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Assessing scientific thinking and inquiry skills can be challenging because of the complexity and divergence in student behaviors. Scholars have advocated the use of more open-ended problems and choice for the assessment of scientific inquiry. In this paper, we interrogate an experimentation mechanic in an educational science game to examine challenges that choice introduces to game-based assessment of science inquiry practices. Descriptive analysis of gameplay elucidates the difference between choices to explore and iterating on choices as a sign of struggling to progress. 

This short paper presents a description and analysis of personal and social immersion features in a life science educational videogame for middle school students. The paper defines personal immersion in relation to the player taking on a virtual role and engaging with the game as if they are themselves a part of in-game events. Social immersion comes from situating the player within a virtual community, in which the player and other characters in the world interact based on their virtual roles. The research study involved interviews with 24 students who used the game over a two-week classroom implementation. The study explored student perceptions about immersive elements of the game, and their impact on student self-efficacy, interest, and identity in science. Student responses indicated that they experienced personal immersion through engaging with the game narrative as a virtual scientist and doing science tasks, and social immersion through role-based interactions with other virtual scientists. Students described how their self-efficacy, interest, and identity in science were impacted by personal and social immersive elements of the game. The paper contributes to the literature through an analysis of these specific immersive game mechanics and their impacts on student attitudes. 

Learning progressions allow researchers to describe key milestones along a pathway of thinking about a topic or practice that ranges from beginner to advanced. For learning related to science practices, some progressions can be abstracted from specific content; others are connected to specific science understandings. This research centers on the design of a middle school science game to support learning of science practices through simulated immersive experiences in which students engage in science practices of experimentation, modeling, and argumentation. This work-in-progress paper describes the application of current research on learning progressions to the design of the game interface and interactions for Aqualab, a game to teach middle school science practices related to aquatic ecosystems. 

While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy and limited analytical depth. In this exploratory methodological paper, we propose a combination of SA with Epistemic Network Analysis (ENA) as an alternative approach for providing richer qualitative and quantitative insights into Twitter discourse. We illustrate the application and potential use of these approaches by visualizing the differences between tweets directed or discussing Democrats and Republicans after the COVID-19 Stimulus Package announcement in the US. SA was integrated into ENA models in two ways: as a part of the blocking variable and as a set of codes. Our results suggest that incorporating SA into ENA allowed for a better understanding of how groups viewed the components of the stimulus issue by splitting them by sentiment and enabled a meaningful inclusion of data with singular subject focus into the ENA models.