Phase One: Interviews with Professionals

Two cycles of interviews occurred over three years to determine best practices in DL learning within marine science and research vessel education. Forty-five interviews were conducted over the three years with professionals, including teachers, researchers, and professional development specialists. All professionals interviewed conduct work within the fields of DL, oceanography, and STEM learning.

The first cycle of interviews focused on identifying best practices in data literacy education through the perspectives of oceanographic researchers, high school teachers, and several professional development specialists. The second cycle of interviews expanded on the preliminary interviews by including DL experts, research vessel education specialists, and oceanographers whose work strongly incorporates justice, equity, diversity, and inclusion (JEDI). The second round of interviews examined DL education strategies that are inclusive, accessible, and equitable for minoritized populations. All interview participants were initially selected purposefully by identifying current experts in both DL and ship-based outreach and education (Yin, 2011). Recommendations and selections for additional people to interview were made by participants, resulting in the use of a Snowball Sampling protocol (Yin, 2011).

The design and analysis of the interviews operated under Grounded Theory. Grounded theory enables the researcher to identify key patterns or areas of interest through a process of constant comparison of data. Rather than beginning the research process with a specific hypothesis, it begins with a general understanding of a phenomenon. Initially, inductive reasoning generates codes from the data, which assists in the development of theory, identification of future data collection, and specifies important questions to ask (Walker & Myrick, 2006). Interviews were conducted via telephone or in person, lasted an average of 30 minutes to an hour, and were transcribed and open-coded using the qualitative analysis software MAXQDA. Open-coding refers to a reiterative review process that involves rereading transcripts and highlighting key trends, words, or phrases associated with the research objective (Khandkar, 2009).

Phase Two: Implementing Research Vessel Data-Based Curriculum in a High School Setting

Informed by the expert interviews and a literature review, the research team developed a three-day curriculum that utilized oceanographic data collected by research vessels. The curriculum was carefully designed based on promising practices identified by the first round of interviews. The data chosen for the lessons had undergone quality control and was simplified, was free of errors and missing information, and provided concrete examples of how data directly impacts student lives and experiences.

To test the effectiveness of the curriculum, two classes of Oregon high school science students were assigned to an experiential learning group (ELG), and another two were assigned to a passive learning group (PLG). According to the Association for Experiential Education website, “experiential education is a philosophy that informs many methodologies in which educators purposefully engage with learners in direct, hands-on experience and focused reflection in order to increase knowledge, develop skills, clarify values, and develop people’s capacity to contribute to their communities.” Experiential learning activities have been shown to increase student perception of value, enjoyment, performance on assignments, and enthusiasm (Hamer, 2000). Utilizing experiential learning activities can also assist in increasing the amount of information students retain, as students feel more connected with the course material. This can also result in students being more engaged in the classroom (Association for Experiential Education, n.d.; Guest, Lotze, & Wallace, 2015; Jelinek, 1998).

Both groups received the same introduction to oceanography and data lesson on day one. On day two, the ELG participated in a hands-on activity that required them to analyze, graph and interpret data and communicate results, whereas the PLG received a lecture on different types of data collection methods. On the final day, both groups participated in a discussion on the relevance and importance of oceanography and data to their daily lives. Students were surveyed using pre (one week before instruction), post (the last day of instruction), and post-post (six weeks after instruction) surveys to evaluate changes in their DL. Data literacy was measured on a continuous scale of 1) strongly disagree, 2) moderately disagree, 3) slightly disagree, 4) neither disagree or agree, 5) slightly agree, 6) moderately agree, 7) strongly agree, and 8) I don’t know (Likert, 1932). Survey data were then analyzed using the quantitative software Statistical Program for the Social Sciences (SPSS).

Phase Three: Further Testing with a Hands-On Afterschool Program for Latinx Students

To expand on lessons learned from the previous phase of the research, the last phase investigated the utility of hands-on learning over a more extended period as a strategy for building a student’s DL. The curriculum was structured into an eight-week afterschool program for Latinx students to examine promising practices in Culturally Responsive DL education, identified from the literature and expert interviews (Nolan, Miller, Crews & Conway, 2020). Curricular frameworks that fit within Dr. Gay’s theory were additionally applied during the curriculum development, namely CDL and the Cycle of Inquiry. Latinx students were selected as the target population, as the Latinx community in Oregon has been growing at a rate faster than the national average. The Latinx population now accounts for 23% of students enrolled in K-12 schools in the state and is the largest minoritized group in Oregon (Ruffenach et al., 2016). The afterschool program was brought to fruition with significant help from OSU’s Juntos program, an organization that empowers Latinx students in their education, and the Bilingual Education Coordinator at the Oregon school where it was piloted. The lead researcher for phase three is a cisgender white woman and took her positionality into account throughout the study.

The curriculum focused on community-centered and actionable data that connects to the ocean: microplastics in the student’s local watershed. Microplastics are an emergent issue that now permeates natural environments globally and with potential ill health effects on human beings (Manikkam et al., 2013). In this afterschool program, students developed research questions (Figure 3), built trawls to collect microplastics in their local creek, examined and sorted plastics under a microscope while filling out a datasheet, then analyzed and presented their data (Figure 4). Developed around the Cycle of Inquiry, this curriculum allowed students to take control of the scientific process.

The first tenet of CDL was applied by examining microplastics, an issue the students could learn more about and have the agency to take action on. The second tenet of providing context was implemented by reinforcing the importance of metadata and making sure students took metadata (date, time, location, etc.) throughout the program. Tenet three of CDL was fulfilled as the process of student-collected data is inherently messy, so students were able to fully realize the strengths and weaknesses of their data. Tenet four of CDL, utilizing learner-centered tools, refers to analysis and visualization tools. To fulfill tenet four, the online software TuvaLabs was utilized in the data analysis. This software is learner-friendly and does not require significant domain expertise. It was also made freely available for teachers during the COVID-19 pandemic for a limited time.

To fulfill the fifth tenet, valuing community-centered output, students were given multiple options for presenting their data in a framework of “community-centered output.” Students were introduced to data sculptures, data art, data music videos, and scientific posters. They worked as a group to determine which output they wanted to collaborate on together. Ultimately, the participating students decided they wanted to create a scientific poster together, in order to learn a skill that may be useful for them in college (Figure 4). After the data cycle was complete, they then came up with more hypotheses based on their results (Figure 4). In the final lesson, they were introduced to and explored large ocean databases.

In the pilot of the afterschool program, seven students from the Latinx community enrolled, and six regularly participated. Five of the six regularly participating students were recent immigrants to the United States, and one was U.S.-born. Four students were from Chile, one was from Peru, and the U.S.-born student’s parents were from Mexico. Spanish was the first language of all of the students, and all of them expressed a desire to be scientists or doctors when they grew up.

To determine the pilot program’s effectiveness, a qualitative study was utilized. Data included pre and post surveys and pre and post interviews. The researcher took reflective notes after each lesson ended. The program lasted longer than the originally planned eight weeks, to provide additional support to the students in the early weeks of the pandemic.

Phase One Results

The researchers determined several themes through the interviews, which informed the development of a plan for equitable and inclusive DL education supported by the RCRV Project. The complete list of recommendations is viewable in Promising Practices and Considerations for RCRV Outreach and Education: Lessons Learned from Research on How the Next Generation of Regional Class Research Vessels Can Support Equitable Data Literacy Education.

The interviews helped clarify the support needed for the successful transfer of marine science data between researchers and teachers. It also highlighted the fact that more research is needed on the effectiveness of utilizing data with the final stakeholders – students. Phase one of this research provided context on teacher needs to inform the design and implementation of phase two. After finishing data analysis in phase one, it was determined that for an effective DL curriculum, students require relevant, consistent, clearly formatted canned data in addition to real-time data, for the best potential impacts on student DL.

Phase Two Results

Ultimately, there was a lack of significant results linking the use of real data in an experiential learning setting to increased DL. However, the lack of significant results originating from the ELG does not necessarily negate a link between them. Instead, the results highlighted a set of underlying issues that must be addressed, including a lack of preexisting DL and limited instruction time.

Per the Next Generation Science Standards, students entering the ninth grade should have already built their foundation in graphing skills and basic data analysis (“Read the Standards|Next Generation Science Standards”). During the design process of the module, the participating teacher suggested expanding the module from two to three days, as they thought students would need additional time. While three days was originally considered to be a potentially adequate period of instructional time, once classroom instruction began, it was clear that the existing level of student comfort in graphing and interpreting results in ELG students was minimal. The lack of existing skills in these students caused a delay in the progression of activities in the learning module, resulting in the omission of the activity incorporating near real-time data, which may have affected post-survey results.

The majority of instructional time for the ELG was spent working with students on building basic skills such as graphing, reading sets of data, and identifying trends. The existing lack of DL skills in these students could indicate a lack of focus on developing DL skills at the middle school level – and highlights the challenges high school teachers often face when attempting to utilize data in their classrooms. Ideally, students need better preparation at each educational level, as well as extended, contextualized data-focused learning rather than one-off activities or a handful of days of learning (Andersen, Humlum, & Nandrup, 2016).

The identification of factors impacting the successful implementation of data-based, hands-on curriculum prompted further investigation in the next phase of the research. This final phase was designed to utilize previously collected information to further understand best practices for DL and determine promising strategies for creating culturally responsive programming.

Phase Three Results

All participating students entered the program with high-level DL for their age as determined by the pre-survey. The students all successfully interpreted a CTD (conductivity, temperature, depth) graph and growth in plastic production graph, and were able to successfully create their own graphs. After participating in the pre-survey and interviews the students then went through an eight-week curriculum. The eight-week program introduced them to the data process, microplastics, and their watershed. As the students came up with easily testable questions, they were able to confirm their hypotheses and found microplastics in their local creek. Interviews and surveys determined they emerged from the program understanding their one day of data collection was not enough to make firm conclusions. None of them had heard about microplastics before the program, and all ended the program feeling concerned about it. In the pre-survey, students were asked what their primary environmental concern was. All of them responded with climate change. The students were asked the same question in the post-survey and four changed their answer to plastic in the environment. At the end of the program, one student said he wanted to go to college to study microplastics and their chemical effects on humans.

Overall, the results indicated that the collaborative, hands-on program was a positive experience for the students. They enjoyed learning that science was collaborative and felt empowered after learning how to collect and analyze their own data. The students also expressed that they enjoyed learning about science with other Spanish first-language peers, as they could communicate about science in their first language.

Data Tool Design and Usability

Many of the teachers interviewed stated they prefer a website-focused approach to accessing ocean data and educational resources during their interviews. However, many said existing data portals are inaccessible and difficult to navigate. The interviews indicated data portals often take too much time to find, are poorly organized, lack usable information, and rarely offer additional user help. These features are necessary for data to be usable to teachers and their students. If programmers do not design data portals with teachers and students in mind, there is little chance the portal will be used in the classroom.

Lack of Existing Data Skills

The first round of curriculum testing in the classroom did not result in an improvement in student DL after completing the education intervention. However, it did highlight a set of underlying issues that need to be addressed, primarily a lack of existing DL in students.

Students struggled with basic graphing and mapping concepts, which meant the original timeline set for the activities was not adequate. Rather than rush students through the activity so they could move on to the extension using near-real-time data, it was decided that it would be more valuable to spend additional time solidifying the DL skills that the students lacked. Because of this, students did not work with near-real-time data and were given additional time to allow for individual and group processing, sense-making, and communicating their results. While the lack of significant results stemming from the ELG was somewhat unexpected, the implementation of this education module revealed the various challenges teachers expressed to the researchers during interviews: the lack of DL in students, large classroom sizes, the struggle to make data relevant and meet NGSS standards, while attempting to implement experiential learning techniques.

Results from the afterschool program showed that participating students were adept at creating and analyzing graphs but did not realize how much time and effort it takes for scientists to collect data and make decisions based on data. The students also struggled to understand the large ocean databases shown in the final lesson.

Single Interventions are Insufficient

During the first curriculum implementation, it was clear that instructional time must be longer than three days to accomplish the identified educational goals. As it was initially written, the module did not result in the robust learning necessary to increase DL and affected students’ positive attitudes and beliefs in their skills. Ideally, students need better preparation at each educational level and have extended, contextualized, data-focused learning rather than one-off activities or a handful of days of learning (Andersen, Humlum, & Nandrup, 2016). Research shows that although an increase in instructional time in school does increase understanding, it also depends on the quality of instruction, the classroom environment, and the time required for student processing of knowledge (Rivkin and Schiman, 2015). In Oregon, students have the lowest instructional time for elementary science in the United States of 1.9 hours per week (Blank, 2012). This insufficient instructional time can potentially impact Oregon students as they progress through their education, as they are not receiving the foundational science concepts and skills as they should be (Blank, 2012).

While the afterschool program helped build students’ awareness of data and the data process, it did not significantly increase their ocean literacy. The students ended the program with an understanding of how their watershed connects to the ocean and how most plastic ends up in the sea. However, when students had the opportunity to examine large ocean databases, they felt confused and challenged, had to be taught additional ocean concepts, and struggled to complete the activity.

Scaffolding

The data process is a cycle that includes: developing a research question, creating data collection and storage protocols, collecting data, analyzing data, presenting data, and then developing a new testable question. Learning how to develop the skills for each of these steps takes time and effort. For students to be able to utilize and understand data streaming from ocean technologies, their DL skills need to be facilitated from an early age. When working with students, they must understand that knowing how to do each step in the data process is a skill. A curriculum that isolates a step in the data process, or moves students through the entire data process, can be an effective tool for helping them understand its value.

Data literacy must build throughout a student’s education, from kindergarten through college. Just like how turning data into information is a process, students understanding each step of the process takes time. In the evolving world of technology, a student cannot become data literate with a stand-alone education intervention. A student must develop DL as they move through their education. Learning how to develop the skills needed for each step in the data process takes time and effort, and knowing how to execute each step accurately itself is a skill. The afterschool program demonstrated the effectiveness of scaffolding the data process, as it isolated each step and explained the necessity of each step to the students. At the end of the program, students understood that generating knowledge from data takes significant time. One day of fieldwork and one data set is not sufficient for drawing scientific conclusions.

Contextual and Relatable

Data used for education must be contextual, consistent, and relatable. Data without context is essentially meaningless. To be data literate, students must comprehend how and why people collect data and that specific protocols exist for collection. Data is ultimately a human construct, which we use to understand our world better. Even large data sets use numbers collected and analyzed through algorithms created by humans. Several professionals interviewed during the research suggested educational datasets should include “a data biography” or information about the people who collected the data, why they collected them, and why they chose certain collection protocols. Overall, many participants suggested that student-led research projects are vital for helping provide context because they support students’ understanding of data collection. Other strategies for providing context include developing data dictionaries, which are explanations of metadata, and acronyms within a dataset.

Alongside providing context, education data should be relatable for the students, holding some sort of meaning for students working with it. Whether that means it is local, place-based data or the data has a clear connection to students or issues they care about, data needs to tell a story. Without that story, students may not be interested, and it would not be considered a “good data set” for use in education. Oceanographic data used in lesson plans intended to build ocean and DL should tell a pertinent story so that students understand the importance and are more deeply engaged.

One of the best strategies for making data relatable is data-driven research projects addressing local community issues. The use of community data can be especially pertinent for engaging minoritized populations (Deahl 2014; D’Ignazio, 2017). Participatory action research in which students can use data to make informed decisions or change within their communities supports effective DL lessons (Deahl, 2014; D’Ignazio, 2017). In addition, it is also believed that data activities that empower a student to impact change in their community may be one of the best strategies for building DL and can align with the principles of culturally responsive teaching. There are topics like local watersheds, bird migration, and climate change that can be used for ocean data learning no matter the students’ proximity to the sea.

The afterschool curriculum demonstrated the impact of collecting data on relevant topics where students have the potential to influence change. Interviews, surveys, and student-generated work suggested all the students emerged with a greater understanding of the utility of data for making decisions that could result in positive change.

Tools for Teachers

The research determined that data portals designed for scientists are not usually effective tools for the classroom, as they are developed for people with expert-level knowledge as opposed to students who are just beginning to learn. Working with repository data often requires coding and software skills. While understanding programs like R, ArcGIS, and Python are now relevant skills for a career in science, a student must be trained to understand and use them first, and not all educators can be expected to teach these skills. To facilitate DL education, educators should have access to easy-to-use software. Effectively designed sites can allow us to bring real-world research experience to teachers and their students (McLaughlin, 2010). Additionally, utilizing software showcasing authentic data can promote deeper learning of science and math concepts and foster engagement using real-world investigations (McKay et al. 2007). TuvaLabs and Tableau were determined to be useful on-the-market software based on interviews and use in curriculum interventions.

Ensuring Cultural Responsiveness

Building and maintaining relationships with leaders from communities that have historically been locked out of ocean science and most STEM professions, such as Black, Latinx, and Indigenous communities, is vital for creating programming and curricular resources relevant to youth from these communities. Leaders embedded in these communities are better equipped to support the development of effective relationships with stakeholders from the community and to recruit youth to ocean science learning experiences. These leaders may also possess a clearer understanding of the topics and activities students may respond to positively (Bevan, Calabrese & Barton, 2018). As previously stated, the last phase of the research would have been unsuccessful without partnering with community leaders and liaisons.

Incorporating the first language of English second-language students was also found to be imperative for creating a culturally responsive environment. Students in the afterschool program interacted with the instructor primarily in English and communicated with each other in Spanish. The Latinx students all expressed the value of having a space where they could learn about science with other Spanish first-language students and communicate about science in their first language. While the six actively participating students all had a strong interest in science, many did not know one another before the program, and several expressed feeling excited to have met other Latinx students with an interest in science at the program’s conclusion.