Query: interdisciplinarity statistics

title: Teaching Statistics: Creating an Intersection for Intra and Interdisciplinarity.
abstract: Statistics is taught in mathematics courses in all school levels. We suggest that using rich tasks in statistics can develop statistical reasoning and create both intra and interdisciplinary links in students. In this paper, we present three case studies where middle school mathematics teachers used different tasks in lessons on pie charts. We analyzed the actions implemented/performed/attempted by teachers to support the development of statistical reasoning and the creation of intra and interdisciplinary links in their lessons. Results show that their procedural vision of statistics led them to focus more on graphical representation, neglecting aspects of statistical reasoning. Results also reveal an interdisciplinary intersection between mathematics and statistics, which may prevent the development of statistical reasoning.

title: TEACHING STATISTICS IN A MATHEMATICS COURSE IN MIDDLE SCHOOL: INTERDISCIPLINARITY, REALLY?
abstract: Statistics is a domain combined with probability that is taught in Mathematics in all school levels. We suggest a potential in using an interdisciplinary approach with this concept, which, according to Fourez Larochelle (2003) implies that the development of one discipline contributes to the development of others. Thus the development of the understanding of a situation might mean to use both mathematical and statistical reasoning. In this paper, we present a case study where a middle school Mathematics teacher created a lesson in Statistics where the students had the task to study a pack of colored candy and create a pie graph representing its distribution. The teacher hoped that her students made links between mathematical concepts while doing this task. Results show us that her procedural vision of Statistics lead her to focus more on a graphical representation and thus led her to avoid all statistical reasoning development (Garfield, 2002)

title: Statistics, Desire, and Interdisciplinarity

title: Interdisciplinarity in Tech Startups Development Case Study of 'Unistartapp' Project
abstract: Empirical data as well as scientific reports confirm that startups require diverse, in house competencies in order to achieve market success. However, while technology related competencies are usually brought to new tech ventures by their founders, there is a significant deficiency of market and management related competencies, which is reflected in the statistics of common startup failure reasons. In this context, a question arises: how interdisciplinarity of knowledge and competencies can be built into the very core of tech startups? The text addresses this question by zeroing in on the role of universities in this process. In the first part, the specificity of tech startups is analyzed. The next chapter overviews the hitherto academy related startup education environment together with its shortcomings as far as interdisciplinarity is concerned. Finally, the case study of innovative and interdisciplinary academic ecosystem, which was built and tested within the project UniStartApp, is presented and discussed together with related lessons learned. The text is concluded with final remarks on challenges involved in embedding interdisciplinarity into startup education ecosystem.

title: Measuring the interdisciplinarity of Big Data research: a longitudinal study
abstract: The purpose of this paper is to measure the degree of interdisciplinary collaboration in Big Data research based on the co occurrences of subject categories using Stirling's diversity index and specialization index.,Interdisciplinarity was measured utilizing the descriptive statistics of disciplines, network indicators showing relationships between disciplines and within individual disciplines, interdisciplinary communities, Stirling's diversity index and specialization index, and a strategic diagram revealing the development status and trends of discipline communities.,Comprehensively considering all results, the degree of interdisciplinarity of Big Data research is increasing over time, particularly, after 2013. There is a high level of interdisciplinarity in Big Data research involving a large number of disciplines, but it is unbalanced in distribution. The interdisciplinary collaborations are not intensive on the whole; most disciplines are aggregated into a few distinct communities with computer science, business and economics, mathematics, and biotechnology and applied microbiology as the core. Four major discipline communities in Big Data research represent different directions with different development statuses and trends. Community 1, with computer science as the core, is the most mature and central to the whole interdisciplinary network. Accounting for all network indicators, computer science, engineering, business and economics, social sciences, and mathematics are the most important disciplines in Big Data research.,This study deepens our understanding of the degree and trend of interdisciplinary collaboration in Big Data research through a longitudinal study and quantitative measures based on two indexes. It has practical implications to study and reveal the interdisciplinary phenomenon and characteristics of related developments of a specific research area, or to conduct comparative studies between different research areas.

title: Measuring interdisciplinarity in clinical practice with IPC59, a modified and improved version of IPC65
abstract: Interdisciplinarity is considered a key concept in the management of complex cases in healthcare. However, working in interdisciplinary teams requires the integration of many concepts and a large amount of effort. To help healthcare managers and professionals identify the strengths and weaknesses of their interdisciplinary team and to ensure its continuous improvement, we developed a tool called the IPC65.  The purpose of this study was to test the reliability and validity of the IPC65.  Based on a comprehensive review of the literature and qualitative and quantitative assessments, the IPC65 was developed. In this study, the analysis was based on 392 healthcare professionals and managers from short term care settings who provided valid responses throughout the province of Quebec in Canada. Descriptive statistics, Cronbach's alpha values, and inter item correlations were measured, and a principal component analysis (PCA) was conducted. Item discrimination was used to provide an improved version of the IPC65.  The IPC65 showed good statistical results. The discriminant procedure provided the basis for shortening and improving the IPC65 to form the IPC59. Cronbach's alpha values ranged from 0.857 to 0.967 in IPC59, demonstrating very good reliability for all four dimensions. The PCA showed good validity.  The IPC59 can be used to assess the degree of integration of key concepts leading to interdisciplinarity.

title: Should we discipline interdisciplinarity?
abstract: Could we overcome the challenges of embedding interdisciplinarity in the academic mainstream if relevant expertise were defined and recognised as a new discipline? Such expertise includes the ability to combine knowledge from different disciplines, as well as to assess which disciplines and stakeholders have relevant perspectives, figure out how elements of problems are interconnected, decide how to deal with critical unknowns, and use research to support evidence based change. A new discipline of integration and implementation sciences (I2S) would codify such knowledge and skills, especially for dealing with complex societal and environmental problems. It would operate in an analogous way to the discipline of statistics, as a: (1) collaborative hub within teams, (2) focus for dedicated methods journals and (3) lobby group for effective application. Key initial tasks are to develop a repository of currently hard to access methods, and an identifiable academic community; both guided by a unifying and motivating vision of where disciplining interdisciplinarity will lead.

title: Application of "2+1" PAD class based on online course in medical statistics undergraduate teaching
abstract: To explore the effects of "2+1" PAD class based on online course in medical statistics undergraduate teaching.  A total of 326 clinical undergraduates who took medical statistics in the spring term of 2017 were randomly divided into experimental group (n=186) and control group (n=140) Students in the experimental group were taught by "2+1" teaching method, which included self study on line, classroom teaching, discussion and so on. While students in the control group were taught traditionally. Feedbacks about the teaching progress and effects from the students in the two groups were compared. SAS 9.4 was used for quantitative data description, t test and rank sum test. Results according to the students, the online based "2+1" teaching method paid more attention to interdisciplinarity than traditional method (P<0.01) and the new teaching method was more beneficial for the improvement of students' capacity (P=0.008) and more conducive for students to engaging in scientific research (P=0.012) Students in the experimental group had higher overall satisfaction scores (P<0.01) and better ability for solving comprehensive problems (P<0.01)  The "2+1" PAD class teaching model based on online courses has more advantages than traditional teaching methods does, which should be recommended and adopted in undergraduate teaching.  Higher education; PAD class; Teaching effect

title: Who Studies MOOCs? Interdisciplinarity in MOOC Research and Its Changes over Time.
abstract: The complexity of digital and online education is becoming increasingly evident in the context of research into networked learning/participation. Interdisciplinary research is often proposed as a way to address complex scientific problems and enable researchers to bring novel perspectives into a field other than their own. The degree to which research on Massive Open Online Courses (MOOCs) is interdisciplinary is unknown. We apply descriptive and inferential statistics to bibliometric data to investigate interdisciplinarity in MOOC research. Results show that MOOC research published in 2013 2015 was (a) mostly conducted by researchers affiliated with Education and Computer Science disciplines, (b) far from monolithic, (c) had a greater representation of authors from Computer Science than in the past, and (d) showed a trend toward being more interdisciplinary than MOOC research published in 2008 2012. Our results also suggest that empirical research on xMOOCs may be more interdisciplinary than research on cMOOCs. Greater interdisciplinarity in xMOOC research could reflect the burgeoning interest in the field, the general familiarity with the xMOOC pedagogical model, and the hype experienced by xMOOCs. Greater interdisciplinarity in the field may also provide researchers with rich opportunities to improve our understanding and practice of digital and online learning.

title: Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations
abstract: A citation based indicator for interdisciplinarity has been missing hitherto among the set of available journal indicators. In this study, we investigate network indicators (betweenness centrality) unevenness indicators (Shannon entropy, the Gini coefficient) and more recently proposed Rao Stirling measures for "interdisciplinarity." The latter index combines the statistics of both citation distributions of journals (vector based) and distances in citation networks among journals (matrix based) The effects of various normalizations are specified and measured using the matrix of 8207 journals contained in the Journal Citation Reports of the (Social) Science Citation Index 2008. Betweenness centrality in symmetrical (1 mode) cosine normalized networks provides an indicator outperforming betweenness in the asymmetrical (2 mode) citation network. Among the vector based indicators, Shannon entropy performs better than the Gini coefficient, but is sensitive to size. Science and Nature, for example, are indicated at the top of the list. The new diversity measure provides reasonable results when (1 cosine) is assumed as a measure for the distance, but results using Euclidean distances were difficult to interpret.