**4. Uncertainties of Big Data in Education**

Ultimately, considering the benefits and drawbacks from an era of big data in education that is already upon us, there are many uncertainties surrounding the future of the field. Perhaps most importantly, educational organizations must build the capacity to maintain pace with technology and the ever-looming threat of cybersecurity breaches and data loss. Unfortunately, government entities and educational organizations must work together to prepare for an uncertain future where one large data breach could threaten the very existence of a school and the data of countless stakeholders. Similarly, educational organizations should diligently capture data that does not require student or stakeholder consent, but these organizations should also develop a sense of trust between the organization and its community. Through establishing this trust, students and other stakeholders may be more interested in providing correct, robust data to allow the school or organization to make the best-informed decisions.

There is also the uncertainty of how big data can actually inform policy, or if big data will simply exist in the cloud or on a server without analysis, contextualization, and policy advocacy. Macfadyen et al. reasoned that, "the challenge of bringing about institution–wide change in such complex and anarchic adaptive systems may rightly be characterized as a 'wicked problem'– a problem that is complex, unpredictable, open ended, or intractable" [18] (p. 22). Here, the very nature of big data and the possibility of overwhelming paralysis or unsteady leadership could lead many big data initiatives down unclear pathways.

As of the writing of this review, many developing nations do not have big datasets nor the means to assemble them, and it is unclear whether developed nations will partner with developing nations to improve educational equity on a global scale. It often remains the responsibility of local or national governments to gather resources, learn from other nations, and launch big data initiatives. For example, in the Caribbean context, Charran et al. reasoned that literature on inclusive education in the Caribbean shows a deficit in the availability of special education services and resources and a lack of teacher training in special education [4]. Here, Charran et al. argued that the challenge for governmentssuch as those of Caribbean nations—is creating education policies that detail specific educational interventions, such as providing appropriate special education services and mandatory teacher preparation for working with students with disabilities [4]. In this regard, government involvement is crucial, but without the necessary resources, developing nations may not be able to gather data, use it to identify interventions, and realize change, thus falling further behind developed nations [4].

Additionally, developing South Asian nations have also struggled with data collection and the public availability of data. Pakistan experienced considerable population growth in the 1980s and 1990s, resulting in consecutive decades of at least 4% population growth and comparable educational enrollment. However, Pakistan's Ministry of Federal Education and Professional Training does not make large or longitudinal education data available to the general public, and the Ministry's website does not house any large or longitudinal datasets at the primary, secondary, or postsecondary level [26]. As recent as 2013, Pakistani educational researchers have bemoaned the fact that Pakistani educational data is not available, claiming that, "To the best of our knowledge, there is none for Pakistan which uses a historical series of disaggregated data of education to investigate both level and growth effect of human capital on the economic growth" [27] (p. 384).

In the Middle East, ravaged by war and faltering national economies, Syria's educational system has been in crisis for over a decade, partially resulting from little or no regular data collection and analysis. In a Syrian educational report from 2022, researchers suggested that data collection, disaggregation, and analysis was a primary factor in limiting Syrian educational progress. As the researchers wrote, "Data in Syria is not disaggregated by hub or geographical region, demonstrating a challenge with data integrity across Syria, both within and beyond the education" [28] (p. 12). Even though Syrian educational leaders have attempted to gather data systematically in recent years in order to "facilitate better coordination across the different hubs, including in terms of information management, this has not yielded robust results in terms of data and data analysis within the education sector" [27] (p. 12). Here, many developing nations have endured years or decades of struggles in terms of meeting the basic needs of their citizens, never mind embarking upon educational data collection and analysis projects to make better informed educational decisions.

In all, educational organizations are operating in an increasingly complex and competitive environment where data is currency. Educational leaders are under increasing pressure to respond to shifts in national and local economies, as well as political and social change such as the growing need to increase access to education for low-income communities, communities of color, people with disabilities, and individuals from marginalized groups. Unfortunately, many developing nations serve large populations of marginalized people, and these are the very nations that could most benefit from big data initiatives to help educational organizations become more efficient and effective with fewer resources and more global competition for students and talent.

#### **5. The Neoliberal Shift away from Educational Equity**

In many nations, neoliberal policies and agendas have prioritized the growth of the private sector and the defunding of public goods, including educational services. Recent work has underscored the necessity for educational organizations to facilitate data collection and dataset construction initiatives [29], but these initiatives may prove futile if national or local-level governments are not supportive of educational initiatives or view privatization of education as preferable. Yet, a wealth of research has found that neoliberal education policies and practices often minoritize the neediest communities and students, including communities of color and communities from low-income backgrounds [30]. Although prevalent in developed nations such as the United States and members of the European Union, governments of developing nations have begun adopting neoliberal policies, leveraging data for supposed accountability purposes to justify a shift towards the privatization of educational services. Subsequently, socioeconomic and racial equity

gaps have emerged in many educational settings, bringing into question the purpose of using data for educational decision making if the aims of those decisions are to dismantle education systems that serve the most underserved in the name of neoliberalism [30].

#### **6. Conclusions**

Whether educational organizations are ready or not, big data is already changing the global education landscape and increasing opportunities for those nations who can leverage big data to make data driven decisions. For many developing nations, the adage "you can't manage what you don't measure," may ring true, while many impoverished school districts simply cannot measure what they cannot manage. School leaders and teachers are already under enormous pressure as it is, so asking these stakeholders to develop big data sets to inform the work they do seems particularly onerous. Additionally, many developing nations may be struggling with national-level concerns such as war and economic challenges that render educational data collection and analysis a potential afterthought [4,26–28,31].

As a result, developing nations should work alongside developed nations to build the human, financial, and technological capacity necessary to chart a pathway toward big data fluency and utility. Within developed nations, educational leaders are already enlarging big data and performing transnational analyses of big data to inform educational change on a global scale [5,7,9,11,25,31]. However, comparisons of developed and developing nations may prove futile, as comparing nations is not only difficult but perhaps nonsensical given the vastly different geopolitical and social divides between nations. Understanding these divides, developed nations also have a responsibility to perform the necessary equity work to partner with developing nations to ensure that this educational change is on a truly global scale and that is inclusive of all nations and their students, schools, and communities.

**Author Contributions:** Conceptualization, Z.W.T.; formal analysis, Z.W.T., C.C. and J.C.; data curation, Z.W.T., C.C. and J.C.; writing—original draft preparation, Z.W.T., C.C. and J.C.; writing—review and editing, Z.W.T.; supervision, Z.W.T.; project administration, Z.W.T.; funding acquisition, Z.W.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**


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