Reading comprehension is a complex process, in which text content information interrelates with readers' prior structural knowledge to form an integrated understanding of the text. Knowledge structure (KS) is an appropriate organization of text information. Using available measures, mainly computer-based technologies developed in the last two decades, researchers have shown that knowledge structure distinctly affects several cognitive processing outcomes, including reading comprehension scores of new texts, second language learning, and performance in problem-solving. In the present report, we introduced some key methods which were widely used in recent years. In the literature, summary essays, concept maps, the scores of pair-wise task, list-wise task, and sorting task, combined with eye-tracking and fMRI data, were considered as indexes of KS, and researchers have formalized how to transform all these various kinds of data into readable and comparable measures. Latent semantic analysis (LSA) developed in the 1990s is one related measure that is based on the underlying structure of the content that is distributed across many documents. LSA establishes a high-dimensional semantic space consisting of associations of words using the computationally demanding singular value decomposition of a large document by terms matrix (based on thousands of documents). In this way, similarity among different texts and between terms in texts is established as the cosine between their vectors in that semantic space. However, the matrix needed in LSA is so large that it takes extensive computing power to create it, so some researchers have turned to more efficient approaches such as SMD (Surface, Matching, Deep Structure) and T-MITOCAR (Text-Model Inspection Trace of Concepts and Relations). The measurements in both SMD and T-MITOCAR can be regarded as Schema structure analysis. SMD is used to analyze concept maps, whereas T-MITOCAR is an adaptation of SMD that can be used to analyze essays. Researchers who prefer using SMD and T-MITOCAR hold that knowledge is usually organized and stored as hierarchical structures or networks in human memory. Under SMD or T-MITOCAR, mental models could be indirectly measured. Analysis of lexical aggregates (ALA) is another alternative method that can be used to investigate knowledge structure from essays. Different from T-MOTICAR, ALA emphasizes the proximity/adjacency of different nodes rather than term-term distance. After obtaining a matrix of data based on preselected keywords from ALA-Reader, the matrix is then analyzed using Pathfinder network analysis to obtain a PFNet (i.e., a kind of graphical representation of KS) that can be used to compare the similarity of each network to a benchmark referent. Generally, results received via ALA are more intuitively displayed than those received via other methods. Generally speaking, both Schema structure analysis and Analysis of lexical aggregates are easy to use and can provide various information. Such network approaches provide new ways to measure and describe learning and behavioral performance, and so researchers are committed to improving the functions of these technologies. For example, AKOVIA (Automated Knowledge Visualization and Assessment; developed version of T-MITOCAR) and GIKS (Graphical Interface of Knowledge Structure; developed version of ALA-Reader) are available now on an internet platform, which can provide immediate visual feedback (as concept map) of student’s essay writing. However, improvements are still required, and suggestions were proposed at the end of the current report.