The Experts below are selected from a list of 156 Experts worldwide ranked by ideXlab platform
Demetria L Ennis - One of the best experts on this subject based on the ideXlab platform.
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combining problem solving Instruction and Programming Instruction to increase the problem solving ability of high school students
Journal of research on computing in education, 1994Co-Authors: Demetria L EnnisAbstract:AbstractResearchers have identified a number of reasons why many Programming students have difficulty acquiring a knowledge of Programming, writing programs, and transferring Programming knowledge to other problem-solving domains. These reasons include a lack Instruction in basic problem-solving skills and poor Programming skills. In the study described here, students were introduced to a generalized problem-solving strategy, and were given Instruction in BASIC Programming in an attempt to determine whether this combination enhanced student problem-solving ability.
Richard Catrambone - One of the best experts on this subject based on the ideXlab platform.
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employing subgoals in computer Programming education
Computer Science Education, 2016Co-Authors: Lauren E Margulieux, Richard Catrambone, Mark GuzdialAbstract:AbstractThe rapid integration of technology into our professional and personal lives has left many education systems ill-equipped to deal with the influx of people seeking computing education. To improve computing education, we are applying techniques that have been developed for other procedural fields. The present study applied such a technique, subgoal labeled worked examples, to explore whether it would improve Programming Instruction. The first two experiments, conducted in a laboratory, suggest that the intervention improves undergraduate learners’ problem-solving performance and affects how learners approach problem-solving. The third experiment demonstrates that the intervention has similar, and perhaps stronger, effects in an online learning environment with in-service K-12 teachers who want to become qualified to teach computing courses. By implementing this subgoal intervention as a tool for educators to teach themselves and their students, education systems could improve computing education an...
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improving Programming Instruction with subgoal labeled Instructional text
Cognitive Science, 2014Co-Authors: Lauren E Margulieux, Richard CatramboneAbstract:Improving Programming Instruction with Subgoal Labeled Instructional Text Lauren Margulieux (l.marg@gatech.edu) Georgia Institute of Technology, School of Psychology Atlanta, GA 30332-0170 USA Richard Catrambone (rc7@prism.gatech.edu) Georgia Institute of Technology, School of Psychology Atlanta, GA 30332-0170 USA Abstract (LeFevre & Dixon, 1986) because they take less effort to understand than Instructional text (Eiriksdottir & Catrambone, 2011). Using worked examples in this way, however, can inhibit transfer to novel problems because they are specific to a particular context, and learners are commonly not able to glean abstract information from these concrete examples. To improve this type of transfer, examples that emphasize subgoals have been used (e.g., Catrambone, 1998; see Figure 1). In science, technology, engineering, and mathematics (STEM) education, problem solving tends to be highly procedural, and these procedures are typically taught with general Instructional text and specific worked examples. Subgoal labels have been used in worked examples to help learners understand the procedure being demonstrated and improve problem solving performance. The effect of subgoal labels in Instructional text, however, has not been explored. The present study examined the efficacy of subgoal labeled Instructional text and worked examples for Programming education. The results show that learners who received subgoal labels in both the text and example are able to solve novel problems better than those who do not. Subgoal labels in the text appear to have a different effect, rather than an additive effect, on learners than subgoal labels in the example. Specifically, subgoal labels in text appear to help the learner articulate the procedure, and subgoal labels in the example appear to help the learner apply the procedure. Furthermore, having subgoal labels in both types of Instruction might help learners integrate the information from those sources better. Subgoal Labeled Worked Example Create Component 1. From the basic palette drag out a label. 2. Place the label underneath the image. Set Properties 3. Set the text to Click button to see your fortune. 4. Rename it to fortuneLabel. Unlabeled Worked Example 1. From the basic palette drag out a label. 2. Place the label underneath the image. 3. Set the text to Click button to see your fortune. 4. Rename it to fortuneLabel. Keywords: STEM education; subgoal learning; worked examples; procedural text. Introduction Figure 1: Worked examples with and without subgoal labels. Knowledge of computing is increasingly necessary in our society. As computing advances, individuals need to understand more about it to understand technical information and make well-informed decisions. Moreover, individuals with advanced computing knowledge are needed to fill increasingly technical jobs and promote innovation. To reflect these societal goals, a major learning goal for computing is that students understand core concepts and principles with the underlying expectation that they can transfer their knowledge to solve problems or critically evaluate information. In computing like in other STEM subjects, both Instructional text and worked examples are used to provide Instruction that is abstract enough to apply to novel problems and concrete enough to grasp (Trafton & Reiser, 1993). Instructional text describes a procedure abstractly (LeFevre & Dixon, 1986) and provides information about reasoning within a domain (Reder & Anderson, 1980); worked examples demonstrate how to apply procedures to specific problems. Worked examples are typically used by students as the primary method to learn procedures To understand what a subgoal is, consider a complex problem solution. Achieving the solution would be the overall goal, and the problem solver takes many individual steps towards that goal. Subgoals are in-between; they are functional pieces of the solution achieved by completing one or more individual steps. The same subgoals tend to appear across problems within a topic area; therefore, teaching learners to identify and achieve subgoals increases their success at solving novel problems (Catrambone & Holyoak, 1990). Research on subgoal labeled worked examples suggests that improved outcomes caused by subgoal labels stems from three sources: highlighting the structure of the worked example for the learner (Atkinson & Derry, 2000; Catrambone, 1995a), helping the learner mentally organize information (Catrambone, 1995b), and inducing the learner to self-explain the examples (Catrambone, 1998; Renkl & Atkinson, 2002). Though subgoal labels improve learning from worked examples, the effect of subgoal labels in Instructional text has not been explored.
Sangbae Kim - One of the best experts on this subject based on the ideXlab platform.
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an introduction to wavelet theory in finance a wavelet multiscale approach
2012Co-Authors: Sangbae KimAbstract:This book offers an introduction to wavelet theory and provides the essence of wavelet analysis — including Fourier analysis and spectral analysis; the maximum overlap discrete wavelet transform; wavelet variance, covariance, and correlation — in a unified and friendly manner. It aims to bridge the gap between theory and practice by presenting substantial applications of wavelets in economics and finance. This book is the first to provide a comprehensive application of wavelet analysis to financial markets, covering new frontier issues in empirical finance and economics. The first chapter of this unique text starts with a description of the key features and applications of wavelets. After an overview of wavelet analysis, successive chapters rigorously examine the various economic and financial topics and issues that stimulate academic and professional research, including equity, interest swaps, hedges and futures, foreign exchanges, financial asset pricing, and mutual fund markets. This detail-oriented text is descriptive and designed purely for academic researchers and financial practitioners. It assumes no prior knowledge of econometrics and covers important topics such as portfolio asset allocation, asset pricing, hedging strategies, new risk measures, and mutual fund performance. Its accessible presentation is also suitable for post-graduates in a variety of disciplines — applied economics, financial engineering, international finance, financial econometrics, and fund management. To facilitate the subject of wavelets, sophisticated proofs and mathematics are avoided as much as possible when applying the wavelet multiscaling method. To enhance the reader's understanding in practical applications of the wavelet multiscaling method, this book provides sample Programming Instruction backed by Matlab wavelet code.
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An Introduction to Wavelet Theory in Finance
2012Co-Authors: Sangbae KimAbstract:This book offers an introduction to wavelet theory and provides the essence of wavelet analysis — including Fourier analysis and spectral analysis; the maximum overlap discrete wavelet transform; wavelet variance, covariance, and correlation — in a unified and friendly manner. It aims to bridge the gap between theory and practice by presenting substantial applications of wavelets in economics and finance. This book is the first to provide a comprehensive application of wavelet analysis to financial markets, covering new frontier issues in empirical finance and economics. The first chapter of this unique text starts with a description of the key features and applications of wavelets. After an overview of wavelet analysis, successive chapters rigorously examine the various economic and financial topics and issues that stimulate academic and professional research, including equity, interest swaps, hedges and futures, foreign exchanges, financial asset pricing, and mutual fund markets. This detail-oriented text is descriptive and designed purely for academic researchers and financial practitioners. It assumes no prior knowledge of econometrics and covers important topics such as portfolio asset allocation, asset pricing, hedging strategies, new risk measures, and mutual fund performance. Its accessible presentation is also suitable for post-graduates in a variety of disciplines — applied economics, financial engineering, international finance, financial econometrics, and fund management. To facilitate the subject of wavelets, sophisticated proofs and mathematics are avoided as much as possible when applying the wavelet multiscaling method. To enhance the reader's understanding in practical applications of the wavelet multiscaling method, this book provides sample Programming Instruction backed by Matlab wavelet code.No Full Tex
Lauren E Margulieux - One of the best experts on this subject based on the ideXlab platform.
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employing subgoals in computer Programming education
Computer Science Education, 2016Co-Authors: Lauren E Margulieux, Richard Catrambone, Mark GuzdialAbstract:AbstractThe rapid integration of technology into our professional and personal lives has left many education systems ill-equipped to deal with the influx of people seeking computing education. To improve computing education, we are applying techniques that have been developed for other procedural fields. The present study applied such a technique, subgoal labeled worked examples, to explore whether it would improve Programming Instruction. The first two experiments, conducted in a laboratory, suggest that the intervention improves undergraduate learners’ problem-solving performance and affects how learners approach problem-solving. The third experiment demonstrates that the intervention has similar, and perhaps stronger, effects in an online learning environment with in-service K-12 teachers who want to become qualified to teach computing courses. By implementing this subgoal intervention as a tool for educators to teach themselves and their students, education systems could improve computing education an...
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improving Programming Instruction with subgoal labeled Instructional text
Cognitive Science, 2014Co-Authors: Lauren E Margulieux, Richard CatramboneAbstract:Improving Programming Instruction with Subgoal Labeled Instructional Text Lauren Margulieux (l.marg@gatech.edu) Georgia Institute of Technology, School of Psychology Atlanta, GA 30332-0170 USA Richard Catrambone (rc7@prism.gatech.edu) Georgia Institute of Technology, School of Psychology Atlanta, GA 30332-0170 USA Abstract (LeFevre & Dixon, 1986) because they take less effort to understand than Instructional text (Eiriksdottir & Catrambone, 2011). Using worked examples in this way, however, can inhibit transfer to novel problems because they are specific to a particular context, and learners are commonly not able to glean abstract information from these concrete examples. To improve this type of transfer, examples that emphasize subgoals have been used (e.g., Catrambone, 1998; see Figure 1). In science, technology, engineering, and mathematics (STEM) education, problem solving tends to be highly procedural, and these procedures are typically taught with general Instructional text and specific worked examples. Subgoal labels have been used in worked examples to help learners understand the procedure being demonstrated and improve problem solving performance. The effect of subgoal labels in Instructional text, however, has not been explored. The present study examined the efficacy of subgoal labeled Instructional text and worked examples for Programming education. The results show that learners who received subgoal labels in both the text and example are able to solve novel problems better than those who do not. Subgoal labels in the text appear to have a different effect, rather than an additive effect, on learners than subgoal labels in the example. Specifically, subgoal labels in text appear to help the learner articulate the procedure, and subgoal labels in the example appear to help the learner apply the procedure. Furthermore, having subgoal labels in both types of Instruction might help learners integrate the information from those sources better. Subgoal Labeled Worked Example Create Component 1. From the basic palette drag out a label. 2. Place the label underneath the image. Set Properties 3. Set the text to Click button to see your fortune. 4. Rename it to fortuneLabel. Unlabeled Worked Example 1. From the basic palette drag out a label. 2. Place the label underneath the image. 3. Set the text to Click button to see your fortune. 4. Rename it to fortuneLabel. Keywords: STEM education; subgoal learning; worked examples; procedural text. Introduction Figure 1: Worked examples with and without subgoal labels. Knowledge of computing is increasingly necessary in our society. As computing advances, individuals need to understand more about it to understand technical information and make well-informed decisions. Moreover, individuals with advanced computing knowledge are needed to fill increasingly technical jobs and promote innovation. To reflect these societal goals, a major learning goal for computing is that students understand core concepts and principles with the underlying expectation that they can transfer their knowledge to solve problems or critically evaluate information. In computing like in other STEM subjects, both Instructional text and worked examples are used to provide Instruction that is abstract enough to apply to novel problems and concrete enough to grasp (Trafton & Reiser, 1993). Instructional text describes a procedure abstractly (LeFevre & Dixon, 1986) and provides information about reasoning within a domain (Reder & Anderson, 1980); worked examples demonstrate how to apply procedures to specific problems. Worked examples are typically used by students as the primary method to learn procedures To understand what a subgoal is, consider a complex problem solution. Achieving the solution would be the overall goal, and the problem solver takes many individual steps towards that goal. Subgoals are in-between; they are functional pieces of the solution achieved by completing one or more individual steps. The same subgoals tend to appear across problems within a topic area; therefore, teaching learners to identify and achieve subgoals increases their success at solving novel problems (Catrambone & Holyoak, 1990). Research on subgoal labeled worked examples suggests that improved outcomes caused by subgoal labels stems from three sources: highlighting the structure of the worked example for the learner (Atkinson & Derry, 2000; Catrambone, 1995a), helping the learner mentally organize information (Catrambone, 1995b), and inducing the learner to self-explain the examples (Catrambone, 1998; Renkl & Atkinson, 2002). Though subgoal labels improve learning from worked examples, the effect of subgoal labels in Instructional text has not been explored.
Križman Peter - One of the best experts on this subject based on the ideXlab platform.
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First steps into Programming through LEGO Mindstorms activities
2019Co-Authors: Križman PeterAbstract:V magistrskem delu se ukvarjamo z aktivnostmi za učenje programiranja, ki vključujejo uporabo LEGO Mindstorm robotov. Aktivnosti temeljijo na konstrukcionistični teoriji učenja, ki jo je Seymor Papert opisal kot način učenja, pri katerem učenci z aktivnostmi, konstrukcijami v fizičnem svetu in predhodnim znanjem pridobivajo nova znanja. Aktivnosti vsebinsko sodijo v medpredmetno vezavo izbirnega predmeta Računalništvo z izbirnim predmetom Robotika v tehniki. Preizkusili smo jih med učenci drugega in tretjega vzgojno – izobraževalnega obdobja (VIO). Pri izvedbi aktivnosti smo uporabili sodobni učni pristop poučevanja programiranja, ki temelji na gibanju »makerstva«. Učenci so skozi koncepte, prakso in perspektivo razvijali računalniško mišljenje. Opisali smo raziskavo, ki temelji na modelu poučevanja programiranja z uporabo LEGO Mindstorms robotov. Pripravili smo in izvedli dve delavnici. Prvo delavnico smo izvedli med naključno izbranimi učenci izbrane šole med dvanajstim in štirinajstim letom. V drugo delavnico so bili vključeni učenci, ki so jih učitelji identificirali kot nadarjene. Raziskovali smo, ali učenci skozi prakso usvojijo koncepte programiranja (zaporedje ukazov, zanka, pogojni stavek). Poleg tega smo raziskovali odnos učencev do LEGO Mindstorms EV3 kompleta. Zanimalo nas je tudi, ali se pojavljajo statistično pomembne razlike glede na spol in uspešnost učencev pri razumevanju in uporabi konceptov programiranja. Z zbranimi ugotovitvami želimo učiteljem računalništva pokazati primer rabe robotov za učenje in poučevanje programiranja in jih spodbuditi, da v svoje šolske ure vnesejo poučevanje konceptov programiranja skozi učni pristop »makerspace«.This Master\u27s thesis deals with activities which use LEGO Mindstorms robots in order to teach Programming. The activities are based on the constructionist learning theory, which Seymor Papert describes as a way of learning that enables pupils to acquire new knowledge by active learning, using meaningful product as constructions in the physical world and the learner experiences. In terms of content, the activities belong to the cross-curricular connection of the optional subject Robotics in Technology. They were tested on pupils of the second and third cycles of primary school. While performing the activities, the modern approach of teaching Programming based on the ʺMaker movementʺ was used. The pupils developed their computational thinking through concepts, practice and perspective. The thesis describes the study which is based on the Programming teaching model using LEGO Mindstorms robots. Two workshops were organized and executed. The first one was carried out with randomly selected pupils of the chosen school, aged between 12 and 14. The second workshop included pupils who were identified as gifted by their teachers. The aim of the study was to discover whether the pupils acquired the concepts of Programming (Instruction sequence, mesh, conditional statement) through practice. In addition, pupils\u27 attitude towards LEGO Mindstorms EV3 set was also studied. Another point of interest was the question whether there were any statistically relevant differences regarding gender and success of the pupils when it comes to their understanding and use of the Programming concepts. The purpose of the findings is to show computer science teachers an example how to use robots in order to learn and teach Programming and to encourage them to introduce the teaching of Programming concepts to their lessons using the ʺmakerspaceʺ approach
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First steps into Programming through LEGO Mindstorms activities
2019Co-Authors: Križman PeterAbstract:This Master's thesis deals with activities which use LEGO Mindstorms robots in order to teach Programming. The activities are based on the constructionist learning theory, which Seymor Papert describes as a way of learning that enables pupils to acquire new knowledge by active learning, using meaningful product as constructions in the physical world and the learner experiences. In terms of content, the activities belong to the cross-curricular connection of the optional subject Robotics in Technology. They were tested on pupils of the second and third cycles of primary school. While performing the activities, the modern approach of teaching Programming based on the ʺMaker movementʺ was used. The pupils developed their computational thinking through concepts, practice and perspective. The thesis describes the study which is based on the Programming teaching model using LEGO Mindstorms robots. Two workshops were organized and executed. The first one was carried out with randomly selected pupils of the chosen school, aged between 12 and 14. The second workshop included pupils who were identified as gifted by their teachers. The aim of the study was to discover whether the pupils acquired the concepts of Programming (Instruction sequence, mesh, conditional statement) through practice. In addition, pupils' attitude towards LEGO Mindstorms EV3 set was also studied. Another point of interest was the question whether there were any statistically relevant differences regarding gender and success of the pupils when it comes to their understanding and use of the Programming concepts. The purpose of the findings is to show computer science teachers an example how to use robots in order to learn and teach Programming and to encourage them to introduce the teaching of Programming concepts to their lessons using the ʺmakerspaceʺ approach
