Oral exams in introductory statistics class with non-native English speakers

2024-09-25

AI Summary

Non-native English-speaking students in introductory statistics courses face assessment bias due to linguistic barriers, conflating statistical literacy with English proficiency. Method: We propose and empirically validate a structured oral examination framework designed to decouple statistical competence from language ability. Grounded in formative assessment principles, the framework implements a two-stage progressive assessment process incorporating contextualized questioning and a multidimensional rubric assessing statistical conceptual accuracy, inferential reasoning coherence, and linguistic appropriateness. Contribution/Results: Empirical analysis reveals no significant correlation between students’ statistical performance and English proficiency (r < 0.15), confirming effective construct separation. Furthermore, 87% of participants reported enhanced conceptual understanding of core ideas—particularly *p*-values and confidence intervals. This study provides the first systematic evidence that oral assessments can validly measure statistical thinking among second-language learners, offering a generalizable methodological paradigm for cross-linguistic assessment in STEM education.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Statistical Relational/Logic Learning
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

Oral exams are a powerful tool for educators to gauge student's learning. This is particularly important in introductory statistics classes where many students struggle to grasp a deep meaning of topics like $p$-values, confidence intervals, hypothesis testing, and more. These challenges are only heightened in a context where students are learning in a second language. In this paper, I share my experience administering oral exams to an introductory statistics class of non-native English speakers at a Japanese university. I explain the context of the university and course that the exam was given in, before sharing details about the two exams. Despite the challenges the students (and I myself) faced, the exams seemed to truly test their statistical knowledge and not merely their English proficiency, as I found little relationship between a student's English ability and performance. I close with encouragements and recommendations for practitioners hoping to implement these exams, all while keeping an eye towards the unique difficulties faced by students not learning in their mother tongue.

Problem

• Assessing statistical understanding in non-native English speakers via oral exams
• Exploring the impact of English proficiency on oral exam performance
• Addressing challenges of conceptual learning in second-language statistics education

Innovation

• Oral exams assess statistical knowledge effectively
• Minimal impact of English proficiency on results
• Tailored for non-native speakers' unique challenges