Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning

• URL: http://arxiv.org/abs/2406.04348v1
• Date: Tue, 7 May 2024 14:19:11 GMT
• Title: Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning
• Authors: Frederik Baucks, Robin Schmucker, Conrad Borchers, Zachary A. Pardos, Laurenz Wiskott,
• Abstract summary: 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.
• Score: 3.9829166809129095
• 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. One desirable property of courses within curricula is that they are not unexpectedly more difficult for students of different backgrounds. While prior work points to likely variations in course difficulty across student groups, robust methodologies for capturing such variations are scarce, and existing approaches do not adequately decouple course-specific difficulty from students' general performance levels. The present 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 given course. Leveraging data from over 20,000 students at a large public university, we demonstrate DCF's ability to detect inequities in undergraduate course difficulty across student groups described by grade achievement. We compare major pairs with high co-enrollment and transfer students to their non-transfer peers. For the former, our findings suggest a link between DCF effect sizes and the alignment of course content to student home department motivating interventions targeted towards improving course preparedness. For the latter, results suggest minor variations in course-specific difficulty between transfer and non-transfer students. While this is desirable, it also suggests that interventions targeted toward mitigating grade achievement gaps in transfer students should encompass comprehensive support beyond enhancing preparedness for individual courses. By providing more nuanced and equitable assessments of academic performance and difficulties experienced by diverse student populations, DCF could support policymakers, course articulation officers, and student advisors.

Related papers

• Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling [81.00825302340984]
  We introduce Speculative Knowledge Distillation (SKD) to generate high-quality training data on-the-fly. In SKD, the student proposes tokens, and the teacher replaces poorly ranked ones based on its own distribution. We evaluate SKD on various text generation tasks, including translation, summarization, math, and instruction following.
  arXiv  Detail & Related papers  (2024-10-15T06:51:25Z)
• CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning [52.63674911541416]
  Few-shot class-incremental learning (FSCIL) faces several challenges, such as overfitting and forgetting. Our primary focus is representation learning on base classes to tackle the unique challenge of FSCIL. We find that trying to secure the spread of features within a more confined feature space enables the learned representation to strike a better balance between transferability and discriminability.
  arXiv  Detail & Related papers  (2024-10-08T02:23:16Z)
• Relative Difficulty Distillation for Semantic Segmentation [54.76143187709987]
  We propose a pixel-level KD paradigm for semantic segmentation named Relative Difficulty Distillation (RDD) RDD allows the teacher network to provide effective guidance on learning focus without additional optimization goals. Our research showcases that RDD can integrate with existing KD methods to improve their upper performance bound.
  arXiv  Detail & Related papers  (2024-07-04T08:08:25Z)
• A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs [0.0]
  We analyze a heterogeneous knowledge graph consisting of students, course videos, formative assessments and their interactions to predict student performance. We then compare the models generated between 5 on-campus and 2 fully-online MOOC-style instances of the same course. The model developed achieved a 70-90% accuracy of predicting whether a student would pass a particular problem set based on content consumed, course instance, and modality.
  arXiv  Detail & Related papers  (2024-05-19T03:33:59Z)
• Students Success Modeling: Most Important Factors [0.47829670123819784]
  The model undertakes to identify students likely to graduate, the ones likely to transfer to a different school, and the ones likely to drop out and leave their higher education unfinished. Our experiments demonstrate that distinguishing between to-be-graduate and at-risk students is reasonably achievable in the earliest stages. The model remarkably foresees the fate of students who stay in the school for three years.
  arXiv  Detail & Related papers  (2023-09-06T19:23:10Z)
• Fair and skill-diverse student group formation via constrained k-way graph partitioning [65.29889537564455]
  This work introduces an unsupervised algorithm for fair and skill-diverse student group formation. The skill sets of students are determined using unsupervised dimensionality reduction of course mark data via the Laplacian eigenmap. The problem is formulated as a constrained graph partitioning problem, whereby the diversity of skill sets in each group are maximised.
  arXiv  Detail & Related papers  (2023-01-12T14:02:49Z)
• When Do Curricula Work in Federated Learning? [56.88941905240137]
  We find that curriculum learning largely alleviates non-IIDness. The more disparate the data distributions across clients the more they benefit from learning. We propose a novel client selection technique that benefits from the real-world disparity in the clients.
  arXiv  Detail & Related papers  (2022-12-24T11:02:35Z)
• Disadvantaged students increase their academic performance through collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
  During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities. We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA. Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
  arXiv  Detail & Related papers  (2022-03-10T20:23:38Z)
• Impacts of Students Academic Performance Trajectories on Final Academic Success [0.0]
  We apply a Hidden Markov Model (HMM) to provide a standard and intuitive classification over students' academic-performance levels. Based on student transcript data from University of Central Florida, our proposed HMM is trained using sequences of students' course grades for each semester.
  arXiv  Detail & Related papers  (2022-01-21T15:32:35Z)
• Course Difficulty Estimation Based on Mapping of Bloom's Taxonomy and ABET Criteria [0.0]
  We propose a methodology that estimates the difficulty level of a course. The estimated difficulty level is validated based on the history of grades secured by the students.
  arXiv  Detail & Related papers  (2021-11-16T12:53:21Z)
• Towards Equity and Algorithmic Fairness in Student Grade Prediction [2.9189409618561966]
  This work addresses equity of educational outcome and fairness of AI with respect to race. We trial several strategies for both label and instance balancing to attempt to minimize differences in algorithm performance with respect to race. We find that an adversarial learning approach, combined with grade label balancing, achieved by far the fairest results.
  arXiv  Detail & Related papers  (2021-05-14T01:12:01Z)

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.