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Priming the STEM Pipeline: A Socially Relevant Approach to Data Analytics for Middle-Grade Students

Tue, April 9, 8:00 to 10:00am, Metro Toronto Convention Centre, 200 Level, Room 203C

Abstract

Rationale
The underperformance of African American and Hispanic students in mathematics and science in U.S. schools is well documented (National Center for Educational Statistics, 2009, 2011a, 2011b, 2012, 2013) and there is considerable research, commentary, and discussion focused on identifying factors associated with these trends (Flores, 2007; Ladson-Billings, 1997, 2006; National Science Board, 2016; Quinn & Cooc, 2015, Vanneman et al., 2009). There is evidence that African American and Latinx students are highly vulnerable to academic disidentification (detaching visions of self and self esteem from academic outcomes) (Billson, 1993; Fordham, 1988; Ogbu, 2003; Steele, 1997). Furthermore, several studies have shown that academic disidentification is developmental, that identification with academics is related to academic outcomes, and the social dynamics of the environment can heavily influence academic disidentification (Osborne, 2001). The research literature related to factors associated with Latinx student underperformance in mathematics and science is less extensive, yet the performance trends are similar to those of African American students, resulting in explorations of similar factors that may contribute to their underperformance (Strayhorn, 2010; Taylor & Graham, 2007). Therefore, there is an urgent need to design, facilitate and study innovative interventions that provide minority students opportunities to support their development as STEM learners and explore STEM careers.

Perspective/Theoretical Framework
To address the aforementioned disidentification theory for African American and Latinx students, a team of educators developed a data analysis project designed to engage local African American and Latinx middle school students in a series of activities referred to as MDAP (pseudonym) that:
1) build their knowledge of statistics concepts and the data science process,
2) increase their motivation and interest in advanced mathematics and statistics courses in high school and college, and
3) expose them to STEM careers, particularly in the cognate area of statistics and data analytics.

Methods
The data analysis project draws on Identity Based Motivation (IBM) theory (Oyserman, 2013) as a framework for program goals, activities, evaluation and research efforts. IBM theory predicts that people prefer to act in ways that fit their personal important identities, including racial-ethnic, gender, age-based, and social class-based identities. In short, IBM suggests that people are motivated to act in identity-congruent ways. IBM is a general model rooted in a situated-cognition perspective (Brown, Collins, & Duguid, 1989) and was developed to understand motivational processes underlying the choices and outcomes of individuals who are racial and ethnic minorities.

Modes of Inquiry/Data Source
A review of available assessments indicated that there were no currently available assessments aligned to the content of academic sessions and developmentally appropriate for middle grades students. The research team, therefore, chose to develop a statistics and data analytics assessment specifically for the data analytics camp. A pre-assessment and post assessment was developed.

Findings
Analysis of data from pre-post assessments show that participants scored higher on the post-assessment for every assessment category (with the exception of visualizing data), indicating that their statistics and data analytics knowledge base was positively impacted by the MDAP experience. Figure 2 is a chart, representing the 2017 results.

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