Motivating Student Effort: Designing Course Assessments in the Presence of Students’ Biased Beliefs
Bibliographic record
Abstract
In my thesis, I investigate the impact of assessment design on student decision-making in the context of a second-year economics course. I begin by estimating the effect of studying on grades, then estimate what students believe are the returns to studying on grades, and also estimate how quickly students update their beliefs about their ability. I combine the components to develop a model of student test-taking to answer questions about how best to design assessments in a course with relative grading. In the first chapter, as part of a joint project with Marc-Antoine Chatelain, Enhua Hu and Xiner Xu, I use novel high-frequency data collected from a competitive large course setting to estimate a structural model determining the transformation from study effort to test grades. I find that returns to study effort are relatively concave, implying that returns to studying fall off as study hours per week increase. In the second chapter, I first derive students' beliefs about their returns to studying, and then estimate their capacity to update under a Biased Bayesian framework. I find that students overestimate their returns to studying, and that students vastly under-react to test grades when updating. I then develop, and simulate, a dynamic model of student study effort where students exert costly effort to earn test grades centered around a target average. Between tests, students update their beliefs about their ability. In the third chapter, I simulate counterfactuals using the model. There are two sets of counterfactuals: decomposition counterfactuals, which analyze the effect of behavioral biases on behavior; and assessment design counterfactuals, which analyze how changing the structure of a course can affect learning and welfare. The Counterfactuals suggest that biased perceptions contribute to large increases in study effort, implying that the phenomenon exerts a positive influence regarding knowledge acquisition. In addition, assessment design counterfactuals suggest that having more tests, and more objective tests positively contributes to student learning.
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How this classification was reachedexpand
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".