Surveys are arguably the most common tool for data collection in higher education research, particularly for the study of student behaviour and social inequalities. The well-known weaknesses of survey research are issues related to sampling, response rates and lack of knowledge about the characteristics of non-respondents (Nayak & Narayan, 2019). The use of administrative data can circumvent these difficulties. Higher Education Administrative Data (HEAD) is a large set of behavioural data based on documentation with administrative software systems of higher education institutions. The data include information on student characteristics, study and examination patterns, learning curves, student success, drop-outs and length of study. It allows comprehensive data to be collected on the diversity, academic performance and behavioural patterns of entire cohorts of students, which is of interest to higher education institutions themselves. In addition to teaching evaluation, the purposes include accountability to societal stakeholders and the development of higher education structures and new public management (Beerkens, 2022).

Compared to traditional survey-based research, the collection and analysis of HEAD requires a much higher level of commitment to ethics and data protection (Florea & Florea, 2020). Therefore, the actual use is limited so far. In Europe, HEAD in the form of registry data is mainly used in the UK (e.g. Campbell et al., 2019; Chowdry et al., 2013) and Austria (e.g. Zucha et al., 2021) due to the strong centralisation of national statistical systems. Apart from a few recent studies of examination data from specific universities (e.g. Behlen et al., 2022; Pannier et al., 2020), the accessibility of HEAD in other European countries, such as Germany, remains difficult and researchers often fail to obtain the data in full (see Bandorski et al., 2019; Grözinger & McGrory, 2020). In summary, the potential of both forms of HEAD, registry data and higher education institution-specific administrative microdata, has not yet been fully exploited across Europe.

Furthermore, the data provided by management information systems is not very systematically structured or clearly organised. Depending on the internal organisation of the higher education institution, data fragments are scattered across different parts of the institutional administration, including registries, departments and faculties, and are not intended to be linked. As a result, the quality of data varies not only between higher education institutions, but also within them (Daniel, 2015). Due to the lack of IT governance modernisation, data quality is mostly affected by deficiencies such as inappropriate data structures, data duplication and conflicts (Wang & Jiang, 2022). For an in-depth study of study courses, it is necessary to find out what data is available in higher education institutions and how researchers can process it.

Using the example of Leibniz University Hannover, Germany, this article presents opportunities and challenges for the use of HEAD. It introduces the form and structure of raw data provided by higher education administration offices, followed by recommendations for the conceptual handling and organisation of HEAD.

In the long term, the growing importance of HEAD for both research and internal evaluations of higher education institutions could lead to increased networking and communication between administrative, examination and enrolment offices. The increased demand for HEAD by researchers could therefore lead to greater efficiency in the internal administration of the higher education sector by reforming IT governance structures.

Using the example of HEAD from Leibniz University Hannover, a large German university, which works with software from HIS Hochschul-Informations-System eG, we present and explain the form and structure of raw data provided by higher education administrative offices. Our data sources are 1) the Admissions Office, 2) the Central Examination Office, 3) the examination boards of the individual institutes, and 4) the examination regulations of the degree programmes. The data include 1) student socio-demographics, information on enrolment, interruption of studies, and exmatriculation, 2) examination behaviour, including grades, study plan, registration and withdrawal from examinations, examination passes and number of attempts, 3) information on credit points, hearings and final failure, and 4) information on examination forms and credit points of programme modules. This example is used to explain how raw data of this type can be transformed into analysis datasets and to provide recommendations for the conceptual use of HEAD. For instance, when merging datasets from different study programmes, comparability between programmes is often limited because it is not possible to match grade point averages (Cunha & Miller, 2014). A potential solution in European higher education systems is to measure workload in terms of credit points (ECTS) per period of study, which also allows international comparative analysis.

In conclusion, the benefits of using HEAD for educational research are clear. Not only researchers but also higher education institutions benefit from the (decentralised) collection and analysis of data. In this way, academic careers, especially those of minorities and disadvantaged groups, are documented in detail. A comprehensive assessment of diversity and quality of studies is possible, which could not be achieved with surveys due to the increasing attrition of participants. In the long term, the growing relevance of HEAD, both for research and for internal evaluation by higher education institutions, could lead to closer links and communication between their administrative, admissions and enrolment offices. It is also conceivable that campus management data could be linked with e-learning data. Greater demand for HEAD by researchers could therefore lead to greater efficiency in the internal administration of the higher education sector by reforming IT governance structures.

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