- Award ID(s):
- 1761178
- NSF-PAR ID:
- 10106715
- Date Published:
- Journal Name:
- International Conference on Artificial Intelligence in Education
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract. Game-based learning environments (GBLEs) are being increasingly utilized in education and training to enhance and encourage engagement and learning. This study investigated how students, who were afforded varying levels of autonomy, interacted with two types of informational text presentations (e.g., non-player character (NPC) instances, traditional informational text) while problem solving with CRYSTAL ISLAND (CI), a GBLE, and their effect on overall learning by examining eye-tracking and performance data. Ninety undergraduate students were randomly assigned to two conditions, full and partial agency, which varied in the amount of autonomy students were granted to explore CI and interactive game elements (i.e., reading informational text, scanning food items). Within CI, informational text is presented in a traditional format, where there are large chunks of text presented at a single time represented as books and research articles, as well as in the form of participant conversation with NPCs throughout the environment. Results indicated significantly greater proportional learning gain (PLG) for participants in the partial agency condition than in the full agency condition. Additionally, longer participant fixations on traditionally presented informational text positively predicted participant PLG. Fixation durations were significantly longer in the partial agency condition than the full agency condition. However, the combination of visual and verbal text represented by NPCs were not significant predictors of PLGs and do not differ across conditions.more » « less
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Abstract Background Goal setting has been established in the literature as a critical component of self‐regulated learning and for effective problem‐solving. Yet, surprisingly little attention has been focused on goal‐directed behaviour in digital game‐based learning environments (GBLEs) despite their expanding use in educational contexts.
Objectives The current study examined the impact of goal assignment in a GBLE aligned with curricular objectives and played in a classroom context.
Methods Eighth‐grade students (
N = 59) in the experiment were randomly assigned to either a mastery goal condition or a performance goal condition before playingCrystal Island—Outbreak . Goal orientation was measured before gameplay, monitoring judgements assessed during gameplay, and enjoyment was measured after game play.Results and Conclusions Two important results emerged that contribute to the existing literature on goals and GBLEs. First, assignment to the mastery goal condition resulted in significantly higher enjoyment ratings and monitoring judgements but not game performance while considering the influence of pre‐existing goal‐orientation. Second, self‐reported mastery goal orientation and videogame play frequency predicted enjoyment in the game.
Implications The results suggest that enjoyment can be facilitated by orienting students to adopt mastery goals and that developers should consider integrating mastery‐based instructions in their GBLEs. Moreover, instructional goals can influence students regardless of incoming goal orientations.
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Undergraduate students ( N = 82) learned about microbiology with Crystal Island, a game-based learning environment (GBLE), which required participants to interact with instructional materials (i.e., books and research articles, non-player character [NPC] dialogue, posters) spread throughout the game. Participants were randomly assigned to one of two conditions: full agency , where they had complete control over their actions, and partial agency , where they were required to complete an ordered play-through of Crystal Island. As participants learned with Crystal Island, log-file and eye-tracking time series data were collected to pinpoint instances when participants interacted with instructional materials. Hierarchical linear growth models indicated relationships between eye gaze dwell time and (1) the type of representation a learner gathered information from (i.e., large sections of text, poster, or dialogue); (2) the ability of the learner to distinguish relevant from irrelevant information; (3) learning gains; and (4) agency. Auto-recurrence quantification analysis (aRQA) revealed the degree to which repetitive sequences of interactions with instructional material were random or predictable. Through hierarchical modeling, analyses suggested that greater dwell times and learning gains were associated with more predictable sequences of interaction with instructional materials. Results from hierarchical clustering found that participants with restricted agency and more recurrent action sequences had greater learning gains. Implications are provided for how learning unfolds over learners' time in game using a non-linear dynamical systems analysis and the extent to which it can be supported within GBLEs to design advanced learning technologies to scaffold self-regulation during game play.more » « less
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Abstract This exploratory paper highlights how problem‐based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from C
rystal Island: EcoJourneys , a collaborative game‐based learning environment centred on supporting science inquiry. In Crystal Island: EcoJourneys , students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in‐game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game‐based learning environment? Drawing on a mixed method approach, we first analyzed students' pre‐ and post‐test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre‐ and post‐test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game‐based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game‐based learning environment: students engaged in low to moderate self‐directed actions with (1) high and (2) moderate collaborative sense‐making actions, (3) low self‐directed with low collaborative sense‐making actions and (4) high self‐directed actions with low collaborative sense‐making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self‐directed and high collaborative sense‐making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in‐game activities.Practitioner notes What is already known about this topic
Learning analytic methods have been effective for understanding student learning interactions for the purposes of assessment, profiling student behaviour and the effectiveness of interventions.
However, the interpretation of analytics from these diverse data sets are not always grounded in theory and challenges of interpreting student data are further compounded in collaborative inquiry settings, where students work in groups to solve a problem.
What this paper adds
Problem‐based learning as a pedagogical framework allowed for the design to focus on individual and collaborative actions in a game‐based learning environment and, in turn, informed the interpretation of game‐based analytics as it relates to student's self‐directed learning in their individual investigations and collaborative inquiry discussions.
The combination of principal component analysis and qualitative interaction analysis was critical in understanding the nuances of student collaborative inquiry.
Implications for practice and/or policy
Self‐directed actions in individual investigations are critical steps to collaborative inquiry. However, students may need to be encouraged to engage in these actions.
Clustering student data can inform which scaffolds can be delivered to support both self‐directed learning and collaborative inquiry interactions.
All students can engage in knowledge‐integration discourse, but some students may need more direct support from teachers to achieve this.
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Introduction Self-regulated learning (SRL), or learners’ ability to monitor and change their own cognitive, affective, metacognitive, and motivational processes, encompasses several operations that should be deployed during learning including Searching, Monitoring, Assembling, Rehearsing, and Translating (SMART). Scaffolds are needed within GBLEs to both increase learning outcomes and promote the accurate and efficient use of SRL SMART operations. This study aims to examine how restricted agency (i.e., control over one’s actions) can be used to scaffold learners’ SMART operations as they learn about microbiology with Crystal Island, a game-based learning environment.
Methods Undergraduate students (
N = 94) were randomly assigned to one of two conditions: (1) Full Agency, where participants were able to make their own decisions about which actions they could take; and (2) Partial Agency, where participants were required to follow a pre-defined path that dictated the order in which buildings were visited, restricting one’s control. As participants played Crystal Island, participants’ multimodal data (i.e., log files, eye tracking) were collected to identify instances where participants deployed SMART operations.Results Results from this study support restricted agency as a successful scaffold of both learning outcomes and SRL SMART operations, where learners who were scaffolded demonstrated more efficient and accurate use of SMART operations.
Discussion This study provides implications for future scaffolds to better support SRL SMART operations during learning and discussions for future directions for future studies scaffolding SRL during game-based learning.