GE monitoring using data analytics tools

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Authors: Silvia Gaftandzhieva and Rositsa Doneva, Plovdivski Universitet Paisiy Hilendarski

In recent years, many policies have encouraged organizations from all sectors to develop and implement Gender Equality Plans (GEPs). The issue of adopting institutional GEP has become particularly relevant since the European Commission introduced GEP as an eligibility criterion for all public bodies, higher education institutions (HEIs) and research organizations from the EU member states and associated countries wishing to participate in the Horizon Europe Framework Programme for Research and Innovation 2021-2027.

To be eligible for Horizon Europe, it is mandatory that organizations collect and publish sex/gender-disaggregated data on personnel (and students, where relevant) and carry out annual reporting based on indicators. Organizations should consider how to select the most relevant indicators, how to collect and analyze the data, including resources to do so, and should ensure that data is published and monitored on an annual basis.

On the one hand, the collection and subsequent analysis of such disaggregated data requires human resources involvement and manually perusing endless data streams. On the other hand, the presented data are up-to-date at the time of their analysis and do not provide information about the current state of the organization. This raises the question of whether the collection and analysis of such data can be automated and thus not only reduce the amount of manual work and the number of people involved but also provide up-to-date information at any time. In this blog post, we describe the attempt of the University of Plovdiv to efficiently monitor GE using data analytics tools.

In recent years, data extraction and analysis at HEIs have become increasingly important. HEIs are looking for software tools that allow them to extract data from different information systems and convert them into knowledge that helps them make superior decisions regarding various processes, optimize workflows and improve process management. Like any modern HEI, the University of Plovdiv uses a lot of software systems to automate its learning and administrative processes (e.g. human resource systems, student information systems, learning management systems, library systems, etc.). These systems store data on faculty and administrative staff, students and PhD students that could be extracted and analyzed from data analytics tools. As a result, the University of Plovdiv has the data sets needed to benefit from data analytic tools to automate GE monitoring. This motivated the development of data analytics tools for GE monitoring that will extract and analyze sex-disaggregated data about academic and non-academic staff, students and PhD students and allow different stakeholders (e.g. top and middle management, members of Gender Equality Group (GEG) formed at the university, etc.) to make data-driven decisions to improve the university environment in terms of equality between women and men.

The solution to the problem of extracting data from university information systems required an in-depth analysis of the university information infrastructure of the University of Plovdiv. This analysis aimed to determine the appropriate data sources, which of the stored data and how they can be extracted and analyzed to be used for GE monitoring. As a result, the student information system, the system for candidate student campaigns, learning management systems, the human resource system, and the research reporting system are defined as potential data sources of the designed data analytic tool.

Based on this analysis, indicators for data collection are selected. The selection of indicators is determined by the available data in potential data sources and the set goal of the tool to collect as much relevant data as possible to enable scrutiny of the differences between men and women in different roles and at different levels within the university its core activities. When choosing indicators, the issue of further breaking down the collected sex-disaggregated data into other categories is considered to study the differences between women and men in the different units of the university. These data allow us to examine the interrelationship of gender with other characteristics that can highlight specific areas requiring attention. The current list of indicators for which the data analytic tool collects data is:

  • Numbers of women and men among candidate students (per study programme and professional field);
  • Numbers of females and males among enrolled students (per study programme and professional field);
  • Numbers/ratio of women and men among students (per faculty, programme and professional field);
  • Numbers/ratio of women and men among dropped out students (per faculty, programme and professional field);
  • Numbers/ratio of women and men among graduate students (per faculty, programme and professional field);
  • The average grade of women and men at the end of the academic year (per faculty and study programme);
  • The average and maximum success of women and men in graduation (per study programme);
  • Numbers of female and male candidate PhD students (per programme);
  • Numbers of female and male enrolled PhD students (per programme);
  • Numbers of female and male PhD students (per programme);
  • Numbers of female and male dropped out PhD students (per programme);
  • Numbers/ratio of women and men among defended doctoral students (per faculty, study programme and professional field);
  • Numbers of women and men in an academic position (per position, faculty and age);
  • Numbers of women and men among academic staff with scientific degrees (per faculty and degree).

Templates of GE monitoring reports are designed and developed through JasperSoft Studio to collect appropriate data for the proposed indicators.

The developed data analytics tool fills these templates with real data (directly retrieved from university information systems or obtained through calculations) and generates GE monitoring reports depending on the user’s role. For example, a dean of a faculty can generate reports that provide data only for the faculty s/he heads, and a representative of top management can generate reports with data for the entire university. The reports can be generated automatically by the tool according to a predetermined schedule and stored in its repository or generated by the user when s/he wants to see the current situation in the faculty/university. All generated reports stored at the repository can be accessed by users who have access rights.

The results of data processing are presented in the form of tables and diagrams and allow members of the GEG and/or middle of top management to:

  • perform monitoring of numerical data on the ratio of women and men among students (candidate students, enrolled students, graduates, dropped out students,
  • perform monitoring of numerical data on the ratio of women and men among PhD students (candidate students, enrolled PhD students, dropped out PhD students);
  • perform monitoring of numerical data on the ratio of women and men among academic staff;
  • perform monitoring of the equal access of students, PhD students, teachers and employees to education, opportunities for professional development, and competitions for academic and administrative positions (by different categories);
  • perform monitoring of the career development in terms of equality between women and men (academic positions and degrees);
  • identify the most desired/undesirable study programmes by candidate students;
  • identify study programmes with the highest/lowest percentage of graduates;
  • establish a baseline situation in relation to gender equality in the university, against which progress can be monitored and evaluated on an annual basis;
  • carry out a gender equality analysis to identify areas of relative strength and weakness, which will allow better targeting of actions and priorities within the GEP;
  • track trends by comparing monitoring results from different time periods.

The picture below presents a generated report through the developed tool by the Vice-rector of the university. The report shows the numbers of women and men in academic positions in each faculty and allows him/her to monitor the numerical data on the ratio of women and men among academic staff.

Once the data analytics tool generates the reports, users can analyze them to understand key gaps between women and men within the university and its activities. This analysis will help users take data-informed decisions to ensure equal access to education and career development in the university and increase the sense of equality among the university community, guide key priorities and adjust these priorities as the situation evolves.

The data analytics tool automatically generates annual monitoring reports for each indicator with summary data for each faculty and the university. All generated annual monitoring reports are accessible from the data analysis tool and can be viewed and downloaded by users who have the right to access them. The members of the GEG review the generated monitoring reports to identify where activities are having an impact and where obstacles persist and compare the monitoring results with those from the generated monitoring reports in the previous year to show the progress or lack of progress made. In the next step, the members of the GEG can present the results of the comparison of monitoring reports from the current and the last year and the findings for the progress made to the university's top management, university staff, and students (where relevant) and other key stakeholders.

In the future, the functionality of the tool will be expanded to allow data extraction for other quantitative indicators (e.g. number of publications of women and men, numbers of women and men in research projects, number of women and men in decision-making positions, average numbers of years needed for women and men to make career advancements, numbers of women and men applying for/taking parental leave etc.), as well as automated analysis of the results of surveys conducted among members of the academic community.

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