Assessing negotiation skill and its development in an online collaborative simulation game: A social network analysis study

Abstract

Simulation games are widely used to teach negotiation skills in political science education. However, existing studies focus on the impact of simulation games on students' knowledge gains and affective changes and largely ignore skill assessment and development in the gaming environment. This study aimed to understand the process of student groups practicing negotiation skills in a collaborative simulation game through social network analysis (SNA). We proposed a conceptual framework to assess negotiation skills by identifying different negotiation skillsets based on participatory roles in collaborative learning, investigated the skill development through the change of the skillsets over time, and examined the relationship between the negotiation skillsets and achievement. The results showed that the majority of student groups practiced more complex negotiation skillsets towards the end of the game, and the complexity of skillsets was positively related to the negotiation outcomes in the simulation game. The study demonstrated the possibilities of using SNA as an analytical tool to measure negotiation skills and explore dynamic skill development in a collaborative simulation game. It has also shown the potential of integrating SNA in a collaborative gaming environment for automated analysis of a large volume of data concerning interactions.

Learning analytics and assessment: Emerging research trends, promises and future opportunities

“…Furthermore, the special section includes four articles that report on using learning analytics methods to assess collaborative learning processes. Sun and Theussen (2022) applied social network analysis to dynamically assess the development of students' negotiation skills and demonstrated that complexity of negotiation skills was positively related to negotiation outcome in a collaborative simulation game. Ba et al (2022) developed text classifiers to detect phases in cognitive presence and then used epistemic network analysis to identify cognitive patterns in student online discussion transcripts.…”