MATH:EAGER:Collaborative Research: SMILES (Student-Made Interactive Learning with Educational Songs) for Introductory Statistics

Project: Research project

Project Details

Description

In our increasingly data-centric world, statistical reasoning--in particular, reasoning about data in the context of uncertainty--has become central to the skills our nation needs for students to develop, especially for their future careers in the workforce. An undergraduate student's first formal experience with statistical reasoning frequently occurs in classrooms dominated by lectures rather than active learning experiences. In addition, the classroom instructors often are relatively untrained in statistics. This is especially true at two-year colleges where adjunct instructors can find it difficult to take part in professional development opportunities, often perceive reform-based pedagogies as taking 'extra work' when they already have an unreasonable workload, perceive new resources as being difficult to integrate into their current mode of instruction, and recognize the frequent severe 'statistics anxiety' in their students. SMILES (Student-Made Interactive Learning with Educational Songs) for Introductory Statistics--a collaborative project from Pennsylvania State University - University Park, the University of Texas at El Paso, and Georgia Perimeter College--will develop and field-test an innovation in online learning in introductory statistics, where students create a song by filling in key words associated with a learning objective. These interactive songs will challenge students to make conceptual connections and construct examples or context, thereby fostering statistical literacy and reasoning skills. Through a reduction in statistics anxiety (a key impediment to student success) and an accompanying enhancement of student learning, the potential impact is striking.

The underlying goal of the project is to develop resources that require little instructor time or expertise, but have a high impact on developing statistical literacy and reasoning and on reducing statistics anxiety. In connection with this, interactive songs will provide a novel learning resource that holds great potential for teaching literacy and reasoning skills in statistics and other STEM disciplines. The web-based, machine-run, and auto-graded characteristic of this resource will provide easy access to students anywhere anytime, and will address instructor hesitations regarding in-class use. For instructors, interactive songs will be readily adoptable regardless of pedagogy (e.g., as easily incorporated in a flipped class as in an online class, or a lecture/lab course), and will provide a simple bridge to the statistics education reform movement for groups like two-year college adjunct faculty members who might remain otherwise disconnected. Most importantly, these professional-quality interactive songs will be designed to engage students, lessen anxiety, and foster active learning, thereby leading to improved statistical reasoning skills. To enhance their value, the interactive songs developed by the SMILES project will involve a unique artist/scientist collaborative to create original high-quality musical resources. To evaluate their efficacy and assess the value of interactive songs in enhancing student learning and reducing student anxiety, the project team will conduct a randomized controlled field test involving twenty (20) college level introductory statistics instructors for which fifteen (15) will be from two-year colleges. Response variables will include student answers to multiple-choice assessment items and levels of anxiety as measured by a pre-Statistics Anxiety Measure (SAM) and post-SAM measurement. Since students from the two-year colleges consist predominately of African American and Hispanic student populations, the research findings of the project will expand knowledge of best practices for addressing the national need to broaden participation in science, technology, engineering, and mathematics (STEM) discipline areas.

StatusFinished
Effective start/end date9/15/158/31/20

Funding

  • National Science Foundation: $177,123.00

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