Robust Learning in Culturally Relevant Algebra Problem Scenarios Candace DiBiano Department of Science and Math Education University of Texas at Austin

Find the Pain *Graph depicting White-Hispanic achievement gap is similar The White-Black Achievement Gap on NAEP Math

Teachers in Austin have cited Cognitive Tutor Algebra problem scenarios as being “silly,” “dry,” and irrelevant to their freshman inner- city minority students. During observations of Cognitive Tutor classrooms, words in the problem scenarios like “factory” or “rainforest” confounded some students (minority freshman rarely discuss these topics in their everyday speech). Cognitive Tutor Algebra will not be used in ANY Austin schools during the school year.

Cultural Relevance From How People Learn (Bransford, Brophy, & William, 2000): Eating Pie and Learning Fractions: Even small differences in cultural knowledge have the potential to affect students' learning. For example, a primary school teacher is helping students to understand fractional parts by using what she thinks is a commonplace reference. "Today, we're going to talk about cutting up a Thanksgiving holiday favorite--pumpkin pie." She continues with an explanation of parts. Well into her discourse, a young African American boy, looking puzzled, asks, "What is pumpkin pie?" As it turns out, most African Americans are likely to serve sweet potato pie for holiday dinners (Tate, 1994).

Attempting a Culturally Relevant Tutor One notable culturally- relevant algebra CAI tutor is the “African- American Distributed Multiple Learning Styles System.” However, culturally relevant algebra tutors with strong underlying cognitive models have not been empirically tested for their effects on robust learning.

Research Question Will we see improved minority engagement and learning in Cognitive Tutor Algebra software problems when we apply personalization through culturally relevant problem scenarios instead of the current problem scenarios? This study is linked to 2 previous PSLC studies: Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems (McLaren et al., 2006) REAP Study on Personalization of Reading Materials by Topic in Order to Increase Interest & Motivation (Eskenazi et al., 2006)

Methodology To answer the question of what is culturally relevant to Hispanic and African- American freshman: –Surveys to assess interests will be distributed –Structured interviews will then be conducted –A pilot study will be run to test the feasibility of the new culturally relevant problem scenarios that will be created –Applicable literature will be reviewed

Once culturally relevant problem scenarios have been created: –A survey will be added to the Cognitive Tutor Algebra software for students to select areas of their interest –This will provide personalization of the Algebra problems in selected units of the Fall semester progression In Vivo Experiment: Control Group Uses standard Cognitive Tutor software Experimental Group Uses Cognitive Tutor software with culturally relevant problems added

What switching to a culturally relevant problem scenario might look like:

You put an average of 30 new songs on to your Ipod Nano every month. 1.How many songs would you put on your Ipod in three months? 2.How many songs would you put on to your Ipod in three years? To write the expression, define a variable for the number of songs on your Ipod and use this variable to write a rule for the number of songs you put on to your Ipod over time.

Predicted Results Culturally relevant problem scenarios may promote robust learning and transfer in terms of: –Improving student’s focus of attention on the problem –Creating better links to prior knowledge –Making the surface features of the problems more understandable, so students can access the deeper features

Summary This study seeks to make two contributions to the field of the learning sciences: Methodology for collecting student interests at the 9th grade level (survey -> interview -> ITS survey) Empirical evidence in an authentic learning environment for personalization of problem statement

References Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. Washington D.C.: National Academy Press. Clark, R. C. and Mayer, R. E. (2003). e-Learning and the Science of Instruction. Jossey- Bass/Pfeiffer. Cordova, D. I. & Lepper, M. R. (1996). Intrinsic Motivation and the Process of Learning: Beneficial Effects of Contextualization, Personalization, and Choice. Journal of Educational Psychology. Vol. 88,l No. 4, Eskenazi,, M.; Juffs, A., Heilman, M., Collins-Thompson, K., Wilson, L., & Callen, J. (2006). REAP Study on Personalization of Readings by Topic (Fall 2006). The PSLC Wiki. Retrieved June 21, 2007, from McLaren, B. M., Koedinger, K., & Yaron, D. (2006). Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems. The PSLC Wiki. Retrieved June 21, 2007, from Wigfield, A. & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, Retrieved June 19, 2007, from the First Search database.