The Impact of the Inherent Language Complexity on Academic Performance: Using Data Analytic Approach to Profile Diverse Student Learning
Bibliographic record
Abstract
The study examines how language complexity in engineering courses affects students' academic performance to create a more inclusive learning environment. While math is crucial, language proficiency also impacts success, often overlooked. We aim to assess language complexity in chemical engineering courses, proposing a methodology to gauge curriculum progression from basic knowledge to problem-solving and design. Chemical engineering courses typically start with core concepts and advance to practical application. Our goal is to establish a method reflecting this progression and identify areas for improvement. Using natural language processing (NLP), we analyze course materials and final exams, defining features like word frequency and syntactic complexity. Performance data from a decade and 1100 graduates validate our analysis, showing increased language complexity in higher-level courses. International students initially outperform citizens, but this diminishes in advanced courses. Our study pioneers a framework for assessing course language difficulty, aiding curriculum evaluation and student support. By integrating NLP and data analytics, it identifies diverse learning challenges, enabling more inclusive education. Future research should expand to include more courses and disciplines, enhancing educational equity and effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".