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

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 ($r=-0.32$ and $r=0.33$, respectively) with future grades. In the CD network, the network measure target entropy shows the highest correlation ($r=0.45$) 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.

Figure 1

Timeline of assessment activities during the first two blocks along with the research activity relevant to this study. The FCI assessments and the approvals of lab log books or reports are not part of the grade.

Figure 2

Illustration of how the survey answers were transformed to networks. The survey software outputted the data as a comma separated variables (.csv) file. The first entry in a line is the selecting student, the second entry is the selected students. The following entries list the categories in which the students where selected. In this example, student B selected student A in the problem solving (PS) and concept discussion (CD) categories. In network terms B is the source of a connection and A is the target. A is also the source of a PS link to the target C, and an in-class social communication link also to C.

Figure 3

Correlation network of measures that correlate significantly with SOG ($p<0.001$). Correlation coefficients are included on the links, the number of degrees of freedom are shown in the nodes, and the layout is determined by the strength of correlation with SOG.