Overview
Phase Four requires students to step into the role of researchers through active data collection and analysis. This phase often provokes anxiety in students as they realize that the stakes are real and that collecting and analyzing data require much more ownership and thought than “traditional” school work, like completing a worksheet or memorizing vocabulary. By learning in the world--interacting with peers, engaging adults in positions of power--students do not usually think of themselves as immediately capable of engaging in these activities typically framed as belonging to “adults” or “real researchers.” Consequently, students will need a lot of modeling, support, check-ins, and reflection.
Collecting data
In a school context, systems for project management are key. Developing checklists, calendars, rubrics, models and systems for sharing documents will be useful. Before collecting data, teachers need to help students think logistically about data collecting. For example, will the teacher need to show a student survey to an administrator before sending it out to the school? How will the teacher support students in gaining buy-in and permission from other teachers to access their classrooms in order to conduct a focus group? If students are to collect information at lunch, what policy requirements must be met for them to be able to do so? Since students and teachers need to ensure assent, do students have ample copies of assent forms for their particular target audience?
To adequately prepare for and reduce student anxiety before taking these activities outside of the classroom, we suggest conducting one or more data collection practice runs. For example, you can pair one group of researchers with another so that each researcher or research group can (a) rehearse what they will say; (b) identify kinks in data tools, e.g., confusing wording in surveys or ineffective prompts; (c) identify potential breakdowns,, e.g., communicating effectively during dialogue; (d) identify ripe moments for follow-up questions, e.g., during interviews; (e) practice presenting and collecting assent forms and ensuring participant anonymity; and (f) practice how to respond to participants during difficult conversations; e.g., requiring participants to refer to systems, not people, particularly when collecting data about their own school. These practice runs not only build student confidence and thwart potential breakdowns, but also ensure quality, which will reinforce student confidence as well as their ability to handle unforeseen breakdowns.
During data collection, it may be anxiety-producing for some teachers to let students go into other classrooms as researchers. Though it is important to adequately prepare students for this task, is equally important to trust your students and allow them to make mistakes in the quest of learning. Researchers’ data collection is not perfect. This is why there are “limitation” sections in peer-reviewed journals that address the limits and complications of collecting and analyzing data. Immediately after students collect data is when they should reflect on what went well and what could have gone better in their data collecting. The following methods are helpful when facilitating data collection:
To adequately prepare for and reduce student anxiety before taking these activities outside of the classroom, we suggest conducting one or more data collection practice runs. For example, you can pair one group of researchers with another so that each researcher or research group can (a) rehearse what they will say; (b) identify kinks in data tools, e.g., confusing wording in surveys or ineffective prompts; (c) identify potential breakdowns,, e.g., communicating effectively during dialogue; (d) identify ripe moments for follow-up questions, e.g., during interviews; (e) practice presenting and collecting assent forms and ensuring participant anonymity; and (f) practice how to respond to participants during difficult conversations; e.g., requiring participants to refer to systems, not people, particularly when collecting data about their own school. These practice runs not only build student confidence and thwart potential breakdowns, but also ensure quality, which will reinforce student confidence as well as their ability to handle unforeseen breakdowns.
During data collection, it may be anxiety-producing for some teachers to let students go into other classrooms as researchers. Though it is important to adequately prepare students for this task, is equally important to trust your students and allow them to make mistakes in the quest of learning. Researchers’ data collection is not perfect. This is why there are “limitation” sections in peer-reviewed journals that address the limits and complications of collecting and analyzing data. Immediately after students collect data is when they should reflect on what went well and what could have gone better in their data collecting. The following methods are helpful when facilitating data collection:
Data Collecting Calendar, Check-Ins, Celebrations
Use a class calendar (we created and publicly posted a poster-sized student-friendly version of this teacher-use calendar) to document when each group will conduct their research. Help students plan backwards to make sure they are ready for the day they collect data. Check-in with students regularly; see this link for a check-in structure we have used. If desired, plan a celebration for groups after completion.
Data Collection Forms
Ensure that students have copies of research consent forms to give to participants and that they can verbally explain why these consent forms are necessary. Click here for a sample participant assent form, and click here for a more detailed consent form sample. Though assent forms are adequate for most student projects, the consent form might be used if the data being collected requires parental consent or for projects that are more complex or high-stakes.
Reflection
Teachers should engage students in reflecting on their data collection process by asking in written or verbal forms: (1) What did you do well? How do you know? (2) What would you do differently next time? Why? (3) Do you have to conduct more rounds of data collection? Why or why not? and (4) What have you learned about the data collection process? What advice would you give next year’s students? Reflecting on the process empowers students to build on strengths, learn from mistakes, and internalize effective data collection procedures.
Analyzing Data
After reflecting on data collection, it is time to analyze the data. Analyzing data requires students to (1) revisit their research questions to be rooted in what answers are trying to find; (2) read through their findings (survey, interview notes, fieldnotes etc.) for patterns and themes, and (3) organize findings based on themes. We suggest giving students this example to see how clustering and coding data leads to the writing of findings (also known as claims).
The following methods can be used to analyze data and are listed alphabetically since there is no required sequencing. Instead, you should decide whether you will teach one method explicitly for all students to use or whether you will allow students to choose after providing an overview for each method and then implementing structures, like seminar rotations or mentor-leaders, to facilitate that particular data analysis method.
The following methods can be used to analyze data and are listed alphabetically since there is no required sequencing. Instead, you should decide whether you will teach one method explicitly for all students to use or whether you will allow students to choose after providing an overview for each method and then implementing structures, like seminar rotations or mentor-leaders, to facilitate that particular data analysis method.
Coding Data Via Clustering
Use index cards to collect data or make physical copies of data collected on paper and cut the responses out with scissors. Spread all responses across a table, student desks, or the floor. Have students move data around and organize in clusters. This picture and this picture illustrates a clustering process with index cards, and this picture illustrates a clustering process with cut-up paper responses. You could do this digitally by creating charts, outlines, slides, etc.
Coding Data via Color Coding
Make physical or digital copies of the data collected. Have students conduct an initial read through to reveal preliminary themes, name the themes, and then assign a color to each theme. Instruct students to color code responses for each theme by highlighting that response with the corresponding color. This can also be done by creating tables in a Google doc. See an example of that on page two of this document.
Revising Categories
As students code, they will need to engage in a recursive process; preliminary themes can often be consolidated, which requires students to examine and categorize the codes several times.
Generating Numeric Representation for Themes
Most forms of qualitative data can be represented as a percentage by dividing the number of similar responses in the code by the total number of participants. However, this requires a review of the required math, and a discussion about quality representation with percentages or numbers. For example, codes that have responses from multiple questions require a slightly different formula that reflects the total number of similar responses divided by the total number of participants for each question; otherwise, the numbers will not properly represent the data. Another way to represent data is to say, “Five out of ten participants said…” Your approach will depend upon your comfort level with teaching math in your classroom. See a model here.
Writing Claims
Once patterns emerge, students will write claims based on each theme that relates and responds to their larger research questions. For example, imagine a group of students are researching the question, “How does school fencing affect students?” To gather data, they ask peers in a focus group, “How do you feel about school fencing?” While analyzing for themes, students notice that the data sets includes repeated words like “cage” and “prison.” Therefore, these students could write a claim stating: “School fencing makes students feel trapped.”
Students then develop arguments for these claims by answering the research question based on this data set and supporting with evidence from academic readings and data themes. This data analysis process allows students to make clearer connections between evidence and claims; as they analyze the data, they also make connections to their readings. Further, as students write claims, they are empowered to make connections to their own experiences. As they engage in thoughtful dialectic between evidence gleaned from their own experiences, their participants’ experiences from the data themes, and the various analyses of the problem through their readings, students engage in reading the word and the world much more deeply than before.
We have found it useful in Humanities Amped, as claims emerge, to engage students in open-ended writing and/or dialogue to scaffold the claim writing process. Since our students have more often than not experienced the “deskilling” associated with Ladson-Billing’s (2014) description of “death in the classroom” (77), they often need to step back from the “academic” writing and discussion to engage in some less formal, exploratory writing and discussion. Conducting open-ended writing or dialogue frequently throughout the process works particularly well to support students in forming an ownership of their expertise and their identities as researchers as well as in developing their capacities to draw conclusions and form valid analyses and arguments.
Students then develop arguments for these claims by answering the research question based on this data set and supporting with evidence from academic readings and data themes. This data analysis process allows students to make clearer connections between evidence and claims; as they analyze the data, they also make connections to their readings. Further, as students write claims, they are empowered to make connections to their own experiences. As they engage in thoughtful dialectic between evidence gleaned from their own experiences, their participants’ experiences from the data themes, and the various analyses of the problem through their readings, students engage in reading the word and the world much more deeply than before.
We have found it useful in Humanities Amped, as claims emerge, to engage students in open-ended writing and/or dialogue to scaffold the claim writing process. Since our students have more often than not experienced the “deskilling” associated with Ladson-Billing’s (2014) description of “death in the classroom” (77), they often need to step back from the “academic” writing and discussion to engage in some less formal, exploratory writing and discussion. Conducting open-ended writing or dialogue frequently throughout the process works particularly well to support students in forming an ownership of their expertise and their identities as researchers as well as in developing their capacities to draw conclusions and form valid analyses and arguments.
Open-Ended Writing and Dialogue
This process helps students think through connections between their experiences and their understanding of data and readings by allowing them to explore their thoughts without referring to specific evidence but instead responding to more open-ended prompts. The ideas recorded through this writing or dialogue will help students to more clearly see these connections when they revisit the evidence in the data and the readings.
Writing Claims
We have found it helpful to use graphic organizers to support students in making connections between their research question and evidence from their data and readings in order to write claims.
Writing claims usually requires a recursive process asking students to visit previous literature review entries. It also usually requires making observations as a teacher to determine which teams need new sources to support their project’s emerging focus. For example, if the students researching the fence did not have an article analyzing how physical structures specifically contribute to the school-to-prison pipeline, then the teacher would need structures to observe these needs, record them, and respond to them by providing a new reading. Therefore, writing quality claims can be a challenging task for students and teachers. However, this also yields excellent opportunities to involve mentors who can choose appropriate readings as the need emerges and guide students through the difficulties of drawing conclusions and committing to quality research.
Writing claims usually requires a recursive process asking students to visit previous literature review entries. It also usually requires making observations as a teacher to determine which teams need new sources to support their project’s emerging focus. For example, if the students researching the fence did not have an article analyzing how physical structures specifically contribute to the school-to-prison pipeline, then the teacher would need structures to observe these needs, record them, and respond to them by providing a new reading. Therefore, writing quality claims can be a challenging task for students and teachers. However, this also yields excellent opportunities to involve mentors who can choose appropriate readings as the need emerges and guide students through the difficulties of drawing conclusions and committing to quality research.
Mentor Support
Research mentors can assist through all parts of the research process. They can help on the day of data collection to add adult support, assist with coding, support students while writing claims, provide new texts to read and incorporate into projects, and/or engage in open-ended writing and dialogue at any point in the research process, but particularly while writing claims.
Former Humanities Amped student Kaiya Smith would say, “Listen to your research.” The data collection and analysis phase is challenging, time consuming, and if you let it, incredibly fun. It is also an opportunity for you to become a student of your students; observe the behaviors your students display as they engage with this challenging task that requires critical thinking and coming to terms with the fact that there is no singular “right” answer. As Kaiya reminds us, it is crucial to listen to the data, especially since deeply knowing what your findings are powerfully informs appropriate and effective action. However, we encourage teachers of problem-posing pedagogies to work through classroom challenges by listening to their research, too, namely by observing, responding to, and reflecting on the structures, activities, and levels of student success in order to hone your practice and take future action in your classroom.
Former Humanities Amped student Kaiya Smith would say, “Listen to your research.” The data collection and analysis phase is challenging, time consuming, and if you let it, incredibly fun. It is also an opportunity for you to become a student of your students; observe the behaviors your students display as they engage with this challenging task that requires critical thinking and coming to terms with the fact that there is no singular “right” answer. As Kaiya reminds us, it is crucial to listen to the data, especially since deeply knowing what your findings are powerfully informs appropriate and effective action. However, we encourage teachers of problem-posing pedagogies to work through classroom challenges by listening to their research, too, namely by observing, responding to, and reflecting on the structures, activities, and levels of student success in order to hone your practice and take future action in your classroom.