Operation ARIES!: A Serious Game for Teaching Scientific Inquiry

Millis, Keith K.; Forsyth, Carol; Butler, Heather A.; Wallace, Patty; Graesser, Arthur C.; Halpern, Diane F.

doi:10.1007/978-1-4471-2161-9_10

Cited by 80 publications

(44 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This work draws on empirical evidence that games are highly motivating and have natural ties with how people learn (Gee 2003;Kapp 2012;McNamara et al 2009). In recent years, games have been used to teach a variety of subjects including scientific inquiry (Clarke and Dede 2009;Rowe et al 2011), mathematics principles (Conati 2002), negotiation skills (Kim et al 2009), foreign languages (Hallinen et al 2009;Johnson 2010), policy argumentation (Easterday et al 2011), and critical reasoning (Millis et al 2011).…”

Section: Game-based Learningmentioning

confidence: 99%

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Sabourin

Shores

Mott

et al. 2013

Int J Artif Intell Educ

View full text Add to dashboard Cite

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 subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has proven challenging. This paper presents an initial investigation into selfregulated learning in a game-based learning environment. Evidence of goal setting and monitoring behaviors is examined through students' text-based responses to update their 'status' in an in-game social network. Students are then classified into SRL-use categories. This article describes the methodology used to classify students and discusses analyses demonstrating the learning and gameplay behaviors across students in different SRL-use categories. Finally, machine learning models capable of predicting these classes early in students' interaction are presented.

show abstract

Section: Game-based Learningmentioning

confidence: 99%

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Sabourin

Shores

Mott

et al. 2013

Int J Artif Intell Educ

View full text Add to dashboard Cite

show abstract

“…Research shows that computer agents can be successfully used for tutoring, collaborative learning, co-construction of knowledge, and CPS (e.g., Biswas et al 2010;Graesser et al 2008;Millis et al 2011). A computer agent can be capable of generating goals, performing actions, communicating messages, sensing its environment, adapting to changing environments, and learning (Franklin and Graesser 1996).…”

Section: Human-to-human and Human-to-agent Approach In Cps Assessmentmentioning

confidence: 99%

“…Using their agent's performance as motivation and a guide, students study the available resources so that they can remediate the agent's knowledge and, in the process, learn the domain material themselves. Operation ARIES (Cai et al 2011;Millis et al 2011) uses animated pedagogical agents that converse with the student in a game-based environment for helping students learn critical-thinking skills and scientific reasoning within scientific inquiry. The system dynamically adapts the tutorial conversations to the learner's prior-knowledge.…”

Section: Human-to-human and Human-to-agent Approach In Cps Assessmentmentioning

confidence: 99%

Computer-based Assessment of Collaborative Problem Solving: Exploring the Feasibility of Human-to-Agent Approach

Rosen

2015

Int J Artif Intell Educ

View full text Add to dashboard Cite

How can activities in which collaborative skills of an individual are measured be standardized? In order to understand how students perform on collaborative problem solving (CPS) computer-based assessment, it is necessary to examine empirically the multi-faceted performance that may be distributed across collaboration methods. Theaim of this study was to explore possible differences in student performance in humanto-agent (H-A), compared to human-to-human (H-H) CPS assessment tasks. One hundred seventy nine 14 years-old students from the United States, Singapore and Israel participated in the study. Students in both H-H and H-A modes were able to collaborate and communicate by using identical methods and resources. However, while in the H-A mode, students collaborated with a simulated computerdriven partner, and in the H-H mode students collaborated with another student to solve a problem. Overall, the findings showed that CPS with a computer agent involved significantly higher levels of shared understanding, progress monitoring, and feedback. However, no significant difference was found in a student's ability to solve the problem or in student motivation with a computer agent or a human partner. One major implication of CPS score difference in collaboration measures between the two modes is that in H-A mode one can program a wider range of interaction possibilities than would be available with a human partner. Thus, H-A approach offers more opportunities for students to demonstrate their CPS skills. This study is among the first of its kind to investigate systematically the effect of collaborative problem solving in standardized assessment settings.

show abstract

“…In complex social interactions, some individuals support and praise other team members, whereas other individuals disagree with team members and provide negative feedback on the basis of false evidence. Furthermore, participants may have a different statuses, perspectives, abilities, and understandings when attempting to solve the problem (Millis et al, 2011;Wiley & Jensen, 2007). Therefore, single and multiple computer…”

Section: Introductionmentioning

confidence: 99%

Collaborative Problem-Solving Behavior of 15-Year-Old Taiwanese Students in Science Education

Liu

2017

EURASIA J MATH SCI T

View full text Add to dashboard Cite

Collaborative problem solving (CPS) is a crucial skill for students and people entering the workforce. Hence, an online CPS assessment system was developed for the Programme for International Student Assessment to test 12 CPS skills. This study compared the CPS skills of students on the basis of a CPS assessment developed for science scenarios in Taiwan. Moreover, a sequential analysis was applied to explore the behavioral patterns of students with CPS skills. The results demonstrated that most Taiwanese students are proficient in the 11 CPS skills (except for D3: "Monitoring, providing feedback and adapting the team organisation and roles"), and that female students are significantly more proficient than male students. The results also indicated that most students can successfully transition between CPS skills. However, the students in a given class may demonstrate a lack of behavioral patterns. Teachers can design activities and develop strategies to address this lack of behavioral patterns and thus increase students' CPS skills and behavioral patterns.

show abstract

Operation ARIES!: A Serious Game for Teaching Scientific Inquiry

Cited by 80 publications

References 31 publications

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Computer-based Assessment of Collaborative Problem Solving: Exploring the Feasibility of Human-to-Agent Approach

Collaborative Problem-Solving Behavior of 15-Year-Old Taiwanese Students in Science Education

Contact Info

Product

Resources

About