Abstract:

Commonly evaluation and quality management or quality enhancement units in Higher Education Institutions (HEI) help to coordinate and record quality related actions along empirical evidence (found with the help of evaluation projects or instruments) for quality related issues in the institutions and attempt to increase the communication about quality related issues within the organization. Although these three fields of activity do not describe the highly diverse tasks within HEI quality enhancement, a lot of specialized units put great effort into them.

Without clearly defining the concept of quality as such, it can be assumed, that quality in a higher educational setting is not one-dimensional. Unidimensionality would be neither true for quality related topics (such as grades, curricular related questions, resource based questions…) nor for the group of stakeholder quality is “produced” for (if higher education cannot be seen as product, who is serving just one group of customers).

Consequently one goal of evaluation and quality enhancement units could be to gather empirical evidence about a wide range of issues, which relates directly, or indirectly to “quality” of higher education from different sources, condense it and inform relevant internal stakeholders and decision makers, such as lecturers, program managers or deans on a regular basis about quality related topics.

To reach this goal, the evaluation and quality enhancement team at WU Vienna used a solution that allows:
- to input data from a wide range of data sources
- to solve a wide range of multivariate statistical problems
- to visualize data highly effective
- high end reporting
- a limited software license budget

Given the requirements stated above, the free open source software environment for statistical computing and graphics R (http://cran.r-project.org/) and its various free packages is used for internal quality reporting.

In the full paper a way of reporting quality related issues will be presented, that integrates various data sources and evaluation channels (e.g.: a student panel, learning logfiles (for learning analytics) or internal databases) condense the retrieved information with various statistical methods (from descriptive statistics over inferential statistics to data mining techniques), visualizes the results with the powerful possibilities embedded in different R packages (e.g.: lattice or ggplot2) and report the results dynamically (based on up to date data(sub)sets) as well as customized (e.g.: different program directors receive different program based reports) with the help of knitR, a R package that supports any output markup language such as LaTeX or HTML. Additionally, ideas how this kind of reporting structure can help to solve the quality management problem of “closing the loop” (i.e.: the Deming or PDCA cycle) and helps to support the quality discourse at an HEI.

Keywords:

Empirical evidence, statistical reporting, evaluation, quality enhancement.