IES Grant

Title: | National Research & Development Center on Cognition and Mathematics Instruction | ||

Center: | NCER | Year: | 2010 |

Principal Investigator: | Schneider, Steve | Awardee: | WestEd |

Program: | Education Research and Development Centers [Program Details] | ||

Award Period: | 5 years | Award Amount: | $9,998,406 |

Goal: | Multiple Goals | Award Number: | R305C100024 |

Description: |
Bringing together leading experts in cognition, instruction, assessment, research design and measurement, mathematics education, and teacher professional development, the Center team will apply research-based design principles to revise mathematics curricular materials for the grade span of 6 to 8, when fundamental concepts required for algebra and advanced mathematics are addressed. The redesign will be based upon principles derived from experimental studies in classrooms and controlled laboratory settings to enhance the conditions of instruction and improve learning outcomes for students. The Math Center will conduct an integrated series of design studies; develop and test practical guidelines that will enable mathematics teachers, curriculum developers, and publishers to apply the design guidelines; as well as conduct supplementary studies on important issues in mathematics teaching and learning. The Math Center will first complete a series of controlled experiments (RCTs) aimed at examining the effects of revised curricular units with 50 participating teachers, and then a large-scale, school-level random assignment efficacy study to examine the effects of the redesigned CMP in 78 schools. The Math Center will also widely disseminate findings and provide leadership to the education field. Established through a five-year, $10.0 million grant from the Institute of Education Sciences (IES) of the U.S. Department of Education, the National Center on Cognition and Mathematics Instruction is housed at WestEd, and operated in collaboration with partners at the University of Illinois at Chicago, Carnegie Mellon University, Temple University, University of Wisconsin-Madison, and Worcester Polytechnic Institute.
Booth, J.L., McGinn, K.M., Barbieri, C., Begolli, K.N., Chang, B., Miller-Cotto, D., Young, L.K., and Davenport, J.L. (2017). Evidence for Cognitive Science Principles that Impact Learning in Mathematics. In D. Geary, D.B. Berch, R. Ochsendorf and K. Koepke (Eds.), Clinton, V., Cooper, J. L., Michaelis, J. E., Alibali, M. W., and Nathan, M. J. (2017). How Revisions to Mathematical Visuals Affect Cognition: Evidence from Eye Tracking. In C. Was, F.J. Sansoti, and B.J. Morris (Eds.), Hawkins, W.J., Heffernan, N.T., and Baker, R.S. (2014). Learning Bayesian Knowledge Tracing Parameters With a Knowledge Heuristic and Empirical Probabilities. In S. Trausan-Matu, K.E. Boyer, M. Crosby, and K. Panourgia (Eds.), Kelly, K., Heffernan, N., Heffernan, C., Goldman, S., Pellegrino, J., and Goldstein, D.S. (2013). Estimating the Effect of Web-Based Homework. In H.C. Lane, K. Yacef, J. Mostow, and P. Pavlik (Eds.), Selent, D., and Heffernan, N. (2014). Reducing Student Hint use by Creating Buggy Messages From Machine Learned Incorrect Processes. In S. Trausan-Matu, K.E. Boyer, M. Crosby, and K. Panourgia (Eds.), Wang, Y., and Heffernan, N.T. (2014). The Effect of Automatic Reassessment and Relearning on Assessing Student Long-Term Knowledge in Mathematics. In S. Trausan-Matu, K.E. Boyer, M. Crosby, and K. Panourgia (Eds.),
Alibali, M.W., Stephens, A.C., Brown, A.N., Kao, Y.S., and Nathan, M.J. (2014). Middle School Students' Conceptual Understanding of Equations: Evidence From Writing Story Problems. Booth, J.L., and Davenport, J.L. (2013). The Role of Problem Representation and Feature Knowledge in Algebraic Equation-Solving. Booth, J.L., McGinn, K.M., Young, L.K., and Barbieri, C. (2015). Simple Practice Doesn't Always Make Perfect Evidence From the Worked Example Effect. Clinton, V., Alibali, M.W., and Nathan, M.J. (2016). Learning About Posterior Probability: Do Diagrams and Elaborative Interrogation Help?. Clinton, V., Morsanyi, K., Alibali, M.W., and Nathan, M.J. (2016). Learning About Probability From Text and Tables: Do Color Coding and Labeling through an Interactive User Interface Help. Cooper, J. L., Sidney, P. G. and Alibali, M. W. (2018). Who Benefits from Diagrams and Illustrations in Math Problems? Ability and Attitudes Matter. Goldman, S.R., and Pellegrino, J.W. (2015). Research on Learning and Instruction Implications for Curriculum, Instruction, and Assessment. Heffernan, N.T., and Heffernan, C.L. (2014). The ASSISTments Ecosystem: Building a Platform That Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching. Heffernan, N.T., Ostrow, K.S., Kelly, K., Selent, D., Van Inwegen, E.G., Xiong, X., and Williams, J.J. (2016). The Future of Adaptive Learning: Does the Crowd Hold the Key?. McGinn, K.M., Lange, K.E., and Booth, J.L. (2015). A Worked Example for Creating Worked Examples. Ostrow, K. S., Wang, Y., and Heffernan, N. T. (2017). How Flexible is Your Data? A Comparative Analysis of Scoring Methodologies Across Learning Platforms in the Context of Group Differentiation. Ostrow, K.S., Heffernan, N.T., and Williams, J.J. (2017). Tomorrow's EdTech Today: Establishing a Learning Platform as a Collaborative Research Tool for Sound Science.
Adjei, S.A., Botelho, A.F., and Heffernan, N.T. (2016). Predicting Student Performance on Post-Requisite Skills Using Prerequisite Skill Data: An Alternative Method for Refining Prerequisite Skill Structures. In Clinton, V., Alibali, M.W., and Nathan, M.J. (2013). Individual Differences in Calculating Posterior Probability: Do Statistics Education and Math Poficiency Matter?. In Clinton, V., Alibali, M.W., and Nathan, M.J. (2013). The Effects of Diagrams and Questioning While Reading on Learning From a Statistics Lesson. In Clinton, V., Alibali, M.W., and Nathan, M.J. (2013). Why do Diagrams Increase Learning From Lessons?. In Clinton, V., Nathan, M.J., and Alibali, M.W. (2013). The Influence of Visual Representations on Learning From Lessons on Functions. In Cooper, J.L., Clinton, V., Riggs, E.A., Brey, E., Alibali, M.W., and Nathan, M.J. (2013). Contextual Visual Information in Middle School Problem Solving: A Puzzling Situation. In Davenport, J., Kao, Y.S., and Schneider, S.A. (2013). Integrating Cognitive Science Principles to Redesign a Middle School Math Curriculum. In Davenport, J.L., Kao, Y, Hubbard, A, and Schneider, S.A. (2014). Testing Cognitive Science Principles in a Middle School Mathematics Curriculum. In Gu, J., Cai, H., and Beck, J.E. (2014). Investigate Performance of Expected Maximization on the Knowledge Tracing Model. In Gu, J., Wang, Y., and Heffernan, N.T. (2014). Personalizing Knowledge Tracing: Should we Individualize Slip, Guess, Prior or Learn Rate?. In Jiang, Y., Baker, R.S., Paquette, L., San Pedro, M., and Heffernan, N.T. (2015). Learning, Moment-by-Moment and Over the Long Term. In Kehrer, P., Kelly, K.M., and Heffernan, N.T. (2013). Does Immediate Feedback While Doing Homework Improve Learning?. In Kelly, K., Arroyo, I., and Heffernan, N. (2013). Using ITS Generated Data to Predict Standardized Test Scores. In Kelly, K., Heffernan, N., Heffernan, C., Goldman, S., Pellegrino, J.W., and Soffer-Goldstein, D. (2014). Improving Student Learning in Math Through Web-based Homework Review. In Kelly, K.M., and Heffernan, N.T. (2016). Optimizing the Amount of Practice in an On-Line Platform. In Koedinger, K.R. and Mclaughlin, E.A. (2017). Closing the Loop with Quantitative Cognitive Task Analysis. In Lang, C., Heffernan, N., Ostrow, K., and Wang, Y. (2015). The Impact of Incorporating Student Confidence Items Into an Intelligent Tutor: A Randomized Controlled Trial. In Li, N., Stampfer, E., Cohen, W.W., and Koedinger, K.R. (2013). General and Efficient Cognitive Model Discovery Using a Simulated Student. In Lomas, J. D., Forlizzi, J., Poonwala, N., Patel, N., Shodhan, S., Patel, K., and Brunskill, E. (2016). Interface Design Optimization as a Multi-Armed Bandit Problem. In Ostrow, K., and Heffernan, N.T. (2014). Testing the Multimedia Principle in the Real World: A Comparison of Video vs. Text Feedback in Authentic Middle School Math Assignments. In Ostrow, K., Donnelly, C., Adjei, S., and Heffernan, N. (2015). Improving Student Modeling Through Partial Credit and Problem Difficulty. In Ostrow, K., Donnelly, C., and Heffernan, N. (2015). Optimizing Partial Credit Algorithms to Predict Student Performance. In Ostrow, K., Heffernan, N., Heffernan, C., and Peterson, Z. (2015). Blocking vs. Interleaving: Examining Single-Session Effects Within Middle School Math Homework. In Ostrow, K.S., and Heffernan, N.T. (2015). The Role of Student Choice Within Adaptive Tutoring. In Ostrow, K.S., and Heffernan, N.T. (2016). Studying Learning at Scale with the ASSISTments TestBed. In Ostrow, K.S., Selent, D., Wang, Y., Van Inwegen, E.G., Heffernan, N. T., and Williams, J.J. (2016). The Assessment of Learning Infrastructure (ALI): The Theory, Practice, and Scalability of Automated Assessment. In Selent, D., and Heffernan, N. (2015). When More Intelligent Tutoring in the Form of Buggy Messages Does not Help. In Stampfer, E., and Koedinger, K.R. (2013). When Seeing Isn't Believing: Influences of Prior Conceptions and Misconceptions. In Van Inwegen, E., Adjei, S., Wang, Y., and Heffernan, N. (2015). An Analysis of the Impact of Action Order on Future Performance: The Fine-Grain Action Model. In Van Inwegen, E., Wang, Y., Adjei, S., and Heffernan, N.T. (2015). The Effect of the Distribution of Predictions of User Models. In Walkington, C., Clinton, V., and Mingle, L. (2016). Considering Cognitive Factors in Interest Research: Context Personalization and Illustrations in Math Curricula. In Wang, Y., Heffernan, N.T., and Heffernan, C. (2015). Towards Better Affect Detectors: Effect of Missing Skills, Class Features and Common Wrong Answers. In Wang, Y., Ostrow, K., Adjei, S., and Heffernan, N. (2016). The Opportunity Count Model: A Flexible Approach to Modeling Student Performance. In Wang, Y., Ostrow, K., Beck, J., and Heffernan, N. (2016). Enhancing the Efficiency and Reliability of Group Differentiation Through Partial Credit. In Wiese, E., Patel, R., Olsen, J.K., and Koedinger, K (2015). Transitivity is not Obvious: Probing Prerequisites for Learning. In Wiese, E.S., Patel, R., and Koedinger, K.R. (2016). Why Sense Making Through Magnitude may be Harder for Fractions than for Whole Numbers. In |
||

Back |