Abstract:

Quality assurance in the higher education area is provided by evaluation and accreditation procedures. These procedures are conducted by a group of reviewers according to a set of defined standards summarized in ESG (European Standards and Guidelines). A Criteria system based on these standards facilitates the stakeholders involved in the process. It consists of two types of indicators (qualitative and quantitative) used to measure the quality of education in separate professional fields or whole institutions. The quantitative indicators give objective comparable information about the situation in the higher education area and can be used to help reviewers make the most appropriate solution.
The main objective, that we are trying to achieve is: to create a software solution, used to facilitate the process of accreditation by automated quality measurement. An important part of the work while trying to reach this goal is to efficiently manage the raw data, in order to calculate the quantity indicators values. This is the focus of the current paper.
The data used to determine the level of educational service in the HE area is inconsistent and cannot be collected in a standardized manner. There are two main difficulties that have to be overcome in order to calculate the quantitative indicators values correctly:
Problem 1. The lack of information at any moment of the accreditation period;
Problem 2. The variety of data resources and the format of the data used to make a comparative analysis by HEI.
Approaches that can be used to manage these two problems are discussed in this work.
The first problem will be solved by the use of an appropriate approximation function, which will return the approximate values for the missing periods. Details about the type of function and the concrete circumstances that impact its results are described in the paper.
The second problem is solved by a two-step process:
Step. 1: Recognize and describe the list of resources, where the data will be extracted from (including different types of software systems and storage) + summarization of the data format types
Step.2: Create a set of queries with a collection of criteria, that serve the automated calculation of the quantitative indicators.
Solutions for the automation of the raw data collection and relation to these indicators are analyzed and summarized in order to achieve the main objective: the construction of a software system that utilizes the whole accreditation process.

Keywords:

Evaluation, Accreditation, Quality Assurance, Higher Education Institutions, European Standards and Guidelines, ESG.