Presentation and Analysis of Structural Knowledge in Learning Tasks Using the Example of a Complex Literary Text

The lack of reliable instruments for automated assessment of learning outcomes leads to an overload of teachers who do not have sufficient resources to fully and objectively assess numerous students. A promising approach to solving this problem seems to be the use of markers of the formation of students’ structural knowledge, describing the interrelations of the components of the object being studied. The purpose of this study, based on the novel The Master and Margarita by M.A. Bulgakov, is to show that the structural knowledge of the subjects reflects the features and current level of understanding of the novel, as well as the change in this level as a result of learning. The study used PathFinder network scaling algorithm to extract and visualize significant connections between the novel’s characters based on subjective assessments of their pairwise connectivity. The experimental group consisted of high school seniors who studied the novel as part of the school curriculum under the guidance of their teacher. The comparison group included readers of the novel with different levels of experience in comprehending the text. The results of the study confirmed the possibility of assessing the structural knowledge of complex literary texts by the parameters of their network representation (coherence, expansion, and conciseness, balance of tree-like and coalition relationships). A significant difference was found in key indicators of the structural knowledge in the experimental and comparison group. According to the learning results in the experimental group, a statistically significant increase was revealed in the correlation of the students’ structural knowledge with that of their teacher. The analysis of the shortcomings of the extracted knowledge structures makes it possible to individualize teaching, reduce labor intensity and increase objectivity of the assessment of the learning results. The proposed approach requires verification on large samples and in other knowledge areas (in particular, in the training of operators of complex technical systems).