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Research & Teaching

Using Structured Decision-Making in the Classroom to Promote Information Literacy in the Context of Decision-Making

Journal of College Science Teaching—July/August 2022 (Volume 51, Issue 6)

By Jenny M. Dauer, Amanda E. Sorensen, and P. Citlally Jimenez

An important facet of college students’ science literacy and job market preparation is developing skills for finding and applying information to decision-making about complex real-world problems. We developed a multidisciplinary science course to support development of these skills using a decision-making framework based on normative models of structured decision-making. Students practiced decision-making skills in the context of several socioscientific issue modules. We documented a postcourse increase in students’ self-efficacy for finding, evaluating, and using technical information in decision-making. We also review prior findings that articulate potential beneficial student learning outcomes as a result of this teaching approach. This course may provide a model for introductory courses to better align with institutional goals for science literacy and critical-thinking.


College students will be making decisions about societally and personally relevant issues in the future, many of which have scientific underpinnings. Most scientists, science instructors, and science education researchers acknowledge a need to develop students’ science literacy skills to support decision-making. For example, the National Research Council (NRC) defined science literacy with a particular focus on decision-making—that is, science literacy is “knowledge and understanding of scientific concepts and processes required for personal decision making, participation in civic and cultural affairs, and economic productivity” (1996, p. 192). Instructors have traditionally almost exclusively focused on increasing students’ science content knowledge, a perspective that continues to dominate K–16 instruction, yet a sizeable body of research shows that this type of teaching approach is not enough to result in development of effective decision-making skills in the context of challenging real-world issues (National Academies of Sciences, Engineering, and Medicine [NASEM], 2016). Instead, students must have opportunities to make sense of and synthesize complex pieces of information from multiple sources; negotiate information’s intersection with social, cultural, and economic values; and employ this information to evaluate real alternatives for action (NASEM, 2016). In addition, more than ever, employers are calling for college graduates who have the knowledge and, furthermore, the ability to apply this knowledge to complex issues and problem-solving. In a survey of 260 employers by the National Association of Colleges and Employers (2014), 71% of hiring managers said they highly desire decision-making skills in college graduates. Therefore, educators at all levels of the K–16 system have a responsibility to help students master “21st-century skills” such as integrating evidence and decision-making (NRC, 2013).

A key aspect of decision-making involves using scientific and other information to understand the consequences of potential actions and reducing uncertainty when choices are being made. Therefore, it is important that we train students to find, evaluate the reliability of, and apply information to a problem. This is challenging to do: Research indicates, for example, that students have difficulty integrating scientific evidence with real-world problems (Kolsto, 2006). Students need more opportunities to practice the skills of information literacy and sense-making, particularly in the context of decision-making.

In this article, we describe an instructional approach to developing students’ decision-making that has been designed as a multidisciplinary introductory course required for science, technology, engineering, and mathematics (STEM) and non-STEM majors (enrollment is approximately 600 students per academic year). The course is called Science Literacy 101: Science and Decision-Making for a Complex World (SCIL 101). The major learning goals for the course are to support students’ (a) science-informed decision-making skills, (b) information literacy, and (c) systems thinking. The course is structured around the use of structured decision-making (SDM) to think about potential solutions to complex real-world challenges related to food, energy, water, and health. During the semester, students are presented with three modules, each focusing on a specific issue that provides context for instruction around the learning goals. These issues are framed as questions to be addressed using SDM: How do we find a sustainable source of transportation fuel? How do we conserve groundwater and simultaneously support agriculture? How do we conserve wild pollinators? SDM guides students in their thinking as they consider various alternatives for solving the issue, the objectives by which they would judge the success of each alternative, and the trade-offs of each potential alternative given the objectives. Much of the classroom instruction emphasizes students searching for scientific information that explains the potential consequences of each alternative and whether the alternative would satisfy the objectives. (The teaching approach is explained further in SCIL 101 Teaching Approach later in this article.)

SDM provides an opportunity for students to practice the following:

  • finding information
  • evaluating information
  • applying information to a real-world problem
  • systematically evaluating and applying values to decision-making
  • thinking about multiple stakeholders and communicating with people whose perspectives may differ from their own
  • recognizing the complexity of real-world problems
  • applying civics to real-world problems
  • understanding of the role and value of scientific information
  • engaging in scientific habits of mind (e.g., skepticism, objectivity, and curiosity)
  • engaging in scientific argumentation skills (e.g., how to back up ideas with evidence)

While we think our teaching approach using SDM (as described in this article) affords students the opportunity to develop many types of skills, we will limit our claims about the effectiveness of the course to areas that we have assessed at this point in our research (see Course Outcomes). In particular, we highlight our findings on the gains in students’ confidence in their information literacy skills after participating in SCIL 101.

Framing decision-making in SCIL 101

In SCIL 101, we frame decision-making instruction around locally relevant socioscientific issues. A socioscientific issue is a real-world problem that is informed by science as well as societal elements (e.g., economic, ethics, and culture; Zeidler, 2014). Using socioscientific issues as the basis for classroom instruction aims to motivate student learning by contextualizing scientific issues in the relevant social landscapes in which they exist (Sadler, 2011). Once the issue has been introduced to students (as discussed in the Course Structure section in this article), students must then grapple with how to choose an alternative for solving the problem. We ask students to consider the “decision” they are asked to make in the course to be an imagined personal action (e.g., voting, writing a letter to a legislator, or donating money to a cause) that has societal implications (e.g., global climate change, local and national economics, political change). In our module about sustainable transportation fuel, the alternatives included (a) continuing with the status quo of essentially 10% corn ethanol in our fuel, (b) supporting second-generation biofuels, (c) promoting and subsidizing electric cars and renewable electricity, and (d) implementing programs that encourage people to drive and fly less. Then the SDM process helps students figure out which alternative they would personally support for solving the problem.

There is no predetermined choice that the instructors have identified as “the best,” nor do instructors hope that students choose a specific alternative. Instead, the objective is to help students make a high-quality decision. We define a “quality” decision (Alred & Dauer, 2020) as one depending on a quality process of decision-making that displays (a) the ability of the decision-maker to understand scientific and other information and apply it to the decision, and (b) a final choice that reflects priorities resulting from an evaluation of trade-offs among the decision-maker’s conflicting values (Brewer & Stern, 2005). Therefore, informed decisions should be made based not only on values but also with reasoned logic that weighs trade-offs and is attentive to how scientific and other information predicts the performance of each choice (Gregory et al., 2012). The SDM process allows students to select an alternative for solving the problem that aligns most closely with their personal prioritization of what they value in an outcome. Therefore, we say there could be two equally well-informed students who support two different alternatives, and each student could have made a “quality” decision that best fits their values.

In course instruction, we frame the importance to improve students’ decision-making skills in multiple ways. First, we discuss with students how potential employers value employees who have good decision-making and communication skills (National Association of Colleges and Employers, 2014) and the fact that many employers evaluate employees yearly based on these skills. Additionally, we talk about how all people (not just undergraduates in SCIL 101) are susceptible to cognitive biases and simplified heuristics (Arvai et al., 2004). For example, having “confirmation bias” means that someone only pays attention to evidence that confirms existing beliefs, and the “availability heuristic” refers to a mental shortcut that relies too heavily on information that comes to mind quickly. We give explicit instruction on cognitive biases targeted toward helping students understand the difference between formal and informal decision-making and when each is appropriate, using the concepts of “fast” and “slow” thinking (Kahneman, 2011). The course is framed with the idea that some issues are important enough to slow down and do a more structured analysis to avoid suboptimal decision-making because of poor decision-making processes.

SCIL 101 teaching approach

Course structure

The course comprises lecture sections (approximately 120 students per lecture) that meet for two 75-minute sessions per week, as well as four recitation sections (approximately 30 students per recitation) within each lecture section that meet for one 60-minute session per week. The purpose of the lecture is to explore the practices and theory behind decision-making and discuss the biological, physical, societal, and economic content knowledge that is necessary for students to engage in the decision-making process. The purpose of the recitation is for students to enact different steps of the decision-making process in small groups. Recitations are led by graduate students who are paid hourly, attend lectures, give feedback to students on assignments, and work closely with the instructors. Graduate students across the lecture sections receive training in two workshops early in the semester that include teaching techniques and standardization in grading; they also meet weekly with their lecture instructor for additional training, standardization, and discussions on course proceedings.

Using structured decision-making as a tool

This course uses a structured decision-making process designed to improve decision-making practices. The SDM process is based on normative models that aim to reduce cognitive biases (Hammond et al., 2015; Gregory et al., 2012). The SDM process (modified from Ratcliffe, 1997; Hammond et al., 2015) includes seven steps that support students’ decision-making process for solving socioscientific issues (outlined in Table 1).

The first step (Define the issue) occurs when the instructor gives background information on the topic and asks students to consider why the issue is a problem. At the end of the first day, the instructor defines the issue (“Should we use corn ethanol to satisfy our need for a renewable transportation fuel?”) and asks students to list three desired outcomes. The instructor summarizes student responses into three or four specific objectives. These refined objectives become the objectives (Step 2) that the whole class uses to evaluate the alternatives (Step 3). We found that most students converge on similar outcomes, allowing the instructor to craft anticipated objectives before the semester as they develop course materials. For the biofuels module, the objectives were as follows: (a) greatest reduction of greenhouse gases (CO2), (b) economic benefit to farmers and rural communities, and (c) preservation of the health of natural resources (water, soil, biodiversity, air quality). Next, students assigned a “weight” to each objective, with higher numbers indicating the objective was more important, for a total weight of 1.0. Weights appear in the first column in Figure 1. The weights are representative of each student’s individual priorities and values, so no two students may necessarily share the same weight distribution across the objectives. The potential alternatives for solving the problem (Step 3) are predetermined by the instructor based on expert opinion and actual proposed actions for resolving the issue, then discussed throughout the remaining lectures.

After establishing Steps 1 through 3, students are asked to complete an analysis table (Figure 1) as part of Steps 4 and 5 so they can systematically compare alternatives and objectives. During recitation, student groups are tasked with researching and explaining the implications of one alternative for a particular objective (Step 4). For example, one group may be asked to research the consequences of implementing the alternative “second-generation biofuels” for the objective “fewest greenhouse gas emissions.” During this process, students must use provided evidence as well as search for additional scientific or other information. The group writes a summary of their findings and gives a presentation about the potential consequences of the alternative and if it would satisfy the objective. During this time, students in the recitation discuss how well all the alternatives accomplish the given objective based on presented evidence, then come to a consensus to assign all of the performance scores (e.g., the asterisks in Figure 1). In the final module assessment for each socioscientific issue topic (see the online Appendixes A and B), students complete the analysis table independently by writing explanations for each alternative objective combination (although they can use the same evidence that was discussed in recitation), linking the evidence to a justification for the performance scores they assigned, and completing the remaining decision-making steps (Steps 5 through 7).

For Step 5, students perform a quantitative analysis of trade-offs by multiplying their unique objectives weights by each performance score (across a row), then summing the weighted performance scores for each alternative (down a column). For example, if a student weighted “fewest greenhouse gas emissions” as a 0.3 and the alternative “second-generation biofuels” had a four-star performance score, the weighted performance score is 1.2 (0.3 × 4.0 = 1.2). Then, for the “second-generation biofuels” alternative, the student must add the three weighted performance scores for each objective (1.2 + 1.5 + 0.2) to get a total weighted performance score of 2.9 (Figure 1). Students can then compare the total weighted performance scores for all of the alternatives. Using objective weights in this way allows for differences in students’ priorities to be reflected in the overall final scores for each alternative.

Table 1. Description of the steps in the structured decision-making framework.




Define the issue: What is the problem that needs to be solved?


Objectives: What is important to consider in an outcome? What do you care about regarding the issue? Indicate the importance to you personally for each objective by assigning a weight.


Alternatives: List or identify the possible alternative courses of action when considering the problem or issue.


Information: Find information that explains what will happen if this alternative is chosen. Use scientific knowledge or evidence that explains how or why (mechanisms) or by how much the alternative will satisfy each outcome objective. Use the evidence you find to assign performance scores that estimate the rank order of how well each alternative meets the desired outcome.


Analysis of alternatives based on the objectives (trade-offs): Evaluate each alternative against the objectives identified.


Choice: Choose an alternative based on the analysis.


Review: Evaluate the decision-making process.

Figure 1
Figure 1 Example of a fall 2016 student’s completed decision-making analysis table for the biofuels module.

Example of a fall 2016 student’s completed decision-making analysis table for the biofuels module.

In Step 6, students make a choice. Students often choose the alternative that had the highest total weighted performance scores, as this choice should represent the alternative that best satisfies their personal prioritization of the objectives. However, they may select other alternatives as long as their reasoning is clearly justified.

Finally, students are asked a series of reflection questions (Step 7) to review their decision: What are the trade-offs of the alternative you chose? Did working through this SDM process result in your thinking differently about the issue? To further illustrate how SDM is used in class, we included a timeline (see online Appendix C).

The utility of the SDM process is that it gives students the framework to nest their ideas and knowledge about the alternatives and objectives, reducing the cognitive load of retaining the various pieces of information they need to make an informed decision. In doing so, the SDM process also makes explicit the distinct role of scientific information (to better understand socioscientific issues) and personal values (drivers of decision-making). This process allows students to evaluate trade-offs between multiple alternatives for each outcome (objectives) they have individually identified, then account for their priorities among those various outcomes (weighting the objectives). In contrast, if the hypothetical student in Figure 1 made a decision solely based on the highest weighted value, economic benefit to farmers, the status quo would appear to be a good decision. However, SDM helps highlight that this option does not perform well based on other criteria, such as reducing greenhouse gases and preserving the health of natural resources. The option to promote second-generation biofuels may not have been on the students’ mind before engaging in formal decision-making, but it represents better trade-offs among all of the students’ values.

The course is structured such that the burden of responsibility on students for completing all steps in the SDM process independently increases throughout the semester. In the course’s first module, the students proceed through the steps using an analysis table that already contains performance scores. In a second module, students generate performance scores for a smaller subset of the table (one or two objectives). In the final 5 weeks of the semester, student groups are asked to pick a socioscientific issue of their choice and complete the entire SDM process, with the instructor providing support as needed to complete the final project. The student groups present their final project during a public poster session during the final week of the semester.

Course outcomes for information literacy


Given the emphasis in SCIL 101 on finding information to use in service of decision-making about complex socioscientific issues, we sought to document student gains in proficiency around finding and applying evidence. We evaluated development of students’ perceptions of their information literacy by administering a pre- and postcourse survey that included nine Likert-like items from an established information literacy assessment (Fuselier & Nelson, 2011). This survey was used during the fall 2016 semester in the first week of class and during the last week of class in two sections of SCIL 101. These items were analyzed using a paired t-test with an adjusted alpha value (α = 0.005) to account for multiple comparisons.


A total of 177 of paired pre-post survey responses from consenting students within two lecture sections taught by the authors were used for this analysis. We saw significant gains in the mean scores from pre (mean = 3.56, SD = 0.52) to post (mean = 3.91, SD = 0.48; paired t-test p < 0.001). For further information, we compared individual items on the assessment and found significant pre- to postcourse gains across eight of the nine items of information literacy (Table 2). Students were more likely to agree that they felt able to identify relevant information, look for information effectively, critically evaluate information, and engage in appropriate attribution practices.

Table 2. Changes in student response (n = 177) to information literacy survey after participating in Science Literacy 101.


Mean precourse score

Difference pre- to postcourse

I can determine the kind of information I need to answer a research question.



The thought of reading a scientific research article scares me.



I know how to select appropriate keywords for searching databases effectively.



I am confident as an information researcher in finding scientific articles.



I know how to evaluate the authority behind information from the internet.



When necessary, I revise my selection of keywords to find information more efficiently.



I question the validity of information, including that from textbooks or teachers.



I cite (acknowledge) all sources of information I include in my reports.



I can understand journal articles written by scientists about their research experiments.



Note. Items were rated on a 5-point Likert scale, with 1 = strongly disagree, 3 = neutral, and 5 = strongly agree. Changes were assessed with a paired t-test with adjusted alpha value (α = 0.005). * indicates significant pre-post change (p < 0.001).

Additional documented learning outcomes

In our broader research efforts in SCIL 101, we found other student gains that connect to learning goals around decision-making and information literacy. In open-ended statements about what students think should be done about the complex issues covered in the course, students were more likely to have greater awareness of alternatives and their potential consequences after the course ended (Dauer, Lute, & Straka, 2017). In the same study, we observed a decrease in students’ emotive arguments and an increase in students’ arguments that had clearer justifications and connections to the root cause of the issue. Additionally, we often observed students changing their minds by moving toward a more moderate stance by the end of the semester (Alred & Dauer, 2020), which may indicate that effective reasoning and argumentation have occurred in the classroom. In another study, we observed that students gained significantly in socioscientific reasoning, particularly in terms of recognizing complexity and the ability to identify multiple perspectives (Romine et al., 2020). We also documented significant increases in students’ attitudes about social justice, interpersonal and problem-solving skills, and political awareness at the end of the semester (Dauer, Sorensen, & Wilson, 2021).


There are multiple potential affordances to using SDM in science classrooms. We have anecdotally, as well as qualitatively and quantitatively, noted many of these affordances during our experience as instructors—in the rich conversation students engage in around the topics, in their motivation to understand the complexity of the issue, and in their quest to make sense of science information and apply it to a real-world issue. In particular, providing STEM and non-STEM majors with the opportunity to practice finding and using information is important so they can develop decision-making skills that are the foundation of science literacy and become more competitive in today’s job market, which demands potential employees who are adept in evaluating and applying information.

Given the benefits we found in terms of significant gains in students’ self-reported confidence with information skills and other outcomes, as well as the course’s connection to 21st-century skills, a course structure in this vein would likely be useful at many higher education institutions. Indeed, many science education reforms have focused on increasing emphasis on higher-order cognitive skills in science instruction (Leou et al., 2006; Miri et al., 2007; Zoller, 2000; Antonenko et al., 2014). The SCIL 101 course described in this article can provide an outline for other institutions looking to include a similar course in their curriculum. However, there may be limitations in terms of institutional and instructional effort when attempting to create a stand-alone course of this kind. We suggest that SDM may also be adapted in abbreviated ways into existing disciplinary courses, such as by using a mostly complete analysis table and researching just one alternative and objective, or by using a completed analysis table in a non-quantitative way during discussion or role-play regarding controversial topics.

Developing a course around SDM is one way to create explicit instruction of critical-thinking practices—in this case, decision-making, information literacy, and systems-thinking—rather than specific disciplinary content. While we are still investigating learning outcomes related to this course, we believe this classroom model may provide broad potential desirable student outcomes. The SCIL 101 model is one example, among others (Miri et al., 2007), of shifting the focus of instruction from content knowledge to science literacy skills that are important for citizenship and desired by employees. SCIL 101 may provide an important classroom model for thinking about how to change our expectation for college courses, particularly at an introductory level, to better align with the current need for students—and citizens—who are competent in higher-order thinking in the context of social complexity.


This work was supported in part by the National Science Foundation under Grant IUSE: EHR - 1711683.

Jenny M. Dauer ( is an associate professor and associate director in the School of Natural Resources at the University of Nebraska–Lincoln. Amanda Sorensen is an outreach specialist in the Department of Community Sustainability at Michigan State University. P. Citlally Jimenez is a postdoctoral research associate in the School of Natural Resources at the University of Nebraska–Lincoln.


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