Jonathan P. Rowe

Problem Solving and Game-Based Learning: Effects of Middle Grade Students' Hypothesis Testing Strategies on Learning Outcomes

Targeted as a highly desired skill for contemporary work and life, problem solving is central to game-based learning research. In this study, middle grade students achieved significant learning gains from gameplay interactions that required solving a science mystery based on microbiology content. Student trace data results indicated that effective exploration and navigation of the hypothesis space within a science problem-solving task was predictive of student science content learning and in-game performance. Students who selected a higher proportion of appropriate hypotheses demonstrated greater learning gains and completed more in-game goals. Students providing correct explanations for hypothesis selection completed more in-game goals; however, providing the correct explanation for hypothesis selection did not account for greater learning gains. From the analysis, we concluded that hypothesis testing strategies play a central role in game-based learning environments that involve problem-solving tasks, thereby demonstrating strong connections to science content learning and in-game performance.

Story-Based Learning: The Impact of Narrative on Learning Experiences and Outcomes

Within the intelligent tutoring systems community, narrative is emerging as an effective medium for contextualizing learning. To date, relatively few empirical studies have been conducted to assess learning in narrative-centered learning environments. In this paper, we investigate the effect of narrative on learning experiences and outcomes. We present results from an experiment conducted with eighth-grade middle school students interacting with a narrative-centered learning environment in the domain of microbiology. The study found that students do exhibit learning gains, that those gains are less than those produced by traditional instructional approaches, but that the motivational benefits of narrative-centered learning with regard to self-efficacy, presence, interest, and perception of control are substantial.

The effects of empathetic virtual characters on presence in narrative-centered learning environments

Recent years have seen a growing interest in the role that narrative can play in learning. With the emergence of narrative-centered learning environments that engage students by drawing them into rich interactions with compelling characters, we have begun to see the significant potential offered by immersive story-based learning experiences. In this paper we describe two studies that investigate the impact of empathetic characters on student perceptions of presence. A study was initially conducted with middle school students, and was then replicated with high school students. The results indicate that, for both populations, employing empathetic characters in narrative-centered learning environments significantly increases student perceptions of presence. The studies also reveal that empathetic characters contribute to a heightened sense of student involvement and control in learning situations.

Archetype-Driven Character Dialogue Generation for Interactive Narrative

Recent years have seen a growing interest in creating virtual agents to populate the cast of characters for interactive narrative. A key challenge posed by interactive characters for narrative environments is devising expressive dialogue generators. To be effective, character dialogue generators must be able to simultaneously take into account multiple sources of information that bear on dialogue, including character attributes, plot development, and communicative goals. Building on the narrative theory of character archetypes, we propose an archetype-driven character dialogue generator that uses a probabilistic unification framework to generate dialogue motivated by character personality and narrative history to achieve communicative goals. The generators behavior is illustrated with character dialogue generation in a narrative-centered learning environment, Crystal Island .

Individual differences in gameplay and learning: a narrative-centered learning perspective

Narrative-centered learning environments are an important class of educational games that situate learning within rich story contexts. The work presented in this paper investigates individual differences in gameplay and learning during student interactions with a narrative-centered learning environment, Crystal Island. Findings reveal striking differences between high- and low-achieving science students in problem-solving effectiveness, attention to particular gameplay elements, learning gains and engagement ratings. High-achieving science students tended to demonstrate greater problem-solving efficiency, reported higher levels of interest and presence in the narrative environment, and demonstrated an increased focus on information gathering and information organization gameplay activities. Lower-achieving microbiology students gravitated toward novel gameplay elements, such as conversations with non-player characters and the use of laboratory testing equipment. The findings have implications for the design of broadly effective gameplay activities for narrative-centered learning environments, as well as investigations of scaffolding techniques to promote effective problem solving, improved learning outcomes and sustained engagement for all students.

Toward intelligent support of authoring machinima media content: story and visualization

The Internet and the availability of authoring tools have enabled a greater community of media content creators, including nonexperts. However, while media authoring tools often make it technically feasible to generate, edit and share digital media artifacts, they do not guarantee that the works will be valuable or meaningful to the community at large. Therefore intelligent tools that support the authoring and creative processes are especially valuable. In this paper, we describe two intelligent support tools for the authoring and production of machinima. Machinima is a technique for producing computer-animated movies through the manipulation of computer game technologies. The first system we describe, ReQUEST, is an intelligent support tool for the authoring of plots. The second system, Cambot, produces machinima from a pre-authored script by manipulating virtual avatars and a virtual camera in a 3D graphical environment.

Supporting Self-Regulated Science Learning in Narrative-Centered Learning Environments

Narrative-centered learning environments provide engaging, story-centric virtual spaces that afford opportunities for discreetly embedding pedagogical guidance for content knowledge and problem-solving skill acquisition. Students’ abilities to self-regulate learning significantly impact performance in these environments and are critical for academic achievement and lifelong learning. This chapter explores the relationship between narrative-centered learning environments and self-regulation for science learning. Connections are drawn between the salient characteristics of narrative-centered learning environments and principles for promoting self-regulation in science education. These relationships are further explored through an examination of the Crystal Island learning environment. The chapter investigates the hypothesis that narrative-centered learning environments are particularly well suited for simultaneously promoting learning, engagement, and self-regulation. Empirical support is provided by a summary of findings from a series of studies conducted with over 300 middle school students.

Improving Student Problem Solving in Narrative-Centered Learning Environments: a Modular Reinforcement Learning Framework

Narrative-centered learning environments comprise a class of game-based learning environments that embed problem solving in interactive stories. A key challenge posed by narrative-centered learning is dynamically tailoring story events to enhance student learning. In this paper, we investigate the impact of a data-driven tutorial planner on students’ learning processes in a narrative-centered learning environment, Crystal Island. We induce the tutorial planner by employing modular reinforcement learning, a multi-goal extension of classical reinforcement learning. To train the planner, we collected a corpus from 453 middle school students who used Crystal Island in their classrooms. Afterward, we investigated the induced planner’s impact in a follow-up experiment with another 75 students. The study revealed that the induced planner improved students’ problem-solving processes—including hypothesis testing and information gathering behaviors—compared to a control condition, suggesting that modular reinforcement learning is an effective approach for tutorial planning in narrative-centered learning environments.

Enhancing Student Models in Game-based Learning with Facial Expression Recognition

Recent years have seen a growing recognition of the role that affect plays in learning. Because game-based learning environments elicit a wide range of student affective states, affect-enhanced student modeling for game-based learning holds considerable promise. This paper introduces an affect-enhanced student modeling framework that leverages facial expression tracking for game-based learning. The affect-enhanced student modeling framework was used to generate predictive models of student learning and student engagement for students who interacted with CRYSTAL ISLAND, a game-based learning environment for microbiology education. Findings from the study reveal that the affect-enhanced student models significantly outperform baseline predictive student models that utilize the same gameplay traces but do not use facial expression tracking. The study also found that models based on individual facial action coding units are more effective than composite emotion models. The findings suggest that introducing facial expression tracking can improve the accuracy of student models, both for predicting student learning gains and also for predicting student engagement.

Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island

Game-based learning environments (GBLEs) have been touted as the solution for failing educational outcomes. In this study, we address some of these major issues by using multi-level modeling with data from eye movements and log files to examine the cognitive and metacognitive self-regulatory processes used by 50 college students as they read books and completed the associated in-game assessments (concept matrices) while playing the Crystal Island game-based learning environment. Results revealed that participants who read fewer books in total, but read each of them more frequently, and who had low proportions of fixations on books and concept matrices exhibited the strongest performance. Results stress the importance of assessing quality vs. quantity during gameplay, such that it is important to read books in-depth (i.e., quality), compared to reading books once (i.e., quantity). Implications for these findings involve designing adaptive GBLEs that scaffold participants based on their trace data, such that we can model efficient behaviors that lead to successful performance.