An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixtyeight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning. Objectives and Theoretical Framework When learning about complex science topics such as the human circulatory system, research indicates that individuals can gain deep conceptual understanding through effective use of self-regulated learning (SRL). The successful use of cognitive and metacognitive SR processes involves setting meaningful goals for one’s learning, planning a course of action for attaining these goals, deploying a diverse set of effective learning strategies in pursuit of the goals, continuously monitoring one’s own understanding of the material and the appropriateness of the current information, and making adaptations to one’s goals, strategies, and navigational patterns, based on the results of such monitoring processes and their resulting judgments (Azevedo, 2005; Azevedo & Witherspoon, 2009; Opfermann, Azevedo, & Leutner, in press; Pintrich, 2000; Winne, 2001; Winne & Hadwin, 2008; Zimmerman, 2001; Zimmerman & Schunk, in press). Although learners should attempt to follow these guidelines when attempting difficult topics, exploration of typical learning has demonstrated that few learners, in fact, engage in effective self-regulated learning. We assume that, while motivation and affect play a role in determining learners’ willingness to self-regulate, a lack of selfregulatory skills is the main obstacle to adequate regulation and therefore deficient learning gains and conceptual understanding (Azevedo & Jacobson, 2008; Shapiro, 2008; Schwartz et al., 2009; White, Frederiksen, & Collins, 2009). Therefore, our current research is directed toward scaffolding learners’ use of self-regulation using artificial pedagogical agents (PAs) during learning with MetaTutor, a multi-agent adaptive hypermedia learning environments that models, scaffolds, and fosters learners’ use of cognitive and metacognitive SRL processes during learning about human biology. Learners attempting to self-regulate often face limitations in their own metacognitive skills, which, when compounded with lack of domain knowledge, can result in cognitive overload in open-ended learning environments like hypermedia (Azevedo, Johnson, Chauncey, & Graesser, in press; Leelawong & Biswas, 2008; McQuiggan, Robinson, & Lester, 2010). One method of relieving the cognitive burden placed on learners in this situation is to provide assistance in the form of adaptive scaffolding. Previous experiments conducted by Azevedo and colleagues (e.g., Azevedo, Cromley, Winters, Moos, & Greene, 2005; Azevedo, Moos, Greene, Winters, & Cromley, 2008) established that adaptive scaffolding 11 Cognitive and Metacognitive Educational Systems: Papers from the AAAI Fall Symposium (FS-10-01)

L’apprendimento autoregolato rappresenta una modalità di apprendimento di fondamentale importanza quando ci si avvale del supporto di ambienti ipermediali. Obiettivo di questo articolo è presentare quattro assunzioni chiave che consentono la misurazione dei processi cognitivi e metacognitivi attivati durante l’apprendimento tramite ipermedia. Innanzi tutto, assumiamo che sia possibile individuare, tracciare, modellare e favorire processi di apprendimento auto-regolato durante lo studio con gli ipermedia. La seconda assunzione si focalizza sul comprendere come la complessità dei processi regolatori che avvengono durante l’apprendimento mediato da sistemi ipermediali sia importante per determinare il perché alcuni processi vengono messi in atto durante l’esecuzione di un compito. Le terza assunzione è relativa al considerare che l’utilizzo di processi di apprendimento auto-regolato possa dinamicamente cambiare nel tempo e che tali processi sono di natura ciclica (influenzati dalle condizioni interne ed esterne e da meccanismi di feedback). Infine, l’identificazione, raccolta e classificazione dei processi di apprendimento autoregolato utilizzati durante lo studio con sistemi ipermediali, può risultare un compito alquanto difficoltoso.

This experiment examined the role of emotion and motivation on metacognitive judgments and learning performance during multimedia learning. A false-biofeedback paradigm was used to induce emotional states and track learners’ metacognitive monitoring and control behaviors in a self-paced, linearly structured multimedia learning environment. Our results indicate that induced emotional states significantly impact these processes in college students. We will discuss the implications for these findings on the design of intelligent tutoring systems and multimedia learning environments to help learners achieve optimal self-regulation and deep learning.

MetaTutor is a multi-agent, adaptive hypermedia learning environment that trains and fosters high school and college students' use of self-regulatory processes in the context of learning about science topics such as human body systems. The purpose of the MetaTutor environment is to examine the effectiveness of pedagogical agents (PAs) as external regulatory agents used to detect, trace, model, and foster students' self-regulatory processes during science learning with multiple representations of information. The multi-agent system provides adaptive tutoring based on students' evolving conceptual understanding of the topic and their strategic use of cognitive and metacognitive processes. Each of the four agents is responsible for specific aspects of SRL, including task definition, planning, metacognitive processes, and learning strategies. Based on their specialized roles, each PA has been designed to detect a specific set of SRL processes. For example, Mary the Monitor is in charge of detecting when students deploy metacognitive processes and make metacognitive judgments such as expressing a judgment of learning (JOL; e.g., used in relation to judging one's understanding of the current content) and also in determining the valence (e.g., JOL - or JOL +) associated with the metacognitive judgment. The presupposition of an accurate detection method (by each agent) leads the agent to model the temporal dynamics associated with each SRL process, across all SRL processes, and how they relate to several learning outcomes such as declarative, procedural, and inferential knowledge and mental models of the science topic. This evolving model is then used to foster SRL and content understanding by providing several levels of scaffolding. Instructional scaffolding involves the coordination of other architectural modules of MetaTutor that coordinate and manage the dialogue system between agents and the learner. Instructional scaffolding is provided based on current research on human and computerized tutoring research (Chi et al., 2004; Graesser, D'Mello & Person, in press; VanLehn et al., 2007; Wolff, 2009) and recent studies comparing SRL with ERL (externally-regulated learning; Azevedo et al., 2007, 2008). The types of scaffolding range from having the learner vicariously watch as the agent models the SRL process to having the student use a specific SRL process while being provided with elaborate feedback regarding the effective use of the process (based on Zimmerman & Moylan, in press).

L’apprendimento autoregolato rappresenta una modalita di apprendimento di fondamentale importanza quando ci si avvale del supporto di ambienti ipermediali. Obiettivo di questo articolo e presentare quattro assunzioni chiave che consentono la misurazione dei processi cognitivi e metacognitivi attivati durante l’apprendimento tramite ipermedia. Innanzi tutto, assumiamo che sia possibile individuare, tracciare, modellare e favorire processi di apprendimento auto-regolato durante lo studio con gli ipermedia. La seconda assunzione si focalizza sul comprendere come la complessita dei processi regolatori che avvengono durante l’apprendimento mediato da sistemi ipermediali sia importante per determinare il perche alcuni processi vengono messi in atto durante l’esecuzione di un compito. Le terza assunzione e relativa al considerare che l’utilizzo di processi di apprendimento auto-regolato possa dinamicamente cambiare nel tempo e che tali processi sono di natura ciclica (influenzati dalle condizioni interne ed esterne e da meccanismi di feedback). Infine, l’identificazione, raccolta e classificazione dei processi di apprendimento autoregolato utilizzati durante lo studio con sistemi ipermediali, puo risultare un compito alquanto difficoltoso.

Self-regulated learning (SRL) with hypermedia environments involves a complex cycle of temporally unfolding cognitive and metacognitive processes that impact students' learning. We present several methodological issues related to treating SRL as an event and strengths and challenges of using online trace methodologies to detect, trace, model, and foster students' SRL processes. We first describe a scenario illustrating the complex nature of SRL processes during learning with hypermedia. We provide our theoretically driven assumptions regarding the use of several cognitive methodologies, including concurrent think aloud protocols, and provide several examples of empirical evidence regarding the advantages of treating SRL as an event. Last, we discuss challenges for measuring cognitive and metacognitive processes in the context of MetaTutor, an intelligent adaptive hypermedia learning environment. This discussion includes the roles of pedagogical agents in goal-generation, multiple representations, agent-learner dialogue, and a system's ability to detect, track, and model SRL processes during learning.

Learning about complex and challenging science topics with advanced learning technologies requires students to regulate their learning. The deployment of key cognitive and metacognitive regulatory processes is key to enhancing learning in open-ended learning environments such as hypermedia. In this paper, we propose a metaphor—Computers as MetaCognitive tools—to characterize the complex nature of the learning context, self- regulatory processes, task conditions, and features of advanced learning technologies. We briefly outline the theoretical and conceptual assumptions of self-regulated learning (SRL) underlying MetaTutor, a hypermedia environment designed to train and foster students’ SRL processes in biology. Lastly, we provide preliminary learning outcome and SRL process data on the deployment of SRL processes during learning with MetaTutor.

We report preliminary data of an initial laboratory study examining the effectiveness of self-regulated learning (SRL) training versus no training on learners' ability to deploy SRL processes and learn about the circulatory system with MetaTutor. MetaTutor is an intelligent tutoring system (ITS) designed to train and foster learners' SRL processes while learning about several complex human body systems. We used a mixed methodology approach and include the results of a subset of the participants (N=30) whose product and process data we have analyzed. Overall, the results indicate that the SRL training group significantly outperformed the control group.