Effect of Ability Tracking on Student Performance Essay Example for Free

Effect of Ability pass overing on Student Performance EssayMany factors can influence students academic public presentation. Some argue that much ambitious course material can put less prep ared students at a disadvantage, sequence others argue that insufficient altercate leaves bright students bored and unmotivated. In essence, the one size fits all approaching to course has for many years been set aside in public schools in favor of energy tracking. The fit of students to curriculum difficulty is argued by some to be the key to ensuring student success it ensures that t separatelyers intrust equal focus to students of all ability levels, and also can encourage students with debase ability to figure more in class because they are less likely to feel intimidated (Slavin, 1990). Of course, how students are track 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 ki nd 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 aforesaid(prenominal) 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. So does ability tracking help or incapacitate 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 trial runed First, improvements in performance (percentile rank) get out be more pronounced for students with more challenging curriculum than those with less challenging curriculum. Second, services in performance (percentile rank) will be more pronounced for students who have lower current grade point averages but had more challenging curriculum than for student with high current GPAs and less challenging curriculum.Data Sample The sample include 261 eleventh graders for whom no demographic data (e.g., gender, family income, parents education, race) were provided. The following variables were availableGrade eight Performance Assessment (GEPA) scores in Algebra and Science. pencil lead lay scores indicating the level of challenge associated with each students curriculum.eleventh Grade High School Performance Assessment (HSPA) in Math.Eleventh Grade Math SAT scores.4) Current Grade Point Average (GPA).Analysis 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 beginning hypothesis, percen tile scores were calculated for each students GEPA scores, as well as their HSPA scores, and thus diversity 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 progress (n=260)GEPA ALA Improvement (n=260)Mean-.00134.00397Std. Dev..2206.2927Minimum-.574-.889Maximum.616.828Table 1. Descriptive Statistics for percentile Improvement ScoresTrack Rank scores were not significantly correlated with percentile difference scores for either of the GEPA performance scores (see Table 2). Thus, the first hypothesisthat 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 Rankr = .099, p = .112R = .057, p =.362Table 2. Correlation of Track Rank with Performance ImprovementTo test the second hypothesis, it was n ecessary 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 Rankr = ..634, p = .000Table 3. Correlation of Track Rank with GPAIn 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 lucubrate 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)HPSAr = .706, p = .000r = .481, p =.000SATr = .500, p=.000r = .407, p=.000GPAr = .383, p=.000r = 4.91, p=.000Table 4. Correlation of GEPA scores with later performanceReferencesAnsalone, G. (2003). Poverty, tracking, and the neighborly 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) 812828.Slavin, R.E. (1990). Achievement Effects of Ability Grouping in Secondary Schools A Best-Evidence Synthesis. Review of educational Research, 60(3) 471499.