In Study 2, semi-structured interviews were used to gather data about the cues teachers use to judge their students’ learning, and then to see how they align with fixed and growth mindsets. As the results of Study 1 linked judgment accuracy to teacher mindset, in this study interviews were conducted with teachers holding a fixed mindset and teachers holding a growth mindset. The researcher was blind to each interviewee’s mindset to avoid bias and contamination. The purpose of Study 2 was to bring the correlation data of Study 1 to life through interviews examining the cues those teachers with fixed and growth mindsets use to make their predictions of students’ academic performance.
Setting
The researcher interviewed 10 teachers in the privacy of their own classrooms at their school. Both schools were public elementary schools (Kindergarten – 5th grade), with a diverse population of students, and located in a suburban school district in the Mountain West region of the United States.
Participants
Based on the data from Study 1, a total of ten (10) elementary teachers were deliberately selected for a semi-structured interview of Study 2. These 10 teacher
participants had participated in the ITML research study and were assigned to one of four professional development conditions described in Chapter 3, and whose data were
included in Study 1 above. This group of participants came specifically from the mathematical instruction professional development called Developing Mathematical Thinking (DMT) (Brendefur, 2008). To avoid confounding mindset and ITML conditions, teachers were selected from the same group. That is, all the participant teachers had been assigned to the same professional development group for the ITML project; thus, differences in self-reported cue use could not be attributed to being in different ITML groups receiving different professional development.
Due to the fact that all of the teacher participants had been part of the DMT-only group, it is important to note that mean judgment accuracy was greater for the DMT-only group and the DMT-Formative Assessment group than for the other groups in Year 1 of the ITML project (Thiede et al., 2015), and this pattern also held in Year 2. Therefore, the participating teachers in Study 2 had higher judgment accuracy than the FA-only and control groups, although judgment accuracy had adequate variability in this group. Judgment accuracy for math skills ranged from .16 to .95. Judgment accuracy for conceptual understanding ranged from .12 to .83. Growth and fixed mindset scores also had variability for this group.
These ten teachers participated in semi-structured interviews about the cues they used to judge student learning. Because judgment accuracy was related to growth and fixed mindset (see Study 1), these ten teachers were chosen as the highest GM and highest FM. As previously mentioned, their judgment accuracy for math skills and for conceptual understanding ranged, allowing for the analysis of how the cues usage relates to mindset and accuracy. This selection procedure created groups that were significantly different on all four measures, all ts > 3.3, ps < .001. Accordingly, teachers who differed
on both judgment accuracy and mindset were interviewed. These 10 teachers included 2 males and 8 females, and ranged in age and teaching experience (from 6 – 19 years). The group was comprised of three Kindergarten teachers, one 1st grade teacher, three 2nd grade teachers, one 3rd grade teacher, one 4th grade teacher, and one 5th grade teacher. See Table 5.1 for participants’ demographics.
Table 5.1
Teachers’ Demographics
Participants:
Teacher # Years Teaching Sex
Carter 20 M Katrina 11 F Hanna 10 F Nina 19 F Janet 6 F Karen 16 F Cathy 17 F Annie 5 F Mark 18 M Bridgette 15 F
Research Design and Approach
Through inductive qualitative analysis of ten (10) semi-structured interviews, the purposes of Study 2 were, (1) to examine the cues teachers use to make their predictions of students’ academic performance, and (2) to explore whether the teachers’ mindset influences the cues used. These teachers had already been identified and categorized as having either a growth or a fixed mindset through the administration of the Mindset Survey in 2013. However, they took the Mindset Survey again before their interviews of Study 2 in the spring of 2015 to detect any changes in their mindsets.
The qualitative data collected from these semi-structured interviews were
teachers with fixed and growth mindsets use to make their predictions of students’ academic performance. A semi-structured interview was chosen as the ideal interview format for numerous reasons. Compared to a completely structured interview with standardized questions and a protocol that has to be followed consistently with each interviewee (respondent), a semi-structured interview allows for more open-ended and depth-probing investigations of the research topic (Glesne, 2011) through the use of a framework of guiding questions. On the opposite side of the design continuum, a completely free form, unstructured interview without guiding questions exposes the risk of not eliciting the themes more closely connected to the research question under investigation (Rabionet, 2011). Because this study knowingly probed into a possibly sensitive and/or self-incriminating topic—the bases of teacher predictions—the format of a semi-structured interview was also deliberately chosen because it helps reduce the risk of socially desirable answers through its interactive and rapport-building qualities (Patton, 1990, as cited by Barriball & While, 1994). The format of a semi-structured interview compliments the general inductive approach used for its analysis.
A general inductive approach (Thomas, 2006) was chosen as the optimal method of analysis because the semi-structured interviews were driven by specific evaluation objective—in this case to investigate the cues that teachers with fixed and growth mindsets use to make their predictions of students’ academic performance. By
implementing this inductive approach, themes emerged from the interpretations of this raw data, and the connections between the specific research objectives—to investigate the cues that teachers with fixed and growth mindsets use to make their predictions—and the findings from the interviews became transparent and defensible. The qualitative evidence
found in the text data allowed for the theory about the underlying links between teachers’ mindsets, judgment accuracy, and cues used to form their predictions.
As mentioned previously, the researcher was blind to the teachers’ mindsets before and during the interviews. This design was purposefully used to mitigate any threats to validity from experimenter/researcher bias, and to allow for the deliberate search of potentially disconfirming evidence while in the classroom setting (Erickson, 1990). Especially because the semi-structured interviews and their analysis through an inductive approach were both driven by the specific research objective—uncovering what cues teachers use to make predictions of their students’ performance, and seeing if this aligns with growth and fixed mindsets—it was very important to avoid looking only for evidence that supports this objective. With semi-structured interview questions, research categories exist behind each question. Therefore, both sides of the question needed to be examined for confirming and disconfirming evidence, because every good interview question has a hypothesis and/or “reasonable answer” behind it (Wolcott, 2008, p. 75).
Measures
To triangulate the mindset categorization process and the correlation between teacher’s mindset and judgment accuracy, the researcher—who was blind to the mindset of each teacher—conducted semi-structured interviews to uncover the cues teachers use to make their predictions. Revealing these cues could then possibly examine the
correlation between mindset and judgment accuracy.
Qualitative semi-structured interviews were chosen as the optimal way to elicit and investigate the cues that teachers with fixed and growth mindsets use to make their predictions of students’ academic performance. Semi-structured interviews allow for
more open-ended and depth-probing investigations of the research topic (Glesne, 2011) through the use of a framework of guiding questions that focus on the main research question under investigation. Pilot interviews (Griffee, 2005; Glesne, 2011) took place with respondents drawn from a group of teachers to authenticate the pilot interview process. Piloting the questions on members of the actual group this study investigates— teachers—clearly informed the interview protocol used in the final interviews. These pilot interviews not only allowed for the rehearsal of the questions, but also the critical feedback and reflection on behalf of the respondents and the researcher as to the usability of the interview questions (Griffee, 2005; Glesne, 2011).
Data Collection Procedure and Time Line
Before the qualitative semi-structured interviews in the spring of 2015, one of the directors of the ITML project, who had worked with these teachers the year before, made the initial contact with the two schools’ principals and the ten teachers, to explain the purpose of the research project and to verify their willingness to partake in the interviews. After IRB consent and approval from the principals and teachers, the researcher
contacted each teacher directly to schedule the interviews.
Prior to the interviews, these 10 teachers completed the Mindset Survey again to compare their scores from 2013 and note any changes to their mindsets. Teachers were emailed a pdf of the survey so they could print it out themselves and complete it before the interview. The researcher then entered their responses in an Excel spreadsheet and then in SPSS after the interviews were completed. The researcher was blind to these 10 teachers’ mindsets as identified both in 2013 and in 2015. The researcher did not know
the interviewee’s mindsets until after the completion of the interviews and the initial data analysis.
Data collection on the bases of teacher predictions was through semi-structured interviews that took place in the teachers’ classrooms during regular school hours in each teacher’s classroom, and lasted about 30 minutes each. With a class roster listing each student’s name and a copy of the ITML math assessment in front of them, teachers predicted how each of their students would score on this 10-item assessment of
mathematics skills and concepts. After these predictions were made, teachers were asked to respond to questions pertaining to their predictions of their students’ future
performance, and the cues they used to make these predictions. When looking at the teacher’s predictions, the researcher then probed the teachers further by asking them to explain any large discrepancies in scores and to elaborate on why they predicted one student would correctly answer 5-out-of-5 on the skills section, when they predicted another student would correctly answer 0-out-of-5, for example.
Threats to Internal Validity
With semi-structured interviews, it is important to mitigate threats to validity. Therefore, an informed and critical colleague was consulted to verify and validate the plausibility of the interview data (Griffee, 2005; Glesne, 2011; Miles, Huberman, & Saldaña, 2013). This critical colleague looked at the interview data, its coding, summary, and interpretation, to verify the path from data to interpretation; this colleague verified whether plausible conclusions were drawn from the interview data; and this colleague validated that an alternative interpretation could not be drawn based on the same
evidence (Griffee, 2005). This validation of interview data from an informed and critical colleague helped mitigate the threats to internal validity.
Data Analysis
Semi-structured interviews were used to examine the qualitative Research Questions of Study 2—What are the cues that teachers use to make their predictions of students’ academic performance, and does the teacher’s mindset influence these cues? Ten teachers were interviewed after making predictions of their students’ expected performance on a math assessment of skills and concepts. Under investigation were the reasons they gave their students the predicted scores that they did.
Immediately following the completion of the interviews, the qualitative data collected from these semi-structured interviews were analyzed using a general inductive approach (Thomas, 2006) to investigate the cues that teachers with fixed and growth mindsets use to make their predictions of students’ academic performance. A general inductive approach was chosen as the optimal method of analysis because of its “efficient and defendable procedures for analyzing qualitative data” (Thomas, 2006, p. 237). By using an inductive approach, the analysis of the semi-structured interviews was driven by the study’s evaluation objective—to investigate the cues that teachers with fixed and growth mindsets use to make their predictions of students’ academic performance.
Therefore, by implementing this inductive approach, the cues teachers use to make their predictions of students’ academic performance could be thoroughly
investigated. The connections between this specific research objective and the findings from the interviews became transparent and defensible. The qualitative evidence found in the text data allowed for the theory about the underlying links between teachers’
mindsets, judgment accuracy, and the cues they used to form their predictions, as described in more detail in Chapter 6.
Ethical Considerations
Approval to conduct this research was given by the Boise State University IRB (Approval Number: 101-SB15-037) and the schools’ principals and teachers. Because only the teachers were interviewed, Study 2 did not use a vulnerable or protected population. To protect the participants from pressure to participate as well as from privacy threats, the participants in this study were allowed to withdraw at any time, and they each signed Consent Forms documenting their consent to participate. Moreover, all of the data were coded allowing for any and all name identifiers to be removed from the data. All data were kept confidential and stored in a password protected electronic file or in a locked office. Lastly, all teachers participating in Study 2 received a thank you gift card of $50.
Summary
In summary, this mixed-methods study used correlational analysis for Study 1, and qualitative semi-structured interviews for Study 2. Data used for Study 1 included the identification and categorization of teachers with either a growth or a fixed mindset, and then computed teachers’ judgment accuracy (operationalized by computing the intra- individual correlation between students’ predicted score and their actual performance on the tests of mathematical skills and concepts). A correlation was then run to investigate the existence of a correlation between teachers’ judgment accuracy and their mindsets. This research study utilized quantitative data collected over the fall and spring semesters of the 2013/2014 school year, and qualitative data collected through interviews
during the spring semester of 2015 in a suburban school district in the Mountain West region of the United States. Study 2’s interviews utilized stratified convenience sampling, and the sample obtained was 10 Elementary (Kindergarten – 5th grade) schoolteachers of various ages, with varying years in teaching experience, and containing both females and males.
The purpose of this mixed-methods study was two-fold. The first quantitative study (Study 1) examined data to investigate if a correlation existed between teachers’ mindsets (growth and fixed) and their ability to accurately predict students’ academic performance. The second qualitative study (Study 2) explored the cues that teachers use to make their predictions of students’ academic performance, and to see if their fixed or growth mindset influenced these cues. The researcher remained blind to these teachers’ mindsets until after both the interviews and the preliminary data analysis. The next section, Chapter 6, details the results of this study.