Exploring the structure of misconceptions in the Force Concept Inventory with modified module analysis

Wells, James; Henderson, Rachel; Stewart, John; Yang, Jie; Traxler, Adrienne L.

doi:10.1103/physrevphyseducres.15.020122

Cited by 40 publications

(39 citation statements)

References 80 publications

(95 reference statements)

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“…As such, some of the complexity in Table I results from these items. If items 5,6,15,20,21,33,35,37, and 39 are eliminated from the analysis, the structure of Table I simplifies substantially to produce Table II. The communities in Table II are shown graphically in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…This work applies Modified Module Analysis (MMA) described in Study 1 to the FMCE. Although the method is described in detail in Study 1 [21], we provide an overview of the method here.…”

Section: B Analysis Methodsmentioning

confidence: 99%

“…"Coin Toss -Force" items (items [11][12][13] ask students to select the force on a coin tossed in the air. "Force Graph" items (items [14][15][16][17][18][19][20][21] ask students about the force on a toy car as it moves across a low-friction surface; students select from a number of graphs. "Acceleration Graph" items (items [22][23][24][25][26] ask students to select the graph which correctly represents the acceleration of a toy car moving on a horizontal surface.…”

Section: A the Fmce Instrumentmentioning

confidence: 99%

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Exploring the structure of misconceptions in the Force and Motion Conceptual Evaluation with modified module analysis

Wells

Henderson

Traxler

et al. 2020

Phys. Rev. Phys. Educ. Res.

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Investigating student learning and understanding of conceptual physics is a primary research area within Physics Education Research (PER). Multiple quantitative methods have been employed to analyze commonly used mechanics conceptual inventories: the Force Concept Inventory (FCI) and the Force and Motion Conceptual Evaluation (FMCE). Recently, researchers have applied network analytic techniques to explore the structure of the incorrect responses to the FCI identifying communities of incorrect responses which could be mapped on to common misconceptions. In this study, the method used to analyze the FCI, Modified Module Analysis (MMA), was applied to a large sample of FMCE pretest and post-test responses (Npre = 3956, Npost = 3719). The communities of incorrect responses identified were consistent with the item groups described in previous works. As in the work with the FCI, the network was simplified by only retaining nodes selected by a substantial number of students. Retaining nodes selected by 20% of the students produced communities associated with only four misconceptions. The incorrect response communities identified for men and women were substantially different, as was the change in these communities from pretest to post-test. The 20% threshold was far more restrictive than the 4% threshold applied to the FCI in the prior work which generated similar structures. Retaining nodes selected by 5% or 10% of students generated a large number of complex communities. The communities identified at the 10% threshold were generally associated with common misconceptions producing a far richer set of incorrect communities than the FCI; this may indicate that the FMCE is a superior instrument for characterizing the breadth of student misconceptions about Newtonian mechanics. *

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Section: Resultsmentioning

confidence: 99%

“…This work applies Modified Module Analysis (MMA) described in Study 1 to the FMCE. Although the method is described in detail in Study 1 [21], we provide an overview of the method here.…”

Section: B Analysis Methodsmentioning

confidence: 99%

Section: A the Fmce Instrumentmentioning

confidence: 99%

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Exploring the structure of misconceptions in the Force and Motion Conceptual Evaluation with modified module analysis

Wells

Henderson

Traxler

et al. 2020

Phys. Rev. Phys. Educ. Res.

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“…Their initial work was applied to a sample containing only 143 FCI student responses. Wells et al [5] sought to replicate this study with a large sample of FCI pretest and post-test responses (N pre = 4509 and N post = 4716) and found the original MAMCR method did not scale to large datasets. To extend network analysis to large datasets, they introduced a modification of the original MAMCR algorithm called Modified Module Analysis (MMA).…”

Section: Study 1 : Modified Module Analysismentioning

confidence: 88%

“…The network analytic method used in this work extends Modified Module Analysis (MMA) introduced by Wells et al [5] as an extension to the original algorithm pioneered by Brewe, Bruun, and Bearden [4]. Both methods were only productive when applied to the incorrect answers in the FCI; the correct answers formed a single tightly connected community that prevented identification of additional structure.…”

Section: A Research Questionsmentioning

confidence: 99%

Extending modified module analysis to include correct responses: Analysis of the Force Concept Inventory

Yang¹,

Wells²,

Henderson³

et al. 2020

Phys. Rev. Phys. Educ. Res.

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Brewe, Bruun, and Bearden first applied network analysis to understand patterns of incorrect conceptual physics reasoning in multiple-choice instruments introducing the Module Analysis for Multiple-Choice Responses (MAMCR) algorithm. Wells et al. proposed an extension to the algorithm which allowed the analysis of large datasets called Modified Module Analysis (MMA). This method analyzed the network structure of the correlation matrix of the responses to a multiple-choice instrument. Both MAMCR and MMA could only be applied to networks of incorrect responses. In this study, an extension of MMA is explored which allows the analysis of networks involving both correct and incorrect responses. The extension analyzes the network structure of the partial correlation matrix instead of the correlation matrix. The new algorithm, called MMA-P, was applied to the FCI and recovered much of the structure identified by MMA. The algorithm also identified sets of correct answers requiring similar physical reasoning reported in previous studies. Beyond groups of all correct and all incorrect responses, some groups of responses which mixed correct and incorrect responses were also identified. Some of these mixed response groups were produced when a correct response was selected for incorrect reasons; some of the groups helped to explain the gender unfairness previously reported for the FCI.

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Navigating student uncertainty for productive struggle: Establishing the importance for and distinguishing types, sources, and desirability of scientific uncertainties

Chen,

Jordan,

Park

et al. 2024

Science Education

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An essential aspect of scientific practice involves grappling with the generation of predictions, representations, interpretations, investigations, and communications related to scientific phenomena, all of which are inherently permeated with uncertainty. Transferring this practice from expert settings to the classroom is invaluable yet challenging. Teachers often perceive struggles as incidental, negative, and uncomfortable, assuming they stem from students' deficiencies in knowledge or understanding, which they feel compelled to promptly address to progress. While some empirical research has explored the role of scientific uncertainties in driving productive student struggle, few studies have explicitly examined or provided a framework to unpack scientific uncertainty as it manifests in the classroom, including the sources that lead to student struggle and how teachers can manage it effectively. In this position paper, we elucidate the importance of incorporating scientific uncertainties as pedagogical resources to foster student struggles through uncertainty from three perspectives: scientific literacy, student agency, and coherent trajectories of sensemaking. To develop a theoretical framework, we consider scientific uncertainty as a resource for productive struggle in the sensemaking process. We delve into two types (e.g., conceptual, epistemic), four sources (e.g., insufficiency, ambiguity, incoherence, conflict), and three desirability considerations (e.g., relevance, timing, complexity) of scientific uncertainties in student struggles to provide a theoretical foundation for understanding what students struggle with, why they struggle, and how scientific uncertainties can be effectively managed by teachers. With this framework, researchers and teachers can examine the (mis)alignments between uncertainty‐in‐design, uncertainty‐in‐practice, and uncertainty‐in‐reflection.

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Exploring the structure of misconceptions in the Force Concept Inventory with modified module analysis

Cited by 40 publications

References 80 publications

Exploring the structure of misconceptions in the Force and Motion Conceptual Evaluation with modified module analysis

Exploring the structure of misconceptions in the Force and Motion Conceptual Evaluation with modified module analysis

Extending modified module analysis to include correct responses: Analysis of the Force Concept Inventory

Navigating student uncertainty for productive struggle: Establishing the importance for and distinguishing types, sources, and desirability of scientific uncertainties

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