**6. Conclusions**

Ultimately, developed nations have provided guidance for developing nations when building large educational datasets to improve educational decision-making and fill equity gaps. As most developed nations have done, central governments in developing nations should continue to build both human and financial capacities to survey their population and pay close attention to demographic information that has been found to impact students' educational success and economic development. This implies broad, equitable surveying of diverse geographic areas to ensure that all people are counted, and their data contributed to the local- or national-level dataset. Here, developing nations will likely need to develop relationships with community-based organizations and collaborate with local communities to understand how to survey the people and understand local demographics. Building trust and communicating clearly with local communities could help ensure that surveying is robust and accurate, as well as ensure that resources can be distributed equitably once data are collected and analyzed.

Moreover, researchers should develop survey instruments that capture a wide range of races and/or ethnicities, gender identities, disability statuses, and other personal demographics to ensure people are accurately and authentically counted and supported. As robust as their data are, the shortcomings of the U.S. and E.U. datasets are that demographics are often reported by categories that are far too large and miss the nuance that is required to provide targeted educational interventions. Whether survey instruments are newly developed or new iterations of old designs, researchers should expand demographic categories to better understand—and respect—all people to improve their educational opportunities and outcomes.

In general, developing nations must consider the five major limitations of building large educational datasets: human capacity, financial capacity, technological capacity, geography, and sociopolitical contexts and variability. Although developing nations such as India and developed nations such as the United States, the European Union, Australia, and China have enviable datasets and educational resources, other developing nations can follow their lead and begin developing inclusive survey instruments and collaborating with communities to build rich datasets capable of being integrated with international data exchanges, such as SDMX. In modern society, forging a path toward educational equity will require data-driven decision-making to uncover equity gaps and distribute resources equitably, and developing nations can serve their people through the equitable building of educational datasets to improve lives everywhere.

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

**Funding:** This Special Issue Was Funded by the International Joint Research Project "Education Development and Social Justice", Faculty of Education, Beijing Normal University.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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