# An Ontology-Driven Learning Assessment Using the Script Concordance Test
## 2 Methods and Materials
### Proposed Framework
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Disease Case** \\ **(Hypothesis)** \\ \end{tabular} } & \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{\begin{tabular}{c} **No. of** \\ **Candidates** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Parsing and** \\ **Inspection** \\ **[s]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Score** \\ **Calculation** \\ **[s]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Query** \\ **Execution** \\ **[s]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Google** \\ **Sheets** \\ **Inspection** \\ \end{tabular} } & \multirow{2}{*}{**Total Time** \\  & & & & & & **[s]** \\ \hline \begin{tabular}{c} Chronic \\ **Obstructive** \\ **Pulmonary Disease** \\ \end{tabular} & Direct & 9 & 1.673 & 0.004 & 1.014 & 0.913 & 3.603 \\ \hline \
\begin{tabular}{c} Diabetes Type 1 \\ **Diabetes Type 2** \\ \end{tabular} & Indirect—Hard—Diabetes & 2 & 1.132 & 0.002 & 1.126 & 1.017 & 3.277 \\ \hline
\begin{tabular}{c} Diabetes Type 2 \\ \end{tabular} & Indirect—Easy—Stroke & 2 & 1.432 & 0.002 & 1.32 & 0.931 & 3.685 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results for automated generation of questions relying on SCT ontology. settings and computer-based case scenarios would provide a more valid and reliable way of assessing clinical reasoning and clinical competence [2,16], the COVID-19 pandemic forced medical schools to quickly find solutions in offering best digital teaching and assessing for medical students with various online possibilities [50,51]. This issue brought new challenges for research society in terms of how to further improve web-based assessment for clinical reasoning. Following the recent involvement of intelligent systems in all spheres of life, researchers are increasingly focusing on the inclusion of ontologies in the learning assessment area, particularly in terms of automatically generating various types of questions. A literature review revealed that MCQ is the dominant type of question for which different e-assessment platforms are made, while other types of questions such as the script concordance test are less represented, if at all. On the other hand, the script concordance test emerges as one of the most promising and widely used assessment methods in medical education. It uses a short patient vignette (that is weakly defined) with a diagnostic hypothesis. After being presented a new piece of information relevant to each given hypothesis, students should assign a relevance score to the hypothesis--new information pairs on a scale from \(-2\) to \(+2\), with a score of 0 considered as \"no change\". Students' judgment is then compared with those of experts (who also score the test). The SCT has been validated in several medical disciplines with satisfactory results [10, 11, 12, 13, 14, 15]. In this way, SCT seeks to provide a practical, objective method for evaluating clinical reasoning that is currently assessed subjectively and rather informally in most training programs [6]. The main question addressed in this paper is whether the usability of SCT could be raised to a level high enough to match the current education requirements by exploiting opportunities that new technologies provide, particularly semantic knowledge graphs (SCGs) and ontologies. In other words, could SCT overcome the main drawbacks of traditional standardized tools, such as resource intensiveness, time consumption, and cumbersome administration and scoring? In order to answer this question, we developed an ontology-driven automated script-concordance-test generation platform. Resource intensiveness was resolved through ontology mapping from medical records stored in a database to previously created SCTonto ontology. Since patient data are populated in EHR's on daily basis, our platform could constantly generate new questions. In order to obtain Likert scale scores, direct and indirect strategies were proposed and explained in detail.