The Impact of Assignments on Academic Performance
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Bibliographic record
Abstract
INTRODUCTION In this paper, we examine the impact of graded homework on the test performance of students taking economics courses. Recently, researchers have done extensive amounts of work on how to improve performance of economics students (Anderson, Benjamin & Fuss, 1994; Arias & Walker, 2004; Borg & Shapiro, 1996; Greene, 1997; Jensen & Owen, 2001). These studies focused on factors such as class size, personality type, verbal abilities, gender, and interest in economics. One of the least researched issues is the impact of graded assignments on student performance, even though assigning problem sets is now an important part of teaching strategies employed in economics courses (Geide-Stevenson, 2009). Assignments that are graded, with the score used as part of the final course grade, are expected to improve test performance. The logic is that students will be motivated to work on the graded assignment and will learn from it; consequently, test scores will improve. Graded assignments, however, do impose costs on both instructors and students. Instructors spend time grading the assignments and providing adequate feedback. As for students, they may need to forgo other, more productive learning processes and methods to make the time to work on graded assignments (Geide-Stevenson, 2009). Thus, it is necessary to examine whether and to what extent graded assignments benefit students. Although many studies have examined the impact of homework assignments on student performance at the elementary and secondary education levels, only a few studies have investigated this important issue in a university-level setting. Cooper (1989) provides an excellent review of the studies on the impact of homework on student performance in elementary and secondary schools. Grove and Wasserman (2006), using data from economics students in a U.S. university, compared exam performance of students for whom assignments counted toward the final grade with the performance of a control group. Using Ordinary Least Squares regression analysis, the study found that a grade incentive to complete assignments boosted the exam performance of academically average freshman students but not those who were academically above or below average, or of any other class standing. Geide-Stevenson (2009) used data from economics students at another U.S. university and found from Ordinary Least Squares regression analysis that graded assignments had no impact on academic performance. Thus, not only is there a paucity of studies on the impact of assignment on academic performance of university students, but the results so far also are conflicting. In the present study, we aim to fill the gap in the literature and extend the earlier studies in a number of ways: to the best of our knowledge, this study is the first of its kind using Canadian data; this study uses the Ordered Probit method as well as Ordinary Least Squares and Propensity Score Matching methods; and unlike previous studies, which used data from either lower-level or upper-level economics courses, this study uses data from both levels of economics courses. A major contribution of this paper is that it examines the impact of assignments on academic performance of various student subgroups: male vs. female and domestic vs. international. International students have been enrolling in Canadian universities in increasing numbers, so it is important to identify factors that influence their academic performance. In this respect also, this study aims to make an important contribution. The paper has the following format: section 2 deals with data and methodology, section 3 presents results of the study, and section 4 offers conclusions. DATA AND METHODOLOGY Data Data for this study come from 387 students who were taking various levels of economics courses at Thompson Rivers University, a small Canadian primarily undergraduate institution, during the winter term (January-April) of the 2009-2010 academic year. …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it