Studying the Effects of Personalized Language and Worked Examples in the Context of a Web-Based Intelligent Tutor

Previous studies have demonstrated the learning benefit of personalized language and worked examples. However, previous investigators have primarily been interested in how these interventions support students as they problem solve with no other cognitive support. We hypothesized that personalized language added to a web-based intelligent tutor and worked examples provided as complements to the tutor would improve student (e-)learning. However, in a 2 x 2 factorial study, we found that personalization and worked examples had no significant effects on learning. On the other hand, there was a significant difference between the pretest and posttest across all conditions, suggesting that the online intelligent tutor present in all conditions did make a difference in learning. We conjecture why personalization and, especially, the worked examples did not have the hypothesized effect in this preliminary experiment, and discuss a new study we have begun to further investigate these effects.

[1]  D. Kolb.  Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[2]  Vincent A. W. M. M. Aleven,et al.  Teaching case-based argumentation through a model and examples , 1997 .

[3]  Cristina Conati,et al.  Toward Computer-Based Support of Meta-Cognitive Skills: a Computational Framework to Coach Self-Explanation , 2000 .

[4]  J. Sweller.  COGNITIVE LOAD THEORY, LEARNING DIFFICULTY, AND INSTRUCTIONAL DESIGN , 1994 .

[5]  Kenneth R. Koedinger,et al.  An Empirical Assessment of Comprehension Fostering Features in an Intelligent Tutoring System , 2002, Intelligent Tutoring Systems.

[6]  Richard E. Mayer,et al.  Constructing computer-based tutors that are socially sensitive: Politeness in educational software , 2006, Int. J. Hum. Comput. Stud..

[7]  Neil T. Heffernan,et al.  Opening the Door to Non-programmers: Authoring Intelligent Tutor Behavior by Demonstration , 2004, Intelligent Tutoring Systems.

[8]  Alexander Renkl,et al.  Learning from Worked-Out-Examples: A Study on Individual Differences , 1997, Cogn. Sci..

[9]  P. Bonate.  Analysis of pretest-posttest designs , 2000 .

[10]  Michelene T. H. Chi,et al.  Self-Explanations: How Students Study and Use Examples in Learning To Solve Problems. Technical Report No. 9. , 1987 .

[11]  B. Wilson.  Constructivist Learning Environments: Case Studies in Instructional Design , 1995 .

[12]  Alan M. Lesgold,et al.  Tutoring Transfer of Technical Competence. , 1995 .

[13]  F. Paas.  Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. , 1992 .

[14]  Margaret G. McKeown,et al.  Questioning the Author: A Yearlong Classroom Implementation to Engage Students with Text , 1996, The Elementary School Journal.

[15]  R. Mayer,et al.  Engaging students in active learning: The case for personalized multimedia messages. , 2000 .

[16]  E MayerRichard,et al.  Constructing computer-based tutors that are socially sensitive , 2006 .

[17]  Slava Kalyuga,et al.  When problem solving is superior to studying worked examples. , 2001 .

[18]  Tom Murray,et al.  Authoring tools for advanced technology learning environments : toward cost-effective adaptive, interactive and intelligent educational software , 2003 .

[19]  G. Hanley.  e‐Learning and the Science of Instruction , 2004 .

[20]  Kenneth R. Koedinger,et al.  Recasting the feedback debate: benefits of tutoring error detection and correction skills , 2003 .