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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.

  • CogSci - 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.

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.

  • CogSci - 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.

Atsushi Shimizu - One of the best experts on this subject based on the ideXlab platform.

  • f-treeGC: a questionnaire-based family tree-creation software for genetic counseling and genome cohort studies
    BMC Medical Genetics, 2017
    Co-Authors: Tomoharu Tokutomi, Akimune Fukushima, Kayono Yamamoto, Yasushi Bansho, Tsuyoshi Hachiya, Atsushi Shimizu
    Abstract:

    Background The Tohoku Medical Megabank project aims to create a next-generation personalized healthcare system by conducting large-scale genome-cohort studies involving three generations of local residents in the areas affected by the Great East Japan Earthquake. We collected medical and genomic information for developing a biobank to be used for this healthcare system. We designed a questionnaire-based Pedigree-creation software program named “f-treeGC,” which enables even less experienced medical practitioners to accurately and rapidly collect family health history and create Pedigree Charts. Results f-treeGC may be run on Adobe AIR. Pedigree Charts are created in the following manner: 1) At system startup, the client is prompted to provide required information on the presence or absence of children; f-treeGC is capable of creating a Pedigree up to three generations. 2) An interviewer fills out a multiple-choice questionnaire on genealogical information. 3) The information requested includes name, age, gender, general status, infertility status, pregnancy status, fetal status, and physical features or health conditions of individuals over three generations. In addition, information regarding the client and the proband, and birth order information, including multiple gestation, custody, multiple individuals, donor or surrogate, adoption, and consanguinity may be included. 4) f-treeGC shows only marriages between first cousins via the overlay function. 5) f-treeGC automatically creates a Pedigree Chart, and the Chart-creation process is visible for inspection on the screen in real time. 6) The genealogical data may be saved as a file in the original format. The created/modified date and time may be changed as required, and the file may be password-protected and/or saved in read-only format. To enable sorting or searching from the database, the file name automatically contains the terms typed into the entry fields, including physical features or health conditions, by default. 7) Alternatively, family histories are collected using a completed foldable interview paper sheet named “f-sheet”, which is identical to the questionnaire in f-treeGC. Conclusions We developed a questionnaire-based family tree-creation software, named f-treeGC, which is fully compliant with international recommendations for standardized human Pedigree nomenclature. The present software simplifies the process of collecting family histories and Pedigrees, and has a variety of uses, from genome cohort studies or primary care to genetic counseling.

  • f-treeGC: a questionnaire-based family tree-creation software for genetic counseling and genome cohort studies
    BMC medical genetics, 2017
    Co-Authors: Tomoharu Tokutomi, Akimune Fukushima, Kayono Yamamoto, Yasushi Bansho, Tsuyoshi Hachiya, Atsushi Shimizu
    Abstract:

    The Tohoku Medical Megabank project aims to create a next-generation personalized healthcare system by conducting large-scale genome-cohort studies involving three generations of local residents in the areas affected by the Great East Japan Earthquake. We collected medical and genomic information for developing a biobank to be used for this healthcare system. We designed a questionnaire-based Pedigree-creation software program named “f-treeGC,” which enables even less experienced medical practitioners to accurately and rapidly collect family health history and create Pedigree Charts. f-treeGC may be run on Adobe AIR. Pedigree Charts are created in the following manner: 1) At system startup, the client is prompted to provide required information on the presence or absence of children; f-treeGC is capable of creating a Pedigree up to three generations. 2) An interviewer fills out a multiple-choice questionnaire on genealogical information. 3) The information requested includes name, age, gender, general status, infertility status, pregnancy status, fetal status, and physical features or health conditions of individuals over three generations. In addition, information regarding the client and the proband, and birth order information, including multiple gestation, custody, multiple individuals, donor or surrogate, adoption, and consanguinity may be included. 4) f-treeGC shows only marriages between first cousins via the overlay function. 5) f-treeGC automatically creates a Pedigree Chart, and the Chart-creation process is visible for inspection on the screen in real time. 6) The genealogical data may be saved as a file in the original format. The created/modified date and time may be changed as required, and the file may be password-protected and/or saved in read-only format. To enable sorting or searching from the database, the file name automatically contains the terms typed into the entry fields, including physical features or health conditions, by default. 7) Alternatively, family histories are collected using a completed foldable interview paper sheet named “f-sheet”, which is identical to the questionnaire in f-treeGC. We developed a questionnaire-based family tree-creation software, named f-treeGC, which is fully compliant with international recommendations for standardized human Pedigree nomenclature. The present software simplifies the process of collecting family histories and Pedigrees, and has a variety of uses, from genome cohort studies or primary care to genetic counseling.

Angela Z Wagner - 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.

  • CogSci - 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.

Linda R Kauffman - 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.

  • CogSci - 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.