A new approach to modelling student retention through an application of complexity thinking

Forsman, Jonas; Linder, Cedric; Moll, Rachel; Fraser, Duncan; Andersson, Staffan

doi:10.1080/03075079.2011.643298

Cited by 40 publications

(36 citation statements)

References 36 publications

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“…Within physics education research (PER), studies are emerging using social network analysis (SNA) of student interactions, and other types of network analysis in the learning of physics are emerging [3][4][5][6][7]. In the broader field of education, SNA has been used to investigate, for example, how social networks relate to educational change [8], leadership at schools [9][10][11], and the relation between student online interactions and academic performance [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…This study of a Danish introductory physics course at the University of Copenhagen was undertaken to investigate the roles social interactions play in learning physics. This study employs social network analysis, which has been used in PER to show the growth and variation of student-student interactions in instruction [15,16], to identify the roles that gender and ethnicity play in an informal network of students in a physics learning center [3], to model student retention [6,7], and to model interactions between students and teachers in nonphysics learning environments [9,17,18]. Bodin recently used network analysis to document changes in student epistemic networks [19] after engaging in numerical modeling of a physics problem [4].…”

Section: Introductionmentioning

confidence: 99%

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Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

Bruun

Brewe

2013

Phys. Rev. ST Phys. Educ. Res.

115

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The role of student interactions in learning situations is a foundation of sociocultural learning theory, and social network analysis can be used to quantify student relations. We discuss how self-reported student interactions can be viewed as processes of meaning making and use this to understand how quantitative measures that describe the position in a network, called centrality measures, can be understood in terms of interactions that happen in the context of a university physics course. We apply this discussion to an empirical data set of self-reported student interactions. In a weekly administered survey, first year university students enrolled in an introductory physics course at a Danish university indicated with whom they remembered having communicated within different interaction categories. For three categories pertaining to (1) communication about how to solve physics problems in the course (called the PS category), (2) communications about the nature of physics concepts (called the CD category), and (3) social interactions that are not strictly related to the content of the physics classes (called the ICS category) in the introductory mechanics course, we use the survey data to create networks of student interaction. For each of these networks, we calculate centrality measures for each student and correlate these measures with grades from the introductory course, grades from two subsequent courses, and the pretest Force Concept Inventory (FCI) scores. We find highly significant correlations (p < 0:001) between network centrality measures and grades in all networks. We find the highest correlations between network centrality measures and future grades. In the network composed of interactions regarding problem solving (the PS network), the centrality measures hide and PageRank show the highest correlations (r ¼ À0:32 and r ¼ 0:33, respectively) with future grades. In the CD network, the network measure target entropy shows the highest correlation (r ¼ 0:45) with future grades. In the network composed solely of noncontent related social interactions, these patterns of correlation are maintained in the sense that these network measures show the highest correlations and maintain their internal ranking. Using hierarchical linear regression, we find that a linear model that adds the network measures hide and target entropy, calculated on the ICS network, significantly improves a base model that uses only the FCI pretest scores from the beginning of the semester. Though one should not infer causality from these results, they do point to how social interactions in class are intertwined with academic interactions. We interpret this as an integral part of learning, and suggest that physics is a robust example.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

Bruun

Brewe

2013

Phys. Rev. ST Phys. Educ. Res.

115

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show abstract

“…Social network analysis (SNA) can improve our understanding of student network formation in and outside of classrooms, quantify their participation in the learning community, and explore the impacts of these networks on student learning. Different types of network analysis in the learning of physics are emerging [2][3][4][5][6]. SNA has been widely used in the field of education to investigate leadership at schools [7][8][9], the relationship between social networks and educational change [10], and how students' online interactions related to their academic performance [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Study of Informal Learning Communities: a Tale of Two Physics Courses

Yang¹,

Nainabasti²,

Brookes³

et al. 2015

2014 Physics Education Research Conference Proceedings

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Abstract:We asked students in two second-semester introductory college physics courses to report on a weekly basis who they worked with on physics outside of class time. One course was a lecture-based course while the second was a studio-based student-centered course implementing the Investigative Science Learning Environment (ISLE). We used social network analysis to visualize and quantify each student's position and engagement in the informal learning community that formed outside of class. Our study analyzed the relationship between students' network positions as they worked together in groups outside the classroom with their performance in the course. We interpreted our results through a participationist viewpoint on learning. Comparisons between the two courses revealed interesting similarities and differences. While the learning communities in these two distinct settings may look very different, our results showed the overarching importance of informal learning communities irrespective of course type.

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“…And furthermore, we can expect that the new domain, whatever we want to call it, will have new entities of interest emerge [19]. For example, the idea of 'rules of interaction' in the sense that it has been discussed in recent PER literature [3,6] is new. In this literature, 'rules of interaction' are social norms from which observable network structures will likely emerge.…”

Section: A Blend Spaces and Mixed Methodologiesmentioning

confidence: 99%

“…These results can be used to create candidate rules of interaction [6]. Here, one candidate rule of interaction could be: "When doing experimental work, these students are unlikely to share their experiences outside a small group."…”

Section: A Social Networkmentioning

confidence: 99%

Networks as integrated in research methodologies in PER

Bruun¹

2016

2016 Physics Education Research Conference Proceedings

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In recent years a number of researchers within the PER community have started using network analysis as a new methodology to extend our understanding of teaching and learning physics by viewing these as complex systems. In this paper, I give examples of social, cognitive, and action mapping networks and how they can be analyzed. In so doing I show how a network can be methodologically described as a set of relations between a set of entities, and how a network can be characterized and analyzed as a mathematical object. Then, as an illustrative example, I discuss a relatively new example of using networks to create insightful maps of learning discussions. To conclude, I argue that conceptual blending is a powerful framework for constructing "mixed methods" methodologies that may integrate diverse theories and other methodologies with network methodologies.

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A new approach to modelling student retention through an application of complexity thinking

Cited by 40 publications

References 36 publications

Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

A Study of Informal Learning Communities: a Tale of Two Physics Courses

Networks as integrated in research methodologies in PER

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