The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform

Michael L Tushman - One of the best experts on this subject based on the ideXlab platform.

  • open innovation and organizational boundaries task decomposition Knowledge distribution and the locus of innovation
    Chapters, 2013
    Co-Authors: Karim R Lakhani, Hila Lifshitzassaf, Michael L Tushman
    Abstract:

    This chapter contrasts traditional, organization- centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when Problem- Solving Knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, the National Aeronautics and Astronomical Agency (NASA) and LEGO. We suggest that task decomposition and Problem- Solving Knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries,

  • open innovation and organizational boundaries the impact of task decomposition and Knowledge distribution on the locus of innovation
    2012
    Co-Authors: Karim R Lakhani, Hila Lifshitzassaf, Michael L Tushman
    Abstract:

    This paper contrasts traditional, organization-centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when Problem-Solving Knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, NASA, and LEGO. We suggest that task decomposition and Problem-Solving Knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries, design, and identity.

  • open innovation and organizational boundaries the impact of task decomposition and Knowledge distribution on the locus of innovation
    2012
    Co-Authors: Karim R Lakhani, Hila Lifshitzassaf, Michael L Tushman
    Abstract:

    This paper contrasts traditional, organization-centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when Problem-Solving Knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, NASA, and LEGO. We suggest that task decomposition and Problem-Solving Knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries, design, and identity.

Albert T. Corbett - One of the best experts on this subject based on the ideXlab platform.

  • enhancing robust learning through Problem Solving in the genetics cognitive tutor
    Cognitive Science, 2013
    Co-Authors: Albert T. Corbett, Angela Z Wagner, Benjamin A Maclaren, Linda R Kauffman, Aaron P Mitchell, Ryan S Baker
    Abstract:

    Enhancing Robust Learning Through Problem Solving in the Genetics Cognitive Tutor Albert Corbett (corbett@cmu.edu) Ben MacLaren (maclaren@andrew.cmu.edu) Angela Wagner (awagner@cmu.edu) Human-Computer Interaction Institute, Carnegie Mellon University Pittsburgh, PA 15213 USA Linda Kauffman (lk01@andrew.cmu.edu) Aaron Mitchell (apm1@andrew.cmu.edu) Department of Biological Sciences, Carnegie Mellon University Pittsburgh, PA 15213 USA Ryan S. J. d. Baker (baker2@exchange.tc.columbia.edu) Department of Human Development, Columbia University Teachers College, New York, NY 10027 Abstract In this paper, we examine the impact of three learning ac- tivities designed to foster more robust learning in a Ge- netics Cognitive Tutor module on pedigree analysis prob- lem Solving, in an experimental study. The three activi- ties are (1) interleaved worked examples with student ex- planations; (2) enhanced feedback with tutor-provided explanations of Problem Solving steps; and (3) explicit scaffolding of the reasoning steps in this abductive proc- ess-of-elimination reasoning task. The study included four between-subject conditions, a baseline condition in which students exclusively solved standard Problems, and three conditions in which students engaged in one of the new learning activities along with standard Problem Solving. The scaffolded-reasoning condition was most successful in fostering robust learning, as measured by transfer, retention, and preparation for future learning tests. The enhanced feedback condition, in contrast, yielded the poorest performance on the robust learning measures. Keywords: Education; Problem Solving; Robust Learn- ing; Intelligent Tutors. Introduction Problem Solving is an essential learning activity across STEM domains. Successful Problem Solving results in “robust” Knowledge: Knowledge that is well-grounded in domain Knowledge, and as a consequence, is well- retained by students, transfers more readily to related Problem situations and prepares students for more suc- cessful future learning (Koedinger, Corbett & Perfetti, 2012). One of the well-documented risks in Problem Solving, across STEM domains, is that students can develop superficial Knowledge that fails these tests of robust learning. In particular, when students are not well-prepared for Problem Solving, they can develop Problem Solving Knowledge which focuses on surface elements in Problem situations, formal representations, and features of the learning environment itself (Chang, Koedinger & Lovett, 2003; Chi, Feltovich & Glaser, 1981; Rittle-Johnson & Siegler, 1998). In this paper we examine how to structure Problem Solving in an intelligent tutoring system to support ro- bust learning in the domain of genetics. Because of its foundational place in the biological sciences, genetics is a large and growing component of high school biology courses, but it is also viewed as one of the hardest top- ics in biology by both students and instructors, at the secondary and at the post-secondary level (Tsui & Treagust, 2006). Genetics Problem Solving is character- ized by abductive reasoning. In contrast with deductive hypothesis testing, abductive reasoning starts with a set of observations and reasons backwards to infer proper- ties of the genetic processes that produced the data (e.g., whether a trait is dominant or recessive). In this paper, we study these issues within a tutor les- son for pedigree analysis in the Genetics Cognitive Tutor (Corbett, Kauffman, MacLaren, Wagner & Jones, 2010), which has been successfully piloted in both high school and college classrooms. Pedigree analysis relies on a complex reasoning process, which nonetheless lends itself to straightforward natural language descrip- tion. This study examines whether robust learning is supported by a scaffolded reasoning activity prior to conventional Problem Solving, or by incorporating ex- plicit explanations during Problem Solving. The Domain: Pedigree Analysis Basic pedigree analysis Problems pose an interesting challenge both for students and for an intelligent tutor- ing system. Figure 1 displays a typical pedigree analysis Problem, in the Genetics Cognitive Tutor (GCT). This pedigree chart displays four generations in a small fam- ily. Females are represented as circles and males as squares. In this family, the founding parents have a daughter affected by a rare genetic trait, as represented by the dark circle. No other family members are af- fected. The student’s task is to determine whether this genetic trait is dominant or recessive, and whether it is X-linked, or transmitted on one of the twenty-two auto- somal chromosomes in humans.

  • preparing students for effective explaining of worked examples in the genetics cognitive tutor
    Cognitive Science, 2011
    Co-Authors: Albert T. Corbett, Angela Z Wagner, Benjamin A Maclaren, Linda R Kauffman, Ryan S Baker, Aaron P Mitchell, Sujith M Gowda
    Abstract:

    Preparing Students for Effective Explaining of Worked Examples in the Genetics Cognitive Tutor Albert Corbett (corbett@cmu.edu) Ben MacLaren (maclaren@andrew.cmu.edu) Angela Wagner (awagner@cmu.edu) Human-Computer Interaction Institute, Carnegie Mellon University Pittsburgh, PA 15213 USA Linda Kauffman (lk01@andrew.cmu.edu) Aaron Mitchell (apm1@andrew.cmu.edu) Department of Biological Sciences, Carnegie Mellon University Pittsburgh, PA 15213 USA Ryan S. J. d. Baker (rsbaker@wpi.edu) Sujith M. Gowda (sujithmg@wpi.edu) Department of Social Science and Policy Studies, Worcester Polytechnic Institute Worcester, MA 01609 USA Abstract This study examines the impact of integrating worked examples into a Cognitive Tutor for genetics Problem Solving, and whether a genetics process modeling task can help prepare students for explaining worked examples and Solving Problems. Students participated in one of four conditions in which they engaged in either: (1) process modeling followed by interleaved worked examples and Problem Solving; (2) process modeling followed by Problem Solving without worked examples; (3) interleaved worked examples and Problems without process modeling; or (4) Problem Solving alone. Tutor data analyses reveal that process modeling led to faster reasoning and greater accuracy in explaining Problem solutions. Process modeling and worked examples together led to faster reasoning in Problem Solving than did any of the other three conditions. Students in all conditions achieved equivalent Problem-Solving Knowledge, as measured by posttest accuracy, although the tutor results suggest reasoning speed may be a more sensitive measure of learning. Keywords: Education; Problem Solving; Learning; Intelligent Tutors; Worked Examples. Introduction It is well-documented that integrating worked examples with Problem Solving, either by interleaving full Problem solutions with Problems to be solved (Pashler, Bain, Bottge, Graesser, Koedinger, McDaniel & Metcalfe, 2007; Sweller & Cooper, 1985) or by gradually fading the number of solved steps that are provided (Renkl & Atkinson, 2003), serves to decrease total learning time and yields improved learning outcomes. Recently, several studies have examined the benefits of incorporating worked examples into intelligent tutoring systems (ITSs) for Problem Solving in a variety of domains: stoichiometry (Mclaren, Lim & Koedinger, 2008) algebra (Anthony, 2008; Corbett, Reed, Hoffman, MacLaren & Wagner, 2010b); geometry (Salden, Aleven, Schwonke & Renkl, 2010; Schwonke, Renkl, Krieg, Wittwer, Aleven & Salden, 2009; Schwonke, Renkl, Salden & Aleven, 2011) and statistics (Weitz, Salden, Kim & Heffernan, 2010). In these ITS studies, the chief benefit of incorporating worked examples has been to increase learning efficiency. The studies that report learning time universally find that interleaving worked examples (Corbett, et al, 2010b; McLaren, et al, 2008; Weitz, et al, 2010) or fading solution steps (Schwonke, et al, 2009) reduces learning time for a fixed set of activities compared to pure Problem Solving, primarily because students process worked solutions more rapidly than they can solve corresponding Problems. But unlike the classic worked-example literature, these ITS studies generally do not find that incorporating worked examples leads to more accurate posttest Problem-Solving than Problem Solving alone (Anthony, 2008; Corbett et al, 2010b; McLaren, et al, 2008; Schwonke, et al, 2009, 2011; Weitz, et al, 2010). The exception is Salden, et al (2010), who found that adaptively fading examples based on a model of each student’s Knowledge led to some relative improvement on posttest Problem Solving. Similarly, the evidence that students learn more deeply when worked examples are integrated into ITSs is mixed at best, although two papers report better retention of Problem Solving Knowledge (Anthony, 2008; Salden, et al, 2010) and Schwonke, et al (2009) found evidence of greater conceptual transfer in one of two studies. The present study examines the hypothesis that • integrating worked examples and Problem Solving in an ITS will yield better learning outcomes when preceded by ITS learning activities that focus on domain Knowledge relevant to the student explanations This study examines worked examples and Problem Solving in the domain of genetics. The study employs an existing Cognitive Tutor for genetics Problem Solving, which has

  • a cognitive tutor for genetics Problem Solving learning gains and student modeling
    Journal of Educational Computing Research, 2010
    Co-Authors: Albert T. Corbett, Ben Maclaren, Angela Z Wagner, Linda R Kauffman, Elizabeth W. Jones
    Abstract:

    Genetics is a unifying theme of biology that poses a major challenge for students across a wide range of post-secondary institutions, because it entails complex Problem Solving. This article reports a new intelligent learning environment called the Genetics Cognitive Tutor, which supports genetics Problem Solving. The tutor presents complex, multi-step Problems and is constructed around a cognitive model of the Knowledge needed to solve the Problems. This embedded cognitive model enables the tutor to provide step-by-step assistance, and to maintain a model of the student's Problem-Solving Knowledge. The tutor consists of 16 modules with about 125 total Problems, spanning five general topics: Mendelian inheritance, pedigree analysis, genetic mapping, gene regulation, and population genetics. This article reports two evaluations of the tutor. A pretest/posttest evaluation of student learning gains for individual tutor modules across multiple colleges and universities yielded average gains equivalent to almo...

Karim R Lakhani - One of the best experts on this subject based on the ideXlab platform.

  • open innovation and organizational boundaries task decomposition Knowledge distribution and the locus of innovation
    Chapters, 2013
    Co-Authors: Karim R Lakhani, Hila Lifshitzassaf, Michael L Tushman
    Abstract:

    This chapter contrasts traditional, organization- centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when Problem- Solving Knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, the National Aeronautics and Astronomical Agency (NASA) and LEGO. We suggest that task decomposition and Problem- Solving Knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries,

  • open innovation and organizational boundaries the impact of task decomposition and Knowledge distribution on the locus of innovation
    2012
    Co-Authors: Karim R Lakhani, Hila Lifshitzassaf, Michael L Tushman
    Abstract:

    This paper contrasts traditional, organization-centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when Problem-Solving Knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, NASA, and LEGO. We suggest that task decomposition and Problem-Solving Knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries, design, and identity.

  • open innovation and organizational boundaries the impact of task decomposition and Knowledge distribution on the locus of innovation
    2012
    Co-Authors: Karim R Lakhani, Hila Lifshitzassaf, Michael L Tushman
    Abstract:

    This paper contrasts traditional, organization-centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when Problem-Solving Knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, NASA, and LEGO. We suggest that task decomposition and Problem-Solving Knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries, design, and identity.

Elsbeth Stern - One of the best experts on this subject based on the ideXlab platform.

  • the relative merits of explicit and implicit learning of contrasted algebra principles
    Educational Psychology Review, 2018
    Co-Authors: Esther Ziegler, Peter A Edelsbrunner, Elsbeth Stern
    Abstract:

    Knowledge representations that result from practicing Problem Solving can be expected to differ from Knowledge representations that emerge from explicit verbalizing of principles and rules. We examined the degree to which the two types of learning improve Problem-Solving Knowledge and verbal explanation Knowledge in classroom instruction. We presented algebraic addition and multiplication Problems to 153 sixth graders randomly assigned to two conditions. Students in the explicit learning condition had to verbally compare contrasted algebra Problems. Students in the implicit learning condition had to generate and solve new Problems. On three follow-up tests over 10 weeks, students in the explicit learning condition exhibited better Problem-Solving Knowledge than students in the implicit learning condition, as well as some advantages in verbal concept Knowledge. Implicit learning showed some advantages on not directly taught but incidentally learned aspects. Overall, this outcome favors the explicit learning of concepts. Explicit comparison fostered student performance on non-verbal and verbal measures, indicating that verbalization facilitates effective comparison.

  • the role of situational context in Solving word Problems
    Cognitive Development, 1992
    Co-Authors: Elsbeth Stern, Anne Lehrndorfer
    Abstract:

    Abstract Word Problems depicting the comparison of quantities have been shown to be difficult for elementary school children in several studies. One reason for this may be that young children lack situational understanding because they are not familiar with the quantitative comparison of sets. To test this assumption, we presented 45 first graders with “compare” Problems that were embedded in a familiar situational context. The results showed that children who received compare Problems following stories that induced situational understanding of qualitative comparisons performed better than children who received compare Problems following stories that had nothing to do with the comparison of sets. The data suggest that most first graders have access to the mathematical Problem-Solving Knowledge necessary to understand and solve compare Problems only when these Problem are embedded in a familiar situational context.

Ryan S Baker - One of the best experts on this subject based on the ideXlab platform.

  • enhancing robust learning through Problem Solving in the genetics cognitive tutor
    Cognitive Science, 2013
    Co-Authors: Albert T. Corbett, Angela Z Wagner, Benjamin A Maclaren, Linda R Kauffman, Aaron P Mitchell, Ryan S Baker
    Abstract:

    Enhancing Robust Learning Through Problem Solving in the Genetics Cognitive Tutor Albert Corbett (corbett@cmu.edu) Ben MacLaren (maclaren@andrew.cmu.edu) Angela Wagner (awagner@cmu.edu) Human-Computer Interaction Institute, Carnegie Mellon University Pittsburgh, PA 15213 USA Linda Kauffman (lk01@andrew.cmu.edu) Aaron Mitchell (apm1@andrew.cmu.edu) Department of Biological Sciences, Carnegie Mellon University Pittsburgh, PA 15213 USA Ryan S. J. d. Baker (baker2@exchange.tc.columbia.edu) Department of Human Development, Columbia University Teachers College, New York, NY 10027 Abstract In this paper, we examine the impact of three learning ac- tivities designed to foster more robust learning in a Ge- netics Cognitive Tutor module on pedigree analysis prob- lem Solving, in an experimental study. The three activi- ties are (1) interleaved worked examples with student ex- planations; (2) enhanced feedback with tutor-provided explanations of Problem Solving steps; and (3) explicit scaffolding of the reasoning steps in this abductive proc- ess-of-elimination reasoning task. The study included four between-subject conditions, a baseline condition in which students exclusively solved standard Problems, and three conditions in which students engaged in one of the new learning activities along with standard Problem Solving. The scaffolded-reasoning condition was most successful in fostering robust learning, as measured by transfer, retention, and preparation for future learning tests. The enhanced feedback condition, in contrast, yielded the poorest performance on the robust learning measures. Keywords: Education; Problem Solving; Robust Learn- ing; Intelligent Tutors. Introduction Problem Solving is an essential learning activity across STEM domains. Successful Problem Solving results in “robust” Knowledge: Knowledge that is well-grounded in domain Knowledge, and as a consequence, is well- retained by students, transfers more readily to related Problem situations and prepares students for more suc- cessful future learning (Koedinger, Corbett & Perfetti, 2012). One of the well-documented risks in Problem Solving, across STEM domains, is that students can develop superficial Knowledge that fails these tests of robust learning. In particular, when students are not well-prepared for Problem Solving, they can develop Problem Solving Knowledge which focuses on surface elements in Problem situations, formal representations, and features of the learning environment itself (Chang, Koedinger & Lovett, 2003; Chi, Feltovich & Glaser, 1981; Rittle-Johnson & Siegler, 1998). In this paper we examine how to structure Problem Solving in an intelligent tutoring system to support ro- bust learning in the domain of genetics. Because of its foundational place in the biological sciences, genetics is a large and growing component of high school biology courses, but it is also viewed as one of the hardest top- ics in biology by both students and instructors, at the secondary and at the post-secondary level (Tsui & Treagust, 2006). Genetics Problem Solving is character- ized by abductive reasoning. In contrast with deductive hypothesis testing, abductive reasoning starts with a set of observations and reasons backwards to infer proper- ties of the genetic processes that produced the data (e.g., whether a trait is dominant or recessive). In this paper, we study these issues within a tutor les- son for pedigree analysis in the Genetics Cognitive Tutor (Corbett, Kauffman, MacLaren, Wagner & Jones, 2010), which has been successfully piloted in both high school and college classrooms. Pedigree analysis relies on a complex reasoning process, which nonetheless lends itself to straightforward natural language descrip- tion. This study examines whether robust learning is supported by a scaffolded reasoning activity prior to conventional Problem Solving, or by incorporating ex- plicit explanations during Problem Solving. The Domain: Pedigree Analysis Basic pedigree analysis Problems pose an interesting challenge both for students and for an intelligent tutor- ing system. Figure 1 displays a typical pedigree analysis Problem, in the Genetics Cognitive Tutor (GCT). This pedigree chart displays four generations in a small fam- ily. Females are represented as circles and males as squares. In this family, the founding parents have a daughter affected by a rare genetic trait, as represented by the dark circle. No other family members are af- fected. The student’s task is to determine whether this genetic trait is dominant or recessive, and whether it is X-linked, or transmitted on one of the twenty-two auto- somal chromosomes in humans.

  • preparing students for effective explaining of worked examples in the genetics cognitive tutor
    Cognitive Science, 2011
    Co-Authors: Albert T. Corbett, Angela Z Wagner, Benjamin A Maclaren, Linda R Kauffman, Ryan S Baker, Aaron P Mitchell, Sujith M Gowda
    Abstract:

    Preparing Students for Effective Explaining of Worked Examples in the Genetics Cognitive Tutor Albert Corbett (corbett@cmu.edu) Ben MacLaren (maclaren@andrew.cmu.edu) Angela Wagner (awagner@cmu.edu) Human-Computer Interaction Institute, Carnegie Mellon University Pittsburgh, PA 15213 USA Linda Kauffman (lk01@andrew.cmu.edu) Aaron Mitchell (apm1@andrew.cmu.edu) Department of Biological Sciences, Carnegie Mellon University Pittsburgh, PA 15213 USA Ryan S. J. d. Baker (rsbaker@wpi.edu) Sujith M. Gowda (sujithmg@wpi.edu) Department of Social Science and Policy Studies, Worcester Polytechnic Institute Worcester, MA 01609 USA Abstract This study examines the impact of integrating worked examples into a Cognitive Tutor for genetics Problem Solving, and whether a genetics process modeling task can help prepare students for explaining worked examples and Solving Problems. Students participated in one of four conditions in which they engaged in either: (1) process modeling followed by interleaved worked examples and Problem Solving; (2) process modeling followed by Problem Solving without worked examples; (3) interleaved worked examples and Problems without process modeling; or (4) Problem Solving alone. Tutor data analyses reveal that process modeling led to faster reasoning and greater accuracy in explaining Problem solutions. Process modeling and worked examples together led to faster reasoning in Problem Solving than did any of the other three conditions. Students in all conditions achieved equivalent Problem-Solving Knowledge, as measured by posttest accuracy, although the tutor results suggest reasoning speed may be a more sensitive measure of learning. Keywords: Education; Problem Solving; Learning; Intelligent Tutors; Worked Examples. Introduction It is well-documented that integrating worked examples with Problem Solving, either by interleaving full Problem solutions with Problems to be solved (Pashler, Bain, Bottge, Graesser, Koedinger, McDaniel & Metcalfe, 2007; Sweller & Cooper, 1985) or by gradually fading the number of solved steps that are provided (Renkl & Atkinson, 2003), serves to decrease total learning time and yields improved learning outcomes. Recently, several studies have examined the benefits of incorporating worked examples into intelligent tutoring systems (ITSs) for Problem Solving in a variety of domains: stoichiometry (Mclaren, Lim & Koedinger, 2008) algebra (Anthony, 2008; Corbett, Reed, Hoffman, MacLaren & Wagner, 2010b); geometry (Salden, Aleven, Schwonke & Renkl, 2010; Schwonke, Renkl, Krieg, Wittwer, Aleven & Salden, 2009; Schwonke, Renkl, Salden & Aleven, 2011) and statistics (Weitz, Salden, Kim & Heffernan, 2010). In these ITS studies, the chief benefit of incorporating worked examples has been to increase learning efficiency. The studies that report learning time universally find that interleaving worked examples (Corbett, et al, 2010b; McLaren, et al, 2008; Weitz, et al, 2010) or fading solution steps (Schwonke, et al, 2009) reduces learning time for a fixed set of activities compared to pure Problem Solving, primarily because students process worked solutions more rapidly than they can solve corresponding Problems. But unlike the classic worked-example literature, these ITS studies generally do not find that incorporating worked examples leads to more accurate posttest Problem-Solving than Problem Solving alone (Anthony, 2008; Corbett et al, 2010b; McLaren, et al, 2008; Schwonke, et al, 2009, 2011; Weitz, et al, 2010). The exception is Salden, et al (2010), who found that adaptively fading examples based on a model of each student’s Knowledge led to some relative improvement on posttest Problem Solving. Similarly, the evidence that students learn more deeply when worked examples are integrated into ITSs is mixed at best, although two papers report better retention of Problem Solving Knowledge (Anthony, 2008; Salden, et al, 2010) and Schwonke, et al (2009) found evidence of greater conceptual transfer in one of two studies. The present study examines the hypothesis that • integrating worked examples and Problem Solving in an ITS will yield better learning outcomes when preceded by ITS learning activities that focus on domain Knowledge relevant to the student explanations This study examines worked examples and Problem Solving in the domain of genetics. The study employs an existing Cognitive Tutor for genetics Problem Solving, which has