Higher education institutions are increasingly relying on learning analytics to collect voluminous amounts of data ostensibly to inform student learning interventions. The use of learning analytics, however, can result in a tension between the Council for the Advancement of Standards in Higher Education (CAS) principles of autonomy and non-malfeasance on the one hand, and the principle of beneficence on the other. Given the complications around student privacy, informed consent, and data justice in addition to the (...) potential to do harm, many current practices around learning analytics within higher education can be considered to be in violation of CAS standards. This paper aims to explore this tension in greater detail and argues that the student voice offers a promising way to ensure that students’ autonomy is respected, harm is reduced, and that higher education institutions can still fulfil the principle of beneficence. (shrink)

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries (...) between information used for assessment and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. -/- We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods. (shrink)

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however (...) defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students. (shrink)

Education at all levels is increasingly augmented and enhanced by data mining and analytics, catalysed by the growing prevalence of automated distance learning. With an unprecedented capacity to scale both horizontally and vertically, data mining and analytics are set to be a transformative part of the future of education. We reflect on the assumptions behind data mining and the potential consequences of learning analytics, with reference to an issue brief prepared for the U.S. Department of (...) Education entitled Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics. We argue that the associated gains conceal subtle, but important risks. Data-ism, an underpinning paradigm, assigns unjustified veracity to data-driven science and the application of personalised analytics may compromise individual privacy, agency and inventiveness. This holds serious ethical implications, particularly when considering the impact on minors, rendering wholesale adoption premature. (shrink)

Research has shown that the construction of visual representations may have a positive effect on cognitive skills, including argumentation. In this paper we present a study on learning argumentation through computer-supported argument diagramming. We specifically focus on whether students, when provided with an argument-diagramming tool, create better diagrams, are more motivated, and learn more when working with other students or on their own. We use learning analytics to evaluate a variety of student activities: pre and post questionnaires (...) to explore motivational changes; the argument diagrams created by students to evaluate richness, complexity and completion; and pre and post knowledge tests to evaluate learning gains. (shrink)

The paper is concerned with the psychological relevance of a logical model for deductive reasoning. We propose a new way to analyze logical reasoning in a deductive version of the Mastermind game implemented within a popular Dutch online educational learning system (Math Garden). Our main goal is to derive predictions about the difficulty of Deductive Mastermind tasks. By means of a logical analysis we derive the number of steps needed for solving these tasks (a proxy for working memory load). (...) Our model is based on the analytic tableaux method, known from proof theory. We associate the difficulty of Deductive Mastermind game-items with the size of the corresponding logical trees obtained by the tableaux method. We derive empirical hypotheses from this model. A large group of students (over 37 thousand children, 5–12 years of age) played the Deductive Mastermind game, which gave empirical difficulty ratings of all 321 game-items. The results show that our logical approach predicts these item ratings well, which supports the psychological relevance of our model. (shrink)

In this paper I examine two passages in Aristotle's 'Analytics' that deal with the topics of inductive knowledge, in general, and with the specific issue of "simultaneous learning", the grasping of two distinct, new pieces of information in a single act of thought. Interpreters have generally followed the treatment of these passages by Ross and Barnes, but difficulties have recently been raised for this interpretation, especially by Gifford. I attempt to deal with these difficulties and to show that (...) the problems with the received interpretation are not so great as to render it untenable. In doing so I develop an account of Aristotelian epagoge that Lesher, Hamlyn, and McKirahan originated, and show how this account enhances our understanding of the process by which, according to Aristotle, knowledge is acquired and applied. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to (...) investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

David Bronstein sheds new light on Aristotle's Posterior Analytics--one of the most important, and difficult, works in the history of western philosophy--by arguing that it is coherently structured around two themes of enduring philosophical interest: knowledge and learning. He argues that the Posterior Analytics is a sustained examination of scientific knowledge, an elegantly organized work in which Aristotle describes the mind's ascent from sense-perception of particulars to scientific knowledge of first principles. Bronstein goes on to highlight Plato's (...) influence on Aristotle's text, and argues against current orthodoxy that Aristotle is committed to the Socratic Picture of inquiry, according to which one should seek what a thing's essence is before seeking its demonstrable attributes and their causes. (shrink)

Iowa Gambling Task (IGT) is one of the most widely used tools to assess economic decision-making. However, the research tradition on aging and the Iowa Gambling Task (IGT) has been mainly focused on the overall performance of older adults in relation to younger or clinical groups, remaining unclear whether older adults are capable of learning along the task. We conducted a meta-analysis to examine older adults’ decision-making on the IGT, to test the effects of aging on reversal learning (...) (45 studies) and to provide normative data on total and block net scores (55 studies). From the accumulated empirical evidence, we found an average total net score of 7.55 (± 25.9). We also observed a significant reversal learning effect along the blocks of the IGT, indicating that older adults inhibit the prepotent response towards immediately attractive options associated with high losses, in favor of initially less attractive options associated with long-run profit. During block 1, decisions of older adults led to a negative gambling net score, reflecting the expected initial pattern of risk-taking. However, the shift towards more safe options occurred between block 2 (small-to-medium effect size) and blocks 3, 4, 5 (medium-to-large effect size). These main findings highlight that older adults are able to move from the initial uncertainty, when the possible outcomes are unknown, to decisions based on risk, when the outcomes are learned and may be used to guide future adaptive decision-making. (shrink)

Readings, topics, and suggestions for a course on the analytic/synthetic distinction.

The dropout rate among Spanish university students is very high compared to the European mean, creating a pressing need for the introduction of policies and programmes aimed at increasing rates of persistence. In this article, we study this problem by combining students? perceived learning outcomes with their dropout intentions, and we propose a research model that considers subjective factors that might impact this decision. The model is estimated for two degree courses: Business Administration and Nursing. The estimation method uses (...) structural equations based on the partial least squares algorithm. This allows the construction of indices for the variables of interest, enabling us to make comparisons between courses and over time. To reduce dropout intentions, efforts need to be focused on obtaining better cognitive outcomes, as well as on achieving a higher level of student satisfaction with their university experience. (shrink)

Reasons underlying retractions of papers authored by the Iran-affiliated highly cited researchers have not been documented. Here, we report that 229 of the Iran-affiliated researchers were listed by the Clarivate Analytics as HCRs. We investigated the Retraction Watch Database and found that, in total, 51 papers authored by the Iran-affiliated HCRs were retracted from 2006 to 2019. Twenty-three of the 229 HCRs had at least one paper retracted. One of the listed HCRs had 22 papers retracted; 14 of the (...) 23 had only one paper retracted. Among the 51 retracted papers, three had been authored by two female authors. Eight retracted papers had international co-authorships. The shortest and longest times from publication to retraction were 20 and 2610 days, respectively. Of the 51 papers, 43 had a single reason for retraction, whereas eight had multiple reasons. Among the 43 papers, 23 were retracted due to fake peer-review, eight were duplications, six had errors, four had plagiarism, and two were labelled as “limited or no information.” Duplication of data, which is easily preventable, amounted to 27%. Any publishing oversight committed by an HCR may not be tolerated because they represent the stakeholders of the scientific literature and stand as role-models for other peer researchers. Future policies supporting the Iranian academia should radically change by implementation of educational and awareness programs on publishing ethics to reduce the rate of retractions in Iran. (shrink)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium (...) is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

The rise of learning analytics, the application of complex metrics developed to exploit the proliferation of ‘Big Data’ in educational work, raises important moral questions about the nature of what is measurable in education. Teachers, schools and nations are increasingly held to account based on metrics, exacerbating the tendency for fine-grained measurement of learning experiences. In this article, the origins of learning analytics ontology are explored, drawing upon core ideas in the philosophy of computing, such (...) as the general definition of information and the information-theoretic account of knowledge. Drawing upon a reading of Descartes Meditatio II, which extends the phenomenology of Jean-Luc Marion into a pedagogy of intentionality, the article identifies a fundamental incompatibility between the subjective experience of learning and the information-theoretic account of knowledge. Human subjects experience and value their own information incommensurably with the ways in which computers measure and quantify information. The consequences of this finding for the design of online learning environments, and the necessary limitations of learning analytics and measurement are explored. (shrink)

Learning Management System (LMS) platforms provide a wealth of information on the learning patterns of students. Learning Analytics (LA) techniques permit the analysis of the logs or records of the activities of both students and teachers on the on-line platform. The learning patterns differ depending on the type of Blended Learning (B-Learning). In this study, we analyse: (1) whether significant differences exist between the learning outcomes of students and their learning patterns (...) on the platform, depending on the type of B-Learning [Replacement blend (RB) vs. Supplemental blend (SB)]; (2) whether a relation exists between the metacognitive and the motivational strategies (MS) of students, their learning outcomes and their learning patterns on the platform. The 87,065 log records of 129 students (69 in RB and 60 in SB) in the Moodle 3.1 platform were analyzed. The results revealed different learning patterns between students depending on the type of B-Learning (RB vs. SB). We have found that the degree of blend, RB vs. SB, seems to condition student behavior on the platform. Learning patterns in RB environments can predict student learning outcomes. Additionally, in RB environments there is a relationship between the learning patterns and the metacognitive and (MS) of the students. (shrink)

Background: Differential learning is a motor learning method characterized by high amounts of variability during practice and is claimed to provide the learner with a higher learning rate than other methods. However, some controversy surrounds DL theory, and to date, no overview exists that compares the effects of DL to other motor learning methods.Objective: To evaluate the effectiveness of DL in comparison to other motor learning methods in the acquisition and retention phase.Design: Systematic review and (...) exploratory meta-analysis.Methods: PubMed, Web of Science, and Google Scholar were searched until February 3, 2020. To be included, studies had to be experiments where the DL group was compared to a control group engaged in a different motor learning method, studies had to describe the effects on one or more measures of performance in a skill or movement task, and the study report had to be published as a full paper in a journal or as a book chapter.Results: Twenty-seven studies encompassing 31 experiments were included. Overall heterogeneity for the acquisition phase as well as for the retention phase was large, and risk of bias was high. The meta-analysis showed an overall small effect size of 0.26 [0.10, 0.42] in the acquisition phase for participants in the DL group compared to other motor learning methods. In the retention phase, an overall medium effect size of 0.61 [0.30, 0.91] was observed for participants in the DL group compared to other motor learning methods.Discussion/Conclusion: Given the large amount of heterogeneity, limited number of studies, low sample sizes, low statistical power, possible publication bias, and high risk of bias in general, inferences about the effectiveness of DL would be premature. Even though DL shows potential to result in greater average improvements between pre- and post/retention test compared to non-variability-based motor learning methods, more high-quality research is needed before issuing such a statement. For robust comparisons on the relative effectiveness of DL to different variability-based motor learning methods, scarce and inconclusive evidence was found. (shrink)

The research question addressed in this paper is: How do the activities of writing mediate knowledge of writing, disciplinary knowledge, and professional knowledge as intertwined sites of learning? To conceptualise the role that writing can take in these complex processes, we apply an analytical framework comprising two core concepts; mediation and learning trajectories. We draw on an empirical study from the context of initial teacher education in Norway. From our analysis, we identify three qualities of writing as important. (...) First, the writing process should in- clude responding to and sharing drafts. Other important qualities include high teacher expectations and continuous reflection. From the perspective adopted here, learning is understood to be distributed and situated. In particular, in situated cultural contexts, collaborative writing can become a significant mediational tool for learning. Initial teacher education seeks to prepare the student teacher for a highly complex professional competency, developing both professionally and in individual subjects. To do so, students must transform social structures and the tools embedded in practices into psychological tools. We contend that writing is one significant tool in moving through complex trajectories of learning towards becoming professional teachers. (shrink)

Autism spectrum disorder is an inherited long-living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, miscommunication, and limited repetitive interest. If the autism spectrum disorder is detected in the early childhood, it will be very beneficial for children to enhance their mental health level. In this study, different machine and deep learning algorithms were applied to classify the severity of autism spectrum disorder. (...) Moreover, different optimization techniques were employed to enhance the performance. The deep neural network performed better when compared with other approaches. (shrink)

The purpose of this paper is to draw out the consequences of the communal character of learning approach promoted by a sociocultural framework. This approach has both descriptive-analytical and prescriptive-guiding power: it helps to analyze existing practices be they traditional, exclusive, or innovative but, what is, probably, even more important, it also helps to guide practitioners in the design of more inclusive educational practices. In the first part of the paper, we will provide a framework for analyzing the case (...) of a shift from a traditional institutionalized perspective that understands learning as an individual process located in the head of the learner to the institutionalization of learning as a communal process — a regime which helps avoid constructing children in terms of a deficit model, disability, and academic failure. In the second part of the paper, we will discuss how treating learning as a communal process can guide an educational practitioner to develop a new pedagogical regime of a learning community of social activists that leads to inclusive pedagogy and eliminate “zones of teacher-student disability. (shrink)

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

How can teacher development be characterised? In this paper we offer a conceptualisation of teacher development as the enhancement of knowledge and capabilities to function in the activity of a teacher and illustrate with a case study. Our analytic focus is on the development of a science teacher, David, as he engaged in an innovative, collaborative project on learning photonics at a metropolitan secondary school in Australia. Three dimensions of development emerged: technical confidence and competence, pedagogical development and personal (...) agency. We explore the transformative effects of intrapersonal tensions within the teacher’s constitution of his role in the emerging community of enquiry — positioning him in turn as learner, instructor and facilitator. We view the context for David’s actions as a complex and dynamic system and interpret David’s development as arising from his responses to the differences in his emerging roles in the project. (shrink)

Although there is no consensus on what distinguishes analytic from Continental philosophy, I focus in this paper on one source of disagreement that seems to run fairly deep in dividing these traditions in recent times, namely, disagreement about the relation of natural science to philosophy. I consider some of the exchanges about science that have taken place between analytic and Continental philosophers, especially in connection with the philosophy of mind. In discussing the relation of natural science to philosophy I employ (...) an analysis of the origins of natural science that has been developed by a number of Continental philosophers. Awareness and investigation of interactions between analytic and Continental philosophers on science, it is argued, might help to foster further constructive engagement between the traditions. In the last section of the paper I briefly discuss the place of natural science in relation to global philosophy on the basis of what we can learn from analytic/Continental exchanges. (shrink)

The ability to analyze arguments is critical for higher-level reasoning, yet previous research suggests that standard university education provides at best modest improvements in students’ analytical reasoning abilities. What techniques are most effective for cultivating these skills? Here we investigate the effectiveness of a 12-week undergraduate seminar in which students practice a software-based technique for visualizing the logical structures implicit in argumen- tative texts. Seminar students met weekly to analyze excerpts from contemporary analytic philosophy papers, completed argument visualization problem sets, (...) and received individualized feedback on a weekly basis. We found that Seminar students improved substantially more on LSAT Logical Reasoning test forms than Control students (d = .71, p < .001), suggesting that learning how to visualize arguments in the seminar led to large generalized improvements in students’ analytical reasoning skills. Moreover, blind scoring of final essays from Seminar students and Control students, drawn from a parallel lecture course, revealed large differences in favor of seminar students (d = 0.87, p = .005). Seminar students understood the arguments better, and their essays were more accurate and effectively structured. Taken together, these findings deepen our understanding of how visualizations support logical reasoning, and provide a model for improving analytical reasoning pedagogy. (shrink)

Volume 98, Issue 4, December 2020, Page 831-833.

Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language (...) processing. Additionally, we share pre-trained legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others. (shrink)

The research aims to identify the learning organizations and their role in achieving organizational excellence in the Palestinian universities in Gaza Strip. The researchers used descriptive analytical approach and used the questionnaire as a tool for information gathering. The questionnaires were distributed to senior management in the Palestinian universities. The study population reached (344) employees in senior management is dispersed over (3) Palestinian universities. A stratified random sample of (182) workers from the Palestinian universities was selected and the recovery (...) rate was (69.2%). Statistical analysis (SPSS) program was used for analysis and processing the data. The research found the following results: there is a fair degree of approval on "cognitive dimension", there is a high approval about the importance of "organizational dimension", there is moderately consent of the importance of "Community dimension", there is a large degree of consent about the importance of axis of the "leadership excellence", there is a large degree of consent about the importance of axis of the "service-excellence", there is a fair degree approval about the importance of the axis of "cognitive excellence", and there is a moderately consent of the importance of "Organizational Excellence". The research found a group of recommendations including: the need to develop appropriate strategies for the University, employ modern technology in information systems, in addition to provide appropriate environment that achieve learning organizations, develop technological infrastructure, adopt universities for knowledge management in the academic and administrative departments because knowledge is the core of the work of these departments, create technological incubators in universities to adopt excellence academic research projects, and protected them, support them, and market them. Establishing of centers of excellence for scientific research in the university.The need for cooperation and coordination between local, Arab and international universities for the exchange of knowledge, information, and participation programs and training courses, as it provides an opportunity to develop the capabilities and skills of personnel and expertise. (shrink)

David Bronstein’s book tackles Aristotle’s account, as presented in the Posterior Analytics, of knowledge, or rather a privileged form of it, ‘scientific knowledge’ or ‘understanding.’ We know in this way by grasping arguments of a certain kind, demonstrations, for which reason Aristotle devotes much of his attention in the Posterior Analytics to demonstrative argument. The subject is as important as anything in Aristotle, and it presents challenges as difficult as any confronting his interpreters elsewhere, which Bronstein’s book tackles (...) skillfully and to illuminating effect. The book is especially noteworthy for the high degree of systematic unity it attributes to the Posterior Analytics. The solutions... (shrink)

Cooperative Learning. When you're in the presence of teachers you hear about it. When you pick up a professional journal, you read about it. When you see learners experiencing it, obviously, they are learning and having fun in the process. Yet, when I walk through the halls of schools and peep through the windows of the closed doors, I observe most learners seated, in rows, being taught individually, with lecture, work-book, and ditto sheets dominating the scene. Why is (...) this so? (shrink)

The article addresses the question of how should scientific peers revise their beliefs (if at all) upon recognized disagreement. After presenting the basics of peer disagreement in sections 1 and 2, we focus, in section 3, on a concrete case of scientific disagreement, to wit, the dispute over the evidential status of randomized control trials in medical practice. The examination of this case motivates the idea that some scientific disagreements permit a steadfast reaction. In section 4, we support this conclusion (...) by providing a normative argument in the same direction; if we are correct, typical reasons for conciliation are absent in this kind of scientific disagreements. (shrink)

Analytic theology can flourish in the secular academy, and flourish as authentically Christian theology. Analytic Theology and the Academic Study of Religion explains analytic theology to other theologians and scholars of religion, while simultaneously explaining those other fields to analytic theologians. William Wood defends analytic theology from some common criticisms, but also argues that analytic theologians have much to learn from other forms of inquiry. Analytic theology is a legitimate form of theology, and a legitimate form of academic inquiry, and (...) it can be a valuable conversation partner within the wider religious studies academy. Analytic Theology and the Academic Study of Religion articulates an attractive vision of analytic theology, fosters a more fruitful inter-disciplinary conversation, and enables scholars across the religious studies academy to understand one another better. (shrink)

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting (...) from adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions. (shrink)

In this article I propose that a postphenomenological approach to science and technology can open new analytical understandings of how material artifacts, embodiment and social agency co-produce learned perceptions of objects. In particle physics, physicists work in huge groups of scientists from many cultural backgrounds. Communication to some extent depends on material hermeneutics of flowcharts, models and other visual presentations. As it appears in an examination of physicists’ scrutiny of visual renderings of different parts of a detector, perceptions vary in (...) relation to social and bodily experiences. Vision in physics has seemingly allowed an objective perception at a convenient distance of the body. This article challenges this view and proposes that the variations can be analysed as cultural at two echelons with the help of a postphenomenological approach combined with cultural psychological theory of artifacts. A third echelon presumably constitutes the phenomenological limit to culture in science. Even this last resort of subjectivity can be embraced by a postphenomenological approach. The process of culturalization in physics can be defined as a process of situating knowledge in a body whose continuous learning of micro-and macro perceptions makes scientific renderings unstable. Taken together postphenomenology, following the distinctions between body one and body two, and combined with cultural psychological learning theory, enables new insight into what constitutes culture in science. (shrink)

This article presents a new framework for unobtrusive observation analytics of knowledge and skills-in-action through continuous collection of data from individuals while they interact with digital assets either as individuals or on problem-solving teams. The framework includes measures of the skill and knowledge areas of collaboration, creativity, personal learning, problem solving, and global sustainability, which are observed during natural production and use of communications, intentional artifacts, and resources in a digital learning space designed for self-directed and team-based (...) learning challenges. The article describes the digital context for data collection and shows some example data and analyses. (shrink)

Interventions that incorporated the teaching of self-regulated learning strategies are assumed to be effective in improving students' second language performance as they support students' SRL activity and self-efficacy. Nevertheless, previous meta-analyses largely focused on students' language learning achievement, while neglecting the instructional effects on their SRL strategy use and self-efficacy, two key factors in SRL models. This meta-analytic study was thus conducted to address the gap by synthesizing the evidence of SRL interventions in influencing students' L2 learning (...) achievement, strategy use, and self-efficacy. The largest effect was obtained for L2 learning achievement, followed by self-efficacy and strategy use. Moderator analysis revealed similar instructional effects on students of different age groups and education levels. The duration and intensity of intervention significantly moderated the effectiveness of SRL interventions in the L2 context, especially for strategy use and self-efficacy. The findings obtained in the current study could inform practitioners and researchers of the cumulative effects of SRL interventions in L2 classrooms and study design and student characteristics that moderate the instructional effectiveness. (shrink)

This study aimed to identify the learning and business incubation processes and their impact on developing the performance of business incubators in Gaza Strip, and the study relied on the descriptive analytical approach, and the study population consisted of all employees working in business incubators in Gaza Strip in addition to experts and consultants in incubators where their total number reached (62) individuals, and the researchers used the questionnaire as a main tool to collect data through the comprehensive survey (...) method, where (55) questionnaires were retrieved with a recovery rate of (88.7%). The results of the study showed that there is a high approval of the variables of the study, where the dimension of incubation operations came with a relative weight (84.89%), followed by the learning dimension which got a relative weight (82.50%), while the performance level of the incubators got a relative weight (80.12%) and as the study results showed There is a positive relationship statistically between the two dimensions of learning and business incubation processes and performance development in business incubators, and the linear regression analysis model showed that the main effect in improving the performance of business incubators is "after business incubation operations" while it showed weak effect after learning. The study came out with several recommendations, the most important of which is that business incubators seek to reinforce the learning process and incubation processes for business, and the need to periodically evaluate their performance, and that a more effective mechanism be designed to follow up with companies after the end of the incubation period and monitor the progress of these companies. (shrink)

I review David Bronstein's "Aristotle on Knowledge and Learning: The Posterior Analytics".

Evolution and learning are two analytically distinct concepts. People learn yet evolution (`change') does not necessarily take place. To clarify this problem the concept of learning is explicated. The first problem addressed is the question of who is learning. Here a shift from the single actor perspective to an interaction perspective is proposed (using Habermas and Luhmann as theoretical arguments for such a shift). Both, however, idealize the preconditions that interactants share while learning collectively. Against rationalist (...) assumptions it is argued that in order to learn people need a narratively based shared world. What do they learn? They acquire knowledge and they learn how to learn. This still does not solve the problem why they learn. Learning, it is argued, does not guarantee evolution but provides the mutations for evolutionary processes to take place. (shrink)

ABSTRACT This is a review of David Bronstein's book "Aristotle on Knowledge and Learning: The Posterior Analytics" (Oxford: Oxford University Press, 2016).

Deep brain stimulation (DBS) is an effective treatment for the debilitating motor symptoms of Parkinson’s disease and other neurological disorders. However, clinicians and commentators have noted that DBS recipients have not necessarily experienced the improvements in quality of life that would be expected, due in large part to what have been described as the ‘psychosocial’ impacts of DBS. The premise of this paper is that, in order to realise the full potential of DBS and similar interventions, clinical services need to (...) be arranged in such a way that these psychosocial dimensions are recognised and managed. Our starting point is that the psychosocial effects of DBS ‘in the field’ present us with analytically-useful disruptions: they disturb and foreground deeply held assumptions relating to the individual, health and its treatment, and which in a crude form manifest as the myth of technological solutionism within health care. Drawing on scholarship in medical sociology and science and technology studies (STS), we argue that DBS brings to the fore the relational dimensions of personhood, and demonstrates the emotional and social turmoil that can result if the relational dimensions of personhood are ignored by clinical services. In light of this, we argue that DBS should be implemented within a regime of care. Drawing on ethnographic research of a paediatric DBS clinical service, we provide an example of a regime of care, and conclude by reflecting on what other DBS services might learn from this paediatric service. (shrink)

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To (...) build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial—but not temporal—variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing. (shrink)