New Jersey’s urban students traditionally don’t do well on the high stakes NJ High School Proficiency Assessment. Most current remedial mathematics curricula provide students with a plethora of problems like those traditionally found on the state test. This approach is not working. Finding better ways to teach our urban students may help close this achievement gap. This study examined whether a problem/project-based data analysis unit incorporating the document features of the TI-Nspire would help students master data analysis concepts. The study used a quasi-experimental pre/Post-test design enhanced by a qualitative component. A four-week problem/project based data analysis unit served as the curriculum for the intervention treatment. Students were assigned either the TI-84 or the TI-Nspire calculator. Twelve sections of ninth grade students were divided into four basic study groups: (Intervention (TI-84), Traditional (TI-84), Intervention (TI-Nspire), and Traditional (TI-Nspire)).
The quantitative component of the study analyzed differences between students’ pre/post- Total, Multiple-choice, Open-ended mean scores and quantified attitudinal responses. The analysis showed students in the TI-Nspire groups improved more on the Total test and Multiple-choice questions while students in the TI-84 group performed better on Open-ended questions. The Intervention Curriculum was more effective for Multiple-choice questions, Traditional Curriculum for Open-ended questions and Total scores. Student interviews revealed they didn’t like taking notes and answering questions on the TI-Nspire. Some students liked referring to the information in the calculator while others felt that accessing information was too time consuming. The merits of the TI-Nspire document feature needs further exploration. Analysis of the quantified attitudinal survey showed an increase in the positive attitudes of students using the TI-Nspire.
Both qualitative and quantitative evidence showed the Traditional TI-84 group had fewer changes in attitude and content knowledge than everyone else combined, suggesting the need to change how we teach data analysis. Problem/project-based learning, if introduced gradually, may prove to be an effective teaching/learning educational practice.
Further exploration needs to match students’ technological and data analysis proficiencies when determining readiness for student-centered learning that expects students to be calculator proficient and comfortable with basic quantitative procedures such as finding measures of central tendency and variation.