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.
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This content will become publicly available on November 9, 2024
Identifying the effects of scaffolding on learners’ temporal deployment of self-regulated learning operations during game-based learning using multimodal data
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.
Undergraduate students (
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.
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.
- Award ID(s):
- 1761178
- NSF-PAR ID:
- 10475814
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Psychology
- Volume:
- 14
- ISSN:
- 1664-1078
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Providing adaptive scaffolds to help learners develop effective self‐regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open‐ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks.
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Methods We detect key cognitive/metacognitive
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Implications This paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context‐sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer‐based learning environments.
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Abstract Intelligent tutoring systems (ITSs) incorporate pedagogical agents (PAs) to scaffold learners' self‐regulated learning (SRL) via prompts and feedback to promote learners' monitoring and regulation of their cognitive, affective, metacognitive and motivational processes to achieve their (sub)goals. This study examines PAs' effectiveness in scaffolding and teaching SRL during learning with MetaTutor, an ITS on the human circulatory system. Undergraduates (
N = 118) were randomly assigned to a condition:Control Condition (i.e. learners could only self‐initiate SRL strategies) andPrompt and Feedback Condition (i.e. PAs prompted learners to engage in SRL). Learners' log‐file data captured when strategies were used, the initiator of the strategy (i.e. learner and PA), and the relevance of instructional content pages in relation to learner subgoals. While results showed that PAs were effective scaffolders of SRL in which they prompted learners to engage in SRL strategies more when content was relevant towards their subgoals and as time on page and task increased, there were mixed findings about the effectiveness of PAs as teachers of SRL. Findings show how production rules guiding PA prompts can improve their scaffolding and teaching of SRL across the learning task – through contextualizing SRL strategies to the instructional content and in relation to the relevance of the content to learners' subgoals.Practitioner notes What is already known about this topic Most learners struggle to efficiently and effectively use self‐regulated learning (SRL) strategies to attain goals and subgoals.
There is a need for SRL to be scaffolded for learners to manage multiple goals and subgoals while learning about complex STEM topics.
Intelligent tutoring systems (ITSs) typically incorporate pedagogical agents (PAs) to prompt learners to engage in SRL strategy and provide feedback.
There are mixed findings on the effectiveness of PAs in scaffolding learners' SRL.
What this paper adds We consider PAs not only scaffolders but also teachers of SRL.
Results showed that while PAs encouraged the use of SRL strategies when the content was relevant to subgoals, they did not discourage the use of SRL strategies when the content was not relevant.
Results for this study were mixed in their support of PAs as teachers of SRL.
Learners increasingly depended on PAs to prompt SRL strategies as time on task progressed.
Implications for practice and/or policy PAs are effective scaffolders of SRL with more research needed to understand their role as teachers of SRL.
PA scaffolding is more essential as time on task progresses.
When deploying specific cognitive and metacognitive SRL strategies, the relevance of the content to learners' subgoals should be taken into account.
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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.
