MetaTutor: A MetaCognitive Tool for Enhancing Self-Regulated Learning

AAAI Conferences

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.


Dysregulated Learning with Advanced Learning Technologies

AAAI Conferences

Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitive monitor the use of strategies and repeatedly make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.


Detecting, Tracking, and Modeling Self-Regulatory Processes during Complex Learning with Hypermedia

AAAI Conferences

Self-regulated learning (SRL) involves a complex set of interactions between cognitive, metacognitive, motivational and affective processes. The key to understanding the influence of these self-regulatory processes on learning with open-ended, nonlinear learning computer-based environments involves detecting, capturing, identifying, and classifying these processes as they temporally unfold during learning. Understanding the complex nature of the processes is key to building intelligent learning environments that adapt to learners' fluctuations in their SRL processes and emerging understanding of the topic of domain. The foci of this paper are to: (1) introduce the complexity of SRL with hypermedia, (2) briefly present an information processing theory (IPT) of SRL and using it to analyze the temporally, unfolding sequences of processes during learning, (3) present and describe sample data to illustrate the nature and complexity of these processes, and (4) present challenges for future research that combine several techniques and methods to design intelligent learning environments that trace, model, and foster SRL.


Issues in the Measurement of Cognitive and Metacognitive Regulatory Processes Used During Hypermedia Learning

AAAI Conferences

The goal of this paper is to present four key assumptions regarding the measurement of cognitive and metacognitive regulatory processes used during learning with hypermedia. First, we assume it is possible to detect, trace, model, and foster SRL processes during learning with hypermedia. Second, understanding the complex nature of the regulatory processes during learning with hypermedia is critical in determining why certain processes are used throughout a learning task. Third, it is assumed that the use of SRL processes can dynamically change over time and that they are cyclical in nature (influenced by internal and external conditions and feedback mechanisms). Fourth, capturing, identifying, and classifying SRL processes used during learning with hypermedia is a rather challenging task.


A Semantic Metacognitive Learning Environment

AAAI Conferences

In the last years, knowledge technologies have been exploited for self-regulation functionalities inside e-learning systems. The definition of integrated system suitably scaffolding learners to improve their experi- ence is still lacking though. In this work, we propose an innovative Web-based educational environment that sustains metacognitive self-regulated learning processes upon Semantic Web and Social Web methods and technologies.