Effect of Ability Tracking on Student Performance Essay Example for Free

Effect of Ability Tracking on Student Performance Essay Many factors can influence students’ academic performance. Some argue that more challenging course material can put less prepared students at a disadvantage, while others argue that insufficient challenge leaves bright students bored and unmotivated. In essence, the â€Å"one size fits all† approach to curriculum has for many years been set aside in public schools in favor of ability tracking. The fit of students to curriculum difficulty is argued by some to be the key to ensuring student success; it ensures that teachers give equal focus to students of all ability levels, and also can encourage students with lower ability to participate more in class because they are less likely to feel intimidated (Slavin, 1990).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Of course, how students are tracked varies; some schools allow students to be placed into an advanced class for one subject and a lower ranking class for others, while others do not allow this kind of mobility (Slavin, 1990). Even if done carefully, tracking can influence choice of peers and views toward other students. Gamoran (1992) finds that friendships are more easily formed among students in the same tracks than among students in different tracks. A related concern is that tracking leads to students being stigmatized, and ultimately leads to poor academic performance and negative attitudes toward education (Gamoran, 1992). Ansalone (2003) discusses how tracking may perpetuate the cycle of poverty, and the effect of tracking on learning compared to educational systems outside the United States.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   So does ability tracking help or hinder performance? Analyzing historical and present academic performance of eleventh graders in the context of the level of challenge attached to their curriculum, may help to answer this complex question. Specifically, two hypotheses were tested:   First, improvements in performance (percentile rank) will be more pronounced for students with more challenging curriculum than those with less challenging curriculum. Second, Improvements in performance (percentile rank) will be more pronounced for students who have lower current GPAs but had more challenging curriculum than for student with higher current GPAs and less challenging curriculum. Data Sample   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The sample included 261 eleventh graders for whom no demographic data (e.g., gender, family income, parent’s education, race) were provided. The following variables were available: Grade Eight Performance Assessment (GEPA) scores in Algebra and Science. Track Rank scores indicating the level of challenge associated with each student’s curriculum. Eleventh Grade High School Performance Assessment (HSPA) in Math. Eleventh Grade Math SAT scores. 4)   Current Grade Point Average (GPA). Analysis   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Track Rank scores in the sample ranged from 1.17 to 4.17, with a mean of 2.75 and a standard deviation of .68. To test the first hypothesis, percentile scores were calculated for each student’s GEPA scores, as well as their HSPA scores, and then difference scores were calculated between each of the GEPA percentiles and the HSPA percentile. Descriptive statistics for the percentile improvement variable are shown in Table 1. GEPA SCI Improvement (n=260) GEPA ALA Improvement (n=260) Mean -.00134 .00397 Std. Dev. .2206 .2927 Minimum -.574 -.889 Maximum .616 .828 Table 1. Descriptive Statistics for Percentile Improvement Scores Track Rank scores were not significantly correlated with percentile difference scores for either of the GEPA performance scores (see Table 2). Thus, the first hypothesis—that students with more challenging curriculum will experiences more pronounced score improvements than those with less challenging curriculum can be rejected. GEPA SCI Improvement (n=260) GEPA ALA Improvement (n=260) Track Rank r = .099, p = .112 R = .057, p =.362 Table 2. Correlation of Track Rank with Performance Improvement To test the second hypothesis, it was necessary first to determine whether some students had higher GPAs and lower Track Ranks, while others had lower GPAs and higher Track Ranks.   In fact, Track Rank was significantly correlated with GPA (see Table 3). This indicates that Curriculum difficulty is a strong predictor of GPA, and makes it impossible to test the remainder of the second hypothesis. GPA (n=261) Track Rank r = ..634, p = .000 Table 3. Correlation of Track Rank with GPA In contrast, both GEPA scores were significantly correlated with Track Rank (as shown in Table 2), and with GPA, HPSA and SAT scores (see Table 4). Additional data, such as demographics, would have allowed more detailed analysis of this sample. However, with the available evidence, in the current sample, the surest predictor of current performance is past performance. GEPA SCI Improvement (n=260) GEPA ALA Improvement (n=260) HPSA r = .706, p = .000 r = .481, p =.000 SAT r = .500, p=.000 r = .407, p=.000 GPA r = .383, p=.000 r = 4.91, p=.000 Table 4. Correlation of GEPA scores with later performance References Ansalone, G. (2003). Poverty, tracking, and the social construction of failure: International perspectives on tracking. Journal of Children and Poverty, 9(1): 3-20. Gamoran, A. (1992). The Variable Effects of High School Tracking. American Sociological Review, 57(6): 812–828. Slavin, R.E. (1990). Achievement Effects of Ability Grouping in Secondary Schools: A Best-Evidence Synthesis. Review of Educational Research, 60(3): 471–499.