James C. Lester

The persona effect: affective impact of animated pedagogical agents

Animated pedagogical agents that inhabit interactive learning environments can exhibit strikingly lifelike behaviors. In addition to providing problem-solving advice in response to students’ activities in the learning environment, these agents may also be able to play a powerful motivational role. To design the most effective agent-based learning environment software, it is essential to understand how students perceive an animated pedagogical agent with regard to affective dimensions such as encouragement, utility, credibility, and clarity. This paper describes a study of the affective impact of animated pedagogical agents on students’ learning experiences. One hundred middle school students interacted with animated pedagogical agents to assess their perception of agents’ affective characteristics. The study revealed the persona eflecr, which is that the presence of a lifelike character in an interactive learning environment~ven one that is not expressive— can have a strong positive effect on student’s perception of their learning experience. The study also demonstrates the interesting effect of multiple types of explanatory behaviors on both affective perception and learning performance.

The Case for Social Agency in Computer-based Teaching: Do Students Learn More Deeply When They Interact with Animated Pedagogical Agents?.

College students (in Experiment 1) and 7th-grade students (in Experiment 2) learned how to design the roots, stem, and leaves of plants to survive in 8 different environments through a computer-based multimedia lesson. They learned by interacting with an animated pedagogical agent who spoke to them (Group PA) or received identical graphics and explanations as on-screen text without a pedagogical agent (Group No PA). Group PA outperformed Group No PA on transfer tests and interest ratings but not on retention tests. To investigate further the basis for this personal agent effect, we varied the interactivity of the agent-based lesson (Experiment 3) and found an interactivity effect: Students who participate in the design of plant parts remember more and transfer what they have learned to solve new problems better than students who learn the same materials without participation. Next, we varied whether the agents words were presented as speech or on-screen text, and whether the agents image appeared on the screen. Both with a fictional agent (Experiment 4) and a video of a human face (Experiment 5), students performed better on tests of retention and problem-solving transfer when words were presented as speech rather than on-screen text (producing a modality effect) but visual presence of the agent did not affect test performance (producing no image effect). Results support the introduction of interactive pedagogical agents who communicate with students via speech to promote meaningful learning in multimedia lessons.

Increasing believability in animated pedagogical agents

Animated pedagogical agents o er great promise for knowledge based learning environments In addition to coupling feedback capabilities with a strong visual presence these agents play a critical role in motivating students The extent to which they exhibit life like behaviors strongly increases their motivational impact but these behaviors must always complement and never interfere with students problem solving To address this problem we have developed a framework for dynamically sequencing animated pedagogical agents believability enhancing be haviors By monitoring a student s problem solving history and the agent s past activities a competition based behavior sequencing engine produces realtime life like character animations that are pedagogically appropriate Behaviors in the agent s repertoire compete with one another At each moment the strongest eligible behavior is heuristically selected as the winner and is ex hibited We have implemented this framework in Herman the Bug an animated pedagogical agent that inhabits a knowledge based learning envi ronment for the domain of botanical anatomy

Lifelike pedagogical agents and affective computing: an exploratory synthesis

Lifelike pedagogical agents have been the subject of increasing attention in the agents and knowledge-based learning environment communities [2, 17, 19—21]. In parallel developments, recent years have witnessed great strides in work on cognitive models of emotion and affective reasoning [4,18, 22]. As a result, the time is now ripe for exploring how affective reasoning can be incorporated into pedagogical agents to improve students’ learning experiences.

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 naïve 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.

Enhancing 5th graders' science content knowledge and self-efficacy through game-based learning

Many argue that games can positively impact learning by providing an intrinsically motivating and engaging learning environment for students in ways that traditional school cannot. Recent research demonstrates that games have the potential to impact student learning in STEM content areas and that collaborative gameplay may be of particular importance for learning gains. This study investigated the effects of collaborative and single game player conditions on science content learning and science self-efficacy. Results indicated that there were no differences between the two playing conditions; however, when conditions were collapsed, science content learning and self-efficacy significantly increased. Future research should focus on the composition of collaboration interaction among game players to assess what types of collaborative tasks may yield positive learning gains.

Evaluating the consequences of affective feedback in intelligent tutoring systems

The link between affect and student learning has been the subject of increasing attention in recent years. Because of the possible impacts of affective state on learning, it is a goal of many intelligent tutoring systems to attempt to control student emotional states through affective interventions. While much work has gone into improving the quality of these interventions, we are only beginning to understand the complexities of the relationships between affect, learning, and feedback. This paper investigates the consequences associated with providing affective feedback. It represents a first step toward the long-term objective of designing intelligent tutoring systems that can utilize this information for analysis of the risks and benefits of affective intervention. It reports on the results of two studies that were conducted with students interacting with affect-informed virtual agents. The studies reveal that emotion-specific risk/reward information is associated with particular affective states and suggests that future systems might leverage this information to make determinations about affective interventions.

U-director: a decision-theoretic narrative planning architecture for storytelling environments

Recent years have seen significant growth in work on interactive storytelling environments. A key challenge posed by these environments is narrative planning, in which a director agent orchestrates all of the events in a storyworld to create an optimal experience for a user, who is herself an active participant in the unfolding story. To create effective stories, the director agent must cope with the tasks inherent uncertainty, including uncertainty about the users intentions and the absence of a complete theory of narrative. Director agents must be efficient so they can operate in real time. In this paper, we present U-Director, a decision-theoretic narrative planning architecture that dynamically models narrative objectives (e.g., plot progress, narrative flow), storyworld state (e.g., plot focus), and user state (e.g., goals, beliefs) with a dynamic decision network that continually selects storyworld actions to maximize narrative utility on an ongoing basis. The U-DIRECTOR architecture has been implemented in a narrative planner for Crystal Island, an interactive storyworld in which users play the role of a medical detective solving a science mystery. Preliminary evaluations suggest that the U-DIRECTOR architecture satisfies the real-time constraints of interactive environments and creates engaging narrative experiences.

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.