# An Ontology-Driven Learning Assessment Using the Script Concordance Test
## 1 Introduction
Therefore, there is a significant need to incorporate EHR in academic training settings [21]. The adoption of ontologies and semantic knowledge graphs (SKGs) for knowledge representation has become the cornerstone technology [22] that could enable SCT to advance further. Ontologies have been used for over a decade, for automatic question generation, and its successful application is presented in a number of research papers [23; 24; 25; 26], etc. On another front, automated question generation (AQG) plays a major role in educational assessment nowadays. It reduces the time and expenses needed for the manual construction of questions and also produces a continuous supply of new questions [27]. Therefore, building systems for generating questions has become imperative for researchers all over the world [28]. Although there are a plethora of research papers regarding AQG, we focus our literary review on ontology-based systems. In what follows, we give an overview of relevant ontology-based question generation solutions from the available literature. [PERSON] et al. developed the Ontology eLearning (OeLe) E-Assessment platform, which generates open questions for free text answers. In the developed OeLe platform, authors used ontologies, semantic annotations, and similarity functions, as well as natural language processing techniques. However, ontology is created externally, and this process may seem discouraging for non-IT-savvy teachers [29]. OntoQue is an ontology-based system for MCQ item generation presented by [PERSON] [27]. Although it includes true/false, fill-in, and multiple-choice items, the study was focused on MCQs solely. The proposed system generates items by iterating over all entities in the particular ontology. Although MCQs are satisfactory to a certain extent, the evaluation shows that the majority of MCQs also concentrate only on factual knowledge [30]. [PERSON] developed an automatic question generation (AQG) system that selects an informative sentence and keywords for a question based on the semantic labels and names of entities in the sentence [31]. The system chooses distractors through the application of string similarity measured between sentences in a dataset. This research was also limited to MCQ questions. In one of their latest studies, [PERSON] and [PERSON] [32] elaborated on the details of their prototype system called extended automatic test generation (E-ATG) used for MCQs generation. E-ATG can generate MCQ sets of particular sizes and find the difficulty values. It also controls the overall difficulty level of MCQ sets. The evaluation that the authors conducted shows that the system proposed can generate domain-specific MCQ sets, which are close to the one generated by domain experts regarding semantic similarity [3]. Ontology-based personalized feedback generator (OntoPeFeGe) framework was proposed by [PERSON] et al. OntoPeFeGe consists of two components that generate true/false, multiple-choice, and short-answer questions, with five different types of feedback and the personalized feedback algorithm that provide students with appropriate feedback after answering the questions. However, teachers are limited to the above-mentioned type of questions [25]. OntoQuest, a framework for the generation of multiple-choice questions, was presented by [PERSON] et al. [33]. In order to determine relevant sub-topics and auxiliary topics, domain and granular ontologies were used. OntoQuest uses a strategy for e-assessment by generating MCQ from various crawled web corpora. Research has confirmed the reliability of the OntoQuest framework and states that its accuracy in key and distractor generation is higher than the existing models [33]. [PERSON] and [PERSON] proposed fuzzy-MCS-algorithm-based ontology generation for e-assessment, in which MCQs are generated from a given ontology [34]. Java was used as a working platform for the implementation of the proposed ontology for e-assessment.