Effects of Question Stem on Student Descriptions of Histograms

Kaplan, Jennifer J.; Lyford, Alexander; Jennings, Jeremy Kyle

doi:10.52041/serj.v17i1.177

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The opposite equally occurs where students have trouble linking the histogram to the original data collection or context . This is in line with research from Kaplan et al, (2018) who showed that students' descriptions of histograms systematically differ depending on the specific wording of the question (including the word distribution or variable or both in the question) as well as the context (income or hours of sleep).…”

Section: Contextsupporting

confidence: 89%

Histograms

Boels

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Many high-school students are not able to draw justified conclusions from statistical data in histograms. A literature review showed that most misinterpretations of histograms are related to difficulties with two statistical key concepts: data and distribution. The review also pointed to a lack of knowledge about students’ strategies when solving histogram tasks. As the literature provided little guidance for the design of lesson materials, several studies were conducted in preparation. In a first study, five solution strategies were found through qualitative analysis of students’ gazes when solving histograms and case-value plot tasks. Quantitative analysis of several histogram tasks through a mathematical model and a machine learning algorithm confirmed these results, which implied that these strategies could reliably and automatically be identified. Literature also suggested that dotplot tasks can support students’ learning to interpret histograms. Therefore, gazes on histogram tasks were compared before and after students solved dotplot tasks. The "after" tasks contained more gazes associated with correct strategies and fewer gazes associated with incorrect strategies. Although answers did not improve significantly, students’ verbal descriptions suggest that some students changed to a correct strategy. Newly designed materials thus started with dotplot tasks. From the previous studies, we conjectured that students lacked embodied experiences with actions related to histograms. Designed from an embodied instrumentation perspective, the tested materials provide starting points for scaling up. Together, the studies address the knowledge gaps identified in the literature. The studies contribute to knowledge about learning histograms and use in statistics education of eye-tracking research, interpretable models and machine learning algorithms, and embodied instrumentation design.

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Section: Contextsupporting

confidence: 89%

Histograms

Boels

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“…In their discussion about creating useful representations of data, Konold and Higgins (2003) point out that we want students to move from concrete representations (John spent $10 on his last haircut) to abstract representations that use aggregates (50% of the students spent between $10 and $15 dollars on their last haircut). Kaplan et al (2018) reported that students were much more likely to use the shape and the center when describing a histogram in a STEM context. However, in our matching task, when reporters described the rationale for finding a match from their breakout rooms, they mostly referred to the "ranges" of numbers to describe spread (min and max values), and to a lesser extent to the center.…”

Section: Class Discussionmentioning

confidence: 99%

Data in Search of a Context: An Online Icebreaker Activity

White¹,

Guyot²

2022

Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference

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Because context plays a critical part in data collection, analysis, and interpretation, we offer an icebreaker activity during which students explore data with an unknown context. We previously developed this activity for small, in-person, introductory statistics courses (White et al., 2018), and we adapted the instructions and materials for online instruction. Surprisingly, we observed more robust conversations of critical concepts during the online implementation. We report how students engaged in the activity, by creating graphs of their own and identifying features of data representations that help them reveal the context. Finally, we discuss how concepts introduced in the icebreaker activity can be developed in later classes.

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Undergraduate Students’ Use of Primitive Notions When Reasoning About Variability

Koklu

Kaplan²

2022

Int J of Sci and Math Educ

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Effects of Question Stem on Student Descriptions of Histograms

Cited by 5 publications

References 33 publications

Histograms

Histograms

Data in Search of a Context: An Online Icebreaker Activity

Undergraduate Students’ Use of Primitive Notions When Reasoning About Variability

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