Networks identify productive forum discussions

Traxler, Adrienne L.; Gavrin, A.; Lindell, Rebecca

doi:10.1103/physrevphyseducres.14.020107

Cited by 41 publications

(50 citation statements)

References 49 publications

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“…After sparsifying with LANS (using code from Traxler et al [85], Supplemental Material), the Infomap CDA was applied. Infomap is based on information theoretic methods.…”

Section: A Module Analysismentioning

confidence: 99%

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

Wells

Henderson

Stewart

et al. 2019

Phys. Rev. Phys. Educ. Res.

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Module Analysis for Multiple-Choice Responses (MAMCR) was applied to a large sample of Force Concept Inventory (FCI) pretest and post-test responses (Npre = 4509 and Npost = 4716) to replicate the results of the original MAMCR study and to understand the origins of the gender differences reported in a previous study of this data set. When the results of MAMCR could not be replicated, a modification of the method was introduced, Modified Module Analysis (MMA). MMA was productive in understanding the structure of the incorrect answers in the FCI, identifying 9 groups of incorrect answers on the pretest and 11 groups on the post-test. These groups, in most cases, could be mapped on to common misconceptions used by the authors of the FCI to create distactors for the instrument. Of these incorrect answer groups, 6 of the pretest groups and 8 of the post-test groups were the same for men and women. Two of the male-only pretest groups disappeared with instruction while the third male-only pretest group was identified for both men and women post-instruction. Three of the groups identified for both men and women on the posttest were not present for either on the pretest. The rest of the identified incorrect answer groups did not represent misconceptions, but were rather related to the the blocked structure of some FCI items where multiple items are related to a common stem. The groups identified had little relation to the gender unfair items previously identified for this data set, and therefore, differences in the structure of student misconceptions between men and women cannot explain the gender differences reported for the FCI. *

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“…After sparsifying with LANS (using code from Traxler et al [85], Supplemental Material), the Infomap CDA was applied. Infomap is based on information theoretic methods.…”

Section: A Module Analysismentioning

confidence: 99%

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

Wells

Henderson

Stewart

et al. 2019

Phys. Rev. Phys. Educ. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Expanding the network to include correct and incorrect responses could address this limitation, but this is beyond the scope of the current study. We attempted to follow the methods for MAMCR presented by Brewe, Bruun, and Bearden as closely as possible [22]: we used the igraph package in the R programming language to create networks from students' response patterns [28,29]; we created a "backbone" network by applying the LANS algorithm (with α = 0.05) using code developed by Traxler, Gavrin, and Lindell [30,31]; and we attempted to use the InfoMap community detection algorithm multiple times to identify modules that are consistent across random fluctuations in the analysis [32,33]. Unfortunately, like others, we were not able to obtain meaningful results using InfoMap, either within the igraph package or as a standalone program [34].…”

Section: Module Analysismentioning

confidence: 99%

Using psychometric tools as a window into students’ quantitative reasoning in introductory physics

Smith¹,

Eaton

Brahmia

et al. 2020

2019 Physics Education Research Conference Proceedings

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The Physics Inventory of Quantitative Literacy (PIQL), a reasoning inventory under development, aims to assess students' physics quantitative literacy at the introductory level. The PIQL's design presents the challenge of isolating types of mathematical reasoning that are independent of each other in physics questions. In its current form, the PIQL spans three principle reasoning subdomains previously identified in mathematics and physics education research: ratios and proportions, covariation, and signed (negative) quantities. An important psychometric objective is to test the orthogonality of these three reasoning subdomains. We present results from exploratory factor analysis, confirmatory factor analysis, and module analysis that inform interpretations of the underlying structure of the PIQL from a student viewpoint, emphasizing ways in which these results agree and disagree with expert categorization. In addition to informing the development of existing and new PIQL assessment items, these results are also providing exciting insights into students' quantitative reasoning at the introductory level. arXiv:1907.05491v2 [physics.ed-ph]

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“…Detailed racial and ethnic demographics are unavailable for the class. The institution is a large, urban, public university whose students were 72% White, 10% African American, 6% Hispanic or Latino, and other groups (including international students) 4% or less [2].…”

Section: A Course Context and Datamentioning

confidence: 99%

“…The motive for this study stems from prior work using social network analysis to examine three semesters of introductory physics online forum data [2]. In that work, network position correlated with course grade in the first and third semesters of data, but not the second semester.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the linguistic persistence of high and low performers in an online student forum

Myers¹,

Fox²,

Traxler³

et al. 2020

2019 Physics Education Research Conference Proceedings

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This work uses recurrence quantification analysis (RQA) to analyze the online forum discussion between students in an introductory physics course. Previous network and content analysis found differences in student conversations occurring between semesters of data from an introductory physics course; this led us to probe which concepts occur and persist within conversations. RQA is a dynamical systems technique to map the number and structure of repetitions for a time series. We treat the transcript of forum conversations as a time series to investigate and apply RQA techniques to it. We characterize the forum behaviors of high and low scoring students, such as their percentage of recurring topics and persistence of discussing a topic over time. We quantify how high scoring and low scoring students use online discussion forum and test whether different patterns exist for these groups. This work is the first adaptation of recurrence quantification methods from the field of psychology for physics education research. Using RQA, there was not a general, observable difference in how the two different groups, high-and low-scoring students, used the forum; however, there were differences when focusing in on and comparing one high-scoring student and one low-scoring student. This technique has the potential for analyzing other PER data such as interviews or student discussions.

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Networks identify productive forum discussions

Cited by 41 publications

References 49 publications

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

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

Using psychometric tools as a window into students’ quantitative reasoning in introductory physics

Quantifying the linguistic persistence of high and low performers in an online student forum

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