Crisis requires society to renew itself, albeit in a disruptive way. The current Covid-19 pandemic is transforming ways of working, living, and relating to each other on a global level, suddenly and dramatically. This paper focuses on the field of education to show how higher education institutions are undergoing radical transformations driven by the need to digitalize education and training processes in record time with academics who lack innate technological capabilities for online teaching. The university system must strive to overcome (...) this situation to be competitive and provide high-quality education in a scenario of digital transformation, disruptive technological innovations, and accelerated change. To achieve these goals, this paper explains some barriers and challenges that universities encounter, as well as technological resources and methodologies they have used in the current scenario to transform higher education to face Covid-19 disruption. The discussion and conclusion synthesize significant insights that can be applied to the digitalization of education in the foreseeable future. (shrink)

In the research on literacy learning the concept of language awareness has come forward as a unifying framework for understanding the underlying knowledge that supports ability in reading and writing. Consensus is gathering around the idea that language awareness is an essential foundation. If subsequent work in this area confirms it, this factor may turn out to be the key cognitive-domain explanation for successful literacy learning in school (and for academic purposes in general). In this review we examine (...) two cross-cultural comparisons regarding this claim. The comparisons point to the need to examine cases that juxtapose contrasting conditions. Relevant contrasts place side by side examples that appear to be typical and examples that appear to be exceptional. Taking what appear on the surface as sharply diverging cases, how is access to requisite underlying competencies similar, and how different, from one instance to the other? (shrink)

Background This study used a scenario- and discussion-based approach to teach preclinical medical students the socio-philosophical aspects of psychiatry and qualitatively evaluated the learning outcomes in a medical humanities course in Taiwan. Methods The seminar session focused on three hypothetical psychiatry cases. Students discussed the cases in groups and were guided by facilitators from multiple disciplines and professions. At the end of the semester, students submitted a narrative report comprising their reflections on the cases and discussions. The authors utilized (...) content analysis to categorize students’ narratives into three facets, namely, the philosophical, social and individual. Results In total 163 preclinical medical students participated in the class; 150 of them mentioned the scenario-based lesson in their reports; 33.3% of these reports discussed the case at the philosophical dimension (n = 50), 45.3% at the social dimension (n = 68), and 26.6% at the individual dimension (n = 40). Four major themes emerged: (1) a psychiatric diagnosis has far-reaching consequences for an individual’s life, (2) the social structure affects how patients experience psychiatric disorders, (3) students related personal experience or those of friends and family to understand psychiatric disorders, and (4) medical humanities are of particular importance in psychiatric education. Conclusions This study demonstrated that the scenario-based discussions led by a multidisciplinary team of facilitators can benefit medical students with limited clinical experience to contemplate the socio-philosophical aspects of psychiatry. The authors suggest that this pedagogical model during preclinical education should be encouraged. (shrink)

We present a test of a revised version of the Second Language Linguistic Perception (L2LP) model, a computational model of the acquisition of second language (L2) speech perception and recognition. The model draws on phonetic, phonological and psycholinguistic constructs to explain a number of L2 learning scenarios. However, a recent computational implementation failed to validate a theoretical proposal for a learning scenario where the L2 has less phonemic categories than the native language (L1) along a given acoustic (...) continuum. According to the L2LP, learners faced with this learning scenario must not only shift their old L1 phoneme boundaries but also reduce the number of categories employed in perception. Our proposed revision to L2LP successfully accounts for this updating in the number of perceptual categories as a process driven by the meaning of lexical items, rather than by the learners’ awareness of the number and type of phonemes that are relevant in their new language, as the previous version of L2LP assumed. Results of our simulations show that meaning-driven learning correctly predicts the developmental path of L2 phoneme perception seen in empirical studies. Additionally, and to contribute to a long-standing debate in psycholinguistics, we test two versions of the model, with the stages of phonemic perception and lexical recognition being either sequential or interactive. Both versions succeed in learning to recognize minimal pairs in the new L2, but make diverging predictions on learners' resulting phonological representations. In sum, the proposed revision to the L2LP model contributes to our understanding of L2 acquisition, with implications for speech processing in general. (shrink)

The emerging field of synthetic biology, the designing and construction of biological parts, devices and systems for useful purposes, may simultaneously resolve some issues and raise others. In order to develop applications robustly and in the public interest, it is important to organize reflexive strategies of assessment and engagement in early stages of development. Against this backdrop, initiatives related to the concept of Responsible Research and Innovation have also appeared. This paper describes such an initiative: the construction of future (...) class='Hi'>scenarios to explore the plausibility and desirability of potential synthetic biology innovations. We guided teams of synthetic biology students who participated in the large international Genetically Engineered Machines competition, in constructing scenarios aimed at exploring the plausibility and desirability of potential synthetic biology innovations. In this paper we aim to examine to what extent, and how, constructing such future scenarios contributes to RRI. In order to do so, we conducted observations and interviews to understand what kind of learning and reflection was promoted by constructing the scenarios in terms of four dimensions, which are discussed prominently in the literature on RRI: anticipation, inclusion, reflexivity and responsiveness. While we focus on how constructing future scenarios can contribute to strengthening RRI at a project level, we also consider how far our experiment may foster RRI in the iGEM competition in general, and perhaps even inspire constructive collaboration between ‘social scientists’ and ‘natural scientists’ in the context of larger scientific research programmes. (shrink)

One of the key learning objectives in any health professional course is to develop ethical and judicious practice. Therefore, it is important to address how medical and pharmacy students respond to, and deal with, ethical dilemmas in their clinical environments. In this paper, we examined how students communicated their resolution of ethical dilemmas and the alignment between these communications and the four principles developed by Beauchamp and Childress. Three hundred and fifty-seven pharmacy and medical students (overall response rate=63%) completed (...) a questionnaire containing four clinical case scenarios with an ethical dilemma. Data were analysed using multiple methods. The findings revealed that 73% of the qualitative responses could be exclusively coded to one of the ‘four principles’ determined by the Beauchamp and Childress' framework. Additionally, 14% of responses overlapped between the four principles (multiple codes) and 13% of responses could not be coded using the framework. The subsequent subgroup analysis revealed different response patterns depending on the case being reviewed. The findings showed that when students are faced with challenging ethical dilemmas their responses can be aligned with the Beauchamp and Childress framework, although more contentious dilemmas involving issues of law are less easily categorised. The differences between year and discipline groups show students are developing ethical frames of reference that may be linked with their teaching environments and their levels of understanding. Analysis of these response patterns provides insight into the way students will likely respond in ‘real’ settings and this information may help educators prepare students for these clinical ethical dilemmas. (shrink)

Theoretical models are widely held as sources of knowledge of reality. Imagination is vital to their development and to the generation of plausible hypotheses about reality. But how can imagination, which is typically held to be completely free, effectively instruct us about reality? In this paper I argue that the key to answering this question is in constrained uses of imagination. More specifically, I identify make-believe as the right notion of imagination at work in modelling. I propose the first overarching (...) taxonomy of types of constraints on scientific imagination that enables knowledge of reality. And I identify two main kinds of knowledge enabled by models, knowledge of the imaginary scenario specified by models and knowledge of reality. (shrink)

One of the open questions in Bayesian epistemology is how to rationally learn from indicative conditionals (Douven, 2016). Eva et al. (Mind 129(514):461–508, 2020) propose a strategy to resolve this question. They claim that their strategy provides a “uniquely rational response to any given learning scenario”. We show that their updating strategy is neither very general nor always rational. Even worse, we generalize their strategy and show that it still fails. Bad news for the Bayesians.

Cross‐situational word learning (XSWL) tasks present multiple words and candidate referents within a learning trial such that word–referent pairings can be inferred only across trials. Adults encode fine phonological detail when two words and candidate referents are presented in each learning trial (2 × 2 scenario; Escudero, Mulak, & Vlach, ). To test the relationship between XSWL task difficulty and phonological encoding, we examined XSWL of words differing by one vowel or consonant across degrees of within‐learning (...) trial ambiguity (1 × 1 to 4 × 4). Word identification was assessed alongside three distractors. Adults finely encoded words via XSWL: Learning occurred in all conditions, though accuracy decreased across the 1 × 1 to 3 × 3 conditions. Accuracy was highest for the 1 × 1 condition, suggesting fast‐mapping is a stronger learning strategy here. Accuracy was higher for consonant than vowel set targets, and having more distractors from the same set mitigated identification of vowel set targets only, suggesting possible stronger encoding of consonants than vowels. (shrink)

Nurses are experiencing new ethical issues as a result of global developments and changes in health care. With health care becoming increasingly sophisticated, and countries facing challenges of graying population, ethical issues involved in health care are bound to expand in quantity and in depth. Blended learning rather as a combination of multiple delivery media designed to promote meaningful learning. Specifically, this study was focused on two questions: (1) the students’ satisfaction and attitudes as members of a scenario-based (...) learning process in a blended learning environment; (2) the relationship between students’ satisfaction ratings of nursing ethics course and their attitudes in the blended learning environment. In total, 99 senior undergraduate nursing students currently studying at a public nursing college in Taiwan were invited to participate in this study. A cross-sectional survey design was adopted in this study. The participants were asked to fill out two Likert-scale questionnaire surveys: CAAS (Case Analysis Attitude Scale), and BLSS (Blended Learning Satisfaction Scale). The results showed what students felt about their blended learning experiences — mostly items ranged from 3.27—3.76 (the highest score is 5). Another self-assessment of scenario analysis instrument revealed the mean scores ranged from 2.87—4.19. Nearly 57.8% of the participants rated the course ‘extremely helpful’ or ‘very helpful.’ This study showed statistically significant correlations (r = 0.43) between students’ satisfaction with blended learning and case analysis attitudes. In addition, results testified to a potential of the blended learning model proposed in this study to bridge the gap between students and instructors and the one between students and their peers, which are typical of blended learning, and to create meaningful learning by employing blended pedagogical consideration in the course design. The use of scenario instruction enables students to develop critical analysis and problem solving skills through active learning and social exchange of ideas. (shrink)

This paper critically examines Japan's recent drive to learn from the West. The paper first presents the historical setting and then goes on to develop future scenarios for Japan. The focus of the paper is learning and change following the collapse of the Japanese economic bubble, and the relevance of the US and Western models of corporate governance. The question it raises is what to change, and from which quadrant(s) of society this drive can be initiated and sustained. (...) The paper proposes various paths to learning and change. It recommends that the most challenging and rewarding path is the eclectic-innovative path, which requires active and non-cosmetic management of change. The paper concludes that the West is but one source of learning for Japan. Japan needs to learn from its mother civilisation, from within which fresh challenges will arise again. (shrink)

Research on authentic learning has been predominantly focussed on skills-based training: there is a paucity of research on models of authentic learning available for adaptation in the humanities undergraduate classroom. In this article, I will seek to address this gap by proposing that legal trials are ideal models for designing authentic learning scenarios in undergraduate teaching and learning contexts, with a specific focus on the humanities. First, I discuss why and how the structure of legal (...) trials can produce authentic learning environments. Second, I present an undergraduate classroom project that combined two disciplinary fields – Shakespearean drama and criminal law – in an effort to enhance student learning and engagement. I outline how the authentic learning scenario was implemented and evaluated and, finally, reflect on the barriers, challenges and potentially transformative effect of authentic learning environments on students and educators. This new intervention combines legal studies... (shrink)

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between (...) models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. (shrink)

“I beg of you, Doctor, please don’t let Dr. Moore see my mother again. My sisters and I do not want him talking with us anymore.”So concluded my first conversation with Samir Zadeh. Our meeting had been purely accidental; as I walked onto the elevator going down from the Surgical ICU, he and one of his sisters were already on, coming down from another one of our hospital’s ICUs, from a floor above. Samir was a man in his early 50s, (...) and his sister, whom I would learn was actually a few years older, appeared younger than he. They were talking quietly when the doors opened, but as is often the case in elevators, especially in a hospital, as soon as I entered, their conversation stopped. We rode down to the bottom floor in silence. As we exited, I let them go first, a courtesy I had been taught by my parents, and they both thanked me, in the usual social way, and walked out into the lobby and then out of the building. I walked the same way.It was when we were about 25 feet from the building that Mr. Zadeh and his sister stopped, so that I caught up to them. As I did, Mr. Zadeh, a large man, maybe 6 feet tall and 250 pounds, with thinning hair and a well-groomed mustache, turned and said my name aloud, “Dr. Finder, may you be so kind as to talk?”. (shrink)

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural (...) networks. To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft. (shrink)

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship (...) between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image, is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors. (shrink)

Purpose – Tabletop online role-playing games enable active learning appropriate for different ages and learner capabilities. They have also been implemented in computer and engineering ethics courses. The paper aims to discuss these issues. Design/methodology/approach – This paper presents the experience of implementing role-playing in several courses embedded in Web 2.0 environment, with an intention to confront complex and sometimes mutually conflicting concepts, and integrate them into a whole. Findings – Typical examples introducing two basic scenarios representing individual (...) and collaborative learning scripts are presented together with the detailed analysis how the games were performed, the effort to participate in, and to maintain them. Particular attention is paid to student feedback. Originality/value – The paper concludes with the basic findings of the effects of role-playing in current learning computer ethics and social responsibilities courses, and recommendations for future implementation of similar asynchronous learning online activities in order to increase their academic value and prepare students for their forthcoming professional integration. (shrink)

In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate to (...) update the background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios. (shrink)

We provide an overview of a transdisciplinary project about sustainable forest management under climate change. Our project is a partnership with members of the Menominee Nation, a Tribal Nation located in northern Wisconsin, United States. We use immersive virtual experiences, translated from ecosystem model outcomes, to elicit human values about future forest conditions under alternative scenarios. Our project combines expertise across the sciences and humanities as well as across cultures and knowledge systems. Our management structure, governance, and leadership behaviors (...) have both fostered and constrained our work and must be continuously responsive to changing group dynamics. Our project presents opportunities for substantial contributions to society, including insights and knowledge about complementary ways of knowing, skills training, and professional development, and opportunities for reflexive learning about effective transdisciplinary, translational, and transformative scientific processes. (shrink)

This study aims to open a critical scenario on the current paradigm of reference for teaching sports techniques by pointing to a methodological perspective that is more in keeping with the theoretical reference framework of the ecological-dynamic approach. Currently, the Constraints-led Approach is considered very useful for teaching sports techniques; however, the transition from pure theory to practice is not so simple and consistent with the heuristic learning paradigm. The purpose is to highlight, starting from the theoretical lines, the (...) methodological applications of the different ways of applying the reference paradigm, analyzing the contrasts of the significant aspects and the uniqueness and unrepeatability of heuristic learning. For this purpose, an in-depth review of the scientific literature was conducted, highlighting the main critical points of the approach, also defined as nonlinear, with the consequent elaboration of operational proposals for the various macro-families of sports. (shrink)

Just over a decade ago, I began teaching medical students in the required preclinical course ethics and professionalism. The point of the course was to introduce basic ethical and professional norms through a small number of large group sessions, but mostly small group tutorials of 10 or 12 students engaging in weekly sessions combining readings from the literature and case scenarios highlighting real-life ethical tensions they either had, or would most likely, encounter in the future. The students wrote perceptively (...) and thoughtfully each week, and class was topical and lively. Some students went on to complete longer essays for a school-wide ethics competition and even to win. By all accounts a resounding... (shrink)

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is (...) strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H , which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words. Key words: lexicon bootstrapping; cross-situational learning; supervised learning; random occupancy problems. (shrink)

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is (...) strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words. Key words: lexicon bootstrapping; cross-situational learning; supervised learning; random occupancy problems. (shrink)

Neurodegenerative Parkinson's Disease is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite and intelligent Motor Assessment Tests, produced within (...) the i-PROGNOSIS European project, are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 and Stage 3 produced from their interaction with PGS/iMAT are analyzed. Five feature vector scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor, Support Vector Machines, and Random Forest, to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation method is adopted for testing the classification performance. The experimental results show that a high classification accuracy is achieved from both data sources, justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions. (shrink)

The paper develops a semi-formal model of learning which modifies the traditional paradigm of artificial neural networks, implementing deep learning by means of a key insight borrowed from the works of Marvin Minsky: the so-called Principle of Non-Compromise. The principle provides a learning mechanism which states that conflicts in the processing of data to be integrated are a mark of unreliability or irrelevance; hence, lower-level conflicts should lead to higher-level weight-adjustments. This internal mechanism augments the external mechanism (...) of weight adjustment by back-propagation, which is typical for the standard models of machine learning. The text is structured as follows: (§1) opens the discussion by providing an informal overview of real-world decision-making and learning; (§2) sketches a typology of decision architectures: the individualistic approach of classical decision theory, the general aggregation mechanism of social choice theory, the local aggregation mechanism of agent-based modeling, and the intermediate hierarchical model of Marvin Minsky's “Society of Mind”; (§3) sketches the general outline of ANIMA – a new model of decision-making and learning that borrows insights from Minsky's informal exposition; (§4) is the bulk of the paper; it provides a discussion of a toy exemplification of ANIMA which lets us see the Principle of Non-Compromise at work; (§5) lists some possible scenarios for the evolution of a model of this kind; (§6) is the closing section; it discusses some important differences between the way ANIMA was construed here and the typical formal rendering of learning by means of artificial neural networks and deep learning. (shrink)

You can find countless books dedicated to student success and resilience. But what about the faculty? What do we do to help college faculty cultivate their professional and personal growth and resilience? During more than three decades as a teacher and workshop facilitator, Steve Piscitelli noticed that many educators can become isolated from their colleagues and their larger institutional culture. They become "islands" disconnected from the potential power of the teaching and learning community. That isolation can affect teaching efficacy (...) and resilience. To encourage educators to embrace the power of collaboration, Piscitelli outlines the "7Rs" for success: relationships, relevance, resources, rainbows, responsibility, reflection, and resilience. He created thirty-seven scenarios, based on real life issues. Each shows how to apply these seven themes to better adapt to professional and personal challenges educators face. The scenarios raise questions about the classroom, work-life integration, collaboration with coworkers, and interpersonal relationships with supervisors. Every teaching and learning situation-every one-gives us the opportunity to take stock of and strengthen our own resilience level. And we do not have to do it alone. That is the beauty of our calling! And that is the strength of this book. It recognizes the power of a collegial teaching and learning community. Piscitelli supplements the information and scenarios with links to brief thought-provoking videos (approximately sixty seconds each) that encapsulate major themes and discussion points. More information at www.stevepiscitelli.com. I think it's brilliant. I often feel like I'm on an island as a professor. -Ashli Archer, Professor I like how you let people know that even though you have developed a table of contents, they might want to skip around. The flexibility will be very appealing.-Erin Hoag, VP of Financial Services and Strategic Development, Innovative Educators This book will be a welcome resource! -Todd Stanislav, Director, Faculty Center for Teaching and Learning Wow, you are covering some controversial topics! This book is going to be different and invaluable!-Pam Ranallo, Co-Owner, Innovative Educators Your scenario addressing the issue of favoritism vs. jealousy demonstrates amazing insight and ability to address the "hard" topics. Bravo!-Karen Armstrong, Career Counselor I think this is a wonderful idea and have never seen anything along these lines before!-Mary Boone Treuting, Director of Center for Academic Success and the Center for Teaching Excellence. (shrink)

Self‐driving cars hold out the promise of being much safer than regular cars. Yet they cannot be 100% safe. Accordingly, they need to be programmed for how to deal with crash scenarios. Should cars be programmed to always prioritize their owners, to minimize harm, or to respond to crashes on the basis of some other type of principle? The article first discusses whether everyone should have the same “ethics settings.” Next, the oft‐made analogy with the trolley problem is examined. (...) Then follows an assessment of recent empirical work on lay‐people's attitudes about crash algorithms relevant to the ethical issue of crash optimization. Finally, the article discusses what traditional ethical theories such as utilitarianism, Kantianism, virtue ethics, and contractualism imply about how cars should handle crash scenarios. The aim of the article is to provide an overview of the existing literature on these topics and to assess how far the discussion has gotten so far. (shrink)

A multidisciplinary faculty committee designed a curriculum to shape biomedical graduate students into researchers with a high commitment to professionalism and social responsibility and to provide students with tools to navigate complex, rapidly evolving academic and societal environments with a strong ethical commitment. The curriculum used problem-based learning (PBL), because it is active and learner-centred and focuses on skill and process development. Two courses were developed: Scientific Professionalism: Scientific Integrity addressed discipline-specific and broad professional norms and obligations for the (...) ethical practice of science and responsible conduct of research (RCR). Scientific Professionalism: Bioethics and Social Responsibility focused on current ethical and bioethical issues within the scientific profession, and implications of research for society. Each small-group session examined case scenarios that included: (1) learning objectives for professional norms and obligations; (2) key ethical issues and philosophies within each topic area; (3) one or more of the RCR instructional areas; and (4) at least one type of moral reflection. Cases emphasised professional standards, obligations and underlying philosophies for the ethical practice of science, competing interests of stakeholders and oversight of science (internal and external). To our knowledge, this is the first use of a longitudinal, multi-semester PBL course to teach scientific integrity and professionalism. Both faculty and students endorsed the active learning approach for these topics, in contrast to a compliance-based approach that emphasises learning rules and regulations. (shrink)

As the development of artificial intelligence technology, the deep-learning -based Virtual Reality technology, and DL technology are applied in human-computer interaction, and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based (...) on the convolutional neural network algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically. (shrink)

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. In (...) the second scenario, a comparable AI model hybridized with genetic algorithm as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient and lowest value root mean square error. However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model. (shrink)

Causal and predictive learning research often employs intuitive and familiar hypothetical scenarios to facilitate learning novel relationships. The allergist task, in which participants are asked to diagnose the allergies of a fictitious patient, is one example of this. In such studies, it is common practice to ask participants to ignore their existing knowledge of the scenario and make judgments based only on the relationships presented within the experiment. Causal judgments appear to be sensitive to instructions that modify (...) assumptions about the scenario. However, the extent to which prior knowledge continues to affect competition for associative learning, even after participants are instructed to disregard it, is unknown. To answer this, we created a cue competition design that capitalized on prevailing beliefs about the allergenic properties of various foods. High and low allergenic foods were paired with foods moderately associated with allergy to create two compounds; high + moderate and low + moderate. We expected high allergenic foods to produce greater competition for associative memory than low allergenic foods. High allergenic foods may affect learning either because they generate a strong memory of allergy or because they are more salient in the context of the task. We therefore also manipulated the consistency of the high allergenic cue-outcome relationship with prior beliefs about the nature of the allergies. A high allergenic food that is paired with an inconsistent allergenic outcome should generate more prediction error and thus more competition for learning, than one that is consistent with prior beliefs. Participants were instructed to either use or ignore their knowledge of food allergies to complete the task. We found that while participants were able to set aside their prior knowledge when making causal judgments about the foods in question, associative memory was weaker for the cues paired with highly allergenic foods than cues paired with low allergenic foods regardless of instructions. The consistency manipulation had little effect on this result, suggesting that the effects in associative memory are most likely driven by selective attention to highly allergenic cues. This has implications for theories of causal learning as well as the way causal learning tasks are designed. (shrink)

This review essay engages critically with Heikki Patomäki's The Political Economy of Global Security: War, Future Crises, and Changes in Global Governance. The book is built around the hypothesis that the current ‘era of Neoliberalism’ shares many similarities to the era of the ‘new imperialism’ of the late nineteenth century, ending, catastrophically, in World War I and the Great Depression. Patomäki undertakes this comparison by focusing on the principal long-term historical processes, structures, tendencies and contradictions that may be responsible for (...) the similarities between these two eras. To do so Patomäki constructs an explanatory model of the underlying causal processes and mechanisms involved. The implications are ominous and urgent. The essay uses a close examination of this exemplary case of critical realism in social science to discuss critically its historical explanations, the comparative analysis it makes, and especially the question of agency as theorised in the author's ‘future scenarios’. (shrink)

An intelligent model of the incremental capacity curve of an automotive lithium-ferrophosphate battery is presented. The relative heights of the two major peaks of the IC curve can be acquired from high-current discharges, thus enabling the state of health estimation of the battery while the vehicle is being operated and in certain cases, aging mechanisms can be suggested. Our model has been validated using a large dataset representing different degradation scenarios, obtained from a recently available open-source database.

If I lose my key in Canada, for instance, and I search for it in the United Kingdom, how long will I take to find it? This paper argues that problems in education are caused by non-professional teachers who are employed when trained teachers move in search of promotion friendly activities or financially rewarding duties. This shift of focus means that policy makers in education act without adequate professional guidance. The problems in education, therefore, result from demands made on mainstream (...) education based on misconceptions about what education can offer. It is argued that the implementation of e-learning in education faces the risk of developing on the basis of unproven theories. This scenario increasingly sees the replacement of formal education activities in institutions of learning with non-formal and informal education practices. Given that the contents and influences of non-formal and informal education are not under the control of the teacher, the experiences that learners bring to education settings are increasingly difficult to manage. The paper proposes that by integrating e-learning in teacher education and rewarding 'good teaching', there is a potential for a successful e-learning revolution in education. (shrink)

If I lose my key in Canada, for instance, and I search for it in the United Kingdom, how long will I take to find it? This paper argues that problems in education are caused by non‐professional teachers who are employed when trained teachers move in search of promotion friendly activities or financially rewarding duties. This shift of focus means that policy makers in education act without adequate professional guidance. The problems in education, therefore, result from demands made on mainstream (...) education based on misconceptions about what education can offer. It is argued that the implementation of e‐learning in education faces the risk of developing on the basis of unproven theories. This scenario increasingly sees the replacement of formal education activities in institutions of learning with non‐formal and informal education practices. Given that the contents and influences of non‐formal and informal education are not under the control of the teacher, the experiences that learners bring to education settings are increasingly difficult to manage. The paper proposes that by integrating e‐learning in teacher education and rewarding ‘good teaching’, there is a potential for a successful e‐learning revolution in education. (shrink)

The study aims to explore the roles of Massive Open Online Courses based on deep learning in college students’ English grammar teaching. The data are collected using a survey. After the experimental data are analyzed, it is found that students have a low sense of happiness and satisfaction and are unwilling to practice oral English and learn language points in English learning. They think that college English learning only meets the needs of CET-4 and CET-6 and does (...) not take it as the ultimate learning goal. After the necessity and problems in English grammar teaching are discussed, the advantages of flipped classrooms of MOOCs are discussed in English grammar teaching. A teaching platform is constructed to study the foreign language teaching mode under MOOCs, and classroom teaching is combined with the advantages of MOOCs following the principle of “teaching students according to their personalities” to improve the listening, speaking, reading, writing, and translation skills of foreign language majors. The results show that high-quality online teaching resources and the deep learning-based teaching environment can provide a variety of interactive tools, by which students can communicate with their peers and teachers online. Sharing open online communication, classroom discussion, and situational simulation can enhance teachers’ deep learning ability, like the ability to communication and transfer thoughts. Constructivism with interaction as the core can help students grasp new knowledge easily. Extensive communication and interaction are important ways for learning and thinking. The new model provides students with profound learning experience, expands the teaching resources of MOOCs around the world, and maximizes the interaction between online and offline teachers and students, making knowledge widely rooted in the campus and realizing the combination of online resources and campus classroom teaching. Students can learn the knowledge through autonomous learning and discussion before class, which greatly broadens the learning time and space. In the classroom and after class, the internalization and sublimation of knowledge are completed through group cooperation, inquiry learning, scenario simulation, display, and evaluation, promoting students to know about new knowledge and highlighting the dominant position of students. (shrink)

Any first step in organisational adaptation starts with individuals’ responses and willingness (or otherwise) to change an aspect of themselves given the transcontextual settings in which they are operating (Bateson in Small arcs of larger circles: framing through other patterns, Triarchy Press, Axminster, 2018). This research explores the implications for organisational adaptation strategies when Artificial Intelligence (AI) is being embedded into the ecology of the organisation, and when employees have a dominant fixed or growth mindset (Dweck in Mindset: changing the (...) way you think to fulfil your potential. Robinson, London, 2017). Research participants were supplied with a single scenario based in 2030, where—as a result of Artificial Intelligence technology implementation—employees were going to be displaced. Using Torbert’s (Organizational wisdom and executive courage, New Lexington Press, San Francisco, 1998) ‘first, second and third person’ research theory, participants were asked to independently review their thoughts, sense, and image of the future from a fixed mindset position (considered to be the worst case), then from a growth mindset perspective (best case), and then do the same collectively. Five key findings are outlined which support the principle that having a growth mindset is a key component of adaptive capacity and futures literacy. The five key findings conclude that AI adaptation processes need to include compassion and authenticity, embodiment, fundamental needs and motivations, mutual learning and considering what lies beyond the edges of the organisation (Bateson in Small arcs of larger circles: framing through other patterns, Triarchy Press, Axminster, 2018). (shrink)

Resumen: El objetivo principal de este escrito es proponer un papel propedéutico de las intuiciones racionales, a fin de defender una propuesta híbrida entre el acceso epistémico a priori y a posteriori. Las intuiciones racionales deben ser informadas empíricamente, y descartadas cuando hay pruebas contra ellas, cuando la evidencia empírica muestra que la intuición racional es errada, por lo que cualquier creencia guiada por ella también lo sería. Se sostiene que las intuiciones racionales cumplen un importante rol propedéutico: ayudan a (...) comprender diferentes escenarios modales a través de ejemplificaciones contrafácticas. Esto no se opone a la variación cultural de intuiciones, por ello se propone un confiabilismo intuicional variable. Esta propuesta comprende a las intuiciones como falibles y como no conductoras de verdad: no es el objetivo de las intuiciones rastrear la verdad de alguna proposición, sino ayudar a comprender los escenarios hipotéticos que pueden ser relevantes en diferentes áreas.: The main target of this paper is to propose a preparatory learning role of rational intuitions, in order to defend a hybrid proposal between a priori and a posteriori epistemic access. Rational intuitions must be informed empirically, and discarded when there is evidence against them, when empirical evidence shows that rational intuition is wrong, so any belief guided by it would also be wrong. It is argued that rational intuitions play an important propaedeutic role: they help to understand different modal scenarios through counterfactual exemplifications. This is not opposed to the cultural variation of intuitions, therefore a variable intuitional reliability is proposed. This proposal includes intuitions as fallible and as not truth conducive: it is not the goal of intuitions to trace the truth of a proposition, but to help understand the hypothetical scenarios that may be relevant in different areas. (shrink)

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse’s Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces (...) circularity, rendering it incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives’ ability to shape a just digital future and diminishes their fundamental epistemic agency. (shrink)

This study presents the first assessment of how an approach based on meeting fundamental human needs can assist regional planning. It uses the Human-scale Development methodology, based on fundamental human needs as a theoretical and methodological framework for scenario building. It offers a structured approach on how non-monetary values and practices (i.e. satisfiers or ways to satisfy needs) can help to open up the planning process, highlighting a regional conflict. The study presents three dimensions of needs to address planning challenges. (...) The data is taken from a case study of a deliberative process for regional planning in Western Europe. The relevance lies in the ways we can learn from individual values and practices, that when shared with others, (1) help to diagnose behaviours and trends toward environmentalism; (2) foster listening and understanding of people's sameness and differences that reduce conflict; and (3) provide a structured tool which predicts society dynamics and develops integrated solutions that facilitate sustainable regional development. (shrink)

In several of his papers, Charles S. Peirce illustrates processes of interpreting and understanding signs by examples from second language vocabulary teaching and learning. The insights conveyed by means of these little pedagogical scenarios are not meant as contributions to the psychology of second language learning, but they aim at elucidating fundamental semiotic implications of knowledge acquisition in general. Peirce's semiotic premise that a well-understood sign is one that represents an object and creates an interpretant is essential (...) to the understanding of how new words and signs in general can be taught and learned. The article argues that Peirce's theory of the object of the sign, especially of the necessity of collateral experience of the object of a sign, can help to understand the riddle posed by of the Meno paradox of the impossibility of learning what we do not yet know. It examines the semiotic implications of the didactic methods of teaching and learning through translation, ostension, mental and real images, as well as metacognition, and it shows how icons, indices, and symbols are essential to learning new words. (shrink)

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

Background Ethical dilemmas that arise in the clinical setting often require the collaboration of multiple disciplines to be resolved. However, medical and nursing curricula do not prioritize communication among disciplines regarding this issue. A common teaching strategy, problem-based learning, could be used to enhance communication among disciplines. Therefore, a university in southern Taiwan developed an interprofessional ethics education program based on problem-based learning strategies. This study described tutors’ experience teaching in this program. Aim To explore the phenomenon of (...) teaching and learning in interprofessional ethics education for medical and nursing students from the perspectives of tutors. Design Phenomenological qualitative research. Methods Medical and nursing students completed a 6-week interprofessional ethics education program moderated by either physician or nurse tutors. At the conclusion of the ethics education program, all 14 tutors were invited to participate in focus group interviews. Among them, six tutors (three nursing tutors and three physician tutors) participated in additional individual interviews. All of the contents from the focus group interviews and individual interviews were recorded and transcribed. Using the phenomenological approach, the phenomenon of teaching and learning in interprofessional ethics education were generated. Ethical consideration The study was approved by the Institutional Review Board. Findings Three themes emerged from the tutors' teaching perspectives, including the instructor’s motivation to teach, the use of narrative case scenarios, and the emphasis on improving interprofessional ethics communication. Discussion Problem-based learning creates an interprofessional communication platform in interprofessional ethics education. The phenomenon of value convergence between tutors and students, between different students’ professions, and between different students’ professional maturities is observed. Conclusion Problem-based learning is an effective teaching strategy for creating a communication platform for interprofessional ethics education. Ethic curriculum should emphasize motivating instructor, use narrative case scenarios, and focus on interprofessional communication. (shrink)

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense mission planning is described as a sequential decision-making problem and formalized as a Markov decision (...) process. Then, the SEAD intelligent planning model based on the proximal policy optimization algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy. (shrink)

In the present scenario of globalisation, knowledge has become the prime factor of production for competitive advantage. This calls for acquisition and utilisation of knowledge for innovation and technical change on a constant basis, which is only possible in a ‘learning organisation’. Innovative activities of a learning organisation are influenced by three main factors: (1) internal learning; (2) external learning; and (3) the innovation strategies decided upon by the enterprise management. An assumption has been made that, (...) particularly in developing countries, absorption and adaptation of technologies, i.e. indigenisation, take place through a process of ‘learning by doing’. Taking this into consideration, this paper focuses on a few case studies carried out at NISTADS, New Delhi, India, on small enterprises in the formal as well as traditional sectors, highlighting the learning process in an organisational context and how it brings in innovation and technological change at enterprise level. The study demonstrates that the learning environment in an organisational context is an indispensable process to be innovative and building up capabilities for technological change. This in turn also calls for strong networking of the enterprises with academia, R&D institutions and other enterprises, to create knowledge clusters. This builds up a strong case for a network approach of learning organisations not only at the regional level but also at the cross-cultural level for constant innovation and technical change. (shrink)

Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary (...) class='Hi'>learning. The model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation. (shrink)