- Email: [email protected]
‘Big Data in Nurse Education’
YNEDT-03347; No of Pages 3 Nurse Education Today xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
Nurse Education Today journal homepage: www.elsevier.com/nedt
Contemporary Issues
‘Big Data in Nurse Education’ Keywords: Big data Tertiary nurse education Health professional Undergraduate Analytics
1. Introduction Recent technological advances have revolutionised the way we collect, store and manage information. The digitalisation of our world has also vastly increased the quantity of data we collect. Everything we do, including teaching health professionals, creates a digital trace (data) which has the potential to be analysed. Big data techniques can be used to extract, manage, analyse and interpret massive datasets and turn them into meaningful hypotheses that can inform practice. The intriguing new possibility of ‘big data and analytics’ is currently happening in health professional education (Harper and Parkerson, 2015). Big data is important to educators and learners because it has the potential to revolutionise nurse education policy, research and practice. While big data could potentially inform positive change to improve teaching and learning experiences, the generation of more data has not yet translated into useable knowledge that can be applied to education best practice (Ellaway et al., 2014). This contemporary issues piece will synthesize the current literature on big data and apply it to the niche of nurse education. The potential for big data implementation will be explored, barriers and dangers discussed and recommendations made that could be applied to other undergraduate health disciplines internationally. 2. What Is Big Data and Analytics? Big data is a powerful and topical field originating from disciplines that routinely collect and analyse vast amounts of data such as genomics, astronomy and meteorology and can be applied to other disciplines including health and education (Gray, 2007). Big data has been described as hypotheses generating machine and the most important technological trend of our time which uses computers to eclipse human limits (Khoury and Ioannidis, 2014). It is widely acknowledged that the more data, both structured and unstructured, that organisations can access and analyse, the more sophisticated their decision making processes become. This superior insight is thought to lead to better performance, reduced risk and improved efficiency (Khoury and Ioannidis, 2014). Big data refers to data sets so large and complex they are impractical to manage with traditional software tools and it relates to data, methods
and techniques that were unavailable a decade ago (Baro et al., 2015). There are three key elements to Big data: volume (the scale of data), variety (different forms of data) and velocity (speed of data processing) (Sagiroglu and Sinanc, 2013). These large volumes of high velocity, complex and variable data require advanced techniques to enable capture, distribution and analysis of the information (TechAmerica Foundation's Federal Big Data Commission, 2012, as cited in Gandomi and Haider, 2015). If big data is the noun, then analytics is the verb and refers to how we can extract, validate, translate and utilise big data as a new currency. Analytics can support nurse education in a number of ways including improving operational and financial decision making, assisting the attainment of specific learning goals and by uncovering relationships and patterns within large data sets that can predict behaviour and events (Barneveld et al., 2012). Data types in nurse education include data about teaching, learning and assessment. Students generate data through e-portfolios, electronic medical records and social media. Furthermore, administrative and academic staff generate data through academic progress reports, class attendance lists, scholarship and research. Stakeholders involved in collecting, analysing and using data to make decisions include learners, educators, administrators, clinical supervisors and academics. These stakeholders collect data about education and assessment from a variety of settings, from first year tutorial class lists, to final year clinical competency documents. These stakeholders encounter students in a myriad of interactions which are opportunities to collect data, from an online polling platform to an interdisciplinary simulation. Taking into account the numerous stakeholders, settings and interactions, a tremendous volume of data points are available (Ellaway et al., 2014). What if educators were able to collate and analyse this data and make it available to stakeholders who play a role in a student's progression through the undergraduate system? This represents the possibility to gather knowledge previously unattainable about how educators teach and how students learn. 3. Challenges and Barriers Currently, some universities are working on data mining, extraction and analytics to benefit academics and optimize student learning experiences. In the current tertiary system data is dispersed with inconsistent access. Data is protected by passwords, stored on personal computers and retained in a way that is not transparent. We lack good tools for both data curation and data analysis (Gray, 2007). Apart from a lack of integration between systems other data management problems include loss of data with platform change, debate on duration of data storage, challenges with staff training, confidentiality, data security issues and misuse of shared drives. There is also a
http://dx.doi.org/10.1016/j.nedt.2016.08.003 0260-6917/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Schwerdtle, P., Bonnamy, J., ‘Big Data in Nurse Education’, Nurse Educ. Today (2016), http://dx.doi.org/10.1016/ j.nedt.2016.08.003
2
Contemporary Issues
contested view of who is responsible for data, so diffused responsibility ensues. Harper and Parkerson (2015) cite an important challenge to big data implementation being a lack of standardised terminology in nursing. If data is to be aggregated and compared it needs to be standardised and therefore comparable across users, venues and regions. Free text documentation also limits the storage of discrete data which negatively impacts aggregation and analysis (Harper and Parkerson, 2015). Dealing with acquiring, storing and accessing data has moral and ethical implications and questions undoubtedly arise about who owns the data. And who is afforded the power to use data to pursue their own agenda? Further stifling big data implementation in nurse education is the absence of a data sharing tradition between universities as they continue to compete for student cohorts and rank themselves against each other internationally. 4. The Potential Mayer-Schonberger and Cukier (2013) sum up the potential of big data in one succinct sentence: ‘things that can be done on a large scale that cannot be done on a smaller scale, to extract new insights, or create new forms of value’. Data has always informed teaching. Assessing and measuring student progress is a process as old as the practice of teaching itself however, never before have educators had the opportunity to customise and understand nurse education to the degree possible if we exploit the potential of big data and analytics (Khan, 2014). Big data has been described to be a new research methodology by and of itself and has been referred to as a ‘data-driven methodology’ (Boyd and Crawford, 2012; Harris et al., 2009). In contrast, Kitchin (2014) argues big data is a disruptive innovation that will redesign the way research is conducted by promoting ‘data-driven’ rather than ‘research-driven’ science. Following this theory, the implications for evidence based nursing are significant. Ellaway et al. (2014) suggest there is an untapped capacity to identify emerging trends, extract meaning, discover knowledge and improve anomaly detection and predictive capacity. Educational institutions can leverage data to adapt nurse education and training programs to better meet student needs and to optimize operations and infrastructure. A further example of the potential of big data to nurse education is increased personalized competency data at the individual level. Specifically, clinical placement performance data could be linked to the complete record of performance in the simulated clinical learning environment to judge whether the student is competent to graduate. Capturing longitudinal data from multiple sources could be used to assess the impact of changes in admission policies or curricula. Aggregated performance data could be analysed across regional universities to assess norms against national standards and set benchmarks for performance and accreditation of courses (Ellaway et al., 2014). The goal of big data is sharable and comparable data. There is potential to utilise data in a more meaningful way, to generate hypotheses, to determine the components of the best possible nursing student and to optimally support the borderline or struggling student. In short, nurse education has the potential to become data driven, rather than simply data generating (Sensmeier, 2015). 5. Dangers of Big Data While big data is thought to be “intrinsically benign and desirable”, with the potential to drive innovation and new thinking there is reason to proceed with caution (Ellaway et al., 2014, p. 219). Big data may also mean big danger (Mayer-Schonberger & Cukier, 2013). Trustworthiness of big data generated hypotheses is essential to its widespread implementation in nurse education. Trustworthiness can
be undermined by incomplete or corrupt datasets and unclear standards and processes for storing, accessing, analysing and presenting big data. The trustworthiness of learning analytics can also be compromised if learners become aware that data is being collected about them (Ellaway et al., 2014). The “Digital Hawthorne Effect” is already being seen in learners who are superficially clicking through online learning modules without comprehension to reach the end and have their completion recorded in their organisations online learning management system (Ellaway et al., 2014, p. 220). It is essential that organisations ensure that data about learners is valid and reliable if that data is to be used as a proxy to support learning and make judgements about learners. Another danger lies in predictive analytics which refers to the temptation to use data-driven hypotheses to judge student's capacity and make decisions about course progression. Predictive analytics could also alter an individual learner's career path or area of specialisation which raises ethical and accountability concerns (Ellaway et al., 2014). Collecting and utilising learner related data raises institutional issues around risk management and data ownership. Ownership of data is becoming increasingly blurred as information is readily and easily transferred between systems and organisations (Bristol, 2011). Clear policies and procedures are lacking related to data types, access rules and permission needed for data analysis or transfer. There have been legal implications for organisations that have automatically included student work in plagiarism detection databases leading to lawsuits, which highlights the danger associated with ambiguity around data ownership (Williams, 2007). Furthermore, requirements for data security are becomingly increasingly difficult to sustain due to the complexity of big data ecosystems and pervasive data collection at every opportunity that plagues many online systems (Navetta, 2013). Disingenuous correlations and data generated fallacies have the potential to multiply when using big data. There is the potential to draw conclusions based on the ‘noise’ or computer-generated hypotheses generated by large data sets, rather than the ‘signal’ or user-generated conclusion that needs to be drawn out. For example, during an influenza outbreak in the US, big data analysis of flu-related internet searches drastically overestimated peak flu levels compared to those predicted by traditional public health surveillance (Khoury and Ioannidis, 2014). Big data analytics are equipped at finding association, but less adept at describing the nature of relationships, confirming causation and deciding whether associations have meaning. It is a hypothesis generating, rather than a hypothesis confirming engine. At this early stage of big data analytics, we must think critically about the theories generated and be mindful that currently, while associations may be the realm of machines, conclusions and recommendations remain very much in the human domain. An example of this in nurse education is institutions that employ text matching software to check the originality of student assessments. Whilst these platforms are capable of matching text in a student's assessment task to voluminous amounts of literature in their databases it is unable to make a decision about whether plagiarism has actually occurred (Whittle and Murdoch-Eaton, 2008). This remains within the domain of academics who must further investigate the student's intentions and make a decision regarding academic misconduct (Bristol, 2011). Disingenuous correlations could lead to dubious decision making and misplaced funds. 6. Recommendations In light of the potential big data offers the discipline and the dangers it poses the following recommendations are suggested. Firstly, the barriers to big data implementation need to be reduced. This can be achieved by increasing digital literacy among stakeholders. Ellaway et al. (2014) purports that implementation is only possible once large datasets exist, are meaningfully integrated and are available for analysis by users other than big data scientists. Software must be useable for the average educator and results must be presented in
Please cite this article as: Schwerdtle, P., Bonnamy, J., ‘Big Data in Nurse Education’, Nurse Educ. Today (2016), http://dx.doi.org/10.1016/ j.nedt.2016.08.003
Contemporary Issues
ways to benefit course convenors, faculty leaders, academics and learners. Fung et al. (2015) agree, stating that big data scientists must forge better relationships if the vision of practical big data implementation is to be realised. Assigning, training and mentoring ‘big data nurse specialists’ who champion the techniques, showcase big data potential in research and apply big data to their teaching may be an innovative way to achieve successful implementation. Students should also be involved in attempts to increase big data literacy in nursing. Preferably this should be integrated into teaching methods rather than added on to the curriculum. For example, using big data visualisation to explain the distribution of disease in population health units will illustrate to students how big data is applicable to practice. Big data is also happening in the clinical environment, so the argument for these skills being attractive in graduates and relevant to clinical practice is strong. Secondly, collaboration is essential for big data integration as we pursue the goal of sharable and comparable data (Harper and Parkerson, 2015). Collaboration at the individual level may involve joint projects involving IT specialists and nurse academics. Combining programming, data mining and analytics expertise with nurse education expertise could lead to transformative innovation and build capacity for future alliances. Finally, an anticipation of pitfalls including data generated fallacies and the slippery slope of predictive analytics will be vital to big data progression. Inherent in these concerns in a need to improve security and clarify the rules around data collection and management. Data can be protected by an internet firewall, data encryption and strong passwords (Ellaway et al., 2014). There must be clear policies and procedures related to what data is being collected, who has access to the data and whose permission is needed for data processing or transfer (Navetta, 2013). 7. Conclusion Big data techniques have the potential to revolutionise health professional education as the applications are endless. The ability to extract, manage, analyse and interpret massive datasets is becoming increasingly important in an online education environment. The potential to generate new knowledge about education and learners not previously possible is widely accepted as tremendous. However, privacy and security risks along with the possibility of misinterpretation and misuse have the potential to create moral, legal and ethical issues for learners and educators. Barriers to implementation are new and numerous and future research should focus on making the daunting task of big data
3
and analytics a workable, safe and dependable reality for nurse education. References Barneveld, A.V., Arnold, K.E., Campbell, J.P., 2012. Analytics in higher education: establishing a common language. Retrieved from http://educause.edu/ir/library. Baro, E., Degoul, S., Beuscart, R., Chazard, E., 2015. Toward a literature-driven definition of big data in healthcare. BioMed Res. Int. 1–9. Boyd, D., Crawford, K., 2012. Critical questions for big data. Inf. Commun. Soc. 15 (5), 662–679. http://dx.doi.org/10.1080/1369118X.2012.678878. Bristol, T.J., 2011. Plagiarism prevention with technology. Teach. Learn. Nurs. 6 (3), 146–149. http://dx.doi.org/10.1016/j.teln.2011.05.002. Ellaway, R., Pusic, M., Galbraith, R., Cameron, T., 2014. Developing the role of big data and analytics in health professional education. Med. Teach. 36 (3), 216–222. http://dx.doi. org/10.3109/0142159X.2014.874553. Fung, I., Tse, Z., Fu, K., 2015. Converting big data into public health. Science 347, 620. http://dx.doi.org/10.1126/science.347.6222.620-b. Gandomi, A., Haider, M., 2015. Beyond the hype: big data concepts, methods and analytics. Int. J. Inf. Manag. 35, 137–144. Gray, J., 2007. Transformed scientific method. Retrieved from http://research.microsoft. com/enus/collaboration/fourthparadigm. Harper, E.M., Parkerson, S., 2015. Powering big data for nursing through partnership. Nurs. Adm. Q. 39 (4), 319–324. Harris, P.A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., Conde, J.G., 2009. Research electronic data capture (REDCap) — a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42 (2), 377–381. Khan, A., 2014. Explainer: what is big data? Retrieved from http://monash.edu/new/ show/explainer-what-is-big-data Khoury, M., Ioannidis, J., 2014. Big data meets public health. Science 346, 1054–1055. Kitchin, R., 2014. Big data, new epistemologies and paradigm shifts. Big Data Soc. 1–12. Mayer-Schonberger, V., Cukier, K., 2013. Big Data: A Revolution That Will Transform How We Live, Work and Think. John Murray Publishers, United Kingdom. Navetta, D., 2013. Legal implications of big data: a primer. ISSA J. 14–19. Sagiroglu, S., Sinanc, D., 2013. Big data: a review. Retrieved from http://ieeexplore.ieee.org. Sensmeier, J., 2015. Big data and the future of nursing knowledge. Nurs. Manag. 46 (4), 22–27. Whittle, S.R., Murdoch-Eaton, D.G., 2008. Learning about plagiarism using Turnitin detection software. Med. Educ. 42 (5), 528. Williams, B., 2007. Trust, betrayal, and authorship: plagiarism and how we perceive students. J. Adolesc. Adult Literacy 51 (4), 350–354.
Patricia Schwerdtle, MIH, PGC-HPE, PGD-CC, Bach HP, RN⁎ James Bonnamy, MNurs, BNurs, RN, MACN Lecturer, Faculty of Medicine, Nursing and Health Sciences, School of Nursing and Midwifery, Monash University, PO Box 527, Frankston, VIC, 3199, Melbourne, Australia ⁎Corresponding author. E-mail addresses: [email protected] (P. Schwerdtle), [email protected] (J. Bonnamy). Available online xxxx
Please cite this article as: Schwerdtle, P., Bonnamy, J., ‘Big Data in Nurse Education’, Nurse Educ. Today (2016), http://dx.doi.org/10.1016/ j.nedt.2016.08.003
Contents lists available at ScienceDirect
Nurse Education Today journal homepage: www.elsevier.com/nedt
Contemporary Issues
‘Big Data in Nurse Education’ Keywords: Big data Tertiary nurse education Health professional Undergraduate Analytics
1. Introduction Recent technological advances have revolutionised the way we collect, store and manage information. The digitalisation of our world has also vastly increased the quantity of data we collect. Everything we do, including teaching health professionals, creates a digital trace (data) which has the potential to be analysed. Big data techniques can be used to extract, manage, analyse and interpret massive datasets and turn them into meaningful hypotheses that can inform practice. The intriguing new possibility of ‘big data and analytics’ is currently happening in health professional education (Harper and Parkerson, 2015). Big data is important to educators and learners because it has the potential to revolutionise nurse education policy, research and practice. While big data could potentially inform positive change to improve teaching and learning experiences, the generation of more data has not yet translated into useable knowledge that can be applied to education best practice (Ellaway et al., 2014). This contemporary issues piece will synthesize the current literature on big data and apply it to the niche of nurse education. The potential for big data implementation will be explored, barriers and dangers discussed and recommendations made that could be applied to other undergraduate health disciplines internationally. 2. What Is Big Data and Analytics? Big data is a powerful and topical field originating from disciplines that routinely collect and analyse vast amounts of data such as genomics, astronomy and meteorology and can be applied to other disciplines including health and education (Gray, 2007). Big data has been described as hypotheses generating machine and the most important technological trend of our time which uses computers to eclipse human limits (Khoury and Ioannidis, 2014). It is widely acknowledged that the more data, both structured and unstructured, that organisations can access and analyse, the more sophisticated their decision making processes become. This superior insight is thought to lead to better performance, reduced risk and improved efficiency (Khoury and Ioannidis, 2014). Big data refers to data sets so large and complex they are impractical to manage with traditional software tools and it relates to data, methods
and techniques that were unavailable a decade ago (Baro et al., 2015). There are three key elements to Big data: volume (the scale of data), variety (different forms of data) and velocity (speed of data processing) (Sagiroglu and Sinanc, 2013). These large volumes of high velocity, complex and variable data require advanced techniques to enable capture, distribution and analysis of the information (TechAmerica Foundation's Federal Big Data Commission, 2012, as cited in Gandomi and Haider, 2015). If big data is the noun, then analytics is the verb and refers to how we can extract, validate, translate and utilise big data as a new currency. Analytics can support nurse education in a number of ways including improving operational and financial decision making, assisting the attainment of specific learning goals and by uncovering relationships and patterns within large data sets that can predict behaviour and events (Barneveld et al., 2012). Data types in nurse education include data about teaching, learning and assessment. Students generate data through e-portfolios, electronic medical records and social media. Furthermore, administrative and academic staff generate data through academic progress reports, class attendance lists, scholarship and research. Stakeholders involved in collecting, analysing and using data to make decisions include learners, educators, administrators, clinical supervisors and academics. These stakeholders collect data about education and assessment from a variety of settings, from first year tutorial class lists, to final year clinical competency documents. These stakeholders encounter students in a myriad of interactions which are opportunities to collect data, from an online polling platform to an interdisciplinary simulation. Taking into account the numerous stakeholders, settings and interactions, a tremendous volume of data points are available (Ellaway et al., 2014). What if educators were able to collate and analyse this data and make it available to stakeholders who play a role in a student's progression through the undergraduate system? This represents the possibility to gather knowledge previously unattainable about how educators teach and how students learn. 3. Challenges and Barriers Currently, some universities are working on data mining, extraction and analytics to benefit academics and optimize student learning experiences. In the current tertiary system data is dispersed with inconsistent access. Data is protected by passwords, stored on personal computers and retained in a way that is not transparent. We lack good tools for both data curation and data analysis (Gray, 2007). Apart from a lack of integration between systems other data management problems include loss of data with platform change, debate on duration of data storage, challenges with staff training, confidentiality, data security issues and misuse of shared drives. There is also a
http://dx.doi.org/10.1016/j.nedt.2016.08.003 0260-6917/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Schwerdtle, P., Bonnamy, J., ‘Big Data in Nurse Education’, Nurse Educ. Today (2016), http://dx.doi.org/10.1016/ j.nedt.2016.08.003
2
Contemporary Issues
contested view of who is responsible for data, so diffused responsibility ensues. Harper and Parkerson (2015) cite an important challenge to big data implementation being a lack of standardised terminology in nursing. If data is to be aggregated and compared it needs to be standardised and therefore comparable across users, venues and regions. Free text documentation also limits the storage of discrete data which negatively impacts aggregation and analysis (Harper and Parkerson, 2015). Dealing with acquiring, storing and accessing data has moral and ethical implications and questions undoubtedly arise about who owns the data. And who is afforded the power to use data to pursue their own agenda? Further stifling big data implementation in nurse education is the absence of a data sharing tradition between universities as they continue to compete for student cohorts and rank themselves against each other internationally. 4. The Potential Mayer-Schonberger and Cukier (2013) sum up the potential of big data in one succinct sentence: ‘things that can be done on a large scale that cannot be done on a smaller scale, to extract new insights, or create new forms of value’. Data has always informed teaching. Assessing and measuring student progress is a process as old as the practice of teaching itself however, never before have educators had the opportunity to customise and understand nurse education to the degree possible if we exploit the potential of big data and analytics (Khan, 2014). Big data has been described to be a new research methodology by and of itself and has been referred to as a ‘data-driven methodology’ (Boyd and Crawford, 2012; Harris et al., 2009). In contrast, Kitchin (2014) argues big data is a disruptive innovation that will redesign the way research is conducted by promoting ‘data-driven’ rather than ‘research-driven’ science. Following this theory, the implications for evidence based nursing are significant. Ellaway et al. (2014) suggest there is an untapped capacity to identify emerging trends, extract meaning, discover knowledge and improve anomaly detection and predictive capacity. Educational institutions can leverage data to adapt nurse education and training programs to better meet student needs and to optimize operations and infrastructure. A further example of the potential of big data to nurse education is increased personalized competency data at the individual level. Specifically, clinical placement performance data could be linked to the complete record of performance in the simulated clinical learning environment to judge whether the student is competent to graduate. Capturing longitudinal data from multiple sources could be used to assess the impact of changes in admission policies or curricula. Aggregated performance data could be analysed across regional universities to assess norms against national standards and set benchmarks for performance and accreditation of courses (Ellaway et al., 2014). The goal of big data is sharable and comparable data. There is potential to utilise data in a more meaningful way, to generate hypotheses, to determine the components of the best possible nursing student and to optimally support the borderline or struggling student. In short, nurse education has the potential to become data driven, rather than simply data generating (Sensmeier, 2015). 5. Dangers of Big Data While big data is thought to be “intrinsically benign and desirable”, with the potential to drive innovation and new thinking there is reason to proceed with caution (Ellaway et al., 2014, p. 219). Big data may also mean big danger (Mayer-Schonberger & Cukier, 2013). Trustworthiness of big data generated hypotheses is essential to its widespread implementation in nurse education. Trustworthiness can
be undermined by incomplete or corrupt datasets and unclear standards and processes for storing, accessing, analysing and presenting big data. The trustworthiness of learning analytics can also be compromised if learners become aware that data is being collected about them (Ellaway et al., 2014). The “Digital Hawthorne Effect” is already being seen in learners who are superficially clicking through online learning modules without comprehension to reach the end and have their completion recorded in their organisations online learning management system (Ellaway et al., 2014, p. 220). It is essential that organisations ensure that data about learners is valid and reliable if that data is to be used as a proxy to support learning and make judgements about learners. Another danger lies in predictive analytics which refers to the temptation to use data-driven hypotheses to judge student's capacity and make decisions about course progression. Predictive analytics could also alter an individual learner's career path or area of specialisation which raises ethical and accountability concerns (Ellaway et al., 2014). Collecting and utilising learner related data raises institutional issues around risk management and data ownership. Ownership of data is becoming increasingly blurred as information is readily and easily transferred between systems and organisations (Bristol, 2011). Clear policies and procedures are lacking related to data types, access rules and permission needed for data analysis or transfer. There have been legal implications for organisations that have automatically included student work in plagiarism detection databases leading to lawsuits, which highlights the danger associated with ambiguity around data ownership (Williams, 2007). Furthermore, requirements for data security are becomingly increasingly difficult to sustain due to the complexity of big data ecosystems and pervasive data collection at every opportunity that plagues many online systems (Navetta, 2013). Disingenuous correlations and data generated fallacies have the potential to multiply when using big data. There is the potential to draw conclusions based on the ‘noise’ or computer-generated hypotheses generated by large data sets, rather than the ‘signal’ or user-generated conclusion that needs to be drawn out. For example, during an influenza outbreak in the US, big data analysis of flu-related internet searches drastically overestimated peak flu levels compared to those predicted by traditional public health surveillance (Khoury and Ioannidis, 2014). Big data analytics are equipped at finding association, but less adept at describing the nature of relationships, confirming causation and deciding whether associations have meaning. It is a hypothesis generating, rather than a hypothesis confirming engine. At this early stage of big data analytics, we must think critically about the theories generated and be mindful that currently, while associations may be the realm of machines, conclusions and recommendations remain very much in the human domain. An example of this in nurse education is institutions that employ text matching software to check the originality of student assessments. Whilst these platforms are capable of matching text in a student's assessment task to voluminous amounts of literature in their databases it is unable to make a decision about whether plagiarism has actually occurred (Whittle and Murdoch-Eaton, 2008). This remains within the domain of academics who must further investigate the student's intentions and make a decision regarding academic misconduct (Bristol, 2011). Disingenuous correlations could lead to dubious decision making and misplaced funds. 6. Recommendations In light of the potential big data offers the discipline and the dangers it poses the following recommendations are suggested. Firstly, the barriers to big data implementation need to be reduced. This can be achieved by increasing digital literacy among stakeholders. Ellaway et al. (2014) purports that implementation is only possible once large datasets exist, are meaningfully integrated and are available for analysis by users other than big data scientists. Software must be useable for the average educator and results must be presented in
Please cite this article as: Schwerdtle, P., Bonnamy, J., ‘Big Data in Nurse Education’, Nurse Educ. Today (2016), http://dx.doi.org/10.1016/ j.nedt.2016.08.003
Contemporary Issues
ways to benefit course convenors, faculty leaders, academics and learners. Fung et al. (2015) agree, stating that big data scientists must forge better relationships if the vision of practical big data implementation is to be realised. Assigning, training and mentoring ‘big data nurse specialists’ who champion the techniques, showcase big data potential in research and apply big data to their teaching may be an innovative way to achieve successful implementation. Students should also be involved in attempts to increase big data literacy in nursing. Preferably this should be integrated into teaching methods rather than added on to the curriculum. For example, using big data visualisation to explain the distribution of disease in population health units will illustrate to students how big data is applicable to practice. Big data is also happening in the clinical environment, so the argument for these skills being attractive in graduates and relevant to clinical practice is strong. Secondly, collaboration is essential for big data integration as we pursue the goal of sharable and comparable data (Harper and Parkerson, 2015). Collaboration at the individual level may involve joint projects involving IT specialists and nurse academics. Combining programming, data mining and analytics expertise with nurse education expertise could lead to transformative innovation and build capacity for future alliances. Finally, an anticipation of pitfalls including data generated fallacies and the slippery slope of predictive analytics will be vital to big data progression. Inherent in these concerns in a need to improve security and clarify the rules around data collection and management. Data can be protected by an internet firewall, data encryption and strong passwords (Ellaway et al., 2014). There must be clear policies and procedures related to what data is being collected, who has access to the data and whose permission is needed for data processing or transfer (Navetta, 2013). 7. Conclusion Big data techniques have the potential to revolutionise health professional education as the applications are endless. The ability to extract, manage, analyse and interpret massive datasets is becoming increasingly important in an online education environment. The potential to generate new knowledge about education and learners not previously possible is widely accepted as tremendous. However, privacy and security risks along with the possibility of misinterpretation and misuse have the potential to create moral, legal and ethical issues for learners and educators. Barriers to implementation are new and numerous and future research should focus on making the daunting task of big data
3
and analytics a workable, safe and dependable reality for nurse education. References Barneveld, A.V., Arnold, K.E., Campbell, J.P., 2012. Analytics in higher education: establishing a common language. Retrieved from http://educause.edu/ir/library. Baro, E., Degoul, S., Beuscart, R., Chazard, E., 2015. Toward a literature-driven definition of big data in healthcare. BioMed Res. Int. 1–9. Boyd, D., Crawford, K., 2012. Critical questions for big data. Inf. Commun. Soc. 15 (5), 662–679. http://dx.doi.org/10.1080/1369118X.2012.678878. Bristol, T.J., 2011. Plagiarism prevention with technology. Teach. Learn. Nurs. 6 (3), 146–149. http://dx.doi.org/10.1016/j.teln.2011.05.002. Ellaway, R., Pusic, M., Galbraith, R., Cameron, T., 2014. Developing the role of big data and analytics in health professional education. Med. Teach. 36 (3), 216–222. http://dx.doi. org/10.3109/0142159X.2014.874553. Fung, I., Tse, Z., Fu, K., 2015. Converting big data into public health. Science 347, 620. http://dx.doi.org/10.1126/science.347.6222.620-b. Gandomi, A., Haider, M., 2015. Beyond the hype: big data concepts, methods and analytics. Int. J. Inf. Manag. 35, 137–144. Gray, J., 2007. Transformed scientific method. Retrieved from http://research.microsoft. com/enus/collaboration/fourthparadigm. Harper, E.M., Parkerson, S., 2015. Powering big data for nursing through partnership. Nurs. Adm. Q. 39 (4), 319–324. Harris, P.A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., Conde, J.G., 2009. Research electronic data capture (REDCap) — a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42 (2), 377–381. Khan, A., 2014. Explainer: what is big data? Retrieved from http://monash.edu/new/ show/explainer-what-is-big-data Khoury, M., Ioannidis, J., 2014. Big data meets public health. Science 346, 1054–1055. Kitchin, R., 2014. Big data, new epistemologies and paradigm shifts. Big Data Soc. 1–12. Mayer-Schonberger, V., Cukier, K., 2013. Big Data: A Revolution That Will Transform How We Live, Work and Think. John Murray Publishers, United Kingdom. Navetta, D., 2013. Legal implications of big data: a primer. ISSA J. 14–19. Sagiroglu, S., Sinanc, D., 2013. Big data: a review. Retrieved from http://ieeexplore.ieee.org. Sensmeier, J., 2015. Big data and the future of nursing knowledge. Nurs. Manag. 46 (4), 22–27. Whittle, S.R., Murdoch-Eaton, D.G., 2008. Learning about plagiarism using Turnitin detection software. Med. Educ. 42 (5), 528. Williams, B., 2007. Trust, betrayal, and authorship: plagiarism and how we perceive students. J. Adolesc. Adult Literacy 51 (4), 350–354.
Patricia Schwerdtle, MIH, PGC-HPE, PGD-CC, Bach HP, RN⁎ James Bonnamy, MNurs, BNurs, RN, MACN Lecturer, Faculty of Medicine, Nursing and Health Sciences, School of Nursing and Midwifery, Monash University, PO Box 527, Frankston, VIC, 3199, Melbourne, Australia ⁎Corresponding author. E-mail addresses: [email protected] (P. Schwerdtle), [email protected] (J. Bonnamy). Available online xxxx
Please cite this article as: Schwerdtle, P., Bonnamy, J., ‘Big Data in Nurse Education’, Nurse Educ. Today (2016), http://dx.doi.org/10.1016/ j.nedt.2016.08.003