Examining evolving performance on the Force Concept Inventory using factor analysis

Semak, Matthew; Dietz, Richard; Pearson, R. H.; Willis, Courtney

doi:10.1103/physrevphyseducres.113.010103

Cited by 2 publications

(8 citation statements)

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“…MIRT identified a 9-factor solution as optimal for the full 30-item FCI. Other studies have identified 5-factor [47] and 6-factor [48] post-test models as optimal. It is possible that the larger sample used in the present study combined with the strong incentives given for correctly answering the items (the FCI score was counted toward the student's grade) allowed this study to resolve more detailed structure in the FCI.…”

Section: Discussionmentioning

confidence: 99%

“…A substantial number of studies have attempted to understand the overall structure of the FCI. These have included purely exploratory or descriptive methods such as factor analysis [46][47][48], module analysis [49], cluster analysis [50,51], item response theory [19,[52][53][54] and item response curves [55,56] The structure of student reasoning on the FCI has also been investigated by methods such as model analysis that require the input of a partial model of the concepts measured by the FCI [57]. Model analysis was later shown to be exact only in certain limiting cases [51].…”

Section: Chapter 3 Evaluation Of the Knowledge Structure Measured By mentioning

confidence: 99%

“…Semak et al explored the evolution of the structure of student thinking on the FCI using factor analysis [48] for 427 algebra-and calculus-based introductory physics students.…”

Section: Factor Analysis and The Fcimentioning

confidence: 99%

“…There is a substantial body of research indicating that some problems within the FCI are unfair to women with a few unfair to men. These problems have often factored together in previous analysis [47,48] leading to the possibility that some factors are identified because of biases in the problems. While this study will not focus on gender fairness, the reduced fair 19-item FCI proposed by Traxler et al [19] was examined using the optimal theoretical FCI model identified by MIRT.…”

Section: Research Questionsmentioning

confidence: 99%

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Using Machine Learning and Traditional Statistics to Explore Retention and Knowledge Structure in STEM with an emphasis on Physics

Zabriskie¹

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and Mathematics (STEM) students is a serious problem as STEM graduation rates continue to lag the growing demand for the skills taught by these degree programs. Critical to fixing this "leaky pipeline" is investment in improving retention in the first two years of college study and increasing and maintaining the interest of K-12 students in STEM. This thesis will address this in three parts. The first is through evaluation of conceptual tests used to evaluate course improvements to determine the structure student knowledge measured by them. The second part uses machine learning to construct early warning models of student failure in introductory physics courses to aid instructors in better targeting of interventions. The final part assesses the effectiveness of the Pulsar Search Collaboratory, an authentic science experience for middle and high school students, at encouraging K-12 students to pursue STEM degrees.

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Section: Discussionmentioning

confidence: 99%

Section: Chapter 3 Evaluation Of the Knowledge Structure Measured By mentioning

confidence: 99%

“…Semak et al explored the evolution of the structure of student thinking on the FCI using factor analysis [48] for 427 algebra-and calculus-based introductory physics students.…”

Section: Factor Analysis and The Fcimentioning

confidence: 99%

Section: Research Questionsmentioning

confidence: 99%

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Using Machine Learning and Traditional Statistics to Explore Retention and Knowledge Structure in STEM with an emphasis on Physics

Zabriskie¹

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“…The properties and performance of the most commonly used conceptual inventories constructed by physics education researchers have been studied through factor analysis [186][187][188], item response theory [189][190][191][192], and network analysis [193]. Most of these studies, however, have been performed using the FCI [21] and have only explored the structure and validity of undifferentiated samples.…”

Section: Chaptermentioning

confidence: 99%

An Exploration of Diversity and Inclusion in Introductory Physics

Henderson¹

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Diversity and inclusion has been a concern for the physics community for nearly 50 years. Despite significant efforts including the American Physical Society (APS) Conferences for Undergraduate Women in Physics (CUWiP) and the APS Bridge Program, women, African Americans, and Hispanics continue to be substantially underrepresented in the physics profession. Similar efforts within the field of engineering, whose students make up the majority of students in the introductory calculus-based physics courses, have also met with limited success. With the introduction of research-based instruments such as the Force Concept Inventory (FCI), the Force and Motion Conceptual Evaluation (FMCE), and the Conceptual Survey of Electricity and Magnetism (CSEM), differences in performance by gender began to be reported. Researchers have yet to come to an agreement as to why these "gender gaps" exist in the conceptual inventories that are widely used in physics education research and/or how to reduce the gaps. The "gender gap" has been extensively studied; on average, for the mechanics conceptual inventories, male students outperform female students by 13% on the pretest and by 12% post instruction. While much of the gender gap research has been geared toward the mechanics conceptual inventories, there have been few studies exploring the gender gap in the electricity and magnetism conceptual inventories. Overall, male students outperform female students by 3.7% on the pretest and 8.5% on the post-test; however, these studies have much more variation including one study showing female students outperforming male students on the CSEM. Many factors have been proposed that may influence the gender gap, from differences in background and preparation to various psychological and sociocultural effects. A parallel but largely disconnected set of research has identified gender biased questions within the FCI. This research has produced sporadic results and has only been performed on the FCI. The work performed in this manuscript will seek to synthesize these strands and use large datasets and deep demographic data to understand the persistent differences in male and female performance.

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Examining evolving performance on the Force Concept Inventory using factor analysis

Cited by 2 publications

References 11 publications

Using Machine Learning and Traditional Statistics to Explore Retention and Knowledge Structure in STEM with an emphasis on Physics

Using Machine Learning and Traditional Statistics to Explore Retention and Knowledge Structure in STEM with an emphasis on Physics

An Exploration of Diversity and Inclusion in Introductory Physics

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