Long-term collaboration with strong friendship ties improves academic performance in remote and hybrid teaching modalities in high school physics

Collaboration among students is fundamental for knowledge building and competency development. Nonetheless, the effectiveness of student collaboration depends on the extent that these interactions take place under conditions that favor commitment, trust, and decision making among those who interact. The worldwide pandemic due to COVID-19 and the transition to emergency remote teaching have added new challenges for collaboration, mainly because now students’ interactions are wholly mediated by information and communication technologies. This study first explores the effectiveness of different collaborative relationships over performance in physics from a sample of secondary students from two schools located in rural and urban areas in southern Chile (exploratory study). We used social network analysis to map academic hierarchies as in the nominations for proficient peers in physics (i.e., physics prestige), collaboration, and friendship ties. We define a strong association if two students who collaborate shared a friendship tie. Using ordinary least squares multiple linear regression models on physics grades, we found positive effects of collaboration over grades, particularly among students working with friends (strong ties). To test whether the effects of collaboration found in the first study were stable throughout two semesters, the following year we designed a quasiexperiment in four classes from the same urban school in the exploratory study. Here, students attended hybrid school sessions where research participants were either in the classroom or participated remotely. The teacher collected the same social networks described in the first study at the end of semester 1, and two times during semester 2. In addition, we followed the same procedures to identify strong and weak collaboration from the network data on each wave of data collection. After fitting ordinary least squares multiple linear regression models, we found that collaborative variables negatively associated with grades on activity 1 (semester 1), yet at the end of the year (activity 3 in semester 2) having strong working ties became positively associated with physics grades. Interestingly, the relationship of academic hierarchies measured in physics prestige with grades transitions from positive on semester 1 to null by the end of the year. These results contribute to the literature of collaboration in physics education and its effectiveness, by taking into account social relationships and the needed time for developing beneficial collective processes among students in the classroom. We discuss these results and their implications for instructional design and guidelines for constructive group-level processes.

Figure 1

Depiction of the methodology utilized to identify and construct collaborative variables number 2, 3, 4, and 5 described in Table 3.

Figure 2

Collaboration network for physics. Node color represents academic performance from low (red) to high grades (black), while its size indicates degree centrality—number of collaborative ties. Edge or link colors depict the type of collaborative relationship (e.g., blue ties are strong collaboration with high prestige students in physics). Note that by default isolates are set to the size of the lowest degree.

Figure 3

Graphic depiction of OLS multiple linear regression models for physics grades regressed on collaborative variables: Degree collaboration, model A; weak and strong collaboration, model B, and weak and strong collaboration with high prestige in physics, model C. Models include confounding variables: School 1 (factor); female students (gender as a factor); physics grade in 2019 (previous academic year); and degree friendship. Positive coefficients are depicted in blue, while red coefficients indicate negative effects. Table 7 in the Appendix reports statistics for models A, B and C. Note that. Note that *$p<0.05\mathrm{y}$ **$p<0.01$.

Figure 4

Collaboration network on activities 1 (end of semester 1) and 3 (end of semester 2) for each class. Node color represents academic performance from low (red) to high grades (black); size of nodes indicates degree centrality—number of collaborative ties.

Figure 5

Strong and weak collaboration network on activities 1 (end of semester 1) and 3 (end of semester 1) for each class. Node color represents academic performance from low (red) to high grades (black); size of nodes indicates degree centrality—number of collaborative ties; edges’ colors indicate strong ties (blue) and weak ties (red).

Figure 6

Strong and weak collaboration network with academic prestige on activities 1 (end of semester 1) and 3 (end of semester 1) for each class. Node color represents academic performance from low (red) to high grades (black); size of nodes indicates degree centrality—number of collaborative ties; edges’ colors indicate strong ties (blue) and weak ties (red) with proficient students. Note that by default isolates are set to the size of the lowest degree.

Figure 7

Graphic depiction of OLS multiple linear regression coefficient comparison between activity 1 at the end of semester 1 (light blue); activity 2 at the beginning of semester 2 (orange); and activity 3 at the end of semester 2 (light blue). Coefficient distributions at the left from the dashed line indicate negative effects, and positive coefficients are located at the right. Key predictors are enclosed in black boxes: collaboration (degree), model A; strong collaboration, model B; and strong collaboration with high prestige students in physics, model C.