Critical Algorithmic Literacy: Bridging Computer Science and Critical Media Literacy in the Elementary Classroom

pi_name

Scott H. Moss

pi_email

scottmoss@ucla.edu

pi_phone

(858) 361-8212

pi_department

UCLA School of Education and Information Studies

pi_title

Doctoral Student

ucla_faculty_sponsor

Nicole Mancevice

other_key_personnel

Cristina Cueblos- Participating Teacher

abstract

Algorithm-driven technologies are transforming our world. Digital algorithms possess the epistemological algorithmic authority to shape and reinforce and limit society by determining what we see and what we do not. While some public and private efforts exist to mitigate the harmful effects of these powerful digital technologies, few measures exist to prepare K-12 students for the algorithm-driven world in which they live. This study describes the implementation of the Critical Algorithmic Literacy (CAL) framework. Using Kellner & Share’s critical media literacy (CML) framework to frame Critical Algorithmic Literacy, this research examines promising practices and challenges to CAL implementation. The study also will examine how students evaluate, challenge, and reconstruct algorithmically-driven media.

project_summary

The purposes of the proposed study are twofold: 1) to understand Critical Algorithmic Literacy instructional practices and challenges from the perspective of the teachers as they work to teach as part of the core curriculum, and 2) to portray how those students understand how digital algorithms affect their lives and the lives of others. This dissertation will draw on the Critical Media Literacy (CML) framework for introducing Critical Algorithmic Literacy (CAL) instruction and projects in one fourth-grade classroom. This single-case study will describe the perceptions of students and two educators participating in Critical Algorithmic Literacy lessons embedded within English, Math, Social Studies, and Science.

goals

This case study will explore the following research questions:
1. How do students demonstrate and apply Critical Algorithmic Literacy knowledge and skills in the classroom?
2. What do teachers perceive as promising practices when implementing Critical Algorithmic Literacy in the core curriculum?
3. What do teachers perceive as challenges when implementing Critical Algorithmic Literacy in the core curriculum?
4. How did the teacher-researcher collaboration contribute to the integration of CAL into Math and ELA activities?

benefits_of_research

Students’ interactions with digital algorithms will profoundly affect them throughout their lives (Dignum et al., 2020). As these algorithmic systems have wide-reaching consequences for children's lives, we must determine how K-12 education can best expand the notion of literacy to include CAL as a component of traditional and media literacy (Valtonen, 2019). Engaging in classroom case studies informs the integration of real-life examples into core content areas in K-12 curricula. If we see algorithmic systems as only the purview of computer science, we miss these opportunities (Ciccone, 2021, p.4). By exploring the experiences and perceptions of teachers and students, I seek to help contribute to the body of knowledge that will inform future CAL and CML efforts in grades 4-6 to foster critical algorithmic literacies in young people.

By examining the student and teacher perceptions, I seek to describe promising practices and challenges for teachers seeking to implement Critical Algorithmic Literacy in grade 4. The need for these skills is particularly true for younger children who are beginning to interact with social media. In addition, schools and teachers can help students on their journey to be more critical consumers and producers of various media. More generally, enhanced Algorithmic Literacy and media literacy skills help improve student critical thinking skills in other real-world contexts.

In general, this study will add to the body of work that seeks to inform the quality of media literacy education. Because media literacy education aims to develop critical thinking beyond the classroom, this study could form a part of a body of work regarding media literacy for teacher and curricular development. Helping children become more media literate in elementary grades has personal and societal consequences as algorithmic literacy and media literacy establish a foundation for children's media literacy throughout their lives. By exploring the experiences and perceptions of fourth and sixth graders, I seek to inform future CML and CAL efforts in grade 4.

dissemination/publications

Doctoral Dissertaion for UCLA Educational Leadership Program

numer_of_subjects

30

selection_criteria

An elementary school teacher is willing to implement CAL lessons.
The teacher has a social justice focus.

The UCLA Lab Elementary School emphasizes a constructivist approach to organizing instruction that supports students’ inquiry, exploration, and play. Teachers at The UCLA Lab Schools design and write the curricula. The school focuses on interdisciplinary, authentic learning to empower student agency to affect change in the world. Consequently, The UCLA Lab School represents a purposeful sample of a best-case classroom likely to engage in CAL constructs in daily practice. First, the school culture that supports student and teacher risk-taking, which, as discussed earlier, prevents many teachers from engaging in any critical pedagogy (Ciccone, 2021). The UCLA Lab School promotes authentic learning, which supports CAL’s focus on real-world content and personally relevant student projects. The school’s inquiry-based learning environments support CAL implementations embedded within the core curriculum. As such, The Lab School’s philosophy supports the tenets of CAL.

I selected a fourth-grade classroom to ascertain how younger students learn and demonstrate CAL skills and knowledge. In today’s algorithmically-saturated world, children of all ages are flooded with media messages that contain a wide range of unfiltered content. Therefore, elementary school students should have opportunities to develop critical algorithmic literacy to engage with and construct media messages in an informed way. Some educators underestimate young children’s capacity to “contribute to their own subjectivity” (Steinberg and Kinchloe, 2004, p.7). Yet by having young students engage with a variety of popular media, they are empowered to examine and create media in authentic contexts (Share, 2015).

methods

My data collection methods will involve teacher interviews, classroom observations, and artifact analysis. The classroom observations and artifact analysis will directly address my first question (How does student engagement with Critical Algorithmic Literacy change over time?). These methods will be cross-referenced with teacher interviews and teacher-created artifacts such as planning documents and curricular materials. The teacher interviews will help explore Research Questions # 2 and #3 (How do specific design elements support students' critical algorithmic literacy learning?
and "What do the teacher and the researcher learn from incorporating Critical Algorithmic Literacy in core curricula?"). Classroom observations, student-created artifacts, and teacher-created documents will inform the teacher interviews.

I will observe every scheduled CAL-integrated lesson implemented over a 6-10-week timespan. There will be 8-16 lessons over the six weeks. The classroom observations help explore all three of my research questions. Observing the students’ performance will help gain more information on how students demonstrate CAL competencies. Throughout the observations, I will also gather data on the teacher’s instructional practices pertaining to CAL implementation. My observations may include brief researcher-student interactions that will take the form of promotes pertaining to critical algorithmic literacy, such as, "How might someone else understand this media message differently than you do?"

I will document teacher behaviors, student-teacher interaction, interactions between students, and independent student behaviors throughout the observed lessons. In addition, I will observe the class three times before implementing the targeted lessons to ascertain the classroom culture, routines, and context. Each observation will last approximately 45 to 60 minutes.

The teacher interviews will take two forms. I will conduct three semi-structured interviews and multiple informal interviews following observed lessons based on teacher time availability. Overall, there will be three semi-structured interviews with the participating teacher: one before, one during, and one after the eight-week observation period. In addition to the semi-structured teacher interviews, I will conduct brief post-lesson unstructured interviews grounded in the observed events, behaviors, and instructional strategies that emerge from the observed lessons. These informal debriefs will provide the teacher’s timely perspectives concerning the research questions about promising practices and challenges of the CAL-integrated lessons. These conversations will last 5-10 minutes, depending on Ms. Sage’s (pseudonym) availability. These lesson debriefs will be audio-recorded and transcribed verbatim.

Artifact collection will consist of student work samples and teacher-created documents. The student artifacts will include reflection journals and final group projects. Both sets of student artifacts directly address the research question pertaining to how students engage with Critical Algorithmic Literacy. Students will be prompted to write in journals to reflect on each CAL-integrated lesson.

Data Analysis
I will analyze the data using a hybrid approach of inductive and deductive data analysis (Braun & Clarke, 2006). I will initially conduct theoretical analyses from data sources that suggest authentic connections to the critical media literacy framework. Codes, themes, and analyses will connect instructional practices, contextual factors, critical media literacy constructs, and student behaviors. I will also use a bottom-up, inductive approach to identify themes unrelated to the CML framework. The inductive provides a richer description as those data are not constrained by the pre-existing theories.

I will then complete the second round of thematic coding framed by the CML framework. During the coding process, particularly illustrative examples will be identified. Student recognition of algorithmic biases, for example, could form a theme based on the CML framework’s foci on bias and semiotics (Kellner & Share, 2019). I will prioritize and review the codes, categories, and themes based on relevant connections to the CML framework, as well as themes that emerge from the inductive analysis.

instruments

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instrument_explanations

Data Collection
My data collection methods will involve teacher interviews, classroom observations, and artifact analysis. The classroom observations, and artifact analysis, will directly address my first question (How do students demonstrate critical algorithmic literacy knowledge and skills in the classroom?). These methods will be cross-referenced with the teacher interviews and teacher-created artifacts such as planning documents and curricular materials. The teacher interviews will help explore Research Questions # 2 and #3 ((How do specific design elements support students' critical algorithmic literacy learning?
and "What do the teacher and the researcher learn from incorporating Critical Algorithmic Literacy in core curricula?"). The teacher interviews will be informed by classroom observations, student-created artifacts, and teacher-created documents.

In addition to supporting a rich description, comparing student, teacher, and researcher perceptions supports the internal validity of emerging findings by reducing researcher bias and reactivity through various techniques of analysis and triangulation (Maxwell, 2013; Merriam & Tisdell, 2016). The multiple data sources will inform and influence each other. Observed student behaviors and student work, for example, will provide corroborating or contradictory data for the artifact analysis. The observations can also provide a context for student behaviors that I will discuss with the teachers in subsequent interviews (Merriam & Tisdell, 2016).

The teacher interviews and teacher-created document data will similarly triangulate with the student-created artifacts, and classroom observation data to ascertain to what extent students achieved the intended goals. The connections between the data sources will address the research questions by providing a richer description from multiple perspectives. Further connections between data sources are described in the validity/reliability section of this chapter.

Classroom Observations
I will observe every scheduled CAL-integrated lesson implemented over a 6-10 week timespan. There will be 8 to 16 lessons over the 6-10 weeks. The classroom observations help explore all three of my research questions. Observing the students’ performance will help gain more information on how students demonstrate CAL competencies. The observations will also gather data on the teacher’s instructional practices pertaining to CAL implementation. Using field notes, I will document teacher behaviors, student-teacher interaction, interactions between students, and independent student behaviors throughout the observed lessons. In addition, I will observe the class three times before implementing the targeted lessons to ascertain the classroom culture, routines, and context. Each observation will last approximately 45 to 60 minutes.

My field notes will be framed by a classroom observation protocol (Appendix D) to describe the teacher’s and students’ behaviors that reflect the lessons’ objectives. This observation instrument uses a three-column structured format. The left column will contain objective open notes reporting what I observe in the classroom. The middle column will include my thoughts, reflections, and questions. In the third column, I will list potential themes for analysis. I will then create analytic memos (Saldaña, 2021) using a word processor. As collection occurs concurrently with the analysis, these memos will include reflections that connect to the research questions, descriptive summaries, and patterns emerging from the classroom observations.

Teacher Interviews
The teacher interviews will take two forms. I will conduct three semi-structured interviews and multiple informal interviews following observed lessons based on teacher time availability. The three formal, semi-structured interviews with Ms. Sage will use open-ended questions (Appendix C) so that I can engage in a more flexible exploration of issues (Merriam & Tisdell, 2016). In addition, I will provide the teacher with the interview questions a week before each interview. Ms. Sage will have the opportunity to seek clarification on any of the interview questions. The teacher interviews will get Ms. Sage’s interpretation of the lessons and allow me to explore emerging themes and respond to opportunities for richer data (Merriam & Tisdell, 2016).

Overall, there will be three semi-structured interviews with the participating teacher: one before, one during, and one after the eight-week observation period. As I am framing CAL as an expanded context of traditional literacy, in the first teacher interview, I will seek to understand Ms. Sage’s overall philosophical approach to literacy education and her thoughts regarding how she conceptualizes critical algorithmic literacy and her role in teaching it. The second interview will explore perceived successes and challenges roughly halfway through the observed lessons by asking questions such as “Please describe specific challenges, if any, you’ve had teaching critical algorithmic literacy to this point” (Appendix C). The third teacher interview will explore Ms. Sage’s perceptions at the conclusion of the observed CAL lessons. Again, I will seek to ascertain the teacher’s perspectives regarding the challenges, successes, and barriers to achieving their intended goals regarding CAL implementation with questions such as “What changes or adjustments would you have made to the CAL lessons?” In the final two interviews, I will ask Ms. Sage to describe the learning goals that motivated her instructional choices, comment on the efficacy of these instructional practices, and share her perceptions of how students understood and applied key critical algorithmic literacy concepts. Each semi-structured interview will last 30-45 minutes and occur in person in a private room in the teacher’s school.

In addition to the semi-structured teacher interviews, I will conduct brief post-lesson unstructured interviews grounded in the observed events, behaviors, and instructional strategies that emerge from the observed lessons. These informal debriefs will provide the teacher’s timely perspectives concerning the research questions about promising practices and challenges of the CAL-integrated lessons. These conversations will last 5-10 minutes, depending on Ms. Sage’s availability. These lesson debriefs will be audio-recorded and transcribed verbatim.
I will record the debriefs and semi-structured interviews using the Rev app for iPhone and, simultaneously, with Zoom for the Macintosh computer. Both applications are accurate and have transcribing capabilities. I will check all interview transcripts for accuracy as the transcription occurs. As a member check, I will email the reviewed transcripts to the teacher so that she may verify their accuracy (Merriam & Tisdell, 2016).

Artifact Collection
Artifact collection will consist of student work samples and teacher-created documents. The student artifacts will include reflection journals and final group projects. Both sets of student artifacts directly address the research question pertaining to how students demonstrate CAL competencies. Students will be prompted to write in journals to reflect on each of the CAL-integrated lessons. In addition to being analyzed for how they address student CAL competencies, the journal entries may also be useful touchstones when students participate in the focus groups. With students’ permission, I will digitally duplicate their paper or word-processed journal entries. These digital reproductions will be anonymized and stored on a password-protected computer and uploaded to cloud storage.
Throughout the CAL lessons, students will work in groups of 3-4 on a culminating project intended to demonstrate knowledge and skills aligned with CAL. The final projects may consist of simple computer programs, oral presentations, program or webpage mock-ups, posters, or videos. I will record or reproduce each group project. These student projects reflect student demonstration of CAL competencies and will inform the student focus groups and final semi-structured teacher interviews. I will conduct audio recordings of student oral presentations. Other student projects will be photographed or duplicated in their respective formats, such as posters, videos, etc.
I will also collect teacher-created documents. These documents include instructional materials and planning documents to facilitate the CAL-integrated lessons. The teacher-created artifacts serve to inform classroom observations, student focus groups, and teacher interviews. During the structured and unstructured interviews, the teacher may refer to both teacher and student-created artifacts to address the research questions about perceived successes and challenges within CAL lessons. The teacher-created instructional materials and planning documents will also inform the classroom observations regarding how the actual instruction compared to what was planned. Comparison between the lesson objectives and observed behavior will frame the analysis and lead to teacher reflections regarding promising practices and challenges, directly addressing the first research question. Both teacher and student-created artifacts will be stored on a non-identifiable laptop, on the researcher’s password-protected Google Drive, and on an external hard drive.


Data Analysis
I will analyze the data using a hybrid approach of inductive and deductive data analysis (Braun & Clarke, 2006). I will initially conduct theoretical analyses from data sources that suggest authentic connections to the critical media literacy framework (Kellner & Share, 201). Codes, themes, and analyses will connect instructional practices, contextual factors, critical media literacy constructs (Kellner and Share, 2019), and student behaviors. This deductive analysis method will provide a more detailed analysis tied to my research questions (Braun & Clarke, 2006). I will also use a bottom-up, inductive approach to identify themes not tied to the CML framework. The inductive provides a richer description as those data are not constrained by the pre-existing theories (Braun & Clarke, 2006).

I will then complete the second round of thematic coding framed by the CML framework. During the coding process, particularly illustrative examples will be identified. Student recognition of algorithmic biases, for example, could form a theme based on the CML framework’s foci on bias and semiotics (Kellner & Share, 2019). I will prioritize and review the codes, categories, and themes based on relevant connections to the CML framework, as well as themes that emerge from the inductive analysis.

Classroom Observations Analysis
The observational data analysis will examine teacher and student behaviors as participants in these CAL-integrated lessons. I will examine interactions between students, student-teacher interaction, independent student behaviors, as well as the teacher’s behaviors throughout the observed lessons. The teacher behaviors involving CAL may include explaining, modeling, posing questions, summarizing, and supporting other students. Initially, I will use deductive coding to connect the observed student and teacher behaviors to the CML framework’s conceptual understandings. If a student, for example, describes how their online behavior influences a platform’s recommendations, that might be coded on the Human-Algorithm Interplay dimension of the Productions/Institutions aspect of the CML framework. Analysis of student and teacher behaviors will be cross-referenced with student work, and teacher-created artifacts interview transcripts to illuminate how the student activities align with the lessons’ goals as they pertain to CAL.

Artifact Analysis
For the student-produced artifacts, the analysis will examine to what extent student work exhibits critical algorithmic literacy framed by Kellner and Share’s (2019) critical media literacy framework. The student work will illustrate the students’ attainment of the desired content and indicate where instruction might be improved. I will also use student work as an impetus for teachers’ reflections during the semi-structured interviews. Teacher reflections on student work may provide insight for teachers’ subsequent implementation efforts.

In addition to reflecting student CAL learning, these classroom artifacts will provide insight into teachers’ instructional decisions throughout each lesson. During the planning sessions, the teachers and I will co-construct project rubrics based on relevant aspects of the CAL and teacher-selected curricular goals. The rubrics and the teacher-created instructions will be used to examine student work on the targeted core standards, teacher-selected student Kellner and Share’s (2019) critical media literacy framework. The student work samples will provide an opportunity to learn how students understand and apply critical algorithmic literacy and the core curricular criteria. This artifact analysis will support the triangulation of the various data sources within the study (Merriam & Tisdell, 2016).

I will not analyze the teacher-created materials directly, as on their own, they do not address any of my research questions. The teacher-created artifacts, however, will serve multiple functions in the analysis of the classroom observations, teacher interviews, and student focus groups. The instructional materials and planning documents will inform the first research question by ascertaining to what extent students demonstrated the intended goals. The teacher artifacts will support analyses of the second and third research questions by functioning as a touchstone for the formal and informal teacher interviews.

Teacher Interviews Analysis
As with all analysis methods, interview data analysis will occur throughout data collection for the three formal interviews and the less formal lesson debriefs. The first step in the analysis will be to read through the interview transcripts to identify possible codes aligned with the CML framework. During this deductive phase of the coding process, particularly illustrative examples will be identified to connect instructional practices, contextual factors, critical media literacy constructs (Kellner and Share, 2019), and student behaviors. For the inductive coding, I will strive to approach the transcripts without preconceived notions to see what emerges (Seidman, 2019). Once all the interviews are coded, frequency counts for each code will be conducted. Such frequency counts will allow me to understand how representative each code was concerning the rest of the data set. I will also review the data for content that is less frequent yet more salient to my research questions (Clarke & Braun, 2013).

justification_of_methods

I will conduct a qualitative single-case study examining student participation, instructional opportunities, and instructional challenges related to CAL lessons in one fourth-grade classroom. A single qualitative case study will support an in-depth examination of student and teacher behaviors and perceptions as participants in critical algorithmic literacy lessons (Creswell & Creswell, 2017). The use of the case study approach aligns with my inquiry as I seek an in-depth description of student and teacher behaviors in an authentic context. (Yin, 2009, p. 18). A qualitative case study also supports my research questions as the classroom observation, teacher interviews, and artifact analysis allow me to conduct a “richly descriptive” case analysis (Merriam, 2016, p.37). Exploring CAL implementations and uncovering promising practices will inform future CAL implementation efforts.

This qualitative single-case study will provide a thick description that addresses my research questions. I will begin by co-planning CAL-integrated lessons with a fourth-grade teacher. Over a period of 6-8 weeks, I will conduct classroom observations of the CAL-integrated lessons. During this time, I will conduct a variety of semi-structured and unstructured teacher interviews. The data collection will conclude with an analysis of student work centered on learner perceptions and learnings during these CAL-integrated lessons.

A quantitative study would not capture the breadth, depth, and complexity that will occur with multiple data collection sources used in this qualitative research. Quantitative research would also not reveal the unique student behaviors and teacher perspectives possible in a qualitative case study. In addition, a quantitative study would not offer the flexibility of design possible with qualitative methods (Maxwell, 2013). Finally, my research questions require more process-focused qualitative work than outcome-focus of quantitative methods (Creswell & Creswell, 2020).

risk_minimization

As a classroom visitor, it is essential that the students and teachers feel as comfortable as possible. Therefore, I will carefully position myself first as a UCLA graduate student exploring a new topic to improve and “modernize” K-12 education. I will then position myself as a former third or sixth-grade teacher (I have taught both grades). The positionality of the researcher and the teachers exert considerable impact on this case study. As mentioned earlier, both teachers have completed a masters-level Critical Media Literacy course as part of their teacher education programs. In addition to completing this course, the participating educators have demonstrated a commitment to implementing CML with their students. These teachers also continue to meet with the instructor from their CML course and other educators committed to the meaningful application of CML in their classrooms.

As the researcher-practitioner, I will work with both teachers to develop the curriculum for the integrated CAL lessons. Throughout my thirty-year career, I’ve worked to integrate educational technology with core curricula with teachers at all levels and for myself. I also taught and was a proponent of block-based coding with students in grades 3-8 for 20 years. As a classroom teacher, my students engaged in media production such as videos, animations, and 3D modeling, but these production efforts lacked critical perspective. With these backgrounds, we position ourselves as passionate advocates and practitioners of critical pedagogies in a modern context.
Ethical Issues

I do not anticipate any ethical issues arising out of this study. I will not collect any personally identifying information about participating students. Teachers and students will be reminded of the confidentiality of their involvement in the study. Both school sites will be anonymous, and I will use pseudonyms for all teachers and students. I will store all research data on password-protected devices. Upon completing my dissertation, I will provide the teachers and the district with a copy and delete all other data.

confidentiality_data_storage

All student and teacher artifacts will be anonymized and stored on a password-protected computer, backed on a remote hard drive, and uploaded to password-protected cloud storage.

relationship_prior_contact

Met with Sandra Smith and Cristina Cubelos on June 10, 2022
Met with Cristina Cubelos On Zoom on August 25, 2022 (One hour)
Met with Cristina Cubelos On Zoom on September 2, 2022 (30 minutes)
Met with Sandra Smith On Zoom on September 2, 2022 (One hour)
Informal observation of Cristina Cubelos' classroom on September 23, 2022 (Two hours)

teachers_staff_consent

Ms. Cubelos agreed to participate in this case study on June 10, 2022.

ucla_lab_school_personnel_involved

Dr, Sandra Smith
Ms. Cristina Cubelos

academic_topic

Math, Art, and Social Studies- expanded to include a critical view of algorithmic-driven media and artificial intelligence.

estimated_start_date

20221015

estimated_end_date

20230131

attachments

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