Narrative-centered Learning Environment Research Articles

Online Reinforcement Learning-Based Pedagogical Planning for Narrative-Centered Learning Environments

Pedagogical planners can provide adaptive support to students in narrative-centered learning environments by dynamically scaffolding student learning and tailoring problem scenarios. Reinforcement learning (RL) is frequently used for pedagogical planning in narrative-centered learning environments. However, RL-based pedagogical planning raises significant challenges due to the scarcity of data for training RL policies. Most prior work has relied on limited-size datasets and offline RL techniques for policy learning. Unfortunately, offline RL techniques do not support on-demand exploration and evaluation, which can adversely impact the quality of induced policies. To address the limitation of data scarcity and offline RL, we propose INSIGHT, an online RL framework for training data-driven pedagogical policies that optimize student learning in narrative-centered learning environments. The INSIGHT framework consists of three components: a narrative-centered learning environment simulator, a simulated student agent, and an RL-based pedagogical planner agent, which uses a reward metric that is associated with effective student learning processes. The framework enables the generation of synthetic data for on-demand exploration and evaluation of RL-based pedagogical planning. We have implemented INSIGHT with OpenAI Gym for a narrative-centered learning environment testbed with rule-based simulated student agents and a deep Q-learning-based pedagogical planner. Our results show that online deep RL algorithms can induce near-optimal pedagogical policies in the INSIGHT framework, while offline deep RL algorithms only find suboptimal policies even with large amounts of data.

Fostering Engagement in Health Behavior Change: Iterative Development of an Interactive Narrative Environment to Enhance Adolescent Preventive Health Services

PurposeAccidents and unintentional injuries account for the greatest number of adolescent deaths, often involving use of alcohol and other substances. This article describes the iterative design and development of Interactive Narrative System for Patient-Individualized Reflective Exploration (INSPIRE), a narrative-centered behavior change environment for adolescents focused on reducing alcohol use. INSPIRE is designed to serve as an extension to clinical preventive care, engaging adolescents in a theoretically grounded intervention for health behavior change by leveraging 3D game engine and interactive narrative technologies. MethodsAdolescents were engaged in all aspects of the iterative, multiyear development process of INSPIRE through over 20 focus groups and iterative pilot testing involving more than 145 adolescents. Qualitative findings from focus groups are reported, as well as quantitative findings from small-scale pilot sessions investigating adolescent engagement with a prototype version of INSPIRE using a combination of questionnaire and interaction trace log data. ResultsAdolescents reported that they found INSPIRE to be engaging, believable, and relevant to their lives. The majority of participants indicated that the narrative’s protagonist character was like them (84%) and that the narrative featured virtual characters that they could relate to (79%). In the interactive narrative, the goals most frequently chosen by adolescents were “stay in control” (60%) and “do not get in trouble” (55%). ConclusionsWith a strong theoretical framework (social-cognitive behavior change theory) and technology advances (narrative-centered learning environments), the field is well positioned to design health behavior change systems that can realize significant impacts on behavior change for adolescent preventive health.

Enhancing Writing Achievement Through a Digital Learning Environment: Case Studies of Three Struggling Adolescent Male Writers

ABSTRACTThe 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.

Self-regulation and gender within a game-based learning environment.

In this study, we examined how self-regulated learning (SRL) and gender influences performance in an educational game for 8th-grade students (N = 130). Crystal Island–Outbreak is an immersive, inquiry-based, narrative-centered learning environment featuring a microbiology science mystery aligned with 8th-grade science curriculum. SRL variables predicted successful in-game performance even after accounting for prior knowledge and perceived gaming skill. Content learning gains were found across both genders, and girls performed at similar levels as boys in the game despite incoming disadvantages for perceived skill and prior gaming experience. Boys were more effective than girls in using a cognitive tool that was critical for solving the Crystal Island–Outbreak mystery; however, these differences disappeared when prior gaming experience was taken into account. Overconfidence on monitoring judgments for boys but not girls was predictive of in-game performance. Findings related to motivational variables such as self-efficacy, situational interest, and goal orientation were mixed with regard to their consistency across genders.

Learning Director Agent Strategies: An Inductive Framework for Modeling Director Agents

Interactive narrative environments offer significant potential for creating engaging narrative experiences that are tailored to individual users. Increasingly, applications in education, training, and entertainment are leveraging narrative to create rich interactive experiences in virtual storyworlds. A key challenge posed by these environments is devising accurate models of director agents’ strategies that determine the most appropriate director action to perform for crafting customized story experiences. A promising approach is developing an empirically informed model of director agents’ decision-making strategies. In this paper, we propose a framework for learning models of director agent decision-making strategies by observing human-human interactions in an interactive narrative-centered learning environment. The results are encouraging and suggest that creating empirically driven models of director agent decision-making is a promising approach to interactive narrative.

Integrating learning, problem solving, and engagement in narrative-centered learning environments

A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learnin...

Modeling User Knowledge with Dynamic Bayesian Networks in Interactive Narrative Environments

Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the Crystal Island interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.

Modeling self-efficacy in intelligent tutoring systems: An inductive approach

Self-efficacy is an individual's belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student's level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naive Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.