Search results for author:"James C. Lester"
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Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later
W. Lewis Johnson; James C. Lester
International Journal of Artificial Intelligence in Education Vol. 26, No. 1 (2016) pp. 25–36
Johnson et al. ("International Journal of Artificial Intelligence in Education," 11, 47-78, 2000) introduced and surveyed a new paradigm for interactive learning environments: animated pedagogical agents. The article argued for combining...
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Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models
Jennifer L. Sabourin; Bradford W. Mott; James C. Lester
International Conference on Educational Data Mining (EDM) 2012 (June 2012)
Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the...
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The Case for Social Agency in Computer-based Teaching: Do Students Learn More Deeply When They Interact with Animated Pedagogical Agents?
Roxana Moreno; Richard E. Mayer; Hiller A. Spires; James C. Lester
Cognition and Instruction Vol. 19, No. 2 (2001) pp. 177–213
Five experiments explored a potentially more productive application of educational technology in which an individual learner has the opportunity to develop a social relation with a computer by interacting with an animated pedagogical agent. Results...
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Integrating Learning, Problem Solving, and Engagement in Narrative-Centered Learning Environments
Jonathan P. Rowe; Lucy R. Shores; Bradford W. Mott; James C. Lester
International Journal of Artificial Intelligence in Education Vol. 21, No. 1 (2011) pp. 115–133
A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments. This view suggests that, on the one...
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Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments
Jennifer L. Sabourin; Lucy R. Shores; Bradford W. Mott; James C. Lester
International Journal of Artificial Intelligence in Education Vol. 23, No. 1 (November 2013) pp. 94–114
Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has...
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Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment
Michelle Taub; Roger Azevedo; Amanda E. Bradbury; Garrett C. Millar; James Lester
Learning and Instruction Vol. 54, No. 1 (April 2018) pp. 93–103
The goal of this study was to assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion during learning with
Language: English
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Problem Solving and Game-Based Learning: Effects of Middle Grade Students' Hypothesis Testing Strategies on Learning Outcomes
Hiller A. Spires; Jonathan P. Rowe; Bradford W. Mott; James C. Lester
Journal of Educational Computing Research Vol. 44, No. 4 (2011) pp. 453–472
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...
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Investigating the Role of Student Motivation in Computer Science Education through One-on-One Tutoring
Kristy Elizabeth Boyer; Robert Phillips; Michael D. Wallis; Mladen A. Vouk; James C. Lester
Computer Science Education Vol. 19, No. 2 (June 2009) pp. 111–135
The majority of computer science education research to date has focused on purely cognitive student outcomes. Understanding the "motivational" states experienced by students may enhance our understanding of the computer science learning process, and ...
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Detecting and Addressing Frustration in a Serious Game for Military Training
Jeanine A. DeFalco; Jonathan P. Rowe; Luc Paquette; Vasiliki Georgoulas-Sherry; Keith Brawner; Bradford W. Mott; Ryan S. Baker; James C. Lester
International Journal of Artificial Intelligence in Education Vol. 28, No. 2 (2018) pp. 152–193
Tutoring systems that are sensitive to affect show considerable promise for enhancing student learning experiences. Creating successful affective responses requires considerable effort both to detect student affect and to design appropriate...