different outcomes of first- versus second-language learning have focused on cognitive and neural factors. In contrast, we explore the possibility that children and adults may rely on different linguistic units throughout the course of language learning, with specific focus on the granularity of those units. Following recent psycholinguistic evidence for the role of multiword chunks in (...) online language processing, we explore the hypothesis that children rely more heavily on multiword units in language learning than do adults learning a second language. To this end, we take an initial step toward using large-scale, corpus-based computational modeling as a tool for exploring the granularity of speakers' linguistic units. Employing a computational model of language learning, the Chunk-Based Learner, we compare the usefulness of chunk-based knowledge in accounting for the speech of second-language learners versus children and adults speaking their first language. Our findings suggest that while multiword units are likely to play a role in second-language learning, adults may learn less useful chunks, rely on them to a lesser extent, and arrive at them through different means than children learning a first language. (shrink)

Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony . This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, (...) we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles. (shrink)

This book illustrates the program of Logical-Informational Dynamics. Rational agents exploit the information available in the world in delicate ways, adopt a wide range of epistemic attitudes, and in that process, constantly change the world itself. Logical-Informational Dynamics is about logical systems putting such activities at center stage, focusing on the events by which we acquire information and change attitudes. Its contributions show many current logics of information and change at work, often in multi-agent settings where social behavior is essential, (...) and often stressing Johan van Benthem's pioneering work in establishing this program. However, this is not a Festschrift, but a rich tapestry for a field with a wealth of strands of its own. The reader will see the state of the art in such topics as information update, belief change, preference, learning over time, and strategic interaction in games. Moreover, no tight boundary has been enforced, and some chapters add more general mathematical or philosophical foundations or links to current trends in computer science. (shrink)

There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and (...) argues that philosophers have a lot to learn by paying closer attention to the practice of simulation. (shrink)

It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...) components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem, we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences. We then examine two possible solutions. On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches. (shrink)

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of (...) disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations. (shrink)

There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments tested this framework with a method in which participants are first provided highly impoverished evidence of a new phonological pattern, and then tested on how they extend this pattern to novel contexts and novel sounds. (...) Participants were found to generalize velar palatalization (e.g., the change from [k] as in keep to [t⌢∫S] as in cheap) in a way that accords with linguistic typology, and that is predicted by a cognitive bias in favor of changes that relate perceptually similar sounds. Velar palatalization was extended from the mid front vowel context (i.e., before [e] as in cape) to the high front vowel context (i.e., before [i] as in keep), but not vice versa. The key explanatory notion of perceptual similarity is quantified with a psychological model of categorization, and the substantively biased framework is formalized as a conditional random field. Implications of these results for the debate on substance, theories of phonological generalization, and the formalization of similarity are discussed. (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)

Computational models can aid in the development of philosophical views concerning the structure and growth of scientific knowledge. In cognitive psychology, computational models have proved valuable for describing the structures and processes of thought and for testing these models by writing and running computer programs using the techniques of artificial intelligence. Similarly, in the philosophy of science models can be developed that shed light on the structure, discovery, and justification of scientific theories. This paper briefly describes a computational (...) model of problem solving and learning that has been used to simulate several kinds of scientific reasoning. (shrink)

Studies of scientific practice demonstrate that the development of scientific models is an enactive and emergent process. Scientists make meaning through processes such as perspective taking, finding patterns, and following intuitions. In this paper, we focus on how a group of fourth grade learners and their teacher engaged in interpretation in ways that align with core ideas and practices in kinematics and computing. Cycles of measuring and modeling––including computer programming––helped to support classroom interactions that highlighted the interpretive nature of (...) modeling and participation in model construction as a knowledge-building process. We draw on literature from the history and philosophy of science in order to analyze the students’ interpretive actions as forms of epistemic and representational agency, constituting a construct we term disciplined interpretation. We demonstrate how students’ disciplined interpretative moves help to position them as owners of their own design decisions and their rights to interpret the phenomena they were modeling, data collected from those phenomena, and the scientific and computational models themselves. We present four extended episodes that characterize the nature of activity in the classroom and the development of students’ disciplined interpretations in terms of learning to recognize scientific patterns amid complex perceptual fields, and to represent them in ways that support sensemaking. (shrink)

Context: The advent of a general artificial intelligence mechanism that learns like humans do would represent the realization of an old and major dream of science. It could be achieved by an artifact able to develop its own cognitive structures following constructivist principles. However, there is a large distance between the descriptions of the intelligence made by constructivist theories and the mechanisms that currently exist. Problem: The constructivist conception of intelligence is very powerful for explaining how cognitive development takes (...) place. However, until now, no computational model has successfully demonstrated the underlying mechanisms necessary to realize it. In other words, the artificial intelligence (AI) community has not been able to give rise to a system that convincingly implements the principles of intelligence as postulated by constructivism, and that is also capable of dealing with complex environments. Results: This paper presents the constructivist anticipatory learning mechanism (CALM), an agent learning mechanism based on the constructivist approach of AI. It is designed to deal dynamically and interactively with environments that are at the same time partially deterministic and partially observable. CALM can model the regularities experienced in the interaction with the environment, on the sensorimotor level as well, as by constructing abstract or high-level representational concepts. The created model provides the knowledge necessary to generate the agent behavior. The paper also presents the coupled agent environment system (CAES) meta-architecture, which defines a conception of an autonomous agent, situated in the environment, embodied and intrinsically motivated. Implications: The paper can be seen as a step towards a computational implementation of constructivist principles, on the one hand suggesting a further perspective of this refreshing movement on the AI field (which is still too steeped in a behaviorist influence and dominated by probabilistic models and narrow applied approaches), and on the other hand bringing some abstract descriptions of the cognitive process into a more concrete dimension, in the form of algorithms. Constructivist content: The connection of this paper with constructivism is the proposal of a computational and formally described mechanism that implements important aspects of the subjective process of knowledge construction based on key ideas proposed by constructivist theories. (shrink)

Decision making often takes place in social environments where other actors influence individuals' decisions. The present article examines how advice affects individual learning. Five social learning models combining advice and individual learning‐four based on reinforcement learning and one on Bayesian learning‐and one individual learning model are tested against each other. In two experiments, some participants received good or bad advice prior to a repeated multioption choice task. Receivers of advice adhered to the advice, so (...) that good advice improved performance. The social learning models described the observed learning processes better than the individual learning model. Of the models tested, the best social learning model assumes that outcomes from recommended options are more positively evaluated than outcomes from nonrecommended options. This model correctly predicted that receivers first adhere to advice, then explore other options, and finally return to the recommended option. The model also predicted accurately that good advice has a stronger impact on learning than bad advice. One‐time advice can have a long‐lasting influence on learning by changing the subjective evaluation of outcomes of recommended options. (shrink)

Formal learning theory is an approach to the study of inductive inference that has been developed by computer scientists. In this paper, I discuss the relevance of formal learning theory to such standard topics in the philosophy of science as underdetermination, realism, scientific progress, methodology, bounded rationality, the problem of induction, the logic of discovery, the theory of knowledge, the philosophy of artificial intelligence, and the philosophy of psychology.

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their (...) steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use. (shrink)

Within the field of human‐computer interaction, the study of the interaction between people and computers has revealed many phenomena. For example, highly interactive devices, such as the Apple Macintosh, are often easier to learn and use than keyboard‐based devices such as Unix. Similarly, consistent interfaces are easier to learn and use than inconsistent ones. This article describes an integrated cognitive model designed to exhibit a range of these phenomena while learning task‐action mappings: action sequences for achieving simple goals, (...) such as opening a file in a word processor. The model, called TAL, is of a user who is familiar with the basic operations of a keyboard and mouse, but unfamiliar with the particular menu structures, words, and actions required to use the device. The model is constructed in Soar and employs a single set of architectural mechanisms. It exhibits behavior that captures human preference for consistent, interactive, and meaningful task‐action mappings. (shrink)

Cognitive Science, Volume 46, Issue 4, April 2022.

In the first year of life, infants' speech perception becomes attuned to the sounds of their native language. This process of early phonetic learning has traditionally been framed as phonetic category acquisition. However, recent studies have hypothesized that the attunement may instead reflect a perceptual space learning process that does not involve categories. In this article, we explore the idea of perceptual space learning by implementing five different perceptual space learning models and testing them on three (...) phonetic contrasts that have been tested in the infant speech perception literature. We reproduce and extend previous results showing that a perceptual space learning model that uses only distributional information about the acoustics of short time slices of speech can account for at least some crosslinguistic differences in infant perception. Moreover, we find that a second perceptual space learning model, which benefits from word‐level guidance. performs equally well in capturing crosslinguistic differences in infant speech perception. These results provide support for the general idea of perceptual space learning as a theory of early phonetic learning but suggest that more fine‐grained data are needed to distinguish between different formal accounts. Finally, we provide testable empirical predictions of the two most promising models and show that these are not identical, making it possible to independently evaluate each model in experiments with infants in future research. (shrink)

Margaret Morrison, (2015) Reconstructing Reality: Models, Mathematics, and Simulations. Oxford University Press, New York. -/- Scientific models, mathematical equations, and computer simulations are indispensable to scientific practice. Through the use of models, scientists are able to effectively learn about how the world works, and to discover new information. However, there is a challenge in understanding how scientists can generate knowledge from their use, stemming from the fact that models and computer simulations are necessarily incomplete representations, and partial descriptions, (...) of their target systems. In order to construct a model, one must make idealizations, approximations, and abstractions. Given these constraints in constructing models, there is a question of whether they can provide new insight into how real systems actually work. So, how is it that highly abstract models inform us about the nature of the world, and more specifically, how do they provide explanatory knowledge? In Reconstructing Reality: Models, Mathematics, and Simulations, philosopher Margaret Morrison undertakes this task of ... (shrink)

In February 2020, as covid-19 infections spread to more than fifty countries, public health officials needed to recommend how the public could protect themselves, balancing safety and urgency. But there was very little data since this novel virus had only been identified three months prior. How could public health officials decide with insufficient data? The multi-armed bandit problem of computer science offers adaptive decision-making procedures that can achieve both safety and urgency. These adaptive methods balance learning information (...) (exploring) with using information (exploiting), adjusting the balance toward learning when uncertainty is high (March 1991; Kaelbling et al. 1996). Related methods are already used in adaptive clinical trials for pharmaceuticals (Pallmann et al. 2018). But we still need to develop these methods for non-pharmaceutical interventions, as I will illustrate with a case study of public mask-wearing to reduce the spread of covid-19. Public health pronouncements impact future learning. (shrink)

A survey on computer use was conducted on 152 college students majoring in elementary education. The students used computers an average of 11.8 hours per week. The uses included academic uses such as writing papers and conducting research over the Internet, as well as leisure uses such as chatting, playing games, and cruising the Web. A strong correlation is seen between the number of hours a student spends on the computer for academics and the number of hours spent (...) on the computer for nonacademics. The students “waste” an average of 4.5 hours per week on the computer, which is less than half the time they spend watching television. (shrink)

The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences, first published in 2006, is the definitive introduction to this innovative approach to teaching, learning, and educational technology. In this significantly revised third edition, leading scholars incorporate the latest research to provide seminal overviews of the field. This research is essential in developing effective innovations that enhance student (...) class='Hi'>learning - including how to write textbooks, design educational software, prepare effective teachers, and organize classrooms. The chapters illustrate the importance of creating productive learning environments both inside and outside school, including after school clubs, libraries, and museums. The Handbook has proven to be an essential resource for graduate students, researchers, consultants, software designers, and policy makers on a global scale. (shrink)

Meta-learning is learning to learn, which includes the development of capacities to transfer what people learned in one specific domain to other domains. It facilitates finetuning learning parameters and setting priors for effective and optimal learning in novel contexts and situations. Recent advances in research on artificial intelligence have reported meta-learning is essential in improving and optimizing the performance of trained models across different domains. In this paper, I suggest that meta-learning plays fundamental roles (...) in practical wisdom and its cultivation within moral domains. I support my argument with findings from studies in psychology, neuroscience, and computer science. Moreover, I suggested concrete ideas for moral education to foster wisdom cultivation along with meta-learning with evidence from studies across various fields. Particularly, I underscored that exemplars, including role models and mentors, play fundamental roles in education. Finally, I discussed the potential implications of the introduction of the concept, meta-learning, to moral education addressing concerns and issues related to socio-cultural diversity. (shrink)

Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this approach's (...) capabilities, utilizing reinforcement learning with human feedback (RLHF). Current research initiatives aim to integrate neural networks with symbolic computing, introducing a new generation of hybrid computational models. (shrink)

An attentional-associative model (Schmajuk et al. 1996), previously evaluated against multiple sets of classical conditioning data, is applied to causal learning. In agreement with Mitchell et al.'s suggestion, according to the model associative learning can be a conscious, controlled process. However, whereas our model correctly predicts blocking following or preceding subadditive training, the propositional approach cannot account for those results.

It has been convincingly argued that computer simulation modeling differs from traditional science. If we understand simulation modeling as a new way of doing science, the manner in which scientists learn about the world through models must also be considered differently. This article examines how researchers learn about environmental processes through computer simulation modeling. Suggesting a conceptual framework anchored in a performative philosophical approach, we examine two modeling projects undertaken by research teams in England, both aiming (...) to inform flood risk management. One of the modeling teams operated in the research wing of a consultancy firm, the other were university scientists taking part in an interdisciplinary project experimenting with public engagement. We found that in the first context the use of standardized software was critical to the process of improvisation, the obstacles emerging in the process concerned data and were resolved through exploiting affordances for generating, organizing, and combining scientific information in new ways. In the second context, an environmental competency group, obstacles were related to the computer program and affordances emerged in the combination of experience-based knowledge with the scientists’ skill enabling a reconfiguration of the mathematical structure of the model, allowing the group to learn about local flooding. (shrink)

This commentary suggests: (1) experimentalists must expand their subject pools beyond university students; (2) the pollution created by deception would not be a problem if experimentalists fully used non-student subjects; (3) one-shot games remain important and repeated games should not ignore social learning; (4) economists need to take better control of context; and (5) using computers in experiments creates potential problems.

Recent events have highlighted large-scale systemic racial disparities in U.S. criminal justice based on race and other demographic characteristics. Although criminological datasets are used to study and document the extent of such disparities, they often lack key information, including arrestees’ racial identification. As AI technologies are increasingly used by criminal justice agencies to make predictions about outcomes in bail, policing, and other decision-making, a growing literature suggests that the current implementation of these systems may perpetuate racial inequalities. In this paper, (...) we argue that AI technologies should be investigated to understand how they recognize racial categories and whether they can be harnessed to fill missing race data. By bridging this gap, we can work towards better understanding racial inequalities in a wide range of contexts, most notably criminal justice. Using a multidisciplinary perspective, we rethink the design and methodology used in facial processing technology (FPT) based on supervised deep learning model (DLM) image classification. By modifying standard FPT pipelines to tackle multiple sources of DLM bias, we propose an experimental methodology based on ethical AI principles to generate binary (Black and White) racial categories using mugshots. We go beyond simply reporting DLM accuracies and address fundamental issues such as generalizability and interpretability by using a “self-auditing” approach. First, we evaluate the inference performances of 42 fine-tuned DLMs using unseen test images from the same dataset but subject to varying data augmentations. Next, to interpret and validate our methodological approach, we apply gradient-based saliency maps to assess the consistency of facial region relevance and attribution. Finally, drawing upon insights from three areas (computer science, sociology, and law), we investigate the efficacy of our DLM-based method as a tool for detecting racial inequalities in criminal justice. (shrink)

In this full review paper, the recent emerging trends in Computing Structures, Software Science, and System Applications have been reviewed and explored to address the recent topics and contributions in the era of the Software and Computing fields. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, framework, implemented approaches and techniques research projects in the areas of Software Technology & Automation, Networking, Systems, Computing Sciences and Software Engineering, Big Data and E-learning. Based on (...) this systematic review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area. (shrink)

This commentary is an elaboration on Schyns, Goldstone & Thibaut's proposal for flexible features in categorization in the light of three areas not explicitly discussed by the authors: connectionist models of categorization, computational learning theory, and constructivist theories of the mind. In general, the authors' proposal is strongly supported, paving the way for model extensions and for interesting novel cognitive research. Nor is the authors' proposal incompatible with theories positing some fixed set of features.

European Computing and Philosophy conference, 2–4 July Barcelona The Seventh ECAP (European Computing and Philosophy) conference was organized by Jordi Vallverdu at Autonomous University of Barcelona. The conference started with the IACAP (The International Association for CAP) presidential address by Luciano Floridi, focusing on mechanisms of knowledge production in informational networks. The first keynote delivered by Klaus Mainzer made a frame for the rest of the conference, by elucidating the fundamental role of complexity of informational structures that can be analyzed (...) on different levels of organization giving place for variety of possible approaches which converge in this cross-disciplinary and multi-disciplinary research field. Keynotes by Kevin Warwick about re-embodiment of rats’ neurons into robots, Raymond Turner on syntax and semantics in programming languages, Roderic Guigo on Biocomputing Sciences and Francesco Subirada on the past and future of supercomputing presented different topics of philosophical as well as practical aspects of computing. Vonference tracks included: Philosophy of Information (Patrick Allo), Philosophy of Computer Science (Raymond Turner), Computer and Information Ethics (Johnny Søraker and Alison Adam), Computational Approaches to the Mind (Ruth Hagengruber), IT and Cultural Diversity (Jutta Weber and Charles Ess), Crossroads (David Casacuberta), Robotics, AI & Ambient Intelligence (Thomas Roth-Berghofer), Biocomputing, Evolutionary and Complex Systems (Gordana Dodig Crnkovic and Søren Brier), E-learning, E-science and Computer-Supported Cooperative Work (Annamaria Carusi) and Technological Singularity and Acceleration Studies (Amnon Eden). (shrink)