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Studies of educational games often treat them as "black boxes" (Black and Wiliam in Phi Delta Kappan 80: 139-48, 1998; Buckley et al. in Int J LearnTechnol 5:166-190, 2010; Buckley et al. in J Sci Educ Technol 13: 23-41, 2010) and measure their effectiveness by exposing a treatment group of students to the game and comparing their performance on an external assessment to that of a control group taught the same material by some other method. This precludes the possibility of monitoring, evaluating, and reacting to the actions of individual students as they progress through the game. To do that, however, one must know what to look for because superficial measures of success are unlikely to identify unproductive behaviors such as "gaming the system." (Baker in Philipp Comput J, 2011; Downs et al. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, USA, 2010) The research reported here advances the ultimate goal of creating educational games that can provide real time, meaningful feedback on the progress of their users, enabling teachers or the game itself to intervene in a timely manner. We present the results of an in-depth analysis of students' actions in "Geniventure," an interactive digital game designed to teach genetics to middle and high school students. "Geniventure" offers a sequence of challenges of increasing difficulty and records students' actions as they progress. We analyzed the resulting log files, taking into account not only whether a student achieved a certain goal, but also the quality of the student's performance on each attempt. Using this information, we quantified students' performance and correlated it to their learning gain as estimated by scores on identical multiple-choice tests administered before and after exposure to "Geniventure." This analysis was performed in classes taught by teachers who had participated in professional development as part of a research project. A two-tailed paired-sample t-test of mean pre-test and post-test scores in these classes indicates a significant positive difference with a large effect size. Multivariate regression analysis of log data finds no correlation between students' post-test scores and their performance on "practice" challenges that invite experimentation, but a highly significant positive correlation with performance on "assessment" challenges, presented immediately following the practice challenges, that required students to invoke relevant mental models. We repeated this analysis with similar results using a second group of classes led by teachers who implemented "Geniventure" on their own after the conclusion of, and with no support from, the research project.

The COVID-19 pandemic produced a dramatic nationwide shift in K-12 education from in-person classroom learning to remote online learning. This shift left teachers and parents facing the challenge of finding engaging online resources to motivate students to become deeply involved in science learning. The pandemic also left educators and researchers, whose work focuses on providing students with experiential learning opportunities in the sciences, with the challenge of adapting to virtual and remote models to continue engaging students in STEM learning activities. In this article we describe: 1) the Health Quest project, which centers on the development of technology-rich learning resources to promote middle grade students' interest in health science careers, with a focus on girls and underrepresented racial and ethnic minorities; and 2) how the project has responded to the challenges presented by the COVID-19 pandemic. In Health Quest, through engaging narrative-based learning scenarios, students work with virtual characters to experience health science careers from multiple perspectives. Although originally envisioned for in-person classroom learning, we discuss how the team is adapting the Health Quest Career Adventure Game to remote learning, including highlighting the role science plays in addressing public health outbreaks. We describe new gameplay features that have been added to support career modeling and how we have adapted the core technology underpinning Health Quest to support broad dissemination to meet the project's broader goal of increasing adolescents' interest in and self-efficacy for pursuing health science careers. We conclude with a discussion of how our evaluation strategies have changed from in-person focus groups and testing to an online data collection model and lessons learned.

Pedagogical agents have been designed to support the significant challenges that learners face when self-regulating in advanced learning environments. Evidence suggests differences in learners' prior skills and abilities, in conjunction with excessive didactic support, can cause overreliance on these external aids, which in turn prevents deeper learning, and pedagogical agents can provide tailored scaffolding to accommodate learners' individual needs. However, there is less evidence about the impact of abstract scaffolding, such as the sharing of non-verbal metacognitive information via a pedagogical agent's facial expressions, on self-regulated learning. To assess factors in the passing of non-verbal metacognitive information via pedagogical agents in a multimedia learning environment, we used growth modeling with self-reports, eye-tracking, and log-file data collected from fifty (n = 50) undergraduates at a large North American university as they learned about human body systems while using MetaTutor-IVH, a multimedia learning environment with a pedagogical agent. We controlled for participant characteristics (perceived utility of emotions for self- and other-centered positive and negative emotions) and characteristics of the metacognitive monitoring information provided by a pedagogical agent (expression type and expression congruency) to assess factors in non-verbally communicating metacognitive information. Results suggest that learners attend to pedagogical agents less over time, but this rate of change is weaker when an agent is providing an expression that is congruent with the ground truth of the environment. Further, only the perceived information utility of other-centered negative emotions has a significant effect on this duration, suggesting learners are driven to consult pedagogical agents to avoid embarrassment or shame. We discuss design implications of these findings for technology-based learning environments.

With accelerating advances in artificial intelligence, it is clear that introducing K-12 students to AI is essential for preparation to interact with and potentially develop AI technologies. To succeed as the workers, creators, and innovators of the future, we argue students should encounter core concepts of AI as early as elementary school. However, building a curriculum that introduces AI content to K-12 students presents significant challenges, such as connecting to prior knowledge, developing curricula that are meaningful for students, and creating content that teachers feel confident to teach. To lay the groundwork for elementary AI education, we investigated the everyday experiences and ideas of students in grades 4 and 5 (ages 9 to 11) about AI to inform possible entry points for learning. This yielded themes around student conceptions, examples, and ethics of AI. For each theme, we juxtapose the student ideas with the teachers' reflections on those ideas as frames of reference to consider in co-designing curricular approaches.

This design case chronicles the efforts of an interdisciplinary team of researchers as they collaborated with middle grades science teachers and students to build and refine an epidemic disease curriculum module. The initial five-day design was delivered in five science classrooms at three nearby schools where researcher classroom observations and teacher feedback drove iterative refinements of the module's materials. The final design of this module consisted of four instructional days of modeling and simulation activities that integrate computational thinking practices, computer science concepts, and life sciences content. The paper aims to illustrate the design motivations to address contextual constraints such as tight curricular schedules and varied levels of exposure to programming for both teachers and students. The instructional materials presented in this design case were the result of a three-year long research-practice partnership with science teachers at nearby middle schools.

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.

Game-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.

The increasing interest in computer science (CS) and CS-integrated STEM teaching and learning has created a need for assessment instruments that can be used to evaluate the efficacy of innovative instructional approaches to K-12 CS education. However, there is a lack of validated assessment tools aligned to core CS concepts for younger students. This paper reports on the development and validation of a CS concept assessment for middle grades (ages 11-13) students. A total of 27 multiple-choice items were developed, guided by focal knowledge, skills and abilities associated with the concepts of variables, loops, conditionals, and algorithms. These items were administered to 457 middle grades students. The items were presented in form of block-based programming code and administered in a week-long computational modeling intervention. A combination of classical test theory and item response theory approaches were used to validate the assessment. Based on results, it was found that only 24 items are considered valid and reliable items to measure CS conceptual understanding. The results also suggested that the assessment can be used as a pre and post-test to investigate students' learning gains. This work fills an important gap by providing a key resource for researchers and practitioners interested in assessing middle grades student CS conceptual understanding.

This 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.

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.

This paper reports findings from the efforts of a university-based research team as they worked with middle school educators within formal school structures to infuse computer science principles and computational thinking practices. Despite the need to integrate these skills within regular classroom practices to allow all students the opportunity to learn these essential 21st Century skills, prior practice has been to offer these learning experiences outside of mainstream curricula where only a subset of students has access. We have sought to leverage elements of the research-practice partnership framework to achieve our project objectives of integrating computer science and computational thinking within middle science classrooms. Utilizing a qualitative approach to inquiry, we present narratives from three case schools, report on themes across work sites, and share recommendations to guide other practitioners and researchers who are looking to engage in technology-related initiatives to impact the lives of middle grades students.

This study examined students' genetics learning in a game-based environment by exploring the connections between the expectancy-value theory of achievement motivation and flow theory. A total of 394 secondary school students were recruited and learned genetics concepts through interacting with a game-based learning environment. We measured their science self-efficacy, science outcome-expectancy beliefs, flow experience, feelings of frustration, and conceptual understanding before and after playing the game, as well as their game satisfaction. Mixed-model ANOVA, correlation tests, and path analysis were run to answer our research questions. Based on the results, we found that the game had a significant impact on students' conceptual understanding of genetics. We also found an acceptable statistical model of the integration between the two theories. Flow experience and in-game performance significantly impacted students' posttest scores. Moreover, science outcome-expectancy belief was found to be a significant predictor of students' flow experiences. In contrast, science self-efficacy and pretest scores were found to be the most significant factors influencing the feeling of frustration during the game. The results have practical implications with regard to the positive role that an adaptive game-based genetics learning environment might play in the science classroom. Findings also underscore the role the teacher should play in establishing productive outcome expectations for students prior to and during gameplay.

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.

This study highlights how middle schoolers discuss the benefits and drawbacks of AI-driven conversational agents in learning. Using thematic analysis of focus groups, we identified five themes in students' views of AI applications in education. Students recognized the benefits of AI in making learning more engaging and providing personalized, adaptable scaffolding. They emphasized that AI use in education needs to be safe and equitable. Students identified the potential of AI in supporting teachers and noted that AI educational agents fall short when compared to emotionally and intellectually complex humans. Overall, we argue that even without technical expertise, middle schoolers can articulate deep, multifaceted understandings of the possibilities and pitfalls of AI in education. Centering student voices in AI design can also provide learners with much-desired agency over their future learning experiences. [ABSTRACT FROM AUTHOR]

Collaborative 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.

A 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 (i.e., 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.

The goal of this study was to examine college students' (n = 61) contextualized emotions during in-game actions while playing "Crystal Island," a game-based learning environment where students are tasked with solving the mystery of what illness impacted all island inhabitants. We examined emotions during in-game actions: during book reading, after scanning food items for the transmission source, and after submitting a final diagnosis. We dichotomized each activity's feedback into a positive or negative outcome: a relevant or irrelevant book for solving the mystery, testing food items that generate a positive or negative result, or submitting a correct or incorrect final diagnosis. Results revealed that expressing joy while reading a relevant book and expressing confusion after a positive scan significantly positively predicted overall game score, which we used as a proxy for problem-solving performance. Implications include understanding different levels of emotions students express during learning with all advanced learning technologies.

A distinctive feature of game-based learning environments is their capacity for enabling stealth assessment. Stealth assessment analyzes a stream of fine-grained student interaction data from a game-based learning environment to dynamically draw inferences about students' competencies through evidence-centered design. In evidence-centered design, evidence models have been traditionally designed using statistical rules authored by domain experts that are encoded using Bayesian networks. This article presents DEEPSTEALTH, a deep learning-based stealth assessment framework, that yields significant reductions in the feature engineering labor that has previously been required to create stealth assessments. DEEPSTEALTH utilizes end-to-end trainable deep neural network-based evidence models. Using this framework, evidence models are devised using a set of predictive features captured from raw, low-level interaction data to infer evidence for competencies. We investigate two deep learning-based evidence models, long short-term memory networks (LSTMs) and n-gram encoded feedforward neural networks (FFNNs). We compare these models' predictive performance for inferring students' knowledge to linear-chain conditional random fields (CRFs) and naïve Bayes models. We perform feature set-level analyses of game trace logs and external pre-learning measures, and we examine the models' early prediction capacity. The framework is evaluated using data collected from 182 middle school students interacting with a game-based learning environment for middle grade computational thinking. Results indicate that LSTM-based stealth assessors outperform competitive baseline approaches with respect to predictive accuracy and early prediction capacity. We find that LSTMs, FFNNs, and CRFs all benefit from combined feature sets derived from both game trace logs and external pre-learning measures.

Science learning is inherently multimodal, with students utilizing both drawings and writings to explain observations of physical phenomena. As such assessments in science should accommodate the many ways students express their understanding, especially given evidence that understanding is distributed across both drawing and writing. In recent years advanced automated assessment techniques that evaluate expressive student artifacts have emerged. However, these techniques have largely operated individually, each considering only a single mode. We propose a framework for the multimodal automated assessment of students' writing and drawing to leverage the synergies inherent across modalities and create a more complete and accurate picture of a student's knowledge. We introduce a multimodal assessment framework as well as two computational techniques for automatically analyzing student writings and drawings: a convolutional neural network-based model for assessing student writing, and a topology-based model for assessing student drawing. Evaluations with elementary students' writings and drawings collected with a tablet-based digital science notebook demonstrate that (1) each of the framework's two modalities provide an independent and complementary measure of student science learning, and (2) the computational methods are capable of accurately assessing student work from both modalities and offer the potential for integration in technology-rich learning environments for real-time formative assessment.

Successful problem-based learning (PBL) often requires students to collectively regulate their learning processes as a group and engage in socially shared regulation of learning (SSRL). This paper focuses on how facilitators supported SSRL in the context of middle-school game-based PBL. Using conversation analysis, this study analyzed text-based chat messages of facilitators and students collected during gameplay. The analysis revealed direct modeling strategies such as performing regulative processes, promoting group awareness, and dealing with contingency as well as indirect strategies including prompting questions and acknowledgment of regulation, and the patterns of how facilitation faded to yield responsibilities to students to regulate their own learning. The findings will inform researchers and practitioners to design prompts and develop technological tools such as adaptive scaffolding to support SSRL in PBL or other collaborative inquiry processes. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we investigate the affective role of off-task behavior by analyzing data from student interactions with CRYSTAL ISLAND, a narrative-centered learning environment for middle school microbiology. We observe that off-task behavior is associated with reduced student learning, but preliminary analyses of students' affective transitions suggest that off-task behavior may also serve a productive role for some students coping with negative affective states such as frustration. Empirical findings imply that some students may use off-task behavior as a strategy for self-regulating negative emotional states during learning. Based on these observations, we introduce a supervised machine learning procedure for detecting whether students' off-task behaviors are cases of emotion self-regulation. The method proceeds in three stages. During the first stage, a dynamic Bayesian network (DBN) is trained to model the valence of students' emotion self-reports using collected data from interactions with the learning environment. In the second stage, a novel simulation process uses the DBN to generate "alternate futures" by modeling students' affective trajectories as if they had engaged in fewer off-task behaviors than they did during their actual learning interactions. The alternate futures are compared to students' actual traces to produce labels denoting whether students' off-task behaviors are cases of emotion self-regulation. In the final stage, the generated emotion self-regulation labels are predicted using off-the-shelf classifiers and features that can be computed in run-time settings. Results suggest that this approach shows promise for identifying cases of off-task behavior that are emotion self-regulation. Analyses of the first two phases suggest that trained DBN models are capable of accurately modeling relationships between students' off-task behaviors and self-reported emotional valence in CRYSTAL ISLAND. Additionally, the proposed simulation process produces emotion self-regulation labels with high levels of reliability. Preliminary analyses indicate that support vector machines, bagged trees, and random forests show promise for predicting the generated emotion self-regulation labels, but room for improvement remains. The findings underscore the methodological potential of considering alternate futures when modeling students' emotion self-regulation processes in narrative-centered learning environments.

Tutoring systems that are sensitive to affect show considerable promise for enhancing student learning experiences. Creating successful affective responses requires considerable effort both to detect student affect and to design appropriate responses to affect. Recent work has suggested that affect detection is more effective when both physical sensors and interaction logs are used, and that context-sensitive design of affective feedback is necessary to enhance engagement and improve learning. In this paper, we provide a comprehensive report on a multi-part study that integrates detection, validation, and intervention into a unified approach. This paper examines the creation of both sensor-based and interaction-based detectors of student affect, producing successful detectors of student affect. In addition, it reports results from an investigation of motivational feedback messages designed to address student frustration, and investigates whether linking these interventions to detectors improves outcomes. Our results are mixed, finding that self-efficacy enhancing interventions based on interaction-based affect detectors enhance outcomes in one of two experiments investigating affective interventions. This work is conducted in the context of the GIFT framework for intelligent tutoring, and the TC3Sim game-based simulation that provides training for first responder skills.

In recent years, significant advances have been made in intelligent tutoring systems, and these advances hold great promise for adaptively supporting computer science (CS) learning. In particular, tutorial dialogue systems that engage students in natural language dialogue can create rich, adaptive interactions. A promising approach to increasing the effectiveness of these systems is to adapt not only to problem-solving performance, but also to a student's characteristics. Self-efficacy refers to a student's view of her ability to complete learning objectives and to achieve goals; this characteristic may be particularly influential during tutorial dialogue for computer science education. This article examines a corpus of effective human tutoring for computer science to discover the extent to which considering self-efficacy as measured within a pre-survey, coupled with dialogue and task events during tutoring, improves models that predict the student's self-reported frustration and learning gains after tutoring. The analysis reveals that students with high and low self-efficacy benefit differently from tutorial dialogue. Student control, social dialogue, and tutor moves to increase efficiency, may be particularly helpful for high self-efficacy students, while for low self-efficacy students, guided experimentation may foster greater learning while at the same time potentially increasing frustration. It is hoped that this line of research will enable tutoring systems for computer science to tailor their tutorial interactions more effectively.

The aim of this study was to explore how "Narrative Theatre," a narrative-centered digital learning environment, supported the writing processes of 3 struggling adolescent male writers. We utilized a multicase study approach to capture 3 sixth-grade participants' experiences with the digital learning environment before, during, and after writing. The case studies provided detailed portraits of the writers as well as insights into their digital writing processes related to student interest, student ability, and value for writing. The across-case analysis revealed 3 themes (i.e., choice, scaffolding, and self-efficacy) that illustrated how the digital learning environment contributed to the students' writing experiences. Future research and development will focus on the addition of text animation for student products and the degree to which this feature further contributes to engagement and proficiency with struggling writers.