Scientific reasoning is a critical foundational skill learners need to practice and know for increased science learning outcomes. Game-based learning environments (GBLEs) provide learners a platform for developing and practicing scientific reasoning skills but little is known about how learners should engage in scientific reasoning during game-based learning. As such, this paper aimed to understand if and how learners engaged in effective scientific reasoning activities during learning with a GBLE. This paper used analytical techniques from Complex Systems Theory to quantify learners’ scientific reasoning actions during game-based learning. High-school students (N = 170) played Crystal Island, a microbiology GBLE requiring learners to engage in scientific reasoning to successfully identify an illness infecting residents of a virtual island. Categorical auto-Recurrence Quantification Analysis was run on participants’ log files as they deployed scientific reasoning actions. This analysis revealed several metrics of complexity, including recurrence rate which is the proportion of repetitive to novel scientific reasoning actions. Results found that as time progressed, recurrence rates decreased. Successful learners (i.e., those who solved the mystery) demonstrated less repetition in their scientific reasoning activities where their recurrence rates decreased at a slower rate over time than learners who were unsuccessful in solving the mystery. Findings provide implications for adaptively scaffolding learners’ emerging complexities in their scientific reasoning processes during game-based learning to increase learners’ GBLE success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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) and Prompt 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.What is already known about this topicWhat this paper addsImplications for practice and/or policyPractitioner notesMost 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.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.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.

Self-regulated learning (SRL), or the ability for a learner to monitor and change their cognitive, affective, metacognitive, and motivational processes, is a critical skill to enact, especially while learning about difficult topics within an intelligent tutoring system (ITS). Learners' enactment of SRL behaviors during learning with ITSs has been extensively studied within the human-computer interaction field but few studies have examined the extent to which learners' SRL behaviors quantitatively demonstrate a functional system (i.e., equilibrium of repetitive and novel behaviors). However, current analytical approaches do not evaluate how the functionality of learners' SRL behaviors unfolds as time on task progresses. This paper reviews two analytical approaches, both based within categorical auto-recurrence quantification analysis (aRQA), for examining how learners' SRL complex behaviors emerge during learning with an ITS. The first approach, binned categorical aRQA, segments learners' SRL behaviors into bins and performs categorical aRQA on the SRL behaviors enacted within those bins to produce metrics of complexity that demonstrate how learners' functionality of their SRL systems change over time. The second approach, cumulative categorical aRQA, continuously calculates complexity metrics as learners enact SRL behaviors to identify the evolution of learners' functional SRL. These two approaches allow researchers to identify how the functionality of SRL behaviors change over time in relationship to the occurrences within the ITS environment. From this discussion, we provide actionable implications for contributing to how learners' SRL functionality can be visualized and scaffolded during learning with an ITS.

Game-based learning environments (GBLEs) supplement classroom instruction so students can demonstrate their scientific reasoning abilities and increase knowledge, providing a platform that promotes interest and engagement in science. The goal of this study was to examine the effectiveness of game mechanics for science learning. This study identifies how two types of game mechanics-learning and assessment mechanics-are used by high school participants (N = 137) as they learn about microbiology with Crystal Island, a game-based learning environment for science education. Participants' learning outcomes were evaluated in two ways: learning gains, which assessed participants' domain knowledge acquisition, and game completion, which assessed participants' ability to successfully demonstrate scientific reasoning abilities. Results from this study showed that game completion is not related to learning gains. However, as participants engaged with increasingly more assessment mechanics, learning gains decreased. Further, profiles of learners were extracted to better understand the learning process that best supports greater learning outcomes. Results showed that learners who engaged in less recurrent transitions across assessment mechanics were more likely to successfully demonstrate scientific reasoning abilities. Implications for the design of games which provide scaffolding based on process data of learners' game mechanic use are provided.

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. Copyright © 2023 Dever, Wiedbusch, Romero, Smith, Patel, Sonnenfeld, Lester and Azevedo.

Learning sciences are embracing the significant role technologies can play to better detect, diagnose, and act upon self-regulated learning (SRL). The field of SRL is challenged with the measurement of SRL processes to advance our understanding of how multimodal data can unobtrusively capture learners' cognitive, metacognitive, affective, and motivational states over time, tasks, domains, and contexts. This paper introduces a self-regulated learning processes, multimodal data, and analysis (SMA) grid and maps joint and individual research of the authors (63 papers) over the last five years onto the grid. This shows how multimodal data streams were used to investigate SRL processes. The two-dimensional space on the SMA grid is helpful for visualizing the relations and possible combinations between the data streams and how the measurement of SRL processes. This overview serves as an analytical introduction to the current special issue “Advancing SRL Research with Artificial Intelligence (AI)” and we encourage to position new research and unexplored frontiers. We emphasize the need for intensive and strategic collaboration to accelerate progress using new interdisciplinary methods to develop accurate measurement of SRL in educational technologies. © 2022 The Authors

Self-regulated learning (SRL), learners' monitoring and control of cognitive, affective, metacognitive, and motivational processes, is essential for learning. However, cognitive and metacognitive SRL strategies are not typically used accurately leading to poor learning outcomes. Intelligent tutoring systems (ITSs) attempt to address this issue by prompting and scaffolding learners to engage in SRL via using pedagogical agents. However, current literature does not examine the extent to which learners' deployed strategies are functional or dysfunctional in relation to pedagogical agent scaffolding. The current study collected 117 undergraduate students' data as they learned with MetaTutor, an ITS about the human circulatory system. Participants were randomly assigned to either the (1) Prompt and Feedback Condition where pedagogical agents scaffolded cognitive and metacognitive SRL strategies or (2) Control Condition where no prompts or feedback were provided. Results demonstrated that learners who received prompts by the pedagogical agents to engage in SRL had higher learning gains as well as greater frequencies across most strategies compared to those in the Control Condition who relied on self-initiated strategy use. While sequential transitions across all strategies were not significant between conditions, further analysis grounded in Complex Systems Theory found that learners who were prompted to engage in strategies demonstrated a significantly lower degree of repetition and balance between repetitive and novel patterns of strategy use. The findings suggest that pedagogical agents within MetaTutor successfully scaffolded the functional deployment of cognitive and metacognitive SRL strategies and are indicative of higher learning after interacting with ITSs.

Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students' SRL while they learn about the human circulatory system. MetaTutor's architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners' cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

Several studies have attempted to capture and analyze the intersect of self-regulated learning (SRL) behaviors and agency (i.e., control over one's own actions) during game-based learning. However, limited studies have attempted to theoretically ground or analytically evaluate these constructs in appropriate theoretical assumptions that can discuss and aptly analyze SRL. As such, this paper argues that complex systems theory, which refers to SRL as a system that is self-organizing, interaction dependent, and emergent, should be integrated into theoretical models of SRL and be analyzed using nonlinear dynamical systems theory techniques to fully capture how learners' SRL behaviors can be captured and scaffolded during game-based learning. This paper guides future discussions and empirical research to understand how to better scaffold learners' SRL behaviors using restricted agency during game-based learning by: (1) understanding scaffolding SRL during game-based learning; (2) reviewing studies that review the intersection of SRL, agency, and game-based learning; (3) discussing the limitations within the field; (4) defining and defend SRL according to complex systems theory; and (5) discussing the open challenges in theoretically, methodologically, and analytically applying complex systems theory to SRL.

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.