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Find out moreData-driven decision-making (DDDM) is an effective approach in higher education. Experts argue that big data analysis offers opportunities for developing relevant educational programs, selecting motivated applicants, monitoring student attendance, and predicting dropout rates. The use of DDDM allows educational institutions to optimize their processes, improve the quality of education, and enhance student satisfaction. Using data to make informed decisions is becoming a key factor for the successful operation of higher education institutions in today's marketplace. In practice, various difficulties arise even at the data collection stage. Universities face numerous challenges when working with big data. These issues were discussed by experts at the roundtable discussion "Data for Student Success Analytics: Deficiencies, Limitations, and Opportunities" as part of the "Transforming Education" forum. The forum, organized by the Tomsk State University Institute of Education, took place from November 17 to 19. This material presents the main points of the speakers' presentations regarding the problem of collecting and analyzing data to improve the effectiveness of the educational process.
What data can universities collect?
Ivan Karlov, Head of the HSE Laboratory for Digital Transformation of Education, presented the results of a study conducted jointly with colleagues. They analyzed over 500 scientific publications devoted to the use of data analytics in universities. The study identified the main types of data most frequently used in higher education. These data play a key role in optimizing educational processes and improving the quality of education at universities.
- Data from the university's internal information systems. This includes demographic data about students, their educational results, the digital footprint left on the LMS platform and in other university systems. In other words, all the information the university receives directly when a student begins their studies.
- Data from open sources. This category primarily includes information from students' personal social media pages.
- Data from external information systems. This category includes information about academic performance, as well as information from social services. According to Ivan Karlov, data from external systems is actively used, particularly in American and Chinese universities, but in the Russian context, it is unavailable to universities.
- Data from specialized devices. This refers to gadgets that users wear, such as smartwatches and fitness trackers, as well as neural interfaces. Some foreign universities use data from such devices to develop predictive and recommendation models.
- Data from sociological research and competency measurements. Data-driven management typically relies on information that is collected en masse and continuously—known as big data. However, sometimes this information is insufficient, and universities conduct research on selected samples—for example, to analyze student competencies or their satisfaction with learning. The resulting data can be used to develop a model: "Under such-and-such conditions, such-and-such educational results were achieved." This model can then become a tool for continuously monitoring certain parameters.
What problems arise in collecting and analyzing data
Katerina Guba, Director of the Center for Institutional Research in Science and Education at the European University at St. Petersburg, noted that with the increase in the volume of research based on big data, various problems arise related to both its accumulation and the analysis process. This situation highlights the need to develop effective methods for working with big data to extract maximum benefit from it for science and education.
Katerina mentioned several problems, but, in her opinion, this list could be significantly expanded.
Researchers are not the source of big data; it is generated as a result of the activities of various departments and organizations. For example, universities accumulate personal data on students, including information on enrollment, academic performance, and other aspects of the educational process. These data provide valuable raw materials for analysis and research, allowing us to identify trends and improve educational practices.
Educational organizations primarily use this data to decide practical administrative tasks. However, if researchers try to obtain the necessary information, they may encounter difficulties in accessing and analyzing it.
According to the expert, a university is a complex organization consisting of various units, such as institutes, faculties, services, and departments. Each of these units has its own methods of collecting and storing data. As a result, researchers face difficulty obtaining a unified database from the university, forcing them to collect information piecemeal. This fragmented approach complicates the research process and requires significant time to consolidate the necessary data.
The difficulty Katerina Guba points out lies in the impossibility of translating operational data into research data without the involvement of university administrators. These employees collect data, but are often overloaded with routine tasks, which hinders their ability to promptly respond to researchers' requests. This creates obstacles to the effective use of data for scientific purposes and reduces the speed of research.
The speaker notes that researchers have questions regarding the quality of administrative data. This is due to the fact that universities collect a large amount of information for reporting, which is used by higher-level agencies to evaluate the effectiveness of universities. Universities, in turn, strive to present this information in the most favorable light, which leads to data distortion and its inconsistency with objective reality.
According to Katerina, researchers often need to "do some digging," which involves, in particular, conducting interviews. This allows you to better understand the real state of affairs with the indicators and determine how much you can trust them.
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Although researchers can obtain data and verify its reliability, as well as thoroughly process and analyze the information, the results of their work do not always lead to the expected solutions. Sometimes, even the highest-quality research may not have practical application.
You can prepare a scientific article based on the results of your research, but implementing these results into university practice is significantly challenging. Successful integration requires the coordinated work of various university departments and divisions, which is complicated by the fragmented structure and the high level of regulation of all processes. Moreover, not all educational institutions are ready to adopt an evidence-based approach to education. The speaker emphasizes the importance of popularizing this approach, as well as demonstrating its value and practical benefits for the educational process.
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