Effectiveness of Cognitive Tutor Algebra I at Scale

Pane, John F.; Griffin, Beth Ann; McCaffrey, Daniel F.; Karam, Rita

doi:10.3102/0162373713507480

Cited by 163 publications

(67 citation statements)

References 14 publications

(14 reference statements)

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“…In light of such reasoning, several funding agencies, including the National Science Foundation, the Institute for Educational Sci ences, and the National Institutes for Health, have been engaged in funding research that has been demonstrated to work in more limited contexts in order to determine whether the results can be replicated on a broader scale (e.g., http://www.nsf.gov/pubs/2002/ nsf02062/nsf02062.pdf). Their support has funded research by several teams (e.g., Clements, 2005;Francis, 2011;Fuchs, 2004;Hurtig, 2004;Pane, 2007;Starkey, 2004) as well as the present study.…”

Section: Scaling Up Educational Interventionsmentioning

confidence: 97%

Testing the theory of successful intelligence in teaching grade 4 language arts, mathematics, and science.

Sternberg

Jarvin²,

Birney

et al. 2014

Journal of Educational Psychology

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This study addressed whether prior successes with educational interventions grounded in the theory of successful intelligence could be replicated on a larger scale as the primary basis for instruction in language arts, mathematics, and science. A total of 7,702 4th-grade students in the United States, drawn from 223 elementary school classrooms in 113 schools in 35 towns (14 school districts) located in 9 states, participated in the program. Students were assigned, by classroom, to receive units of instruction that were based either upon the theory of successful intelligence (SI; analytical, creative, and practical instruction) or upon teaching as usual (weak control), memory instruction (strong control), or critical-thinking instruction (strong control). The amount of instruction was the same across groups. In the 23 comparisons across 10 content units in 3 academic domains, there were only a small number of instances in which students in the SI instructional groups generally performed statistically better than students in other conditions. There were even fewer instances where the different control conditions outperformed the SI students. Implications for the future of SI theory and the scalability of research efforts in general are discussed.Throughout the first decade of the 21st century, educational re searchers and policymakers have placed an increased emphasis on the twin goals of (a) using experimental designs to evaluate educational interventions and (b) gaining a greater understanding of the issues related to the scalability of educational interventions. The value placed on interventions that have been experimentally tested is high lighted by repositories such as the U.S. Department of Education's "What Works" clearinghouse (http://ies.ed.gov/ncee/wwc/). Projects related to issues of scalability were funded by the Department of Education in the early to mid-2000s, and the results of these projects This article was published O nline First April 7, 2014. R obert J. S ternberg, D epartm ent o f H um an D evelopm ent, Cornell U niversity; L in d a Jarvin, Paris C ollege o f Art; D am ian P. B irney, S chool o f Psychology, U niversity o f S ydney; A dam N aples, C hild D evelopm ent C enter, Y ale U niversity; Steven E. S tem ler, D epartm ent o f Psychology, W esleyan U niversity; T ina N ew m an, C enter fo r C h il dren W ith S pecial N eeds, G lastonbury, C onnecticut; R enate O tterbach, D epartm ent o f G eneral E ducation, U niversity o f San F rancisco; C aro lyn P arish, SRA In ternational, Fairfax, V irginia; Judy R andi, D epart m ent o f E ducation, U niversity o f N ew H aven; E lena L. G rigorenko, C hild S tudy C enter and D epartm ent o f P sychology, Y ale U niversity.

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Section: Scaling Up Educational Interventionsmentioning

confidence: 97%

Testing the theory of successful intelligence in teaching grade 4 language arts, mathematics, and science.

Sternberg

Jarvin²,

Birney

et al. 2014

Journal of Educational Psychology

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“…Results such as these have been critical to the design and continual improvement of the "Cognitive Tutor Algebra" course, now in use by some 600,000 middle and high school students a year. The most recent of many full--year randomized field trials involved 147 schools and showed students using the "Cognitive Tutor Algebra" course learned twice as much as students in traditional algebra courses [9].…”

Section: Cognitive Task Analysis Based On Quantitative Analysis Of Edmentioning

confidence: 99%

Data-driven Learner Modeling to Understand and Improve Online Learning

2014

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Editor's IntroductionAdvanced educational technologies are developing rapidly and online MOOC courses are becoming more prevalent, creating an enthusiasm for the seemingly limitless data-driven possibilities to affect advances in learning and enhance the learning experience. For these possibilities to unfold, the expertise and collaboration of many specialists will be necessary to improve data collection, to foster the development of better predictive models, and to assure models are interpretable and actionable. The big data collected from MOOCs needs to be bigger, not in its height (number of students) but in its width-more meta-data and information on learners' cognitive and self-regulatory states needs to be collected in addition to correctness and completion rates. This more detailed articulation will help open up the black box approach to machine learning models where prediction is the primary goal. Instead, a data-driven learner model approach uses fine grain data that is conceived and developed from cognitive principles to build explanatory models with practical implications to improve student learning.

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“…They are commonly understood as predictions of y C as a function of x (e.g. Pane et al, 2013). In fact, much of the intuition behind prognostic scores supports our use ofŷ C (x) here, though the prognostic score theory will not play a direct role in our argument.…”

Section: Matching In Observational Studies: Reviewmentioning

confidence: 67%

Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates

Sales

Hansen²,

Rowan

2017

Journal of Educational and Behavioral Statistics

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In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces “rebar,” a method using high-dimensional modeling to incorporate these commonly discarded data without sacrificing the integrity of the matching design. After constructing a match, a researcher uses the unmatched control subjects—the remnant—to fit a machine learning model predicting control potential outcomes as a function of the full covariate matrix. The resulting predictions in the matched set are used to adjust the causal estimate to reduce confounding bias. We present theoretical results to justify the method’s bias-reducing properties as well as a simulation study that demonstrates them. Additionally, we illustrate the method in an evaluation of a school-level comprehensive educational reform program in Arizona.

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Effectiveness of Cognitive Tutor Algebra I at Scale

Cited by 163 publications

References 14 publications

Testing the theory of successful intelligence in teaching grade 4 language arts, mathematics, and science.

Testing the theory of successful intelligence in teaching grade 4 language arts, mathematics, and science.

Data-driven Learner Modeling to Understand and Improve Online Learning

Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates

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