The Course Difficulty Analysis Cookbook

• URL: http://arxiv.org/abs/2508.13218v1
• Date: Sun, 17 Aug 2025 10:55:37 GMT
• Title: The Course Difficulty Analysis Cookbook
• Abstract summary: Curriculum analytics (CA) studies curriculum structure and student data to ensure the quality of educational programs.<n>This paper provides the first comprehensive review and comparison of existing approaches for assessing course difficulty.<n>It offers a hands-on tutorial on model selection, assumption checking, and practical CA applications.
• Score: 0.7373617024876725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Curriculum analytics (CA) studies curriculum structure and student data to ensure the quality of educational programs. An essential aspect is studying course properties, which involves assigning each course a representative difficulty value. This is critical for several aspects of CA, such as quality control (e.g., monitoring variations over time), course comparisons (e.g., articulation), and course recommendation (e.g., advising). Measuring course difficulty requires careful consideration of multiple factors: First, when difficulty measures are sensitive to the performance level of enrolled students, it can bias interpretations by overlooking student diversity. By assessing difficulty independently of enrolled students' performances, we can reduce the risk of bias and enable fair, representative assessments of difficulty. Second, from a measurement theoretic perspective, the measurement must be reliable and valid to provide a robust basis for subsequent analyses. Third, difficulty measures should account for covariates, such as the characteristics of individual students within a diverse populations (e.g., transfer status). In recent years, various notions of difficulty have been proposed. This paper provides the first comprehensive review and comparison of existing approaches for assessing course difficulty based on grade point averages and latent trait modeling. It further offers a hands-on tutorial on model selection, assumption checking, and practical CA applications. These applications include monitoring course difficulty over time and detecting courses with disparate outcomes between distinct groups of students (e.g., dropouts vs. graduates), ultimately aiming to promote high-quality, fair, and equitable learning experiences. To support further research and application, we provide an open-source software package and artificial datasets, facilitating reproducibility and adoption.

Related papers

• What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning [47.10036442634749]
  Curriculum learning (CL) has become a popular strategy for improving reasoning in large language models (LLMs)<n>Yet prior work employs disparate difficulty metrics and training setups, leaving open fundamental questions: When does curriculum help?<n>We address these questions through a unified offline evaluation framework that decomposes curriculum difficulty into five complementary dimensions: Problem Difficulty, Model Surprisal, Confidence Margin, Predictive Uncertainty, and Decision Variability.
  arXiv  Detail & Related papers  (2025-10-21T21:43:38Z)
• Are You Doubtful? Oh, It Might Be Difficult Then! Exploring the Use of Model Uncertainty for Question Difficulty Estimation [12.638577140117702]
  We show that uncertainty features contribute substantially to difficulty prediction, where difficulty is inversely proportional to the number of students who can correctly answer a question.<n>In addition to showing the value of our approach, we also observe that our model achieves state-of-the-art results on the USMLE and CMCQRD publicly available datasets.
  arXiv  Detail & Related papers  (2024-12-16T14:55:09Z)
• Can Model Uncertainty Function as a Proxy for Multiple-Choice Question Item Difficulty? [12.638577140117702]
  We leverage an aspect of generative large models which might be seen as a weakness when answering questions.<n>We explore correlations between two different metrics of uncertainty, and the actual student response distribution.
  arXiv  Detail & Related papers  (2024-07-07T10:48:04Z)
• Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning [3.9829166809129095]
  This study introduces Differential Course Functioning (DCF) as an Item Response Theory (IRT)-based CA methodology. DCF controls for student performance levels and examines whether significant differences exist in how distinct student groups succeed in a course.
  arXiv  Detail & Related papers  (2024-05-07T14:19:11Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing [68.37496795076203]
  We provide guidance for NLP researchers and practitioners dealing with imbalanced data. We first discuss various types of controlled and real-world class imbalance. We organize the methods by whether they are based on sampling, data augmentation, choice of loss function, staged learning, or model design.
  arXiv  Detail & Related papers  (2022-10-10T13:26:40Z)
• Analysis of Learner Independent Variables for Estimating Assessment Items Difficulty Level [0.0]
  This research proposes a model for predicting the difficulty level of assessment items in engineering courses. Experimental results from three engineering courses provide around 80% accuracy in classification of items using the proposed model.
  arXiv  Detail & Related papers  (2022-06-09T11:01:57Z)
• Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets [74.04426767769785]
  We show that specific types of biases may be preferable, depending on the scenario. We design a novel reinforcement learning algorithm, Balanced Q-learning, in which the target is modified to be a convex combination of a pessimistic and an optimistic term.
  arXiv  Detail & Related papers  (2021-11-03T07:30:19Z)
• Estimating and Improving Fairness with Adversarial Learning [65.99330614802388]
  We propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We evaluate our framework on a large-scale public-available skin lesion dataset.
  arXiv  Detail & Related papers  (2021-03-07T03:10:32Z)
• Uncertainty-aware Score Distribution Learning for Action Quality Assessment [91.05846506274881]
  We propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA) Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Under the circumstance where fine-grained score labels are available, we devise a multi-path uncertainty-aware score distributions learning (MUSDL) method to explore the disentangled components of a score.
  arXiv  Detail & Related papers  (2020-06-13T15:41:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.