Abstract
The AnimalWatch tutoring system provides students with instruction in algebra readiness problem solving, including basic computation, fractions, variables and expressions, basic statistics and simple geometry. Students solve word problems that include authentic environmental science content, and can access a range of multimedia resources that provide instructional scaffolding, such as video lessons and worked examples. Because providing learners with choices is associated with enhanced motivation, AnimalWatch is designed to allow students to decide what science topic they would like to learn about, and when they would like to navigate between different modules in the system. Several evaluation studies in classroom settings have found positive effects of AnimalWatch on study-specific measures of problem solving. Benefits have been strongest for students who are struggling in math, suggesting that technology-based learning can be especially effective for this population.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arroyo I. (2003). Quantitative evaluation of gender differences, cognitive development differences and software effectiveness for an elementary mathematics intelligent tutoring system. Unpublished doctoral dissertation, School of Education, University of Massachusetts, Amherst.
Arroyo, I., Murray, T., Beck, J. E., Woolf, B. P., & Beal, C. R. (2003). A formative evaluation of AnimalWatch. In Proceedings of the 11th International Conference on Artificial Intelligence in Education (pp. 371–373). Amsterdam: IOS.
Arroyo, I., Woolf, B. P., Royer, J. M., Tai, M., & English, S. (2010). Improving math learning through intelligent tutoring and basic skills training. Proceedings of Intelligent Tutoring Systems: Lecture Notes in Computer Science, 6094 (pp. 423–432). Berlin: Springer.
August, D. L., & Shanahan, T. (2006). Developing literacy in second language learners: Report of the National Literacy Panel on Language Minority Youth. Hillsdale: Erlbaum.
Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96, 523–535.
Beal, C. R., Adams, N. M., & Cohen, P. R. (2010). Reading proficiency and mathematics problem solving by high school English language learners. Urban Education, 45, 58–74.
Beal, C. R., & Arroyo, I. (2002). The AnimalWatch project: Creating an intelligent computer math tutor. In S. Calvert, A. Jordan, & R. Cocking (Eds.), Children in the digital age (pp. 183–198). Westport: Greenwood.
Beal, C. R., Arroyo, I., Cohen, P. R., & Woolf, B. P. (2010). Evaluation of AnimalWatch: An intelligent tutoring system for arithmetic and fractions. Journal of Interactive Online Learning, 9, 65–77.
Beal, C. R., & Beck, J. E. (2002). Intelligent user modeling and interactive entertainment. Proceedings of the American Association of Artificial Intelligence Spring Symposium. Menlo Park, CA: AAAI Press.
Beal, C. R., & Lee, H. (2005). Creating a pedagogical model that uses student self reports of motivation and mood to adapt ITS instruction. Proceedings of the Workshop on Emotion and Motivation in Educational Software (EMES). Amsterdam: IOS Press.
Beal, C. R., Qu, L., & Lee, H. (2008). Mathematics motivation and achievement as predictors of high school students’ guessing and help seeking with instructional software. Journal of Computer Assisted Learning, 24, 507–514.
Beal, C. R., & Shaw, E. (2009). An online math problem solving system for middle school students who are blind. Journal of Online Learning and Teaching, 5, 630–638.
Beal, C. R., Shaw, E., & Birch, M. (2007). Intelligent tutoring and human tutoring in small groups: An empirical comparison. In R. Luckin, K. R. Koedinger, & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (pp. 536–538). Amsterdam: IOS.
Beck, J. E., Arroyo, I., Woolf, B. P., & Beal, C. R. (1999). An ablative evaluation. In Proceedings of the Ninth International Conference on Artificial Intelligence in Education (pp. 611–613). Amsterdam: IOS Press.
Beck, J. E., Woolf, B. P., & Beal, C. R. (2000). Learning to teach: A machine learning architecture for intelligent tutor construction. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (pp. 552–557). Austin, TX: AAAI Press.
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13, 4–16.
Boekaerts, M., & Corno, L. (2005). Self regulation in the classroom: A perspective on assessment and intervention. Applied Psychology: An International Review, 54, 199–231.
Brown, A. L., Ellery, S., & Campione, J. (1998). Creating zones of proximal development electronically. In J. Greeno & S. Goldman (Eds.), Thinking practices: A symposium in mathematics and science education (pp. 341–368). Hillsdale: Erlbaum.
Ceci, S. J., & Papierno, P. B. (2005). The rhetoric and reality of gap closing: When the “have-nots” gain but the “haves” gain even more. American Psychologist, 60, 149–160.
Center for Applied Special Technology (CAST). (2007). Summary of 2007 national summit on universal design for learning working groups. Wakefield: CAST.
Cirett, F. G., & Beal, C. R. (2010). Problem solving by English learners and English primary students in an algebra readiness ITS. Proceedings of the 23 rd International FLAIRS Conference. Menlo Park CA: AAAI Press. Retrieved from http://www.aaai.org/ocs/index.php/FLAIRS/2010/paper/view/1250.
Cohen, P. R., & Beal, C. R. (2009). Temporal dating mining for educational applications. International Journal of Software and Informatics, 3, 29–44.
Cordova, D., & Lepper, M. (1996). Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88, 715–730.
Heilman, M., Collins-Thompson, K., Callan, J., & Eskenazi, M. (2007). Combining lexical and grammatical features to improve readability measures for first and second language texts. Proceedings of the Human Language Technology Conference. Rochester, NY.
Kieffer, M. M., Lesaux, N. K., Rivera, M., & Francis, D. J. (2009). Accommodations for English language learners taking large-scale assessments: Meta-analysis on effectiveness and validity. Review of Educational Research, 79, 1168–1201.
Kintsch, W., & Greeno, J. G. (1985). Understanding and solving word arithmetic problems. Psychological Review, 92, 109–129.
Koedinger, K. R., & Nathan, M. J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. The Journal of the Learning Sciences, 13, 129–164.
LeBlanc, M. D., & Weber-Russell, S. (1996). Text integration and mathematics connections: A computer model of arithmetic word problem-solving. Cognitive Science, 20, 357–407.
Lepper, M. R., Woolverton, M., Mumme, D., & Gurtner, J. (1993). Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In S. P. Lajoie & S. J. Derry (Eds.), Computers as cognitive tools (pp. 75–105). Hillsdale: Erlbaum.
Martiniello, M. (2008). Language and the performance of English language learners in math word problems. Harvard Educational Review, 78, 333–368.
Murray, T., & Arroyo, I. (2002). Toward measuring and maintaining the zone of proximal development in adaptive instructional systems. Proceedings of the 6th International Conference on Intelligent Tutoring Systems. In S. A. Cerri, G. Gouardères & F. Paraguaçu (Eds.), Lecture notes in computer science 2363 (pp. 133–145). Berlin: Springer.
Nathan, M. J., & Koedinger, K. R. (2000). Teachers’ and researchers’ beliefs of early algebra development. Journal of Mathematics Education Research, 31, 168–190.
National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston: National Council of Teachers of Mathematics.
Royer, J. M., Tronsky, L. N., Chan, Y., Jackson, S. J., & Merchant, H. (1999). Math fact retrieval as the cognitive mechanism underlying gender differences in math test performance. Contemporary Educational Psychology, 24, 181–266.
Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Burlington: Morgan Kaufman.
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25, 3–17.
Zimmerman, B. J., & Schunk, D. (2011). Handbook of self-regulation of learning and performance. London: Routledge.
Acknowledgments
Over the years, the AnimalWatch project has benefitted from the contributions of many talented people, including Ivon Arroyo, Beverly P. Woolf, Joseph Beck, David Marshall, David Hart, Rachel Wing, and Mary Anne Ramirez at the University of Massachusetts Amherst; Erin Shaw, Jean-Philippe Steinmetz, Mike Birch, and Teresa Dey at the University of Southern California; Thomas Hicks, William Mitchell, Jane Strohm, Timothy Brown, and Wesley Kerr at the University of Arizona; and Niall Adams at Imperial College London. The AnimalWatch project has been supported by grants from the National Science Foundation (HRD 9555737, 9714757) and the Institute of Education Sciences (R305K0500086, R305K090197). The views expressed in this chapter are not necessarily those of the sponsoring agencies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Beal, C.R. (2013). AnimalWatch: An Intelligent Tutoring System for Algebra Readiness. In: Azevedo, R., Aleven, V. (eds) International Handbook of Metacognition and Learning Technologies. Springer International Handbooks of Education, vol 28. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5546-3_22
Download citation
DOI: https://doi.org/10.1007/978-1-4419-5546-3_22
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-5545-6
Online ISBN: 978-1-4419-5546-3
eBook Packages: Humanities, Social Sciences and LawEducation (R0)