CLASS Shifts in Modeling Instruction

Brewe, Eric; Kramer, L. H.; O’Brien, George

doi:10.1063/1.3021278

Cited by 8 publications

(18 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an earlier study comparing two different course formats, Brewe et al [2] found divergent types of community structure depending on course type. Students in a larger traditional lecture class (N = 80) began and ended the semester collaborating with few or no other students.…”

Section: Introductionmentioning

confidence: 99%

“…One resource to enrich this perspective comes from the social sciences, where social network analysis has been used for decades as a way to quantify and explore communities and the interactions that structure them [1]. Recently, discipline-based education researchers have taken up these tools to begin systematically mapping peer interactions and how they correlate with other indicators [2][3][4][5]. These investigations can reach a wider pool of students than in-depth interviews, and the resulting largescale picture provides a valuable counterpart to fine-grained qualitative data [6].…”

Section: Introductionmentioning

confidence: 99%

“…By aggregating this information over the semester, a complex weighted picture of student interactions emerges. Other studies take a less sampling-intensive approach and administer only a few surveys during the semester [2,5] to capture large-scale patterns and changes in students' collaborative behavior. The work reported here takes the second approach, using pre-and postcourse surveys to form a baseline picture of students' physics learning interactions.…”

Section: Introductionmentioning

confidence: 99%

“…Future stages of the project will combine network data with attitudinal and conceptual surveys to explore associations between shifts in those measures and the classroom learning community [10]. To add detail to the picture provided by other researchers [2,11], I use network methods of community detection to characterize student collaborative structure. The data analyzed here show unexpected deviation from previous course network patterns, with lectures containing (and sometimes developing) complex structure, and IE sections "frozen" by pre-existing community.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Community structure in introductory physics course networks

Traxler¹

2015

2015 Physics Education Research Conference Proceedings

View full text Add to dashboard Cite

Student-to-student interactions are foundational to many active learning environments, but are most often studied using qualitative methods. Quantitative network analysis tools complement this picture, allowing researchers to describe the social interactions of whole classrooms as systems. Past results in introductory physics have suggested a sharp division in the formation of social structure between large lecture sections and small studio classroom environments. Extending those results, this study focuses on calculus-based introductory physics courses at a large public university with a heavily commuter and nontraditional student population. Community detection network methods are used to characterize pre-and post-course collaborative structure in several sections, and differences are considered between small and large classes. These results are compared with expectations from earlier findings, and comment on implications for instruction and further study.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Community structure in introductory physics course networks

Traxler¹

2015

2015 Physics Education Research Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…This paper will focus on degree, betweenness, and closeness [6] (defined in Section II), which are common centrality measures that provide different insights into network structure. Brewe et al [7] also found that classes utilizing interactive engagement (IE) techniques showed a more connected post-course network than that of traditional lectures.…”

Section: Introductionmentioning

confidence: 99%

Non-traditional students' conceptual scores and network centrality in SCALE-UP classrooms

Sandt¹,

Traxler²

2016

2016 Physics Education Research Conference Proceedings

View full text Add to dashboard Cite

As classrooms transition from traditional to cooperative learning environments, questions about the details of these environments effectiveness are posed. Does this model equally benefit all students? How do nontraditional (NT) students' gains in conceptual knowledge compare to those of traditional (Trad) students in these classrooms? Do NT students' social differences (i.e. age, employment status, family life, etc.) affect the amount of learning they do in the course or their tendency to form collaborative ties with other students? In three sections of SCALE-UP introductory calculus-based physics, we collected social network survey data about student connections and used the Force Concept Inventory as a pre-and post-course conceptual knowledge diagnostic. Several centrality measures were calculated for the networks and NT and Trad student data were compared to look for significant differences between the two groups' results. We found that NT students are connected to fewer peers but are closer to the inner workings of the network in larger courses.

show abstract

The Influence of Annealing Temperature and Time on the Formation of δ-Phase in Additively-Manufactured Inconel 625

Stoudt

Lass

et al. 2018

Metall Mater Trans A

View full text Add to dashboard Cite

The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic and social integration has been long thought to be important for students’ persistence. Yet, it is rarely investigated. In particular, the relative impact of in-class and out-of-class interactions remains an open issue. Here, we demonstrate that, surprisingly, for students whose grades fall in the “middle of the pack,” the out-of-class network is the most significant predictor of persistence. To do so, we use logistic regression combined with Akaike’s information criterion to assess in- and out-of-class networks, grades, and other factors. For students with grades at the very top (and bottom), final grade, unsurprisingly, is the best predictor of persistence—these students are likely already committed (or simply restricted from continuing) so they persist (or drop out). For intermediate grades, though, only out-of-class closeness—a measure of one’s immersion in the network—helps predict persistence. This does not negate the need for in-class ties. However, it suggests that, in this cohort, only students that get past the convenient in-class interactions and start forming strong bonds outside of class are or become committed to their studies. Since many students are lost through attrition, our results suggest practical routes for increasing students’ persistence in STEM majors.

show abstract

CLASS Shifts in Modeling Instruction

Cited by 8 publications

References 1 publication

Community structure in introductory physics course networks

Community structure in introductory physics course networks

Non-traditional students' conceptual scores and network centrality in SCALE-UP classrooms

The Influence of Annealing Temperature and Time on the Formation of δ-Phase in Additively-Manufactured Inconel 625

Contact Info

Product

Resources

About