Crystal Island Research Articles

The impact of goal assignment in a game‐based learning environment

AbstractBackgroundGoal 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.ObjectivesThe current study examined the impact of goal assignment in a GBLE aligned with curricular objectives and played in a classroom context.MethodsEighth‐grade students (N = 59) in the experiment were randomly assigned to either a mastery goal condition or a performance goal condition before playing Crystal Island—Outbreak. Goal orientation was measured before gameplay, monitoring judgements assessed during gameplay, and enjoyment was measured after game play.Results and ConclusionsTwo 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.ImplicationsThe 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.

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 (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 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.

Early prediction of student knowledge in game‐based learning with distributed representations of assessment questions

AbstractGame‐based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game‐based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine‐grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post‐gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine‐grained individual student performance in game‐based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game‐based learning environment for microbiology, Crystal Island, we investigate the accuracy and early prediction capacity of student models that use a combination of gameplay features extracted from student log files as well as distributed representations of post‐test content assessment questions. The results demonstrate that by incorporating knowledge about assessment questions, early prediction models are able to outperform competing baselines that only use student game trace data with no question‐related information. Furthermore, this approach achieves high generalisation, including predicting the performance of students on unseen questions. Practitioner notesWhat is already known about this topic A distinctive characteristic of game‐based learning environments is their capacity to enable fine‐grained student assessment. Adaptive game‐based learning environments offer individualisation based on specific student needs and should be able to assess student competencies using early prediction models of those competencies. Word embedding approaches from the field of natural language processing show great promise in the ability to encode semantic information that can be leveraged by predictive student models. What this paper adds Investigates word embeddings of assessment question content for reliable early prediction of student performance. Demonstrates the efficacy of distributed word embeddings of assessment questions when used by early prediction models compared to models that use either no assessment information or discrete representations of the questions. Demonstrates the efficacy and generalisability of word embeddings of assessment questions for predicting the performance of both new students on existing questions and existing students on new questions. Implications for practice and/or policy Word embeddings of assessment questions can enhance early prediction models of student knowledge, which can drive adaptive feedback to students who interact with game‐based learning environments. Practitioners should determine if new assessment questions will be developed for their game‐based learning environment, and if so, consider using our student modelling framework that incorporates early prediction models pretrained with existing student responses to previous assessment questions and is generalisable to the new assessment questions by leveraging distributed word embedding techniques. Researchers should consider the most appropriate way to encode the assessment questions in ways that early prediction models are able to infer relationships between the questions and gameplay behaviour to make accurate predictions of student competencies.

Robust Player Plan Recognition in Digital Games with Multi-Task Multi-Label Learning

Plan recognition is a key component of player modeling. Player plan recognition focuses on modeling how and when players select goals and formulate action sequences to achieve their goals during gameplay. By occasionally asking players to describe their plans, it is possible to devise robust plan recognition models that jointly reason about player goals and action sequences in coordination with player input. In this work, we present a player plan recognition framework that leverages data from player interactions with a planning support tool embedded in an educational game for middle school science education, CRYSTAL ISLAND. Players are prompted to use the planning tool to describe their goals and planned actions in CRYSTAL ISLAND. We use this data to devise data-driven player plan recognition models using multi-label multi-task learning. Specifically, we compare single-task and multi-task learning approaches for both goal prediction and action sequence prediction. Results indicate that multi-task learning yields significant benefits for action sequence prediction. Additionally, we find that incorporating automated detectors of plan completion in plan recognition models improves predictive performance in both tasks.

Affective Dynamics and Cognition During Game-Based Learning

Inability to regulate affective states can impact one's capacity to engage in higher-order thinking like scientific reasoning with game-based learning environments. Many efforts have been made to build affect-aware systems to mitigate the potentially detrimental effects of negative affect. Yet, gaps in research exist since accurately capturing and modeling affect as a state that changes dynamically over time is methodologically and analytically challenging. In this paper, we calculated multilevel mixed effects growth models to assess whether seventy-eight participants’ ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 78) time engaging in scientific reasoning (via logfiles and eye gaze) were related to time facially expressing confused, frustrated, and neutral states (via facial recognition software) during game-based learning with Crystal Island. The fitted model estimated significant positive relations between the time learners facially expressed confusion, frustration, and neutral states and time engaging in scientific-reasoning actions. The time individual learners facially expressed frustrated, confused, and neutral states explained a significant amount of variation in time engaging in scientific reasoning. Our finding emphasize that individual differences and agency may play a important role on relations between affective states, their dynamics, and higher-order cognition during game-based learning. Designing affect-aware game-based learning environments that track the dynamics within individual learners’ affective states may best support cognition.

High-quality AlN growth on flat sapphire at relatively low temperature by crystal island shape control method

In this study, we found that the two-step method for heteroepitaxial AlN on sapphire has some limitations. Growing the buffer layer for a too long time will make it challenging to obtain a flat film surface. So, three-step-growth was introduced and an intermediate layer was inserted. In this step, the surface morphology of samples is controlled by NH3 flow rate and the dislocation density is further reduced. Finally, we are able to obtain high-quality AlN samples grown by metalorganic chemical vapor deposition at 1120 ℃, which is a relatively low temperature for the AlN epitaxial layers. The X-ray diffraction ω-scan full width at half maximum values for (002) and (102) reflections are 172 and 145 arcsec, respectively.

Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis.

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.

A learning analytics approach towards understanding collaborative inquiry in a problem‐based learning environment

AbstractThis exploratory paper highlights how problem‐based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from Crystal 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 notesWhat 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.

A novel сalcium trifluoroacetate structure

Objectives. The study was devoted to considering the features of the synthesis and crystal structure of calcium trifluoroacetate Ca2(CF3COO)4·8CF3COOH and investigating the products of its thermal behavior.Methods. The compositions of the proposed structural form were characterized by various physicochemical methods (X-ray diffraction, IR spectroscopy), and the products of thermal decomposition were determined under dynamic vacuum conditions.Results. The reaction between calcium carbonate and 99% trifluoroacetic acid yielded a new structural type of calcium trifluoroacetate Ca2(CF3COO)4·8CF3COOH (I) in the form of colorless prismatic crystals unstable air. X-ray diffraction results confirmed the composition I: space group P21, with unit cell parameters: a = 10.0193(5) Å, b = 15.2612(7) Å, c = 16.3342(8) Å, β = 106.106(2)°, V = 2399.6(2) Å3, Z = 2. The structure is molecular, constructed from Ca2(CF3COO)4·8CF3COOH dimers. The end molecules of the trifluoroacetic acid were involved in the formation of intramolecular hydrogen bonds with oxygen atoms of the bidentate bridging anions CF3COO−. There were strongly pronouncedsymmetric and asymmetric absorption bands of COO and CF3-groups in the IR spectrum of the resulting compound in the range of 1200–1800 cm−1. The definite peak of the oscillation of the OH-group at 3683 cm−1 corresponds to the trifluoroacetic acid molecules present in the structure. The broadpeak of the valence oscillations in the range of 3300–3500 cm−1 is caused by the presence of intramolecular hydrogen bonds. Decomposition began at 250°C and 10−2 mm Hg with calcium fluoride CaF2 as the final decomposition product.Conclusions. We obtained a previously undescribed calcium–trifluoroacetic acid complex whose composition can be represented by Ca2(CF3COO)4·8CF3COOH. The crystal island structure is a dimeric molecule where the calcium atoms are bound into dimers by four trifluoroacetate groups. The complex was deposited in the Cambridge Structural Data Bank with a deposit number CCDC 2081186. Although the compound has a molecular structure, thermal decomposition leads to the formation of calcium fluoride characterized by a small particle size, which may further determine its applications.

Game-Based Learning Analytics for Supporting Adolescents’ Reflection

Reflection is critical for adolescents’ problem solving and learning in game-based learning environments (GBLEs). Yet challenges exist in the literature because most studies lack a theoretical perspective and clear operational definition to inform how and when reflection should be scaffolded during game-based learning. In this paper, we address these issues by studying the quantity and quality of 120 adolescents’ written reflections and their relation to their learning and problem solving with Crystal Island, a GBLE. Specifically, we (1) define reflection and how it relates to skill and knowledge acquisition; (2) review studies examining reflection and its relation to problem solving and learning with emerging technologies; and (3) provide direction for building reflection prompts into GBLEs that are aligned with the learning goals built into the learning session (e.g., learn about microbiology versus successfully solve a problem) to maximize adolescents’ reflection, learning, and performance. Overall, our findings emphasize how important it is to examine not only the quantity of reflection but also the depth of written reflection as it relates to specific learning goals. We discuss the implications of using game-learning analytics to guide instructional decision making in the classroom.

Emotions and the Comprehension of Single versus Multiple Texts during Game-based Learning

ABSTRACT This study examined 57 learners’ emotions (i.e., joy, anger, confusion, frustration) as they engaged with scientific content while learning about microbiology with Crystal Island, a game-based learning environment (GBLE). Measures of learners’ prior knowledge, in-game text comprehension, facial expressions of emotion, and posttest reading comprehension were collected to examine the relationship between emotions and single- and multiple-text comprehension. Analyses found that both discrete and non-discrete emotions were expressed during reading and answering in-game assessments of single-text comprehension. Learners expressed greater joy during reading and greater expressions of anger, confusion, and frustration during in-game assessments. Further results found that learners who expressed a high number of different emotions throughout reading and completing in-game assessments tended to have lower in-game comprehension scores whereas a higher number of different expressed emotions while completing in-game assessments was associated with greater posttest comprehension. Finally, while increased prior knowledge was associated with higher single- and multiple-text comprehension, there was no interaction between prior knowledge and emotions on multiple-text comprehension. Overall, this study found that (1) learners often express more than one emotion during GBLE activities, (2) emotions expressed while learning with a GBLE shift across different activities, and (3) emotions are related to demonstrated comprehension, but the type of activity influences this relationship. Results from this study provide implications for how emotions can be examined as learners engage in GBLE activities as well as the design of GBLEs to support learners’ emotions accounting for different activity demands to increase comprehension of single and multiple texts.

A Modular Reinforcement Learning Framework for Interactive Narrative Planning

A key functionality provided by interactive narrative systems is narrative adaptation: tailoring story experiences in response to users’ actions and needs. We present a data-driven framework for dynamically tailoring events in interactive narratives using modular reinforcement learning. The framework involves decomposing an interactive narrative into multiple concurrent sub-problems, formalized as adaptable event sequences (AESs). Each AES is modeled as an independent Markov decision process (MDP). Policies for each MDP are induced using a corpus of user interaction data from an interactive narrative system with exploratory narrative adaptation policies. Rewards are computed based on users’ experiential outcomes. Conflicts between multiple policies are handled using arbitration procedures. In addition to introducing the framework, we describe a corpus of user interaction data from a testbed interactive narrative, CRYSTAL ISLAND, for inducing narrative adaptation policies. Empirical findings suggest that the framework can effectively shape users’ interactive narrative experiences.

Optimizing Player Experience in Interactive Narrative Planning: A Modular Reinforcement Learning Approach

Recent years have witnessed growing interest in data-driven approaches to interactive narrative planning and drama management. Reinforcement learning techniques show particular promise because they can automatically induce and refine models for tailoring game events by optimizing reward functions that explicitly encode interactive narrative experiences’ quality. Due to the inherently subjective nature of interactive narrative experience, designing effective reward functions is challenging. In this paper, we investigate the impacts of alternate formulations of reward in a reinforcement learning-based interactive narrative planner for the Crystal Island game environment. We formalize interactive narrative planning as a modular reinforcement-learning (MRL) problem. By decomposing interactive narrative planning into multiple independent sub-problems, MRL enables efficient induction of interactive narrative policies directly from a corpus of human players’ experience data. Empirical analyses suggest that interactive narrative policies induced with MRL are likely to yield better player outcomes than heuristic or baseline policies. Furthermore, we observe that MRL-based interactive narrative planners are robust to alternate reward discount parameterizations.

Thin film growth of phase-separating phthalocyanine-fullerene blends: A combined experimental and computational study

Blended organic thin films have been studied during the last decades due to their applicability in organic solar cells. Although their optical and electronic features have been examined intensively, there is still lack of detailed knowledge about their growth processes and resulting morphologies, which play a key role for the efficiency of optoelectronic devices such as organic solar cells. In this study, pure and blended thin films of copper phthalocyanine (CuPc) and the Buckminster fullerene (C60) were grown by vacuum deposition onto a native silicon oxide substrate at two different substrate temperatures, 310 K and 400 K. The evolution of roughness was followed by in-situ real-time X-ray reflectivity. Crystal orientation, island densities and morphology were examined after the growth by X-ray diffraction experiments and microscopy techniques. The formation of a smooth wetting layer followed by rapid roughening was found in pure CuPc thin films, whereas C60 shows a fast formation of distinct islands at a very early stage of growth. The growth of needle-like CuPc crystals loosing their alignment with the substrate was identified in co-deposited thin films. Furthermore, the data demonstrates that structural features become larger and more pronounced and that the island density decreases by a factor of four when going from 310 K to 400 K. Finally, the key parameters roughness and island density were well reproduced on a smaller scale by kinetic Monte-Carlo simulations of a generic, binary lattice model with simple nearest-neighbor interaction energies.

Quantifying Scientific Thinking Using Multichannel Data With Crystal Island: Implications for Individualized Game-Learning Analytics

Individualizing learning by quantifying scientific thinking using multichannel data during game-based learning remains a significant challenge for researchers and educators. Not only do empirical studies find that learners do not possess sufficient scientific-thinking skills to deal with the demands of the 21st century, but there is little agreement in how researchers should accurately and dynamically capture scientific thinking with game-based learning environments (GBLEs). Traditionally, in-game actions, collected through log files, are used to define if, when, and for how long learners think scientifically about solving complex problems with GBLEs. But can in-game actions distinguish between learners who are thinking scientifically while solving problems versus those who are not? We argue that collecting multiple channels of data identifies if, when, and for how long learners think scientifically during game-based learning compared to only in-game actions. In this study, we examined relationships between 68 undergraduates’ pre-test scores (i.e., prior knowledge), degree of agency, eye movements, in-game actions related to scientific-thinking actions during game-based learning about microbiology with Crystal Island, and performance outcomes. Results showed significant predictive relationships between eye-gaze, pre-test scores, and in-game actions related to scientific thinking, suggesting that eye movements, pre-test scores, and degree of agency play a crucial role in scientific thinking and performance with GBLEs. Our findings provide insight into using multichannel data to capture scientific thinking and inform game-learning analytics that is individualized to guide instructional decision making. Findings from this study have implications for enhancing our understanding of scientific thinking within GBLEs. We discuss GBLEs designed to guide individualized and adaptive instructional decision making using learners’ multichannel data to optimize scientific-thinking skills and performance.

Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection

Game-based learning environments are designed to provide effective and engaging learning experiences for students. Predictive student models use trace data extracted from students’ in-game learning behaviors to unobtrusively generate early assessments of student knowledge and skills, equipping game-based learning environments with the capacity to anticipate student outcomes and proactively deliver adaptive scaffolding or notify instructors. Reflection is a key component of self-regulated learning, and it is critical in effective learning. However, there is currently limited work exploring the utility of reflection for inducing accurate predictive student models. This article presents a predictive student modeling framework that leverages natural language responses to in-game reflection prompts to predict student learning outcomes in a game-based learning environment for middle school microbiology, CRYSTAL ISLAND. With data from a pair of classroom studies involving 118 middle school students, we investigate the accuracy of early prediction models that utilize features extracted from student trace data combined with word embedding-based representations (i.e., GloVe, ELMo) of student reflection responses. We evaluate the accuracy of the predictive models over time using data from incremental segments of each student’s interaction with the game-based learning environment, and we compare against models that omit student reflection features. Results reveal that models encoding students’ natural language reflections with ELMo word embeddings yield significantly improved accuracy compared to other representations, with the greatest accuracy demonstrated by an ensemble of predictive models. We discuss the implications of these results for the design of game-based learning environments.

The Impact of Autonomy and Types of Informational Text Presentations in Game-Based Environments on Learning: Converging Multi-Channel Processes Data and Learning Outcomes

Game-based learning environments (GBLEs) focus on enhancing learning by providing learners with various representations of information (e.g., text, diagrams, etc.) while allowing full autonomy, or control over their actions. Challenges arise as research shows that learners inaccurately use cognitive and metacognitive processes when given full autonomy. This study examined 105 undergraduates who were randomly assigned to autonomy conditions (i.e., full, partial, and no autonomy) as they interacted with scientific informational text presentations (i.e., non-player characters [NPCs], books and research articles, posters) during learning with Crystal Island, a GBLE. We assessed how learners’ eye-tracking (e.g., fixation durations on objects) and log-file (e.g., durations of activities) data reflected how learners interacted with text presentations and selected pretest-relevant items (i.e., text providing answers to questions on the pretest). Results showed that participants in the partial autonomy condition (n = 38) demonstrated higher learning gains than those in the full autonomy condition (n = 45). Time spent interacting with all books and research articles within Crystal Island were positively correlated with learning gains. There were significant differences in learners’ duration and fixation duration on informational text presentation interactions between conditions and within types of presentations as well as significant interactions between pretest-relevant items and types of presentations. Overall, autonomy and pretest relevancy impact the time interacting with informational text presentations which influence learning. Implications are provided for applying autonomy during game-based learning, and how this may direct future implementations of AI within GBLEs to provide implicit scaffolding via adaptively limiting learners’ autonomy as they interact with informational text.

Coordinating scaffolds for collaborative inquiry in a game‐based learning environment

AbstractCollaborative inquiry learning affords educators a context within which to support understanding of scientific practices, disciplinary core ideas, and crosscutting concepts. One approach to supporting collaborative science inquiry is through problem‐based learning (PBL). However, there are two key challenges in scaffolding collaborative inquiry learning in technology rich environments. First, it is unclear how we might understand the impact of scaffolds that address multiple functions (e.g., to support inquiry and argumentation). Second, scaffolds take different forms, further complicating how to coordinate the forms and functions of scaffolds to support effective collaborative inquiry. To address these issues, we identify two functions that needed to be scaffolded, the PBL inquiry cycle and accountable talk. We then designed predefined hard scaffolds and just‐in‐time soft scaffolds that target the regulation of collaborative inquiry processes and accountable talk. Drawing on a mixed method approach, we examine how middle school students from a rural school engaged with Crystal Island: EcoJourneys for two weeks (N=45). Findings indicate that hard scaffolds targeting the PBL inquiry process and soft scaffolds that targeted accountable talk fostered engagement in these processes. Although the one‐to‐one mapping between form and function generated positive results, additional soft scaffolds were also needed for effective engagement in collaborative inquiry and that these soft scaffolds were often contingent on hard scaffolds. Our findings have implications for how we might design the form of scaffolds across multiple functions in game‐based learning environments.

The impact of trace data and motivational self-reports in a game-based learning environment

Game-based learning environments (GBLEs) are increasingly present in school settings, primarily because they have been associated with improved academic and motivational outcomes. However, recent research found that GBLEs were no more motivational than conventional instructional methods. Yet, the disproportionate use of self-report measures may not be accurately representing children's motivation in these contexts. For this reason, this study sought to compare fifth-grade children's motivational self-reports with trace measures in their ability to predict both performance and content learning in Crystal Island - Uncharted Discovery, a science-based GBLE. Self-report measures included achievement goals and situational interest; and, trace data included two measures of in-game engagement and one measure of disengagement behaviors. Results revealed that self-reported achievement goals and interest failed to significantly predict off-task behavior and accounted for only 3% and 5% of the variance, respectively, in predicting the two measures of in-game engagement. Most importantly, self-reported achievement goals and interest were not as effective as in-game trace behaviors in predicting performance and science content learning. Findings reinforce the importance of identifying and utilizing trace measures that represent indicators of performance and learning, while also considering the limitations of using self-report measures with children in GBLEs.

Multimodal learning analytics for game‐based learning

AbstractA distinctive feature of game‐based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor‐based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students’ interactions with game‐based learning environments hold significant promise for developing a deeper understanding of game‐based learning, designing game‐based learning environments to detect maladaptive behaviors and informing adaptive scaffolding to support individualized learning. This paper introduces a multimodal learning analytics approach that incorporates student gameplay, eye tracking and facial expression data to predict student posttest performance and interest after interacting with a game‐based learning environment, Crystal Island. We investigated the degree to which separate and combined modalities (ie, gameplay, facial expressions of emotions and eye gaze) captured from students (n = 65) were predictive of student posttest performance and interest after interacting with Crystal Island. Results indicate that when predicting student posttest performance and interest, models utilizing multimodal data either perform equally well or outperform models utilizing unimodal data. We discuss the synergistic effects of combining modalities for predicting both student interest and posttest performance. The findings suggest that multimodal learning analytics can accurately predict students’ posttest performance and interest during game‐based learning and hold significant potential for guiding real‐time adaptive scaffolding.