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Descriptors:
Student Problems; Problem Solving; Grade 8; Grade 6; Algebra; Middle Schools; Secondary School Mathematics; Visual Aids; Individual Differences; Experiments; Child Development; Teaching Methods

Abstract:
Background: High school and college students demonstrate a verbal, or textual, advantage whereby beginning algebra problems in story format are easier to solve than matched equations (Koedinger & Nathan, 2004). Adding diagrams to the stories may further facilitate solution (Hembree, 1992; Koedinger & Terao, 2002). However, diagrams may not be universally beneficial (Ainsworth, 2006; Larkin & Simon, 1987). Aims: To identify developmental and individual differences in the use of diagrams, story, and equation representations in problem solving. When do diagrams begin to aid problem-solving performance? Does the verbal advantage replicate for younger students? Sample: Three hundred and seventy-three students (121 sixth, 117 seventh, 135 eighth grade) from an ethnically diverse middle school in the American Midwest participated in Experiment 1. In Experiment 2, 84 sixth graders who had participated in Experiment 1 were followed up in seventh and eighth grades. Method: In both experiments, students solved algebra problems in three matched presentation formats (equation, story, story + diagram). Results: The textual advantage was replicated for all groups. While diagrams enhance performance of older and higher ability students, younger and lower-ability students do not benefit, and may even be hindered by a diagram's presence. Conclusions: The textual advantage is in place by sixth grade. Diagrams are not inherently helpful aids to student understanding and should be used cautiously in the middle school years, as students are developing competency for diagram comprehension during this time. (Contains 3 footnotes, 5 figures and 3 tables.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Cognitive Science; Educational Research; Research and Development; Theory Practice Relationship; Models; Educational Principles; Learning; Instruction; Classification; Memory; Logical Thinking

Abstract:
Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Internet; Feedback (Response); Computer Software; Data Collection; Computer Uses in Education; Laboratory Experiments; Mathematics Instruction; Educational Games; Research Methodology; Numeracy; Mathematics Skills; Elementary School Mathematics; Educational Technology

Abstract:
Traditional experimental paradigms have focused on executing experiments in a lab setting and eventually moving successful findings to larger experiments in the field. However, data from field experiments can also be used to inform new lab experiments. Now, with the advent of large student populations using internet-based learning software, online experiments can serve as a third setting for experimental data collection. In this paper, we introduce the Super Experiment Framework (SEF), which describes how internet-scale experiments can inform and be informed by classroom and lab experiments. We apply the framework to a research project implementing learning games for mathematics that is collecting hundreds of thousands of data trials weekly. We show that the framework allows findings from the lab-scale, classroom-scale and internet-scale experiments to inform each other in a rapid complementary feedback loop. (Contains 2 figures and 2 tables.) [This research was supported by Next Generation Learning Challenge, Carlow University, and Pellisippi State University. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.] Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Performance Factors; Intelligent Tutoring Systems; Individual Differences; Prediction; Probability; Regression (Statistics); Geometry; Bayesian Statistics; Item Response Theory; Correlation; Problem Solving

Abstract:
Although ITSs are supposed to adapt to differences among learners, so far, little attention has been paid to how they might adapt to differences in how students learn from help. When students study with an Intelligent Tutoring System, they may receive multiple types of help, but may not comprehend and make use of this help in the same way. To measure the extent of such individual differences, we propose two new logistic regression models, ProfHelp and ProfHelp-ID. Both models extend the Performance Factors Analysis model (Pavlik, Cen & Koedinger, 2009) with parameters that represent the effect of hints on performance on the same step on which the help was given. Both models adjust for general student proficiency, prior practice on knowledge components, and knowledge component difficulty. Multilevel Bayesian implementations of these models were fit to data on student interactions with a geometry ITS, where students received on-demand problem-relevant help ranging from first-level hints that facilitate application of principles to specific and immediately actionable bottom-out hints. The model comparison showed that in this dataset students differ in their individual hint-processing proficiency and these differences depend on hint levels. These results suggest that we can assess specific learning skills, e.g., making sense of instructional text, and in future work we may be able to remediate and improve such skills. (Contains 2 figures, 4 tables, and 5 footnotes.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.] Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Educational Technology; Intelligent Tutoring Systems; Educational Improvement; Mathematics; Academic Achievement; Tutors; Models; Evaluation

Abstract:
Student modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational technology data sets from intelligent tutors to games in a variety of domains from math to second language learning. In at least ten of the eleven cases, the method discovers improved models based on better test-set prediction in cross validation. The improvements isolate flaws in the original student models, and we show how focused investigation of flawed parts of models leads to new insights into the student learning process and suggests specific improvements for tutor design. We also discuss the great potential for future work that substitutes alternative statistical models of learning from the EDM literature or alternative model search algorithms. (Contains 6 figures and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.] Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Cognitive Science; Theory Practice Relationship; Interdisciplinary Approach; Praxis; Logical Thinking; Memory; Teaching Methods; Cognitive Psychology; Educational Practices; Educational Principles; Mathematics

Abstract:
Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices. (Contains 9 notes, 6 tables, and 1 figure.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Mathematics Instruction; Problem Solving; Educational Research; Evidence; Teaching Methods; Mathematical Applications; Group Instruction; Self Management; Reflection; Visual Aids; Protocol Analysis; Discussion (Teaching Technique); Mathematical Concepts; Algebra; Grade 4; Grade 5; Grade 6; Grade 7; Grade 8

Abstract:
The Institute of Education Sciences (IES) publishes practice guides in education to bring the best available evidence and expertise to bear on current challenges in education. Authors of practice guides combine their expertise with the findings of rigorous research, when available, to develop specific recommendations for addressing these challenges. The authors rate the strength of the research evidence supporting each of their recommendations. The goal of this practice guide is to offer educators specific, evidence-based recommendations that address the challenge of improving mathematical problem solving in grades 4 through 8. The guide provides practical, clear information on critical topics related to improving mathematical problem solving and is based on the best available evidence as judged by the authors. Appended are: (1) Postscript from the Institute of Education Sciences; (2) About the Authors; (3) Disclosure of Potential Conflicts of Interest; and (4) Rationale for Evidence Ratings. (Contains 9 tables, 21 examples and 303 endnotes.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Cooperative Learning; Peer Teaching; Tutoring; Helping Relationship; Educational Principles; Instructional Design; Intelligent Tutoring Systems; Comparative Analysis; Instructional Effectiveness; Reciprocal Teaching

Abstract:
Adaptive collaborative learning support systems analyze student collaboration as it occurs and provide targeted assistance to the collaborators. Too little is known about how to design adaptive support to have a positive effect on interaction and learning. We investigated this problem in a reciprocal peer tutoring scenario, where two students take turns tutoring each other, so that both may benefit from giving help. We used a social design process to generate three principles for adaptive collaboration assistance. Following these principles, we designed adaptive assistance for improving peer tutor help-giving, and deployed it in a classroom, comparing it to traditional fixed support. We found that the assistance improved the conceptual content of help and the use of interface features. We qualitatively examined how each design principle contributed to the effect, finding that peer tutors responded best to assistance that made them feel accountable for help they gave. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Feedback (Response); Help Seeking; Intelligent Tutoring Systems; Program Effectiveness; Tutors; Geometry; Tutoring; Metacognition; Self Evaluation (Individuals); Intervention

Abstract:
The present research investigated whether immediate metacognitive feedback on students' help-seeking errors can help students acquire better help-seeking skills. The Help Tutor, an intelligent tutor agent for help seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students' help-seeking behavior correlated with other independent measures of help seeking, and that the Help Tutor improved students' help-seeking behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also help-seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved help-seeking skills transferred to learning new domain-level content during the month following the intervention, while the help-seeking support was no longer in effect. Implications for metacognitive tutoring are discussed. (Contains 3 tables and 7 figures.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Performance Based Assessment; Predictive Validity; High Stakes Tests; Rote Learning; Physical Sciences; Scoring; Science Process Skills; Grade 8; Inquiry; Science Instruction; Student Evaluation; Biological Sciences; Middle School Students; Computer Assisted Instruction; Educational Technology; Web Based Instruction; Computer Assisted Testing; Computer Simulation; Models; Pretests Posttests; Measurement Techniques; Rural Schools; Federal Aid; Correlation; Science Experiments

Abstract:
The National frameworks for science emphasize inquiry skills (NRC, 1996), however, in typical classroom practice, science learning often focuses on rote learning in part because science process skills are difficult to assess (Fadel, Honey, & Pasnick, 2007) and rote knowledge is prioritized on high-stakes tests. Short answer assessments of inquiry have been used (cf., Alonzo & Aschbacher, 2004; Songer, 2006), however, these tend to not align well to current national frameworks (Quellmalz, Kreikemeier, DeBarger, & Haertel, 2007) and it is unclear whether they properly identify inquiry skills (Black, 1999; Pellegrino, 2001). Hands-on performance assessments are more authentic (Baxter and Shavelson 1994; Ruiz-Primo & Shavelson, 1996), however, these are seldom used in schools because of difficulty with reliable administration and the resulting high cost. The Science Assistments project (www.scienceassistments.org) has developed a rigorous, technology-based learning environment that assists and assesses hence, "assistments") middle school students in Earth, Life, and Physical Science so that teachers can assess their students' skills rigorously, frequently, and during instruction--in the context in which they are developing (Mislevy et al, 2002). The authors' program of work represents a significant advance over other programs that utilize pencil and paper assessments because theirs makes use of a state-of-the art logging infrastructure to do web-based tutoring (Razzaq et al, 2005). As a proof of concept for automated assessment of scientific inquiry skills, the authors used model-tracing (Corbett & Anderson, 1995; Koedinger & Corbett, 2006) to develop a cognitive model of science inquiry skills, particularly, the control for variables strategy (Chen & Klahr, 1999) and warranting claims with data. This model provides a rich qualitative, process-oriented scoring of students' inquiry "moves" within a guided scientific inquiry simulation for the domain of state change. They address the validity of this automated approach to performance assessment both quantitatively, in terms of reliability and predictive validity, and qualitatively, in terms of providing rich traces of student inquiry steps and "mis-steps" or haphazard inquiry (Buckley, Gobert et al, 2010). Participants were 78 eighth grade students, ranging in age from 12-14 years, from a public middle school in Central Massachusetts. In this paper the authors have shown that they can use model-tracing as a method of performance assessment for science inquiry skills, an ill-defined domain. This builds upon the extensive work that has been done to date for well-defined domains such as math (Corbett & Anderson, 1995; Koedinger & Corbett, 2006). Additionally: (1) the reliability of their machine-scored measures of inquiry are highly consistent across the 4 Assistment activities or "trials", suggesting that they can reliably capture students' inquiry performance on these rich inquiry tasks, and (2) their measures are moderately correlated with post-test measures of inquiry performance for analogous concepts. Lastly, their data show that model-tracing can detect interesting patterns of student inquiry such as confirmation bias and overcoming confirmation basis. These are important data with respect to demonstrating auto-scoring of rich inquiry behaviors, but are also important, particularly the former, in terms of its implications for adaptive scaffolding of student inquiry, such as that being done by the Science Assistments group (www.scienceassistments.org; Gobert et al, 2007, 2009). This work makes contribution to theoretical understanding of scientific inquiry, to its assessment, and to technical methods to auto-score inquiry. This represents an advance in this area since to date there has been difficulty in separating inquiry from the domain-specific context in which it was learned (Mislevy et al., 2002; Gobert, Pallant, & Daniels, 2010), and difficulty measuring inquiry skills due to their complexity and the amount of data required for reliable measurement (Shavelson et al, 1999). (Contains 1 table.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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