• Publications
  • Influence
Inferring Learning from Gaze Data during Interaction with an Environment to Support Self-Regulated Learning
TLDR
In this paper, we explore the potential of gaze data as a source of information to predict learning as students interact with MetaTutor, an ITS that scaffolds self-regulated learning. Expand
  • 54
  • 6
  • PDF
Clustering and Profiling Students According to their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning
TLDR
In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). Expand
  • 72
  • 4
Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems
This chapter emphasizes the importance of using multi-channel trace data to examine the complex roles of cognitive, affective, and metacognitive (CAM) self-regulatory processes deployed by studentsExpand
  • 107
  • 3
Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments?
TLDR
MetaTutor, a multi-agent, hypermedia-based learning environment, was investigated, including how prior knowledge affected their use of self-regulated learning (SRL) strategies. Expand
  • 75
  • 3
Aligning and Comparing Data on Emotions Experienced during Learning with MetaTutor
TLDR
In this study we aligned and compared self-report and on-line emotions data on 67 college students’ emotions at five different points in time over the course of their interactions with MetaTutor. Expand
  • 30
  • 3
  • PDF
Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning
TLDR
Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). Expand
  • 48
  • 3
  • PDF
Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments
TLDR
The current study examined the relationships between learners’ personality traits, the emotions they typically experience while studying (trait studying emotions), and emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Expand
  • 32
  • 3
A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system
TLDR
This paper presents the evaluation of the synchronization of three emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activity) and their agreement regarding learners' emotions. Expand
  • 101
  • 2
  • PDF
The Effectiveness of Pedagogical Agents' Prompting and Feedback in Facilitating Co-adapted Learning with MetaTutor
TLDR
Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g., response to agents' prompting and feedback) conditions, and (2) agents can adequately and correctly detect, track, model, and foster learners' self-regulated processes. Expand
  • 50
  • 2
  • PDF
Can Adaptive Pedagogical Agents' Prompting Strategies Improve Students' Learning and Self-Regulation?
TLDR
This study examines whether an ITS that fosters the use of metacognitive strategies can benefit from variations in its prompts based on learners' self-regulatory behaviors. Expand
  • 13
  • 2
...
1
2
3
4
5
...