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Record W1499109156

The Impact of Assignments on Academic Performance

2011· article· en· W1499109156 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of economics and economic education research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGrading (engineering)Mathematics educationTest (biology)Class (philosophy)PsychologyComputer scienceEngineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.301
GPT teacher head0.542
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it