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A qualitative meta-analysis of computer games as learning tools

9/23/2018

Cited by this article: 368
Ke, Fengfeng. "A qualitative meta-analysis of computer games as learning tools." Gaming and Simulations: Concepts, Methodologies, Tools and Applications. IGI Global, 2011. 1619-1665.
Keywords:

What’s the Big Idea: Review and synthesize theories methods and findings on instructional games

Terms to get familiar with: Need to clearly define: computer game, cognitive learning, metacognition

Research questions/Hypothesis, methods used in research/conclusions: 256 documents on the design, use and evaluation of computer-based games were evaluated. The majority couldn’t be included in the study. 89 research articles that provided empirical data were included and tabulated. Four major recurring themes concerning the effectiveness and key influence factors of computer-based instructional gaming have emerged: gaming research purpose and methodology, learning, learner, and instructional game intervention

Research Papers to read from this article:
Many, this should be circled back to for reference on finding other papers.

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Adapting to when students game an intelligent tutoring system

9/23/2018

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Cited by this article: 236
d Baker, Ryan SJ, et al. "Adapting to when students game an intelligent tutoring system." International Conference on Intelligent Tutoring Systems. Springer, Berlin, Heidelberg, 2006.

Keywords:

What’s the Big Idea: Students exploit properties of the system rather than try and use the educational knowledge to answer correctly. This paper introduces supplemental material that the student bypassed by gaming. Students who receive many supplemental exercises have considerably better learning.

Terms to get familiar with:

Research questions/Hypothesis, methods used in research/conclusions: Gaming can be divided into harmful gaming (steps student knows least and associated with poor learning outcome) and non-harmful gaming (steps student knows and not associated with poor learning outcomes). This paper focuses on harmful gaming. When students try and get around parts of the game, supplemental material was added. This resulted in those students catching up to the rest of the class. Students tend to game harmfully on steps they know least well. Adding the intelligent tutor led to about half the gamers wanting to game. The paper didn’t expand on this.

Research Papers to read from this article:
4: Baker, R.S., Corbett, A.T., Koedinger, K.R. (2004) Detecting Student Misuse of Intelligent Tutoring Systems. Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 531-540.

Questions:
Why can’t gaming be divided into 4 groups, steps student knows (least/most) and learning outcome (poor/good) - make a matrix:

Least/poor = harmful
Most/good = non-harmful
Least/good = most effective
Most/poor = least effective

Investigate why adding an intelligent tutor causes people to lose interest and not want to game.

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Combining software games with education: Evaluation of its educational effectiveness.

9/22/2018

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Cited by this article: 500
Virvou, Maria, George Katsionis, and Konstantinos Manos. "Combining software games with education: Evaluation of its educational effectiveness." Journal of Educational Technology & Society 8.2 (2005).

Keywords
Educational software games, Virtual reality, Evaluation, Intelligent tutoring systems, Student model

Focus: Using an Intelligent Tutoring System (ITS) to explore educational effectiveness, appeal and scope.

Terms to get familiar with: Intelligent Tutoring System (ITS)

High Level Overview: Use Virtual Reality to engage students. VR-ENGAGE, a game similar to “DOOM” from ID Software, is the game that aims to increase students’ engagement. ITS should consist of four components: the domain knowledge, the student modeling component, the tutoring component and the user interface (Self, 1999; Wenger, 1987). The student must navigate through the virtual world to find pages of a book of wisdom. As the student navigates, guard dragons pose questions the students must answer correctly to get them closer to the book of wisdom. Evaluation focused on education effectiveness of the gaming aspect. Conclusions of the experiments show that good students perform well under any circumstance while other students benefit from extra motivation. It appears from this experiment that the learnability and attention span of the student are important factors in educational effectiveness.

Research Papers to read from this article:
Wenger, E. (1987). Artificial Intelligence and Tutoring Systems, Los Altos, CA, USA: Morgan Kaufmann.
Andriessen J., & Sandberg, J. (1999). Where is Education Heading and How about AI? International Journal of Artificial Intelligence in Education, 10, 130-150.

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Building and evaluating an intelligent pedagogical agent to improve the effectiveness of an educational game.

9/22/2018

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Conati, Cristina, and Xiaohong Zhao. "Building and evaluating an intelligent pedagogical agent to improve the effectiveness of an educational game." In Proceedings of the 9th international conference on Intelligent user interfaces, pp. 6-13. ACM, 2004.
Keywords: Intelligent Agents, Educational Games, User Modeling, Dynamic Bayesian Networks.

Focus: Assist learning for children who have lost interest in math and science by using intelligent games

Terms from this paper to get familiar with: intelligent (also animated) pedagogical agent, target instructional domain, pedagogically effective,

High Level Overview: Highly entertaining games don’t always trigger learning. Introducing pedagogical agents that provide individualized instruction is explored. This paper describes an agent developed for Prime Climb, a game on number factorization. Learning to play the game does not necessarily imply learning the target instructional domain. Learning happens only when students actively build the connections between game moves and the underlying knowledge. Two main challenges for this approach are that it’s difficult to assess student’s knowledge and learning from the game and how to trigger individualized interventions without interfering with the high level of engagement. A Microsoft Agent package was added to Prime Climb to interject hints along the game play. Hints appear if the student asks for a hint, if the student performs wrong move, or if the student does a correct move but may have done it through guessing. Dynamic Bayesian Networks (DBNs) handle uncertainty. This technique shows promise in improving effectiveness, but further study needs to be done on how the agent can be used and not try to avoid interfering too much. This might be determined by having the game learn more about the student. (My take on it)

Research papers to read from this article:
2. Conati, C. and J. Fain Lehman. Toward a Model of Student Education in Microworlds. Proc. of the 15th Annual Conference of the Cognitive Science Society, 1993, Boulder, CO, U.S.A.
7. Klawe, M. When Does The Use Of Computer Games And Other Interactive Multimedia Software Help Students Learn Mathematics? NCTM Standards 2000 Technology Conference, 1998, Arlington, VA, U.S.A.
11. Randel, J.M., B.A. Morris, C.D. Wetzel, and B.V. Whitehill,
The effectiveness of games for educational purposes: A review of recent research. Simulation & Gaming, 1992, 23(3).
13. Shute, V.J., A comparison of learning environments: All that glitters..., in Computers as Cognitive Tools, S. Lajoie, P. and S. Derry, Editors, 1993, Lawrence Erlbaum Associates: Hillsdale, NJ.

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A qualitative meta-analysis of computer games as learning tools

Adapting to when students game an intelligent tutoring system

Combining software games with education: Evaluation of its educational effectiveness.

Building and evaluating an intelligent pedagogical agent to improve the effectiveness of an educational game.

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