STATISTICS EDUCATION RESEARCH JOURNAL

Editorial

Susan Peters; Jennifer Kaplan — Sun, 28 Jul 2024 00:00:00 +0000

TEACHING STATISTICS WITH POSITIVE ORIENTATIONS BUT LITTLE KNOWLEDGE? TEACHERS' PROFESSIONAL COMPETENCE IN STATISTICS

SARAH HUBER, FRANK REINHOLD, ANDREAS OBERSTEINER, KRISTINA REISS — Wed, 07 Aug 2024 00:00:00 +0000

Research suggests teachers have positive motivational and emotional orientations regarding statistics but little statistical knowledge. How does this fit together? Since teachers’ professional competence in statistics has not been well explored, we asked 88 in-service mathematics teachers about their orientations regarding teaching statistics and tested their statistical content knowledge. First, we investigated how “positive” their orientations were by comparing them to their orientations regarding teaching fractions. Then, we analyzed relationships between teachers’ orientations and content knowledge in statistics using mixed-effects logistic regression models. The results showed that teachers’ orientations regarding teaching statistics were: (1) poorer than those regarding teaching fractions and (2) related to their statistical knowledge. Teachers with high self-efficacy showed higher knowledge than teachers with low self-efficacy, and anxious female teachers had higher knowledge than less anxious female teachers. We also found that knowledge decreased with increasing age of the teachers. The findings underscore the need to strengthen statistics in teacher education, including both content knowledge and the development of positive orientations.

TEACHING AND LEARNING TO CONSTRUCT DATA-BASED DECISION TREES USING DATA CARDS AS THE FIRST INTRODUCTION TO MACHINE LEARNING IN MIDDLE SCHOOL

YANNIK FLEISCHER, SUSANNE PODWORNY, ROLF BIEHLER — Sat, 10 Aug 2024 00:00:00 +0000

This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test data. They learned to manually construct decision trees to classify food items as recommendable or not. They utilized data cards with a heuristic that is a simplified form of a machine learning algorithm. We report on evidence that this topic is teachable to middle school students, along with insights for refining our teaching approach and broader implications for teaching machine learning at the school level.

EXAMINING THE ROLE OF CONTEXT IN STATISTICAL LITERACY OUTCOMES USING AN ISOMORPHIC ASSESSMENT INSTRUMENT

SAYALI PHADKE, MATTHEW BECKMAN, KARI LOCK MORGAN — Sun, 28 Jul 2024 00:00:00 +0000

The Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report advocates for use of real data with context and purpose. This work contributes to the growing literature on assessing statistical literacy by investigating the influence of context as it relates to assessment performance among post-secondary introductory statistics students. We discuss the development of an isomorphic form of an existing assessment instrument, and report results which concluded that test takers demonstrated lower statistical literacy scores when assessment tasks incorporated real data from published studies as context when compared with functionally similar tasks such as those with a contrived data set and a realistic context.

CAUSAL LANGUAGE AND STATISTICS INSTRUCTION: EVIDENCE FROM A RANDOMIZED EXPERIMENT

JENNIFER HILL, GEORGE PERRETT, STACEY HANCOCK, LE WIN, YOAV BERGNER — Wed, 07 Aug 2024 00:00:00 +0000

Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation.