The Genetics Lab - A new computer-based problem solving scenario to assess intelligence

The purpose of this chapter is to explore how learning, by making games, can provide opportunities for higher-order thinking such as problem solving, decision-making, and knowledge construction in children. As the game design process involves students drawing on multiple intelligences, it often provides students who are typically not successful in school with a chance to see themselves as capable members of the classroom learning community. In the classroom, computer-based game-making activities give students the opportunity to create lively interactive simulations for any subject, for any grade level, and can be used by students with a wide variety of learning styles. Game making can be used as an alternative way for students to communicate information and demonstrate their knowledge and understanding.

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Problem-solving on a computer-based teletype

Educational Studies in Mathematics ◽

10.1007/bf00368982 ◽

1969 ◽

Vol 2 (1) ◽

pp. 1-15 ◽

Cited By ~ 15

Author(s):

Patrick Suppes ◽

Elizabeth F. Loftus ◽

Max Jerman

Keyword(s):

Problem Solving ◽

Computer Based

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Computer-based decision support and training for problem solving tasks

Applied Ergonomics ◽

10.1016/0003-6870(87)90261-4 ◽

1987 ◽

Vol 18 (2) ◽

pp. 172

Keyword(s):

Decision Support ◽

Problem Solving ◽

Computer Based ◽

And Training

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Computer-based simulations and clinical problem-solving

Medical Informatics ◽

10.3109/14639238409015202 ◽

1984 ◽

Vol 9 (3-4) ◽

pp. 277-279 ◽

Cited By ~ 5

Author(s):

Octo Barnett

Keyword(s):

Problem Solving ◽

Clinical Problem ◽

Clinical Problem Solving ◽

Computer Based

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Estimating student ability and problem difficulty using item response theory (IRT) and TrueSkill

Information Discovery and Delivery ◽

10.1108/idd-08-2018-0030 ◽

2019 ◽

Vol 47 (2) ◽

pp. 67-75 ◽

Cited By ~ 1

Author(s):

Youngjin Lee

Keyword(s):

Data Mining ◽

Item Response Theory ◽

Problem Solving ◽

Learning Environment ◽

Item Response ◽

Predictive Power ◽

Response Theory ◽

Content Type ◽

Computer Based Learning ◽

Computer Based

Purpose The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment. Design/methodology/approach Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve. Findings The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems. Originality/value Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.

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