Educational institutions increasingly rely on digital platforms to deliver content and learning, monitor attendance, communicate with stakeholders, and evaluate institutional performance. Despite the efficiency and accessibility gains they offer, digital platforms are powered by personal data which, through a process of datafication, can be used to track, monitor, and profile staff and students. The insights drawn from this data can be used to shape educational and professional futures. This article examines how datafication has become a social justice issue (...) in education, discussing the implications for well-being, decision-making, governance, and power in education. Using Hintz and colleagues framework for data justice, it applies and explores the three dimensions of data justice to the context of education, including: (1) infrastructures (2) regulation, and (3) informed and knowledgeable stakeholders. The paper discusses the unique challenges to achieving data justice in education and concludes by outlining the key questions for a future research agenda. (shrink)

High vocation education is an important foundation for China to cultivate high teaching quality and technical and skilled talents. In the new era, the acceleration of the development of modern vocational education is put in a more prominent position. It is proposed that we should adhere to moral education, closely combine this with the needs of technological change and industrial upgrading, constantly improve the quality of high vocational education teaching, and cultivate more technical and skilled talents (...) with both political integrity and ability for modernization construction. Under the background of social informatization, using artificial intelligent technology to solve these problems can play an important role for improving the teaching quality of high vocational education. This paper proposed a data mining approach in promoting student satisfaction with the teaching quality of high vocational education. We design a questionnaire for Students’ satisfaction with the teaching quality of basic entrepreneurship curriculum. We take the survey data of vocation education as an example and use mining technology analysis software to understand the current status of the teaching quality of basic entrepreneurship curriculum. The results determine the main factors affecting Students’ satisfaction with teaching quality. The results of this paper can be used in student management, education strategy, student education satisfaction, and teaching quality in high vocation college education, and to improve the teaching quality of fundamentals of entrepreneurship curriculum in high vocation education. (shrink)

Educational research is dominated by a particular model: data is gathered and analysed. Much literature on methods concerns either ways of processing data, or ethical issues regarding its collection and handling. The present paper looks beyond these matters to the taken‐for‐granted idea of data itself. What can be meant by ‘data’? How does this connect with ideas of the given? What is the place of giving in education—in teaching and learning, in research itself? These issues (...) are explored in the light of information technology’s impact on research, uncovering ethical dimensions that the seeming naturalness of data otherwise obscures. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. (...) We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

Student performance prediction aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining. This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, problem (...) formalization, model, prediction, and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education. (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)

When analyzing complex longitudinal data, especially data from different educational settings, researchers generally focus only on the mean part (i.e., the regression coefficients), ignoring the equally important random part (i.e., the random effect variances) of the model. By using Project English Language and Literacy Acquisition (ELLA) data, we demonstrated the importance of taking the complex data structure into account by carefully specifying the random part of the model, showing that not only can it affect the variance (...) estimates, the standard errors, and the tests of significance of the regression coefficients, it also can offer different perspectives of the data, such as information related to the developmental process. We used xxM (Mehta, 2013), which can flexibly estimate different grade-level variances separately and the potential carryover effect from each grade factor to the later time measures. Implications of the findings and limitations of the study are discussed. (shrink)

In recent years, learning materials have gradually been applied to flipped classrooms. Teachers share learning materials, and students can preview the learning materials before class. During class, the teacher can discuss students' questions from their notes from previewing the learning materials. The social media platform Facebook provides access to learning materials and diversified interactions, such as sharing knowledge, annotating learning materials, and establishing common objectives. Previous studies have explored the effect of flipped classrooms on students' learning engagement, attitudes, and performance. (...) In this paper, we apply educational data mining to explore the relationship between students' viewing behaviors in accessing learning materials and their performance in flipped classrooms. The participants are classified into an experimental group and a control group to engage in flipped classroom activities. The experimental group uses the social media platform Facebook for flipped learning, and the control group uses a learning management system for flipped learning. The results show that there is a significant difference in the learning performance between the two groups, with the average score of the experimental group being higher than that of the control group. Furthermore, we find that the viewing behaviors and performance of the students within the experimental group differ significantly. (shrink)

Abstract:For Dewey, growth in the educative process means education that enriches and expands one’s experience as it prepares students for not only a vocation but also entry into and transaction with the world. In few places can we see growth, generally understood, to be occurring as fast as in big data technology. This essay begins with an overview of what big data is, specifically what big data looks like in education as understood through learning management (...) system platforms but also data as understood generally on a college campus through dataveillance, or data surveillance understood as the monitoring or possibility of monitoring of one’s data or online activity. In so doing, this essay acknowledges a selection of privacy concerns and educational challenges resulting from big data. I then proceed to the employment of intelligent technology in education with particular emphasis on algorithms and “algorithmic nudging.” I conclude with a Deweyan analysis of technology as continuous with culture and a discussion about the educational needs for technological environments to aid in the proper cultivation of individuals. (shrink)

Like other parts of the social system, education is becoming an information-driven venture: data technologies pervade all levels of the system. This datafication of education seems to take place alongside a general turn to learning that Gert Biesta has called learnification: a progressively singular focus on the manipulable features of individual learning in education. Given rapidly rising levels of datafication, it seems timely to take up Luhmann and Schorr’s contention that education entails a technology deficit (...) and discuss datafication as reflection issue in the system of education. Against their argument that human learning is not amenable to a technology, I develop the counter-argument that data technologies are replacing human learning outright with data at the level of organisation. Data thus present a concretely digital form of what Raf Vanderstraeten has called education as an ersatz order. In a data-driven form of organising education, human dimensions of learning become secondary to a systemic dimension: making learning visible as data and so susceptible to databased manipulation. The text treats school-wide positive behaviour support interventions as an evidence-based exemplar of this trend towards datafication in the system of education. (shrink)

Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big (...) class='Hi'>data, and problematic research can still meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts. (shrink)

As organizations are benefiting from investments in big data analytics capabilities building and education, our study has analyzed the impact of big data analytics capabilities building and education on business model innovation. It has also assessed technological orientation and employee creativity as mediating and moderating variables. Questionnaire data from 499 managers at enterprises in Jiangsu, China have been analyzed using Structural Equation Modeling in SmartPLS. Big data analytics capabilities building and education strengthen technological (...) orientation and increase business model innovation. Technology orientation increases business model innovation and plays a mediating role. Employee creativity also boosts innovation. These findings show that business managers should adopt and promote a technological orientation. They should hire and train employees with big data education and training. Organizations can try to select and support employees who show creativity. (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)

Resumo: Com o rápido desenvolvimento da sociedade da informação moderna, o Big Data (BD) trouxe grande comodidade à vida dos residentes. No contexto do BD, a aplicação da tecnologia da informação em diversas áreas tornou-se uma tendência. Através da transformação da filosofia educacional, a Gestão do Ensino Superior também assumiu novas características e gerou novos rumos de desenvolvimento. Este artigo explora as deficiências e razões do trabalho tradicional de Gestão de Esino Superior, aponta problemas atuais e explica a necessidade (...) de sua inovação. Também analisa as vantagens e o desenvolvimento da tecnologia BD no trabalho de gestão. Através das novas ideias, métodos e ferramentas trazidas pela tecnologia Big Data para a Gestão do Ensino Superior, foi demonstrado que a tecnologia BD apoiou o desenvolvimento de modalidades de ensino inovadoras, proporcionou um ambiente de aprendizagem positivo e reduziu os custos de gestão. Com base nas características do trabalho da Gestão e nas vantagens da tecnologia BD, foram realizadas pesquisas inovadoras para analisar a eficiência e o entusiasmo da aprendizagem dos alunos para ilustrar o efeito inovador desse trabalho. Os autres defendem que a tecnologia BD, incluindo Inteligência Artificial e gestão precisa e flexível, traz novas ideias, métodos e ferramentas para a GEstão, melhorando efetivamente a eficiência, aumentando a felicidade dos alunos e reduzindo os custos de gestão laboral. Ademais, apoiam o desenvolvimento de modelos de ensino inovadores, oferecem aos estudantes universitários um ambiente de aprendizagem positivo e reduzem os custos de gestão. (shrink)

_The Infrastructure of Accountability _brings together leading and emerging scholars who set forth an ambitious conceptual framework for understanding the full impact of large-scale, performance-based accountability systems on education. Over the past 20 years, schools and school systems have been utterly reshaped by the demands of test-based accountability. Interest in large-scale performance data has reached an unprecedented high point. Yet most education researchers focus primarily on questions of data quality and the effectiveness of data use. (...) In this bold and thought-provoking volume, the contributors look beneath the surface of all this activity to uncover the hidden infrastructure that supports the production, flow, and use of data in education, and explore the impact of these large-scale information systems on American schooling. These systems, the editors note, “sit at the juncture of technical networks, work practices, knowledge production, and moral order. (shrink)

In order to improve education and teaching methods and meet the “heart” needs of college students in the era of big data, this paper analyzes the application of data mining technology in college mental health education, and introduces database technology and decision tree algorithm to support college mental health work. This process verifies the feasibility of this kind of system with the help of an example. Using the test standards outlined in this document, 1.5 previous test (...) tasks were completed within the timeframe. During the system test, the error rate was 14% and the number of tests was 7%.However, the error rate in the development stage is 11%, which is lower than 19% of the old version. The error rate in the acceptance stage is 14%, which is lower than 5% of the old version. That is to say, most of the errors were found in time in the system analysis and design stage. 14% of the problems found in the development stage are basically small problems in the interface display, which do not need major changes. However, the old version also includes design defects found in the development stage, and only large-scale rewriting of the involved modules. In the research process, the work of mental health in Colleges and universities has been promoted. At this time, the law of psychological changes of college students has been summarized. Therefore, the support of data mining technology can better meet the needs of mental health education in Colleges and universities. (shrink)

This paper investigates how deans and directors at the top 50 global MBA programs (as rated by the "Financial Times" in their 2006 Global MBA rankings) respond to questions about the inclusion and coverage of the topics of ethics, corporate social responsibility, and sustainability at their respective institutions. This work purposely investigates each of the three topics separately. Our findings reveal that: (1) a majority of the schools require that one or more of these topics be covered in their MBA (...) curriculum and one-third of the schools require coverage of all three topics as part of the MBA curriculum, (2) there is a trend toward the inclusion of sustainability-related courses, (3) there is a higher percentage of student interest in these topics (as measured by the presence of a Net Impact club) in the top 10 schools, and (4) several schools are teaching these topics using experiential learning and immersion techniques. We note a fivefold increase in the number of stand-alone ethics courses since a 1988 investigation on ethics, and we include other findings about institutional support of centers or special programs; as well as a discussion of integration, teaching techniques, and notable practices in relation to all three topics. (shrink)

How humans choose their mates is a central feature of adult life and an area of considerable disagreement among relationship researchers. However, few studies have examined mate choice (instead of mate preferences) around the world, and fewer still have considered data from online dating services. Using data from more than 1.8 million online daters from 24 countries, we examined the role of sex and resource-acquisition ability (as indicated by level of education and income) in mate choice using (...) multilevel modeling. We then attempted to understand country-level variance by examining factors such as gender equality and the operational sex ratio. In every nation, a person’s resource-acquisition ability was positively associated with the amount of attention they received from other site members. There was a marked sex difference in this effect; resource-acquisition ability improved the attention received by men almost 2.5 times that of women. This sex difference was in every country, admittedly with some variance between nations. Several country-level traits moderated the effects of resource-acquisition ability, and in the case of unemployment this moderating role differed by sex. Overall, country-level effects were more consistent with evolutionary explanations than sociocultural ones. The results suggest a robust effect of resource-acquisition ability on real-life mate choice that transcends international boundaries and is reliably stronger for men than women. Cross-cultural variance in the role of resource-acquisition ability appears sensitive to local competition and gender equality at the country level. (shrink)

First Published in 2005. Routledge is an imprint of Taylor & Francis, an informa company.

Contributing to a rising number of Critical Data Studies which seek to understand and critically reflect on the increasing datafication and digitalisation of governance, this paper focuses on the field of school monitoring, in particular on digital data infrastructures, flows and practices in state education agencies. Our goal is to examine selected features of the enactment of datafication and, hence, to open up what has widely remained a black box for most education researchers. Our findings are (...) based on interviews conducted in three state education agencies in two different national contexts, thus addressing the question of how the datafication and digitalisation of school governance has not only manifested within but also across educational contexts and systems. As our findings illustrate, the implementation of data-based school monitoring and leadership in state education agencies appears as a complex entanglement of very different logics, practices and problems, producing both new capabilities and powers. Nonetheless, by identifying different types of ‘doing data discrepancies’ reported by our interviewees, we suggest an analytical heuristic to better understand at least some features of the multifaceted enactment of data-based, increasingly digitalised governance, within and beyond the field of education. (shrink)

This paper considers the use of secondary data analysis in educational research. It addresses some of the promises and potential pitfalls that influence its use and explores a possible role for the secondary analysis of numeric data in the 'new' political arithmetic tradition of social research. Secondary data analysis is a relatively under-used technique in Education and in the social sciences more widely, and it is an approach that is not without its critics. Here we consider (...) two main objections to the use of secondary data: that it is full of errors and that because of the socially constructed nature of social data, simply reducing it to a numeric form cannot fully encapsulate its complexity. However, secondary data also offers numerous methodological, theoretical and pedagogical benefits. Indeed by treating secondary data analysis with appropriate scepticism and respect for its limitations, by demanding that tacit assumptions about the unreliability of secondary data are applied equally to other research methods, and crucially by combining secondary data analysis with small-scale in-depth work, this paper argues for a return to prominence of secondary data analysis in its own right as well as becoming a central component of the new political arithmetic tradition of social research. (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)

Assessment practices have come to dominate much of formalized education, especially within the United States. Currently, learning analytics (la) and educational data mining (edm) are purported by many educational companies and institutions to successfully improve learning through what are often considered as objective collection, classification, and analysis of educational data. Enthusiasm about big data in education has contributed to the naturalization of datafication within the field. Educational data is regarded as a natural resource that (...) exists ‘out there’ to be mined by edm and refined by la. Once refined, it is thought to bear the truth of educational assessment that leads to successful learning outcomes. (shrink)

Providing comprehensive sexuality education in schools is a work in progress in many countries throughout the world. In some countries, the journey is just beginning; in others, investments in this field have been made for many years. It is and has been difficult in Pakistan to implement and promote reproductive health, women’s empowerment, and CSE. In Pakistan, previous implementation efforts revealed the critical role of community influencers in propagating misleading information about the initiatives, inciting organized community resistance, and provoking (...) backlash. This paper looked at several aspects of community resistance, as well as approaches for overcoming the resistance for increasing community engagement in the implementation of CSE in Islamabad, Pakistan. To analyze community perceptions of CSE implementation in Islamabad, the community readiness assessment questionnaire was adapted. Questions and prompts for discussion included leadership, current initiatives, community knowledge, resource availability, community support, and implementation strategies. A total of 35 in-depth interviews were conducted. Data was analyzed and interpreted using qualitative content analysis to explore community perspectives that contribute to resistance around CSE, as well as implementation options. Using inter-code relationship data, network analysis was conducted to provide a graphical representation of the analyzed qualitative data. The study reveals community resistance to CSE being implemented in schools. Misconceptions, a lack of awareness, a lack of priority, and the lack of dedicated resources are just a few of the primary implementation challenges to consider when implementing CSE in practice. Network analysis identified, based on modularity class, five distinct clusters of highly connected nodes/codes: non-governmental organizations, misconceptions, resources and policy, strategies and community support, and personal social and health education and current efforts. In conservative environments and when confronted with resistance, innovative marketing and rebranding are critical for priority setting and community engagement, especially when developing curriculum and implementing CSE. Some of the suggested strategies for implementation include community sensitization through strategic awareness campaigns, involving already established infrastructure and NGOs, endorsement by all major stakeholders, particularly decision-makers, and the use of creative digital platforms for better dissemination. (shrink)

Data sharing between housing and education agencies will provide housing agencies with resources to assist them with efforts to decrease segregation and mitigate the adverse health outcomes experienced by people of color. The Fair Housing Act has the potential to fulfill its original integrationist purpose if housing and education agencies combine resources and data to create and implement fair housing plans. The Biden Administration’s restored rule to affirmatively further fair housing pursuant to the Fair Housing Act (...) of 1968 which seeks to reduce segregation and increase housing equity. However, it omits most of the processes set forth by the Obama Administration whereby federal agencies provide the proper tools to housing agencies so that they are able to make data-based decisions about housing policies. This article advocates for the sharing of data between housing and education agencies to optimize the positive impact of fair housing not only within housing, but also on the education, employment, and health opportunities for communities of color. (shrink)

The present study aimed to examine how to improve the effectiveness of moral exemplar-applied interventions based on the pillars of the Self-Determination Theory (SDT) framework, autonomy, competence, and relatedness. Past research has mainly focused on the relatedness and attainability of moral exemplars for predicting motivation outcomes. The data for this study consisted of synthesized data sets from previous studies examining the motivational impacts of distinct moral exemplars and intervention methods. The main syntheses for these data sets used (...) Multilevel Modeling (MLM) focusing on relatability, attainability, and intervention methods, corresponding to relatedness, competence, and autonomy in the SDT, respectively, as predictors. In general, there was a significant interaction effect between the attainability or relatability, and the intervention method. Autonomous instruction methods, which support autonomy, were demonstrated to boost motivational outcomes. Implications from this study support the employment of SDT to examine the use of moral exemplars in moral education and were consistent with previous exemplar studies. (shrink)

Amidst ongoing technological and social change, this article explores the implications for critical education that result from a data-driven model of digital governance. The article argues that traditional notions of critique which rely upon the deconstruction and analysis of texts are increasingly redundant in the age of datafication, where the production of information is automated and hidden. The article explains the concept of the ‘educative subject’ within the liberal education tradition, with specific focus on the role of (...) critique and reflexivity in their becoming. It explores how the logics and practices of datafication and automation pose unprecedented challenges to the educative subject, examining three features in particular: the creation of data subjects; the rise of correlationalism; and the move from representation to operationalisation. The article concludes by outlining a research agenda to address the existential challenges posed by data education. (shrink)

Purpose As data-driven tools increasingly shape our life and tech ethics crises become strikingly frequent, data ethics coursework is urgently needed. The purpose of this study is to map the field of data ethics curricula, tracking relations between courses, instructors, texts and writers, and present a proof-of-concept interactive website for exploring these relations. This method is designed to be used in curricular research and development and provides multiple vantage points on this multidisciplinary field. Design/methodology/approach The authors use (...) data science methods to foster insights about the field of data ethics education and literature. The authors present a semantic, linked open data graph in the Resource Description Framework, along with proof-of-concept analyses and an exploratory website. Its framework is open-source and language-agnostic, providing the seed for future contributions of code, syllabi and resources from the global data ethics community. Findings This method provides a convenient means of exploring an overview of the field of data ethics’ social and textual relations. For educators designing or refining a course, the authors provide a method for curricular introspection and discovery of transdisciplinary curricula. Research limitations/implications The syllabi the authors have collected are self-selected and represent only a subset of the field. Furthermore, this method exclusively represents a course’s assigned literature rather than a holistic view of what courses teach. The authors present a prototype rather than a finished product. Originality/value This curricular survey provides a new way of modeling a field of study, using existing ontologies to organize graph data into a comprehensible overview. This framework may be repurposed to map the institutional knowledge structures of other disciplines, as well. (shrink)

A number of questions are raised concerning the purposes of data coding in qualitative research. It is suggested that in some cases these purposes may usefully be organised into two broad categories, each of which requires a separate coding response. A research project is briefly described in which it was found useful to employ two distinct, though connected, phases of data coding along the lines proposed.

This article examines the data collected from the semi-structured interviews of nine Quebec elementary pre-service teachers in a practice teaching context. The paper exposes the educational goals inherent in the teaching of Social Studies, the place that this school subject occupies in the pre-service teacher’s practices, and its contribution to the development of the three-part mission of the Quebec school system (to instruct, socialize, and qualify). Even though Social Studies are considered by the participants of this study to be (...) a less important school subjects, they nevertheless remain significant. This importance refers mainly to the contribution of Social Studies to the development of socially acceptable attitudes in the student. It also refers to the development of an understanding and knowledge of history and of the functioning of society with the ultimate goal of adapting to this society. Sur la base des données issues des entrevues semi-dirigées réalisées auprès de neuf futurs enseignants du primaire en contexte de formation en milieu de pratique au Québec, l’article met en exergue les finalités sous-jacentes à l’enseignement des sciences humaines et sociales, la place que cette discipline occupe dans leurs pratiques ainsi que ses contributions au développement de la triple mission octroyée à l’école québécoise (instruire, socialiser, qualifier). Bien que les sciences humaines et sociales soient considérées par les participants à l’étude comme étant une discipline secondaire, elles restent somme toute importantes. Cependant, cette importance serait axée principalement sur la contribution des sciences humaines et sociales au développement chez les élèves d’attitudes socialement acceptables ainsi que sur le développement d’une connaissance et d’une compréhension qu’ils se font de l’histoire et du fonctionnement de leur société dans le but de s’y adapter. (shrink)

This paper interrogates the fundamental logic of data-driven decision-making as it has taken hold in education and argues for a critical analysis of data-driven education via an attitude of historical ontology. Though influenced by Foucault’s understanding of this concept, I center Colin Koopman’s recent analysis of the ‘informational person’ to point attention to the ways in which the very formatting of data may be understood as historically contingent and, thus, more contestable. After examining the background (...) of DDDM and relevant critiques of it, I suggest that investigating the socio-historical formations of data itself are important for indicating its potential dangers within education. Such a critical analysis identifies the doubled politics of data formatting, with its ability to tether subjectivity to data, while potentially baking modes of power into a variety of technological data apparatuses. To conclude, I consider Koopman’s work in terms of how it may extend critical studies of educational datafication. (shrink)

Nowadays, innovation and entrepreneurship courses occupy a very important place in universities and colleges and have also become an important teaching position in the process of building a new science. Colleges and universities actively respond to the challenge of “mass entrepreneurship and innovation” and define the goals and specifications of the talent training mechanism based on data fusion algorithms to cultivate as much high-quality applied talent as possible. In view of some shortcomings and problems in the current talent training (...) mechanism in universities and colleges, this paper proposes a data fusion algorithm based on information fusion theory and proof theory. The aim is to verify the feasibility of establishing a talent training mechanism for innovation and entrepreneurship education in universities and colleges. And this paper analyzes and explores the data fusion algorithm and the elements of innovation and entrepreneurial talent training, and forms an operating mechanism for entrepreneurial talent training according to social needs. Among them, the efficiency of the data fusion algorithm used by the GM model plays a significant role in the final result, and the minimum relative error value is 3.2%. Finally, it is concluded that we should focus on establishing a perfect talent training system for college students’ innovation and entrepreneurship education to improve students’ own comprehensive quality and various abilities, and to solve some social problems that are difficult to find employment in essence. (shrink)

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the (...) personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered. (shrink)

The concept of dual-use encapsulates the potential for well-intentioned, beneficial scientific research to also be misused by a third party for malicious ends. The concept of dual-use challenges scientists to look beyond the immediate outcomes of their research and to develop an awareness of possible future (mis)uses of scientific research. Since 2001 much attention has been paid to the possible need to regulate the dual-use potential of the life sciences. Regulation initiatives fall under two broad categories—those that develop the ethical (...) education of scientists and foster an awareness and responsibility of dual-use issues, and those which assess the regulation of information being generated by current research. Both types of initiatives are premised on a cautious, risk-adverse philosophy which advocates careful examination of all future endpoints of research endeavors. This caution advocated within initiatives such as pre-publication review of journal articles contrasts to the obligation to share underpinning data sharing discussions. As the dual-use debate has yet to make a significant impact on data sharing discussions (and vice versa) it is possible that these two areas of knowledge control may present areas of ethical conflict for scientists, and thus need to be more closely examined. This paper examines the tension between the obligation to share exemplified by data sharing principles and the concerns raised by the risk-cautious culture of the dual-use debates. The paper concludes by reflecting on the issues of responsibility as raised by dual-use as relating to data sharing, such as the chain of custody for shared data. (shrink)

The current behavioral consultation case demonstrates how functional behavioral assessment (FBA) data, basic and applied research, teacher preferences, and contextual variables contribute to the decision making process when developing classroom intervention procedures. A male, African-American, fifth-grade general education student was initially referred for his inappropriate vocalizations duringtime designated for independent seatwork. FBA data suggested that this behavior was being reinforced with teacher attention. Additional data showed that he was failing to complete his assignments. An intervention was (...) implemented where the student was given assignments one a time. He was instructed to solicit teacher attention and his next assignment after completing each assignment. Analysis ofteacher ratings for inappropriate vocalizations and assignment performance data suggest that the intervention was effective in increasing assignment completion and decreasing inappropriate verbalizations. Discussion focuses on how the various data playa role in the development and implementation of classroom intervention procedures. (shrink)

The issue of levels of participation in post-compulsory education has been emphasised by the current policy initiatives to increase the age to which some form of participation is compulsory. One of the acknowledged weaknesses of research in the field of children's intentions with regard to participation is the lack of longitudinal data. This paper offers a longitudinal analysis using the Youth Survey from the British Household Panel Survey. The results show that most children can express intentions with regard (...) to future participation very early in their secondary school careers and that these intentions are good predictors of actual behaviour five years later. Intentions to stay on are more consistent than intentions to leave and most children who finally leave at 16 have at some point said they want to remain in education post-16. The strongest association with participation levels is attainment at GCSE. However, there are also influences of gender and parental background and these remain, even after attainment is held constant. The results show the value of focusing on intentions for participation at a very early stage of children's school careers and also the importance of current attempts to reform curriculum and assessment for the 14–19 age group. (shrink)

Outdoor Education (OE) refers to organized experiential education that takes place in the outdoor, characterized by action-centered and thematic learning processes. Literature shows how OE may have beneficial effects on different areas of child development, including cognitive abilities, social skills and motor development. This relationship is not necessarily linear, but moderated by different variables. Until now, few studies have examined, using rigorous methods, the role of OE in children’s development and studies of preschool aged children remain lacking. The (...) current study aimed to explore teachers’ perceptions of children’s developmental trajectories over 2 school years, investigating whether teachers’ perceptions differed between two kindergartens, one characterized by a consolidated OE approach and the other one characterized by a more traditional method of education. The sample was composed of 20 teachers, evaluating 93 children aged 3-5 (M= 46.95 months, SD= 6.73; 42 males): 13 teachers were from a traditional kindergarten (Traditional Group- TG) and evaluated 52 children; 7 teachers were from an OE kindergarten (Outdoor Group – OE) and observed 41 children. All the teachers completed the Kuno Beller Developmental Tables (Beller, 1995), in order to describe specific child developmental areas in 4 consecutive moments during 2 school years (T1-T2: January-May 2014; T3-T4: October 2014-May 2015). The 20 teachers also completed the “Outdoor Activities/Trips Diary”, an instrument created for this study to collect qualitative data on the characteristics of outdoor activities. Results showed that, in all the developmental areas, OE teachers perceived higher scores over time were found for the Outdoor Group compared to the Traditional one. Specifically, GLM ANOVAs Repeated Measures revealed a significant interaction of the 2 variables Time and Groups (p<0.001): contrast analyses showed that OE children, compared to the TG children, were perceived by their teachers with higher levels in all developmental areas at T1 and T2, but not at T3 and T4. The findings suggest that the OE activites, compared to indoor ones and according to teachers' perceptions, offer greater opportunities to promote the child’s development at different levels. especially when children are younger. Future studies are recommended analyzing possible moderating variables and long term effects of OE. (shrink)

Denmark is a society that has already moved towards Big Data and a Learning Health Care System. Data from routine healthcare has been registered centrally for years, there is a nationwide tissue bank, and there are numerous other available registries about education, employment, housing, pollution, etcetera. This has allowed Danish researchers to study the link between exposures, genetics and diseases in a large population. This use of public registries for scientific research has been relatively uncontroversial and has (...) been supported by facilitative regulation that allows data to be used without the consent of data subjects. However, in the future much of the data will not be held by public authorities but by private companies. What are the implications of this shift for the governance of the research use of the data? This paper will argue that increased involvement of Research Ethics Committees and better training of researchers are necessary; and that some form of consent will have to be re-introduced. Four different consent models will be discussed: Opt-Out, Broad/Blanket consent, Dynamic consent, and Meta consent. It will be argued that a governance model including a possibility for citizens to make meta-choices strikes the best balance between individual and public interests. (shrink)

Background: Education is considered the first function and mission of the university, and observing educational ethics guarantees the health of the teaching–learning process in the university. Aim: The aim of this study was to explore ethical values in nursing education from the perspective of Iranian nursing students and educators. Research design: This qualitative study was conducted using the Thematic Content Analyses method. The data were collected from seven semi-structured individual interviews and three focus group discussions from July (...) to November 2015. Participants and research context: The participants were faculty educators of nursing and nursing students in Tehran, capital of Iran, who were selected through purposive sampling. They were recruited gradually. Sampling was continued until data saturation when no new codes were extracted. Ethical committee: This study was conducted after obtaining the approval of Shahid Beheshti University of Medical Sciences Ethics Committee, and informed consent were ensured before conducting the research. The principles of voluntariness, confidentiality, and anonymity were respected during the research process. Findings: Seven major themes emerged: human dignity, constructive human relations, educational justice, competency enhancement, excellence view, wisdom, and commitment and accountability. Conclusion: The results of this study indicated that although many of the values, as universal values, were similar to those of other countries—which can be a reflection of the globalization process in the nursing profession and the presence of humanistic and spiritual approaches at the roots of the discipline, some differences could be found in the content of values due to factors such as the people’s beliefs, culture, and religion. Iranian nursing students and educators revealed a unique and culture-based set of ethical values. (shrink)