Using network science to analyze concept maps of psychology undergraduates

Abstract

Network science is an emerging area of complexity science that uses mathematical techniques to study complex systems and could represent a new way of quantifying and investigating the internal structure of domain-specific knowledge as approximated by students' concept maps. Students enrolled in introductory psychology constructed concept maps to represent their understanding of a psychology chapter.Concept networks were constructed based on the concept maps generated by students. Network analysis revealed that the structure of concept networks differed across students (i.e., some networks were better connected than others), and network structure significantly predicted quiz scores, such that concept networks with larger average shortest path lengths (a network metric representing the average of the shortest paths between two nodes in a network) were associated with higher quiz scores, after controlling for network size. This paper illustrates how network science techniques can be used to quantify the conceptual structure of a learner's knowledge.

Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth

“…These patterns highlight differences in the specific expertise of individuals. Although the relationship between conceptual associations and domain specific expertise has not been fully explored yet (Aitchison, 2012), a few pioneering approaches have recently established how the structure of semantic networks can be predictive of students' performance in quizzes (Siew, 2019;Tyumeneva, Kapuza & Vergeles, 2017). The further exploration of a link between network structure and domain expertise represents an interesting future research direction for forma mentis networks, also in terms of investigating the effects of shared knowledge and of prior-knowledge over learning outcomes in the sense of Koponen, Kokkonen & Nousiainen (2017).…”

Forma mentis networks quantify crucial differences in STEM perception between students and experts

“…However, recent approaches have constructed association networks at the level of individuals Siew, 2018) and even reported how individualized free association networks were predictive of creativity levels or knowledge mastery (Siew, 2018). With larger sample sizes and a more substantive free association task (leading to denser networks), building forma mentis networks for the individuals represents an exciting research direction for the future.…”

“…Psycholinguistic evidence has shown that the associative structure of the mental lexicon influences language processes such as word learning (Hills & Siew, 2018;Stella, 2019;Stella, Beckage, & Brede, 2017) and processing (De Deyne, Navarro, & Storms, 2013;De Deyne, Navarro, Perfors, Brysbaert, & Storms, 2018;Kenett, Levi, Anaki, & Faust, 2017;Steyvers & Tenenbaum, 2005). This strong link between mental lexicon structure and language usage promoted the use of network models for a variety of processes such as the discovery of writing styles from word co-occurrences in texts (Amancio, 2015) or predicting the creativity of individuals (Kenett, Anaki, & Faust, 2014;Kenett et al, 2018), their curiosity (Zurn & Bassett, 2018), their openness to new experience (Christensen, Kenett, Cotter, Beaty, & Silvia, 2018), their expertise in a given domain (Siew, 2018;Valenzuela Castellanos, Pérez Villalobos, Bustos, & Salcedo Lagos, 2018) and their perceived anxiety toward a topic (Siew, McCartney, & FORMA MENTIS NETWORKS IN STEM 6 Vitevitch, 2019). Forma mentis networks rely on the framework of cognitive network science to represent the associative and emotional structure of concepts in the mental lexicon.…”

Contributions of Modern Network Science to the Cognitive Sciences: Revisiting research spirals of representation and process

“…These studies analyzed the concept maps produced by teachers-in-training and high school students as networks to identify central, important concepts in their conceptual structure (Koponen & Nousiainen, 2018), detect meso-level structures that reflect thematic communities in a domain (Lommi & Koponen, 2019), and compare the overall network structure of experts against that of novices (Koponen & Pehkonen, 2010). Recent work by Siew (2019b) showed that the overall network structure of concept maps is a significant predictor of quiz scores and could serve as one indicator of successful learning and retention of content. Lydon-Staley and colleagues (Lydon-Staley, Zhou, Blevins, Zurn, & Bassett, 2019) showed that the different search strategies that people adopted while navigating Wikipedia resulted in different knowledge networks (i.e., Wikipedia pages that were connected based on the sequence in which they were being navigated).…”