Learning Gains for Core Concepts in a Serious Game on Scientific ReasoningReport as inadecuate




Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning - Download this document for free, or read online. Document in PDF available to download.



International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, Jun 19-21, 2012)

"OperationARIES!" is an Intelligent Tutoring System that teaches scientific inquiry skills in a game-like atmosphere. Students complete three different training modules, each with natural language conversations, in order to acquire deep-level knowledge of 21 core concepts of research methodology (e.g., correlation does not mean causation). The student first acquires basic declarative knowledge and then applies the knowledge by critiquing case studies on scientific methodology and finally generating questions that reflect the core topics. A study using a pretest-training-posttest design was conducted in which 46 college students interacted with the modules of "OperationARIES!", resulting in thousands of logged measures. The goal of this investigation was to discover the different trajectories of learning within 11 of these core concepts by evaluating 3 main constructs (e.g., discrimination, generation, and time on task) represented by key logged measures. Different constructs showed relationships with specific core concepts. Three core concepts were analyzed with stepwise regression and 5-fold cross-validation in order to discover contributing factors to learning gains for these core concepts. [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]

Descriptors: Learning, Cognitive Processes, Logical Thinking, Scientific Methodology, Inquiry, Fundamental Concepts, Intelligent Tutoring Systems, Educational Games, Pretests Posttests, College Students, Regression (Statistics), Validity

International Educational Data Mining Society. e-mail: admin[at]educationaldatamining.org; Web site: http://www.educationaldatamining.org





Author: Forsyth, Carol; Pavlik, Philip, Jr.; Graesser, Arthur C.; Cai, Zhiqiang; Germany, Mae-lynn; Millis, Keith; Dolan, Robert P.; Butle

Source: https://eric.ed.gov/?q=a&ft=on&ff1=dtySince_1992&pg=2569&id=ED537227







Related documents