Data: Gamified Formative Assessment in Mechanics: Insights into Conceptual Learning Across Engineering Disciplines

Description

This dataset contains anonymized student assessment data collected during an introductory mechanics course at Montana Technological University in Fall 2024. The dataset includes Force Concept Inventory (FCI) pre-test and post-test results used to evaluate students' conceptual understanding of Newtonian mechanics. It supports analyses of raw and normalized learning gains across eight engineering majors and six FCI conceptual categories. The associated study also incorporated Kahoot!-based formative assessments to reinforce fundamental mechanics concepts. The dataset supports the examination of conceptual learning outcomes and differences across engineering disciplines in a gamified instructional environment.

Steps to reproduce

### Steps to Reproduce 1. Download the dataset containing anonymized Force Concept Inventory (FCI) pre- and post-test results from 60 students enrolled in an introductory mechanics course at Montana Technological University during Fall 2024. 2. Open the dataset using Excel, Python, R, or other statistical software. 3. Use the anonymized participant identifiers to match pre- and post-test records and identify each participant's engineering major. 4. Examine the pre- and post-test scores to evaluate changes in conceptual understanding. Where item-level responses are available, calculate the percentage of correct answers using the 30-question FCI assessment. 5. Group the observations by engineering major or FCI conceptual category, as supported by the available data, to examine differences in performance. 6. Calculate descriptive statistics and learning gains using the variables provided in the dataset. Apply consistent scoring and aggregation procedures when comparing groups.

Institutions

• University of Central Florida

  Florida, Orlando

Categories

Funders

• U.S. National Science Foundation

  Government of the United States of America

  Virginia, Alexandria

  Grant ID: OIA-2531622

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