Master of Science in Statistical Data Analysis

Quality assurance

Ghent University Conduct of Educational Quality Assurance

(cf. Dutch: ERGO – Eigen Regie in Gents Onderwijsbeleid en Kwaliteitszorg)

In 2015 the existing system of external quality assurance for Flemish higher education institutions was suspended. All universities and university colleges have chosen to replace the former external system with their own internal systems of quality assurance. In so doing, they have taken full charge of the conduct of educational quality assurance (cf. Dutch: kwaliteitszorg in eigen regie). At Ghent University we have decided on a model in which every six years, all our study programmes receive a so-called “peer learning visit”. A typical peer learning visit lasts one day, in the course of which a multidisciplinary team speaks with different stakeholders (students, alumni, lecturers). A peer learning team consists of Ghent University lecturers, an external expert and a student representative. By installing these peer learning visits, we want to encourage our lecturers and our students to look at study programmes beyond the familiar disciplinary boundaries in a critically-constructive way. Afterwards, the peer learning team renders a written account, structured according to Ghent University’s six strategic objectives, to the Educational Quality Commission (cf. Dutch: Onderwijskwaliteitsbureau).

The information below summarizes the report’s main points.

The study programme in 5 assets

  1. Requirements: our one-year programme is open to holders of any Master’s degree in a(n) empirical science discipline and builds on students’ scientific attitude by adding a strong statistical layer.
  2. Freedom of choice: we give our students considerable latitude to put together their study programme according to their own background and field(s) of interest.
  3. Bridging theory and practice: we focus on understanding and applying statistical concepts and bridging the world of statistics with empirical sciences. Our students are trained in modern statistical methods with a strong emphasis on applications.
  4. Multidisciplinarity: we train scientists to become responsible and professional statisticians who can work in multidisciplinary teams and who are equipped with creative problem-solving skills, a firm basis of statistical thinking and knowledge of modern statistical methods and their applications.
  5. Multitalented graduates: students who successfully finish our programme have acquired an advanced level of statistical knowledge and data analytical skills. They are ready to contribute as independent experts to a multidisciplinary team that designs, performs, analyses and reports applied scientific research.

 

Quality assurance: 5 strengths

  1. Our professorial staff consists of motivated lecturers who combine their passion for teaching with excellent research records. All of our staff is engaged in international networks.
  2. Our programme is supported by a multidisciplinary team of lecturers from 7 different faculties and offers a wide range of optional courses. It is this multidisciplinarity that makes our programme so attractive for students from a variety of disciplinary backgrounds.
  3. Our programme regularly interacts with the industry and with leading research institutes. Both parties are actively involved in the shaping of our programme.
  4. Our programme focuses on understanding and applying statistical concepts, with many hands-on sessions, and authentic homework and project assignments which makes that our graduates are well prepared for national and international jobs in the private sector as well as in government and in academia.
  5. Our professorial staff and our study programme committee guarantee a continued attention to quality assurance.

 

Quality assurance: 3 focus points with action plan

1. Communication: we strive to further improve communication with candidate students about the programme’s expectations (positive job outlook, study load, and entrance level of mathematics, programming and communication skills,…)  

Action plan:

  • improve the MaStat website and other communication channels and make them more accessible to candidate students
  • set up a communication channel between candidate students and alumni.

2. Study load: some students experience the programme as heavy and drop out. The many homework and project assignments present an ongoing demand on students’ time and learning efforts. Particularly our part-time students (combining work with study) and students not optimally prepared for this advanced Master’s degree programme experience a heavy workload. However, after graduation, alumni hardly ever repeat the complaint and they agree that the many hands-on assignments are an essential part of a very effective teaching method.

Action plan:

  • improve our communication with candidate students and employers of part-time students
  • confine and distribute the study load or study pressure more evenly
  • tighten our entrance criteria to ensure that incoming students meet with the necessary prerequisites

3. Study efficiency: more of our students should graduate. Our programme still welcomes too many students with a poor preparatory education. Quite a number of students do not finish their Master’s thesis because they find a job before completing the programme. Some students drop out because they experience the workload as too heavy (e.g. in combination with their fulltime job and personal life).

Action plan:

  • improve our communication with candidate students
  • confine and distribute the study load or study pressure more evenly
  • tighten our entrance criteria to ensure that incoming students meet with the necessary prerequisites
  • actively encourage our students to start their thesis work earlier