d.- EFECTO PSICOSOCIAL

ENA results indicated differences in the frequency and types of connections made in Conversation 4 comparing groups that used higher and lower proportions of APT in design meeting discourse. The details about these findings, and how they are different from those found in my analysis of Conversation 3, are presented below.

First, I present Figure 5.14 (below), which is a plot of the mean of the network connections for those groups with differing APT use in Conversation 4. T-tests indicated that the observed differences in engineering discourse comparing groups that used higher (red) and lower (blue) proportions of APTwas statistically significant ((t)-2.882,

p=0.006, d = 0.79). Groups that used higher proportions of APT in Conversation 4 scored lower, on average, on ENA dimension 1 (Client/Design Skill). Cohen’s effect size value (d = 0.79) suggests a strong practical significance regarding how groups that used higher proportions of APT, compared to those that did not, are different with regard to their epistemic discourse.

EN A 2 ( y- ax is ): Co n su lt an t & Da ta An al ys is /D es ig n S k ill

ENA 1 (x-axis): Client/Design Skill

Figure 5.14. Plot of mean of network connections for higher (red, n=28) and lower APT (blue, n=27) use groups in Conversation 4.

The types of network connections that reflect the epistemic discourse of higher and lower use APT use groups can be seen in the mean equiload models presented below in Figure 5.15. These models suggest that groups that used higher proportions of APT (in red), compared to those that used lower (in blue), more frequently connected their

knowledge of the client/patient’s wellbeing (i.e., health, comfort and/or safety) to other aspects of engineering design. These groups also more frequently justified their design decisions (i.e., epistemology) in relation to the client/patient’s wellbeing. Said another way, students in these groups more often integrated considerations of the health, comfort and/or safety of their clients/patients into their design thinking during design meeting discourse.

Figure 5.15. Mean network models of groups’ discourse in Conversation 4. LEFT PANEL: Higher APT use groups (red, n=28). RIGHT PANEL: Lower APT use groups (blue, n=27).

Given the similarity of these models with regard to the density of their

connections, I present a comparative network model (Figure 5.16, below) to illuminate further differences in the relative strength of their connections.

Figure 5.16. Comparative model of groups using higher (red) and lower (blue) APT in Conversation 4.

In particular, the node sizes and line weights in this model highlight how groups with higher APT use, relative to lower use groups, made more and stronger connections between aspects of the client (knowledge and epistemology) and other aspects of engineering practice in this conversation. This finding implies that groups that used higher proportions of APT in their discourse spent more time discussing, and justifying, their design decisions in relation to client’s needs, than did lower APT use groups. The implication of higher APT-use groups more frequently making such connections is that they were better able to attend to this important design constraint as they worked

collaboratively at a key phase of their design process, namely, when they were finalizing their “best” design for submission.

It is worth noting two other differences highlighted in this model. First, higher APT use groups (in red), on average, persisted in making stronger connections to the consultant, as was found in Conversation 3 (reported in the previous section). Second, in contrast to the findings reported for Conversation 3, it was lower APT use groups (in blue) that, on average, more frequently integrated data analysis into their design thinking. However, in both instances, the observed difference in the frequency of these connections in discourse was not statistically significant (i.e., on the y-axis). This suggests that the degree to which higher and lower use APT groups integrated the consultant and data analysis into their design thinking was not substantively (i.e., semantically) different, even if it if was more frequent overall, as will described shown below. That is, collaborative conversational moves did not particularly support or impede whether students made connections to these epistemic elements in their discourse in Conversation 4.

The key difference between these findings and those for Conversation 3 is that they imply that the use of APT was salient with regard to which design constraint was better integrated into a group’s design thinking. In particular, the shift in the effect of increased APT use from the consultant and data analysis in Conversation 3 to the client in Conversation 4 suggests that the effects of APT varied as a function of the focal work in each design meeting. For instance, in Conversation 3, groups were building their knowledge and integrating the findings collected about the variety of materials and consultants’ interests that students’ prior groups worked with (i.e., in Design Cycle 1) in order to test new, and more complex, devices. It therefore makes sense that the

consultant and data analysis were better integrated through more collaborative interaction in this conversation. In Conversation 4, however, groups were expected to consider the results of how these newly tested devices performed in order to come to consensus on a final device that would best satisfy the design constraints with regard to the interests of the consultants and the needs of the client. In this conversation, greater or lesser use of APT was not related to how well groups integrated the consultant and data analysis in their design thinking. This makes sense because groups have already synthesized their knowledge of prior performance data and understandings of various consultants’ interests prior to this conversation. Instead, my findings suggest that increased collaborative interaction in Conversation 4 served a different function in the final design meeting of the simulation. That is, it supported groups’ surfacing and accounting for the health and well being of the client in the specifications for their final device submission in the simulation.

5.6.3 Evidence of ENA results reflected in student discourse in Conversation 4

The relationship between more proportional use of APT moves and the greater frequency of connections in discourse to the client (i.e., epistemology and knowledge) in Conversation 4 is evidenced in student’s conversational data (i.e., chat-logs). In

particular, the examination of student discourse suggests that in this conversation, connections to these elements are better integrated in student’s design thinking when there is a greater use of APT moves in discursive interaction in general, and in particular, two APT moves were specifically associated with the integration of the client’s safety, well-being, etc. in student discourse (i.e., APT-C: Explain and Share Reasoning). In what follows, I present three representative samples of discursive interaction in Conversation 4 (Figures 5.17-5.19). The first two are drawn from higher APT use groups, and the third, for comparative purposes, is from a lower APT use group.

As will be described below, the comparison of these representative samples highlights how groups with higher APT use in the course of this conversation were better able to focus on critical and domain-based content connections to the client in a way that resulted in more nuanced epistemic discourse as they worked to complete their task.

Discourse samples from higher APT use groups in Conversation 4

The first conversational sample (Figures 5.17a-d, below) features the discursive interactions of a higher APT use group. The sample highlights how this group integrated the client, as well as data analysis and the consultant’s interests, into their design thinking through efforts to explain why the ideas of their teammates are good ones for the team’s decisions.

For instance, this conversational exchange begins (Figure 5.17a) with an effort by Dale to affirm that the group has come to consensus about their final decision regarding the device the team will use in their final submission (APT-F: Revoice). However, after Andy confirms (APT-C: Agree/Disagree), Dale challenges this decision, noting that he thinks a different device is better (APT-F: Challenge). This syntactic move initiates a semantically rich series of interactions, discussed below, among the team about the pros and cons of their final decision that integrates data analysis, engineering design, the consultant and the client into the team’s design thinking.

Student Conversational

Category: Move Student Utterance Epistemic Code(s)

Dale APT-F: Revoice So we all decided on device 3 right? Design (Knowledge & Skill)

Andy APT-C:

Agree/Disagree

sure

Dale APT-F: Challenge I think device two is better after doing the assignment for last week though

Design (Knowledge & Skill)

Figure 5.17a. Chat log excerpt from a higher APT use group in Conversation 4.

Following Dale’s challenge, Lucy presents her option that device 3 is her choice (Figure 5.17b, below). Harry, though, not only agrees with Dale’s opinion about device 2, but explains why it is important by raising the issue of the client’s health (APT-C: Explain). Dale then indicates that he agrees with Harry’s explanation (APT-C: Agree/Disagree). Next, Lucy explains her position regarding the use of device 3 (i.e., that it is the best “overall”), referring to the data about the devices (APT-C: Explain). Harry and Dale both challenge her position by presenting evidence about the consultant’s interests and potential limitations to the design of that device (APT-F: Challenge).