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Abstract: Use of data within higher education. How data is being misused in higher education. How higher institutions can reduce the misuse of data. Role of institutional research in facilitating appropriate use of data.
Introduction:
Availability of data drives the introduction of new policies and the recommendation for improving performance (students or institutional). Is this a good way to make use of available data in an institution? Is there a way to use this data and avoid its misuse? Specific concerns over the use of data include the following; power, legitimacy, ideology, transparency, intentionality and relevance (Calderon, 2015).
How data is used in Higher Education:
Data is used based on context, i.e. the situation in which it is being used (Spillane, 2012). This explains the interrelationship between social or organisational structures, projects or reports and analysis involving the data and the outcome of the data use. Data is conceptualised as an interactive endeavour that involves understanding how data flows into streams of ongoing activities and interactions as they occur (Coburn et al, 2012).
The environment, organisation and larger context play a role in influencing how data is being used. In most higher education, there are institutional research units (or depending on the name given) that manage, synthesize and transform data into reusable information. These professionals before anything else, determine the purpose of the data use, i.e. is it to describe a thing or an activity or is it to make an inference? (McLaughlin et al 2012).
The desired outcome from the data analysis will then determine the methodology to be used in analysing the data and also the amount of data to be used, the scope, definition, date and collection of the data. Thus, it is very important for these data professionals to take care in their choices when making use of data, it has to be in a clear, thoughtful and objective way so that they can be neutral and apolitical as possible.
In recent times due to the vast increase in data volume, velocity and variety, data visualization has been achieved in two ways, they are data metrics and data analytics.:
Data metrics give information on standard measurements obtained from past organizational operations.
Data analytics is more strategic and future-focused, identifying patterns from past data performances to help predict a future outcome.
These two methods used provide information for institutions to take action and improve the student experience and institutional performance.
Examples of Data misuse in Higher Education:
- Data that provide inaccurate information or misreporting: This is the data that data consumer believes is true when it is actually false. The data provides false information, this is the kind of (inaccurate or misreporting) data used for propaganda. This creates situations where resources (time, human & financial) are needed to correct the inaccuracies and mitigate the consequences of the misuse.
- Flawed data governance: Flawed or absence of data governance can lead to multiple misuses of institutional data. This usually occurs when institutional data is allowed to be used in a way that it is not intended, or when this data is accessed by outside entities (i.e. data breaches). Flawed data governance is often a result of a lack of data protocols, definitions and structures or a breakdown in their use.
- Data used as it is not intended: This is when data is not used as defined or when data does not measure up to what it intends or claims to measure. i.e. a definitional or a misapplication issue.
- Violation of privacy: When an individual’s data is collected, accessed, or used not in line with institutional policy, this constitutes a violation of privacy.
Recommendations to minimise data misuse in every institution
- Need for data, technical and context expertise. The room for context expertise is critical as it gives room for questions around legitimacy, intentionality and ideology of how data is used within institutions.
- Need to create, strengthen and monitor data governance and access to these data. Have clear path definitions and rules of use, definition and collection.
- Need for clear institutional principles and guidelines for its use and development.
References
Calderon, A. (2015). In light of globalization, massification, and marketization: Some considerations on the uses of data in higher education. In K. Webber and A. Calderon (Eds.), Institutional research and planning in higher education: Global contexts and themes (pp.288-306). Routledge Press: New York.
Coburn, C. & Turner, E. (2012). The practice of data use: An introduction. American Journal of Education, 118(2), 99-111.
Higgins, K. (2016). Post-truth: A guide for the perplexed. Nature, 540, 9.
McLaughlin, G., Howard, R., & Jones-White, D. (2012). Analytic approaches to creating planning and decision support information. In R. Howard, G. McLaughlin, & W. Knight (Eds.), The handbook of institutional research (pp. 459-477). Jossey-Bass: San Francisco.
Mathies, C. (2018). Uses and Misuses of Data. In K. L. Webber (Ed.), Building Capacity in Institutional Research and Decision Support in Higher Education (pp. 95-111). Springer. Knowledge Studies in Higher Education, 4. https://doi.org/10.1007/978-3-319-71162-1_7
Spillane, J. (2012). Data in practice: Conceptualizing the data-based decision-making phenomena. American journal of education. 118(2), 113-141.