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IES Grant

Title: Training in Experimental Design: Developing Scalable and Adaptive Computer-based Science Instruction
Center: NCER Year: 2006
Principal Investigator: Klahr, David Awardee: Carnegie Mellon University
Program: Cognition and Student Learning      [Program Details]
Award Period: 3 years Award Amount: $1,485,318
Type: Development and Innovation Award Number: R305H060034

Purpose: In this project, the researchers proposed to develop a computer-based intelligent tutoring system that would provide instruction to help students learn about experimental design. This intelligent tutoring system, called Intelligent TED Tutor, would provide feedback based on individual learners' knowledge and mastery in real time across a variety of tasks and science content areas. The research team argued that it was important for students to master two aspects of the scientific method: (a) the procedures for executing experiments, and (b) reasoning processes that support valid causal inferences.

Project Activities: The researchers proposed to develop a computer-based intervention that could be delivered either in a one-on-one setting or by a teacher in a whole-class setting. This system would adapt to individual student differences in order to promote transfer of knowledge. Computerized instructional modules were to include simulations, tracking of students' performance, and adaptive algorithms that provide feedback based on students' current actions and knowledge. Students who do not reach mastery in a particular module would receive one-on-one instruction. The researchers proposed to conduct studies in both classrooms and one-on-one tutoring sessions to determine what and how well the students learn, why some do not, and what revisions are likely to improve subsequent modules.

Structured Abstract


Setting: The six urban parochial schools are in Pennsylvania.

Sample: Students will come from two high-SES schools and four low-SES urban schools. In grades 5 through 8, these 6 schools provide 8 classrooms of students (between 120 to 180 students in total) in each grade level.

Intervention Through a series of four increasingly adaptive instructional modules, the researchers will gradually develop the capability of the Intelligent TED Tutor to flexibly choose paths for each individual learner. Such individualization will enable each student to achieve both mastery and transfer goals. Broadly defined, an intelligent tutor is a computer-based instructional system containing an artificial intelligence (AI) component that identifies key instructional components, tracks students' knowledge, compares student performance to expert performance, and tailors multiple features of instruction to the student.

Research Design and Methods: In modules 1 and 2, teacher-led instruction will occur for the whole class or small groups. Module 1 most closely resembles traditional teacher-led, whole-class instruction. The teacher will use a physical apparatus and a structured lesson plan to teach experimental design. Module 2 introduces a more flexible lesson plan and a computer simulation (rather than a physical apparatus) of a "virtual" experiment. In modules 3 and 4, instruction moves to the individual level and becomes increasingly flexible, adaptive, and computer based. The effectiveness of each module will be determined in classroom contexts and compared to the effectiveness of the module that came before it. Quasi-experimental, pretest/posttest, and non-equivalent group designs will be used with individual students (rather than classes) as the units of analyses. By comparing student learning via later modules with that of earlier modules, researchers will be able to determine whether later modules are, indeed, improvements on earlier ones.

Control Condition: Module 1 most closely resembles traditional teaching practices. The effectiveness of module 1 in improving student learning will be compared to the effectiveness of the successive modules 2 through 4.

Key Measures: Researchers will measure students' baseline performance on the training task and transfer measures, performance throughout instruction (namely, formative assessment), and post-training performance on the training task immediately after instruction and transfer measures after a delay.

Data Analytic Strategy: To compare module effectiveness, general linear model analysis (GLM) is used including repeated measures analysis of variance and analysis of covariance. To evaluate the effectiveness of one-on-one, remedial tutoring for students below the mastery cutoff at post-test, regression discontinuity analysis is used.

Related IES Projects: From Cognitive Models of Reasoning to Lesson Plans for Inquiry (R305H030229), Promoting Transfer of the Control of Variables Strategy in Elementary and Middle School Children via Contextual Framing and Abstraction (R305A100404), Contextualizing Experimental Design Instruction Within Related Inquiry Activities: The ISP Tutor (R305A170176)

Products AND Publications

ERIC Citations: Find available citations in ERIC for this award here.

Select Publications:

Journal articles Siler, S.A., Klahr, D., and Price, N. (2013). Investigating the Mechanisms of Learning From a Constrained Preparation for Future Learning Activity. Instructional Science, 41(1): 191–216. Strand-Cary, M., and Klahr, D. (2008). Developing Elementary Science Skills: Instructional Effectiveness and Path Independence. Cognitive Development, 23(4): 488–511.

Proceedings Siler, S.A., Klahr, D., Magaro, C., Willows, K., and Mowery, D. (2010). Predictors of Transfer of Experimental Design Skills in Elementary and Middle School Children. In Proceedings of the 10th ITS 2010 Conference (pp. 198–208). Pittsburgh, PA: Springer-Verlag Berlin Heidelberg.

Siler, S.A., Mowery, Magaro, C., Willows, K., and D., Klahr, D. (2010). Comparison of a Computer-Based to a Hands-On Lesson in Experimental Design. In Proceedings of the 10th ITS 2010 Conference. Lecture Notes in Computer Science (pp. 408–410). Pittsburgh, PA: Springer-Verlag Berlin Heidelberg.