## 2 Literature Review
### Direct Observation Analysis
#### 2.3.3 Usage of Digital Technology in Assessment  
The objective of an educational process is the achievement of the main purposes established in its design phases. One of the main possibilities for achieving these objectives is related to an objective assessment. The objectivity of an assessment process is related to the appropriate design of assessment tools and the valid analysis of assessment results, which can be conceptually achieved by using specific pedagogical methods such as Universal Design for Learning (UDL) [28] or, more precisely, Universal Design for Assessment (UDA) [29], technically implemented using computational methods, such as machine learning, evolutionary algorithms and thoroughly studied using, for example, Learning Analytics (LA) [30] or statistical indicators [31; 32]. The main objective of a successful automated design and analysis of an evaluation test is to be as close as a human-centered approach to a design result with similar requirements, because human experience is still difficult to overcome in terms of the test of specific assessment and article design and analysis [33]. The problematic elements that frequently appear in the general context of the evaluation can be considered:  
* Creating a fair assessment for a group of students who were taught as a group. Equity refers to a fair distribution of balanced assessment items for each student within a group. * The human errors that occur. * Covering an extended area of previously learned subjects. * The mix between practical and theoretical evaluation. There is a wealth of literature on optimizing the evaluation process in terms of the design and analysis of evaluation components. Most of the area of automated educational assessment consists of developing models and assessment tools for Question Generation (QG) and Answer Evaluation (AE). An important part of the AE branch is devoted to Automatic Essay Scoring (AES), as shown in [34; 35; 36], which has been extensively researched in recent years. Regarding the QG branch, most research has been directed toward generating objective questions, such as multiple-choice ([37; 38]), true-false [39] or open-ended questions ([40; 41]). The classical topics of QG research are related to the formulation of questions from learning material, so recent research has been widely related to question sentence generation [42] and question generation from any type of text [43], including artificial intelligence [40]. To visualize the extent of research on this topic, empirical research on the subject of articles in scientific databases revealed that the topic is of wide interest in the research area. This research was carried out based on a search operation on specific keywords (for example, for the specific keyword \"automated question generation\"). Searching the Google Scholar paper database returned 292 unique results for 2022. Regarding the methods used to accomplish this task, one of the most used is Natural Language Processing (NLP), which was developed and perfected over time. For the AE branch, the research focuses on the analysis of short answers and essays, which also uses NLP-based techniques to perform a performant analysis of the text in the answer. One of the most researched topics is the assessment of answer correctness, especially related to a certain type of question (e.g., multiple-choice questions, as in [44; 45]). However, a growing interest can be seen in the evaluation of automated responses to essay-type items ([46; 47]). Another important part of research in educational evaluation is related to Item Analysis (IA), a field located at the border of several fields, such as statistics, psychometrics, evaluation, or education. It presents a wide range of research topics related to the mathematical and statistical aspects of assessment analysis [48], which remain benchmarks in the subject of item analysis and are heavily integrated into learning management systems as core functionalities for the human-centered analysis of educational activity on a specific platform.