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Directed or Random? Student Reasoning about Diffusion across Contexts

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Title: Directed or Random? Student Reasoning about Diffusion across Contexts
Language: English
Authors: Aeryn L. VanDerSlik (ORCID 0009-0007-6590-2602), Emily E. Scott, Mary Pat Wenderoth, Zachary A. Kam, Jasmine D. Parker, Maya B. Shah, Joseph Vieregge, Jennifer H. Doherty (ORCID 0000-0002-2333-1692)
Source: Advances in Physiology Education. 2025 49(4):1014-1025.
Availability: American Physiological Society. 9650 Rockville Pike, Bethesda, MD 20814-3991. Tel: 301-634-7164; Fax: 301-634-7241; e-mail: webmaster@the-aps.org; Web site: https://www.physiology.org/journal/advances
Peer Reviewed: Y
Page Count: 12
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 1661263
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Thinking Skills, Physiology, Scientific Concepts, Motion, Context Effect, Reliability, Public Colleges, Research Universities, Problem Solving, Science Education, Knowledge Level, Undergraduate Students
DOI: 10.1152/advan.00185.2025
ISSN: 1043-4046
1522-1229
Abstract: Diffusion is a critical component of the Physiology Core Concept of flow down gradients and is fundamental to understanding how ions, gases, or signaling molecules travel short distances in the body. When asked about diffusion, students often reason successfully using the "things move from areas of high to low concentration" heuristic but struggle to understand that random motion underlies this movement. We investigated the different knowledge resources students use when reasoning about diffusion across different contexts. Additionally, we determined if item context impacted the resources students activated and how consistent students were in their reasoning. We gave students a pair of questions from three contexts (plant, animal, and nonliving) that asked them to predict and explain where a molecule of gas would be located before and after equilibrium. Using the resources framework, we identified 14 common knowledge resources and 6 different patterns in resource activation. "High to low" and related resources were used in 73% of responses. Only 23% of responses included at least one "random motion" resource, and the vast majority of these responses described random particle motion starting only after equilibrium is reached. Item context did not significantly affect the resources students used. Students were also mostly consistent in their reasoning, with 76% using similar resources across the two items. These findings indicate that "high to low" and related resources have a high cueing priority for many students and that instructors should help students unpack random motion as the mechanism underlying diffusion instead of leaving it "black boxed."
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1490867
Database: ERIC
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  Value: <anid>AN0190644494;apu01dec.25;2026Jan06.04:27;v2.2.500</anid> <title id="AN0190644494-1">Directed or random? Student reasoning about diffusion across contexts </title> <sbt id="AN0190644494-2">INTRODUCTION</sbt> <p>Diffusion is a critical component of the Physiology Core Concept of flow down gradients and is fundamental to understanding how ions, gases, or signaling molecules travel short distances in the body. When asked about diffusion, students often reason successfully using the "things move from areas of high to low concentration" heuristic but struggle to understand that random motion underlies this movement. We investigated the different knowledge resources students use when reasoning about diffusion across different contexts. Additionally, we determined if item context impacted the resources students activated and how consistent students were in their reasoning. We gave students a pair of questions from three contexts (plant, animal, and nonliving) that asked them to predict and explain where a molecule of gas would be located before and after equilibrium. Using the resources framework, we identified 14 common knowledge resources and 6 different patterns in resource activation. "High to low" and related resources were used in 73% of responses. Only 23% of responses included at least one "random motion" resource, and the vast majority of these responses described random particle motion starting only after equilibrium is reached. Item context did not significantly affect the resources students used. Students were also mostly consistent in their reasoning, with 76% using similar resources across the two items. These findings indicate that "high to low" and related resources have a high cueing priority for many students and that instructors should help students unpack random motion as the mechanism underlying diffusion instead of leaving it "black boxed." NEW & NOTEWORTHY We present the first physiology education study that investigates students' understanding of diffusion using a resources framework. Students frequently used "high to low" knowledge resources and rarely coordinated them with "random motion." Of the 23% that included "random motion" resources, the vast majority described random motion starting only after equilibrium. While "high to low" resources are sometimes productive, when students coordinate "random motion" resources, they have tools for a more nuanced understanding of physiological phenomena.</p> <p>Diffusion, modeled by Fick's First Law, is an integral aspect of the Physiology Core Concept of flow down gradients due to its centrality to physiological processes (<reflink idref="bib1" id="ref1">1</reflink>, 2). Diffusion is the net movement of a single species, such as ions, gases, nutrients, neurotransmitters, or hormones, from areas of higher concentration to areas of lower concentration by the mechanism of constant random molecular movement (i.e., Brownian motion). Each particle moves due to its thermal energy, and collisions with other particles make this motion unpredictable, so particles are equally likely to move in any direction. In regions of higher concentration, there are more particles moving randomly, increasing the probability that some will cross into areas of lower concentration. In regions of lower concentration, there are fewer particles moving randomly, decreasing the probability that some will cross into areas of higher concentration. This statistical imbalance in particle exchange leads to a net movement of particles from areas of higher concentration to lower concentration. Over time, this redistribution results in a uniform distribution of particles we refer to as equilibrium. At equilibrium, random motion continues, but there is no net movement of particles between compartments (<reflink idref="bib2" id="ref2">2</reflink>).What is the advantage to students in understanding the underlying mechanism of diffusion, i.e., how unpredictable particle motion leads to the predictable pattern of diffusion from high to low concentration? Why is it not sufficient for students to simply know that substances move from high to low concentration? While this simplified understanding of diffusion is useful in some contexts, it becomes a barrier when students encounter more complex systems where diffusion interacts with other processes (e.g., facilitated diffusion, secondary active transport, and the establishment of dynamic steady states like resting membrane potential). For example, students who think diffusion involves directed motion might incorrectly assume that glucose molecules "seek out" glucose transporters on the cell membrane for transport into the cell. In reality, glucose moves randomly, and only random collisions with glucose transporters allow the glucose molecule to bind to the transporter and facilitated diffusion to occur. This misunderstanding can extend to molecular interactions like ligand-receptor binding or enzyme activity (<reflink idref="bib3" id="ref3">3</reflink>). For instance, students who believe in directed movement for diffusion might reason that acetylcholinesterase "searches for" acetylcholine at the neuromuscular junction to degrade. In reality, acetylcholine moves randomly on and off the acetylcholine receptor, and degradation depends on random collisions between the acetylcholine molecules and the enzyme that is localized adjacent to the receptor. These examples illustrate how the random motion of molecules and the probability of collision are fundamental to understanding physiological processes. Without recognizing randomness, students may fail to grasp how intermolecular forces (e.g., binding affinity), temperature, pH of the environment, or changes in concentrations influence the efficiency and likelihood of interactions.Understanding how random molecular motion is important for diffusion is challenging for students. Across three decades, investigations of students' understanding of diffusion have consistently shown that students are successful in using a "things go from high to low concentration" rule of thumb to reason about particle movement but struggle with understanding the underlying mechanism driving this process (4–9). For example, in 1995, Odom and Barrow (<reflink idref="bib4" id="ref4">4</reflink>) administered the Diffusion and Osmosis Diagnostic Test (DODT) to 117 biology majors and 123 nonmajors and found that only 22% of these students correctly identified random particle motion as the mechanism driving diffusion. Sixteen years later, Fisher et al. (<reflink idref="bib5" id="ref5">5</reflink>) corroborated these findings using the updated Osmosis and Diffusion Conceptual Assessment (ODCA). Results from the ODCA indicated that fewer than 25% of students recognized random motion as the driving process, and many incorrectly believed that molecules stop moving once equilibrium is reached. Similarly, Tunstall et al. (<reflink idref="bib6" id="ref6">6</reflink>) found that in an undergraduate biology-physiology course, most students described diffusion as the directed movement of "things" from high to low concentrations, with only 27% of 119 students incorporating randomness in their reasoning. More recent research with physiology students by Doherty et al. (<reflink idref="bib9" id="ref7">9</reflink>) found that difficulties with the mechanism of diffusion remain widespread, illustrating how these challenges have persisted across decades of research and teaching.Why is the underlying mechanism of diffusion so challenging for students? Previous studies have shown that students struggle with the concepts of randomness and probability in mathematics and as applied in chemistry and biology (10–13). Students' difficulty with the concept of randomness in diffusion has been linked to its emergent and unobservable nature. Chi (<reflink idref="bib14" id="ref8">14</reflink>) argues that diffusion is challenging for students to learn because it is an emergent process, where the overall pattern arises from the interaction of individual components within the system rather than being driven by direct, linear causation. Emergent processes, such as diffusion, require reasoning about probabilities and interactions at the particle level, which can be difficult for students accustomed to thinking in terms of directed or intentional processes. Sevian and Stains (<reflink idref="bib7" id="ref9">7</reflink>) emphasize that what happens on the unobservable particle scale (i.e., random motion of individual particles) is fundamentally different from what happens on the observable scale, where the net movement of substances appears directed from high to low concentration. This disconnect can make it challenging for students to link the mechanisms of random motion to the macroscopic outcomes.In this study, we take a dynamic systems view of thinking and learning because it allows us to view students' reasoning as continuously changing and developing (<reflink idref="bib15" id="ref10">15</reflink>). This view helps us step away from the idea that students "wrong" ideas about concepts are static misconceptions requiring replacement (<reflink idref="bib16" id="ref11">16</reflink>). This dynamic perspective aligns with resource-based theoretical frameworks such as diSessa's Knowledge-in-Pieces (<reflink idref="bib17" id="ref12">17</reflink>) and Hammer's resources framework (<reflink idref="bib18" id="ref13">18</reflink>). These frameworks model students' knowledge as a network of dynamic, context-sensitive pieces or resources that can be activated, reorganized, and coordinated depending on the situation. Learning, therefore, involves restructuring the activation patterns of knowledge resources rather than replacing an unproductive idea with the correct one (<reflink idref="bib19" id="ref14">19</reflink>). This theoretical perspective allows us to view the knowledge resource "things move from high to low concentration" as not inherently incorrect when reasoning about the mechanism of diffusion. In fact, it is a productive resource that aligns with the observable outcome of diffusion. However, the idea that "things move from high to low" would need to be further coordinated with additional knowledge resources, such as "constant molecular movement," rather than being activated alongside ideas like "molecules move because they want to be less crowded" (<reflink idref="bib4" id="ref15">4</reflink>). Instruction can support this more productive coordination by helping students integrate these ideas, enabling them to reason how random, individual particle movements result in the net flow from high to low concentrations.To better design instructional interventions to support students in this coordination process, we need to understand the knowledge resources that students activate when reasoning about diffusion. Previous studies on student reasoning across various biological contexts have shown that the context of a question plays a role in shaping which resources students activate (20–23). Using the resources theoretical perspective, this is understood as students' perceptions of the context influencing which knowledge resources they activate (<reflink idref="bib17" id="ref16">17</reflink>, 24). When some pieces of knowledge are more commonly activated by a context than others, that knowledge is referred to as having a higher cueing priority (<reflink idref="bib17" id="ref17">17</reflink>).While there are many possible knowledge resources students might activate and coordinate within and across contexts, these resources often group into recognizable patterns of use. In our study, we define these patterns of use as "reasoning types." We ask the following three research questions:</p> <p>RQ1: What knowledge resources and reasoning types do students use when solving diffusion problems about random motion?</p> <p>RQ2: Does question context impact the reasoning types students use when solving diffusion problems about random motion?</p> <p>RQ3: To what extent are students consistent in the reasoning types they use across two question contexts?</p> <hd id="AN0190644494-3">METHODS</hd> <p></p> <hd id="AN0190644494-4">Setting and Data Collection</hd> <p>This study was conducted at a selective R1 public university in a lower-division survey of human physiology course with 328 students. The course had no prerequisites and was designed for preallied health students and nonscience majors, although science majors could enroll. We collected data on students' reasoning about diffusion before instruction in week 1 of the 10-wk term via an online homework assignment graded for completion rather than correctness. As students were only required to complete 80% of the homework assigned over the term to receive full credit in the homework category of the gradebook, many students chose not to complete this particular assignment. Therefore, only 60% (197 of 328) of students completed this assignment.To investigate what knowledge resources and types of reasoning students used when reasoning about diffusion (RQ1), we developed three questions that varied in context (animal, plant, and nonliving scenarios) but used the same underlying question structure (Fig. 1). Each question provided a figure, a description of the figure, and posed a scenario. Students were asked to predict, using a forced-choice format, and to explain, using a short-answer format, where they thought a molecule of a gas (indicated by the letter "X" in the figure) would be found before and after equilibrium was reached. Note, molecule X was always initially located in the low-concentration compartment. While a correct answer might acknowledge that net movement of particles occurs from high to low concentration, the answer also recognizes that random motion of particles prevents an accurate prediction of the location of any individual particle at any given time. This distinction highlights the importance of reasoning about diffusion mechanisms rather than relying solely on high-to-low reasoning.</p> <p>Open in Viewer</p> <p>PHOTO (COLOR): Figure 1. Questions for research question 1: What knowledge resources and reasoning types do students use when solving diffusion problems about random motion?</p> <p>To examine if question context impacts the type of reasoning students use (RQ2), we developed multiple versions of the online homework assignment to randomly assign contexts to each student (Table 1). To examine student consistency of reasoning (RQ3), the online homework assignment asked each student to answer the diffusion question in two different contexts, allowing us to compare reasoning within individual students (in contrast to the between-student comparisons in RQ2). We created three different question pairings (animal + plant, animal + nonliving, and plant + nonliving). We separated each question pair by six unrelated assessment questions addressing other course topics. We did this to ameliorate the possibility that any consistency of reasoning observed was an artifact of their similar formats or proximity on the homework. To eliminate the possibility that consistency in reasoning was due to students returning to and changing their answer to the first question after reading the second question in the pair, the homework assignment did not allow students to go back and change their answers after seeing subsequent questions. To ensure there was no bias based on the order of questions within a pair, we used two homework versions per pair, alternating which item came first. In total, we implemented six versions of the homework (Table 1). An equal number of each homework version was assigned to students in the course. However, some students did not follow directions and selected a different version than the one they were assigned or did not complete the homework, causing our sample sizes for each homework version and item pair to be unequal.</p> <p>Open in Viewer</p> <p>Table 1. Description of question contexts on homework assignments</p> <p> <ephtml> <table><thead><tr><th>First Diffusion Question</th><th>Second Diffusion Question</th><th>Question Pair</th><th>Homework Version</th><th>No. of Students</th></tr></thead><tbody><tr><td><p>Animal</p></td><td><p>Plant</p></td><td><p>1</p></td><td><p>1</p></td><td><p>31</p></td></tr><tr><td><p>Plant</p></td><td><p>Animal</p></td><td><p>1</p></td><td><p>2</p></td><td><p>31</p></td></tr><tr><td><p>Plant</p></td><td><p>Nonliving</p></td><td><p>2</p></td><td><p>3</p></td><td><p>33</p></td></tr><tr><td><p>Nonliving</p></td><td><p>Plant</p></td><td><p>2</p></td><td><p>4</p></td><td><p>26</p></td></tr><tr><td><p>Animal</p></td><td><p>Nonliving</p></td><td><p>3</p></td><td><p>5</p></td><td><p>33</p></td></tr><tr><td><p>Nonliving</p></td><td><p>Animal</p></td><td><p>3</p></td><td><p>6</p></td><td><p>43</p></td></tr></tbody></table> </ephtml> </p> <p>Each student was randomly assigned 1 of the 6 homework versions listed below. Students could have received animal and plant, plant and nonliving, or nonliving and plant. The order of the 2 questions in the pair was randomized to control for question order, and 6 unrelated assignment questions separated each of the questions. Students were provided with a figure, figure description, and the related multiple-choice and short-answer questions.</p> <hd id="AN0190644494-5">Data Analysis</hd> <p></p> <hd id="AN0190644494-6">RQ1: What knowledge resources and reasoning types do students use when solving diffusion prob...</hd> <p>To determine patterns in students' diffusion reasoning, we created a preliminary rubric of types of diffusion reasoning based on the diffusion education research literature and knowledge resources we observed in students' responses to our questions. We revised the preliminary rubric through iterative rounds of coding subsamples of responses and discussion. Four researchers performed this initial coding and came to a consensus. After this process, we initially organized the patterns into four reasoning types. Two researchers then assigned each student's response to a reasoning type and came to a consensus if there were any disagreements. Next, we examined the group of responses assigned to each reasoning type and identified the common knowledge resources students used for each type. This analysis allowed us to evaluate whether the reasoning types should be further subdivided. Ultimately, we confirmed that splitting some types was warranted based on differences in knowledge resource use, leading to a final revision of the rubric and coding. The final rubric describes 14 common knowledge resources and 6 types of student reasoning. Students used a wide variety of knowledge resources to reason, but our final rubric only describes knowledge resources that were used in more than five student responses. The coded responses were also used to investigate RQ2 and RQ3 as described below.</p> <hd id="AN0190644494-7">RQ2: Does question context impact the reasoning types students use when solving diffusion pro...</hd> <p>To investigate whether question context impacts the type of reasoning students use, we used multinomial regression with question context as a fixed effect. Multinomial regression was appropriate because the type of reasoning is a categorical outcome variable with more than two categories. Of the six types of reasoning, three types had fewer than eight responses and were dropped from the analysis. As all items were represented in item 1 across homework pairs and again in item 2 (Table 1), we took a more conservative approach to our analysis by limiting it to the response that students provided for the first item. In this way, we avoided any impact answering the first item might have on the answer for the second item. To account for the impact of differences in student academic performance before entering the course on the reasoning type used, we also included students' grade point average (GPA) at the start of the term (obtained from the university registrar) in the model as a fixed effect. The GPA was recorded on a four-point scale with a sample range of 1.68 to 4.0.</p> <hd id="AN0190644494-8">RQ3: To what extent are students consistent in the reasoning types they use across two questi...</hd> <p>We considered each student's reasoning consistent if their explanation on the second item on the homework was coded as the same type of reasoning as the first item. We first reviewed the data set to determine whether inconsistencies reflected true differences. For example, some students referred back to the first question when answering the second, causing occasional discrepancies when coded separately. We calculated the percent of students who reasoned consistently across items.We also explored how students shifted their reasoning across contexts by examining patterns in inconsistent response pairs. For example, some students may have shifted from Reasoning Type Y on the first question to Reasoning Type Z on the second, while others from Reasoning Type Z to Reasoning Type Y, and so on. We looked for potential patterns in resource activation for students for each shift to try to understand the cognitive processes underlying these transitions.We used R statistical software for all statistical tests and graphing (<reflink idref="bib25" id="ref18">25</reflink>). We used packages <emph>stats</emph> and <emph>MASS</emph> for statistical analysis and packages <emph>ggplot2</emph>, <emph>ggeffects</emph>, and <emph>ggalluvial</emph> for graphing (26–29).All procedures were conducted in accordance with approval from the Institutional Review Board at The University of Washington (STUDY00001316). Under STUDY00001316, a waiver of consent was obtained, and students were able to opt out of sharing their data with us, as is typical for studies that use data collected as a normal part of class activities (homework).</p> <hd id="AN0190644494-9">RESULTS</hd> <p></p> <hd id="AN0190644494-10">RQ1: What Knowledge Resources and Reasoning Types Do Students Use When Solving Diffusion Prob...</hd> <p>We identified 14 common knowledge resources that students activated when responding to questions about diffusion across different scenarios. We noticed six different patterns in the ways students coordinated these resources, and therefore identified six reasoning types. Below, we describe each reasoning type and the knowledge resources commonly used in each reasoning type. We provide example responses in Table 2. In Table 3, we provide a summary of the number of responses for each reasoning type given to the first question.</p> <p>Open in Viewer</p> <p>Table 2. Types of diffusion reasoning and associated knowledge resources</p> <p> <ephtml> <table><thead><tr><th>Type of Reasoning</th><th>Knowledge Resources</th><th>Description</th><th>Student Example</th></tr></thead><tbody><tr><td><p>Properties</p></td><td><p>Membranes are semipermeable.Gases have polarity.Oxygen is capable of forming hydrogen bonds with water. Gas molecules can interact with water molecules through IMFs.</p></td><td><p>Students activate knowledge resources that pertain to properties of the gas molecules, the system, or of diffusion itself to explain where "X" will be before and after equilibrium.</p></td><td><p>Before equilibrium: b. In the water/ Molecule "X" will be in the water because oxygen molecules are hydrophilic which means they'd want to bond with the water molecules.After equilibrium: a. In the bag/ After equilibrium is reached, the X would be in the bag since O<sub>2</sub> is likely to travel up into the permeable bag because it is a gas.</p></td></tr><tr><td><p>Do Its Job</p></td><td><p>Photosynthesis occurs in plants. CO<sub>2</sub> fuels photosynthesis in plant cells.Oxygen keeps living things alive.Oxygen must be present in the blood to oxygenate tissues.Breathing in provides a continuous supply of O<sub>2</sub> to the alveoli.</p></td><td><p>Students activate knowledge resources about the function or necessity of the system to explain where "X" will be before and after equilibrium.</p></td><td><p>Before equilibrium: b. In the capillary/ Molecule "X" will be in the capillary because before the next inhale happens, the after effect of the last inhales pushed the oxygen out of the alveoli into the capillary so that the blood can carry the oxygen to cells that need it and the empty alveoli can prepare for the next inhale.After equilibrium: b. In the capillary/ So that the capillary can carry the oxygen to the whole body cells.</p></td></tr><tr><td><p>H to L to Do Its Job</p></td><td><p>Things move from high to low.Concentration gradients drive things from high to low.Photosynthesis occurs in plants/CO<sub>2</sub> fuels photosynthesis in plant cells.Oxygen keeps living things alive.Oxygen must be present in the blood to oxygenate tissues.</p></td><td><p>Students coordinate resources about the functions/necessities of the system with "things move from high to low" and related resources to explain where "X" will be before and after equilibrium.</p></td><td><p>Before equilibrium: a. In the alveoli/ Oxygen is inhaled through the lungs (alveoli) therefore would be there first.After equilibrium: b. In the capillary/ The concentration gradient will lead the oxygen into the bloodstream because the blood flowing past the alveoli will be oxygen deficient. The oxygen from lungs will be absorbed into bloodstream.</p></td></tr><tr><td><p>H to L</p></td><td><p>Things move from high to low.Concentration gradients drive things from high to low.Two compartments with different concentrations will eventually come to equilibrium.Equilibrium is when concentrations/amounts are balanced.</p></td><td><p>Students activate high to low and/or concentration gradient-related resources to explain that "X" will end up in the compartment with the lowest preequilibrium concentration.</p></td><td><p>Before equilibrium: a. In the mesophyll cell/For the system to reach equilibrium, the concentration of carbon dioxide molecules would have to be evenly distributed between in the mesophyll cell and the leaf air space. Hence, since the concentration is higher in the leaf air space, more would go into the mesophyll cell to reach equilibrium. The carbon dioxide molecule X will stay inside the mesophyll cell before that happens.After equilibrium: a. In the mesophyll cell/Since there are more carbon dioxide molecules in the leaf air space, more will go into the mesophyll cell to reach equilibrium. Carbon dioxide molecule X does not have to go into the leaf air space.</p></td></tr><tr><td><p>H to L Then Random</p></td><td><p>Things move from high to low.Concentration gradients drive things from high to low.Two compartments with different concentrations will eventually come to equilibrium.Equilibrium is when concentrations/amounts are balanced.Once equilibrium is achieved, molecules will move randomly. Directed motion occurs before random motion.</p></td><td><p>Students coordinate high to low and/or concentration gradient-related resources with resources pertaining to nonconstant random motion to explain that the location of "X" cannot be predicted once equilibrium is achieved.</p></td><td><p>Before equilibrium: b. In the capillary/"X" will likely be in the capillary BEFORE equilibrium is reached since there is a PO<sub>2</sub> gradient driving O<sub>2</sub> to move into the capillary. It would be unlikely that X would move against the gradient without any sort of energy consumption.After equilibrium: c. Cannot predict/"X" could be anywhere after equilibrium, O<sub>2</sub> molecules are free to move around wherever- there is no PO<sub>2</sub> gradient pushing O<sub>2</sub> one way or another but likely will just stay in the capillary.</p></td></tr><tr><td><p>Random</p></td><td><p>Molecules move randomly constantly.If membranes are semipermeable, molecules can move back and forth randomly.</p></td><td><p>Students activate constant random motion resources to explain that the location of "X" cannot be predicted before or after equilibrium.</p></td><td><p>Before equilibrium: c. Cannot predict/You can't predict where the molecules will be dispersed, because it is random.After equilibrium: c. Cannot predict/Again, I'd guess that the diffusion is random, so there are equal chances that X would be in the bag or in the water.</p></td></tr></tbody></table> </ephtml> </p> <p>We identified 14 common knowledge resources that students used when reasoning about diffusion. While students used a wide variety of resources, only the most common (used in at least 5 responses) are listed. Types of diffusion reasoning were identified based on patterns in the coordination of similar knowledge resources by different students. These data address research question 1: What knowledge resources and reasoning types do students use when solving diffusion problems about random motion? H, high; IMFs, intermolecular forces; L, low.</p> <p>Open in Viewer</p> <p>Table 3. Percent of students using each reasoning type and percent of students who were consistent by reasoning type</p> <p> <ephtml> <table><thead><tr><th rowspan="2">Type of Reasoning</th><th rowspan="2">RQ1 Reasoning Type on First Question</th><th colspan="3">RQ2 Reasoning Type on First Question by Question Context</th><th rowspan="2">RQ3 Consistency by Reasoning Type</th></tr><tr><th>Animal</th><th>Nonliving</th><th>Plant</th></tr></thead><tbody><tr><td><p>Properties</p></td><td><p>21% (41)</p></td><td><p>22% (14)</p></td><td><p>19% (13)</p></td><td><p>22% (14)</p></td><td><p>61% (25)</p></td></tr><tr><td><p>Do Its Job</p></td><td><p>4% (8)</p></td><td><p>8% (5)</p></td><td><p>0% (0)</p></td><td><p>4% (3)</p></td><td><p>25% (2)</p></td></tr><tr><td><p>H to L</p></td><td><p>51% (100)</p></td><td><p>53% (34)</p></td><td><p>54% (37)</p></td><td><p>45% (29)</p></td><td><p>86% (86)</p></td></tr><tr><td><p>H to L to Do Its Job</p></td><td><p>1% (3)</p></td><td><p>3% (2)</p></td><td><p>0% (0)</p></td><td><p>2% (1)</p></td><td><p>0% (0)</p></td></tr><tr><td><p>H to L Then Random</p></td><td><p>21% (41)</p></td><td><p>12% (8)</p></td><td><p>24% (17)</p></td><td><p>25% (16)</p></td><td><p>80% (33)</p></td></tr><tr><td><p>Random</p></td><td><p>2% (4)</p></td><td><p>2% (1)</p></td><td><p>3% (2)</p></td><td><p>2% (1)</p></td><td><p>75% (3)</p></td></tr><tr><td><p>Total responses</p></td><td><p>197</p></td><td><p>64</p></td><td><p>69</p></td><td><p>64</p></td><td /></tr></tbody></table> </ephtml> </p> <p>These data address research question 1 (RQ1): What knowledge resources and reasoning types do students use when solving diffusion problems about random motion? RQ2: Does question context impact the reasoning types students use when solving diffusion problems about random motion? RQ3: To what extent are students consistent in the reasoning types they use across two question contexts? Numbers of students are in parentheses. H, high; L, low.</p> <hd id="AN0190644494-11">Reasoning type "Properties": inappropriate use of properties of gas/diffusion.</hd> <p>Responses using this reasoning type (<emph>n</emph> = 41 in responses to the first question on the homework) indicated activation of a wide range of knowledge resources loosely pertaining to diffusion as a phenomenon. These resources typically included properties of the molecules or the system itself when explaining where molecule X could be located, such as the polarity of the molecule dictating where it will move. Other students explained that molecules move against their gradients from low to high, or generally made incorrect statements about diffusion.</p> <hd id="AN0190644494-12">Reasoning type "Do Its Job": gases move so the organism can do its job.</hd> <p>Responses in this category (<emph>n</emph> = 8) were teleological in nature. Students generally activated knowledge resources about organismal processes and functions and linked them to gas movement. For example, students referred to the necessity of CO<subs>2</subs> moving into plant cells so that the plant can do photosynthesis, or explained that O<subs>2</subs> will move into the capillaries so that it can be delivered to the body. However, these students did not note the difference in concentrations between the two compartments. This reasoning type was not used at all for the nonliving scenario.</p> <hd id="AN0190644494-13">Reasoning type "High to Low": particles move from high to low concentration.</hd> <p>Responses in this category (<emph>n</emph> = 100) used "things move from high to low" or related knowledge resources, such as "things move to equilibrium/become balanced" and "concentration gradients are driving forces for movement." For example, many students within this category activated knowledge resources about achieving equilibrium by balancing out concentrations.</p> <hd id="AN0190644494-14">Reasoning type "High to Low to Do Its Job": particles move from high to low concentration so...</hd> <p>Responses in this category (<emph>n</emph> = 3) drew upon both "things move from high to low" or related knowledge resources, as well as the organismal processes/functions knowledge resources. Students reasoned that molecules move from high to low concentration into the organism's cells, because the organism needs the gases in order to function. This type of reasoning is distinct from "Do Its Job" and "High to Low" because these students activated both types of knowledge resources.</p> <hd id="AN0190644494-15">Reasoning type "High to Low Then Random": particles move from high to low concentration with...</hd> <p>Responses in this category (<emph>n</emph> = 41) activated "things move from high to low" or related knowledge resources, but also included additional resources about random motion. As with the previous reasoning types, these responses described directional movement of molecules from high to low concentration before equilibrium, and this directional movement balances out concentrations. However, these responses further explained that after equilibrium was reached, random motion "took over" so that molecules could move into any compartment as long as the concentrations remained balanced.</p> <hd id="AN0190644494-16">Reasoning type "Random": particles move by random motion before and after equilibrium.</hd> <p>Responses in this category (<emph>n</emph> = 4) explained that you could not predict where molecule X would be either before or after equilibrium because of the dynamic nature of the process of diffusion. These students activated resources similar to the "High to Low Then Random" reasoning type but did not use the "things move from high to low" resource to predict the location of X. Instead, they used the knowledge resource "random motion is constant" to explain that X could be in either compartment at any time.</p> <hd id="AN0190644494-17">RQ2: Does Question Context Impact the Reasoning Types Students Use When Solving Diffusion Pro...</hd> <p>The reasoning type "High to Low" was the most common; it was used by ∼50% of student responses across all three question contexts (Table 3). Reasoning type "High to Low to Do Its Job" was the least common, used by 2% to 3% of responses for the plant and animal (living organisms) questions, respectively. Reasoning types "High to Low to Do Its Job" and "Do Its Job" were not used when answering the nonliving context.We omitted reasoning types with eight or fewer responses before statistically analyzing the impact of question context on student reasoning: "Do Its Job," "High to Low to Do Its Job," and "Random." Removing these three types allowed us to perform multinomial regression on the remaining types, which all had sufficient sample sizes. This analysis found that the question context did not significantly affect the combined reasoning type students used in their explanations (Table 4).</p> <p>Open in Viewer</p> <p>Table 4. Odds ratios, standard errors, and P values for the multinomial regression model addressing RQ2: Does question context impact the reasoning types students use when solving diffusion problems about random motion?</p> <p> <ephtml> <table><thead><tr><th>Predictor/Comparison (Ref: Properties)</th><th>Odds Ratio</th><th>Standard Error</th><th><italic>P</italic> Value</th></tr></thead><tbody><tr><td><p>Animal (ref: nonliving)</p></td><td /><td /><td /></tr><tr><td><p> H to L</p></td><td><p>−0.07</p></td><td><p>0.46</p></td><td><p>0.89</p></td></tr><tr><td><p> H to L Then Random</p></td><td><p>−0.62</p></td><td><p>0.59</p></td><td><p>0.30</p></td></tr><tr><td><p>Plant (ref: nonliving)</p></td><td /><td /><td /></tr><tr><td><p> H to L</p></td><td><p>−0.29</p></td><td><p>0.46</p></td><td><p>0.53</p></td></tr><tr><td><p> H to L Then Random</p></td><td><p>−0.05</p></td><td><p>0.53</p></td><td><p>0.93</p></td></tr><tr><td><p>GPA*</p></td><td /><td /><td /></tr><tr><td><p> H to L</p></td><td><p>0.61</p></td><td><p>0.38</p></td><td><p>0.11</p></td></tr><tr><td><p> H to L Then Random</p></td><td><p>1.28</p></td><td><p>0.55</p></td><td><p>0.02</p></td></tr></tbody></table> </ephtml> </p> <p>*To directly compare the incoming grade point average (GPA) associated with "High to Low" ("H to L") responses and "H to L Then Random" responses, we releveled the outcome variable. This comparison revealed no significant difference between these groups (odds ratio = 0.68; standard error = 0.50; <emph>P</emph> = 0.18).</p> <p>By far the most common reasoning type across all GPAs was "High to Low," in which students used knowledge resources related to directional movement without incorporating random motion (Fig. 2). This reasoning type was used frequently regardless of GPA. However, the other two reasoning types did vary with GPA (Fig. 2 and Table 4). Students with higher GPAs were more likely to incorporate knowledge resources related to random motion ("High to Low Then Random"), while students with lower GPAs were more likely to activate resources related to the properties of the system such as "gases have polarity" ("Properties").</p> <p>Open in Viewer</p> <p>PHOTO (COLOR): Figure 2. Predicted probability of type of diffusion reasoning based on grade point average (GPA). GPA was recorded on a 4-point scale with a sample range of 1.68 to 4.0 and was included in our statistical modeling as a fixed effect. Each combined reasoning type is represented as a separate probability curve. Regardless of GPA, students were more likely to use types "High to Low" or "High to Low to Do Its Job." These data related to research question 1: Does question context impact the reasoning types students use when solving diffusion problems about random motion?</p> <hd id="AN0190644494-18">RQ3: To What Extent Are Students Consistent in the Reasoning Types They Use across Two Questi...</hd> <p>We found that, in general, students were consistent (76%) when reasoning about diffusion across question pairs (Table 3 and Fig. 3). Eighty-two percent of students used the "High to Low" reasoning type on both question one and two, which made this the most consistent reasoning type. Further, of the students who were inconsistent (48 of 197, 24%), we found most students switched from "High to Low" to another reasoning type, or from another reasoning type to "High to Low" (63%).</p> <p>Open in Viewer</p> <p>DIAGRAM: Figure 3. Alluvial diagram of type of diffusion reasoning on homework assignment. Each student was randomly assigned 1 of 3 pairs of questions (plant and animal, plant and nonliving, or animal and nonliving). The question order within each pair was randomized to control for effects of item order. On the alluvial, each stripe of color represents the reasoning level a student used on the first item connected to the level of reasoning that same student used on the second item. These data relate to research question 3: To what extent are students consistent in the reasoning types they use across two question contexts? H, high; L, low.</p> <p>Though most students were consistent, those who used the reasoning types "Do Its Job" (<emph>n</emph> = 8) and "High to Low to Do Its Job" (<emph>n</emph> = 3) were more likely to change their reasoning on the second item, with 25% and 0% consistency, respectively. This is likely due to the nature of the reasoning type itself. Many of these students were given the plant or animal question alongside the bag question, and students might have found it unreasonable to reason about the function or needs of a bag of air in a beaker of water. Thus these students activated alternative knowledge resources to reason.While most students that used "High to Low" reasoning were consistent, we noticed a pattern in how students who were inconsistent shifted their reasoning type from the first item to the second item. Most students that originally used "High to Low" reasoning shifted to "Properties." These students activated "things move from high to low" or related knowledge resources in one question but did not activate these same resources in response to the second question. Similarly, while over half of the students who used the "Random" or "High to Low Then Random" reasoning type were consistent, there was a small group of students that changed reasoning types for the second item and only activated "things move from high to low" or related resources.</p> <hd id="AN0190644494-19">DISCUSSION</hd> <p>Using the resources framework, we identified knowledge resources and patterns in the coordination of those knowledge resources that we have categorized as reasoning types. The resource "things move from high to low" and related resources such as "things move to equilibrium/become balanced" and "concentration gradients are driving forces for movement" were the most commonly used and are a defining feature in three of the six reasoning types ("High to Low," "High to Low to Do Its Job," and "High to Low Then Random"). The three contexts of the diffusion question (animal, plant, or nonliving) did not meaningfully affect the reasoning type used; "High to Low" reasoning was the most frequently used regardless of context and students' incoming GPA. Further, while most students reasoned consistently across question contexts, students who activated "things move from high to low" or related resources were more likely to reason consistently than those who did not. These results are in agreement with studies that found that most students use high to low when reasoning about diffusion (<reflink idref="bib4" id="ref19">4</reflink>, 7–9).Nehm and others (<reflink idref="bib20" id="ref20">20</reflink>, 21, 23) have found that changing the context of isomorphic questions often alters the knowledge resources students use, which leads to students providing different answers to the same basic question. Contrary to these findings, our results suggest that when students are given a question about particle movement or images showing different concentrations in different compartments, regardless of the context, the "things move from high to low" and related resources are much more commonly activated than "random motion" or "properties." Knowledge resources that are more commonly activated by a given context have a higher cueing priority than less commonly activated ones (<reflink idref="bib17" id="ref21">17</reflink>, 30). We propose that because "things move from high to low" has such a strong cueing priority with questions showing different concentrations in different compartments and/or the word "equilibrium," it overcomes question context. This high cueing priority leads to a high consistency of reasoning across contexts, which is consistent with findings from other studies (<reflink idref="bib31" id="ref22">31</reflink>, 32).Why does "things move from high to low" have such a high cueing priority? We propose that its high cueing priority is built on an intuitive understanding of phenomena that people learn through experience with the physical world [i.e., phenomenological primitives (p-prims); Ref. 17]. These intuitive knowledge resources are basic, context dependent, and result in highly satisfying explanations. In the case of reasoning in diffusion, students draw on an intuitive understanding of equilibrium. This intuitive understanding includes the idea that a system has some natural stability and that a return to equilibrium is the natural result of being in disequilibrium. diSessa (<reflink idref="bib17" id="ref23">17</reflink>) offers the following example: "An absence or sparseness of material next to an abundance leads, primitively, to flow and reequilibration. The space left by a scoop taken out of a body of sand or water is refilled. Equilibration here can serve as a replacement for more mechanistic explanations, for instance, that there are forces that cause the return to equilibrium."Intuitive knowledge resources are grounded in students' observations and experiences in the world and are therefore more familiar than the unobservable concept of the random motion of particles. When students see an image with a high concentration of a substance on one side of a barrier and a small amount of a substance on the other, they recognize that the system is out of balance and their intuitive equilibrium resources are cued. Consequently, they reason that the concentrations will naturally come to be balanced over time by the movement of particles from high to low concentrations. "High to Low" and related resources are built off of these intuitive resources, hence their high cueing priority.For some students, the living contexts of the plant and animal questions activated knowledge resources about the "needs" of these organisms (which defined the reasoning type "Do Its Job"). We suggest that for these students the "needs" resources hold a higher cueing priority than "things move from high to low" resources. Furthermore, we propose that the "needs" resources may be so satisfying for these students that they may impede the activation of the "things move from high to low" resource. This is consistent with studies by Anderson and colleagues (<reflink idref="bib33" id="ref24">33</reflink>, 34), who found that force-dynamic reasoning, which explains events as being performed by actors with needs that they must meet, is very satisfying for students. When given the question about the nonliving bag of air, these students' "needs" resources were not cued in the same way, and they now had to consider alternative explanations. Only in this situation of cognitive dissonance were resources with lower cueing priority activated. For some of the students, these were knowledge resources about the properties of the system such as "gases have polarity," while for others they activated "things move from high to low" resources.Another group of students activated both "things move from high to low" and "random motion" knowledge resources (reasoning type "High to Low Then Random"). These students recognized that random motion exists and plays a role in particle movement, but only after equilibrium is reached, as their "things move from high to low" resources held a higher cueing priority. We propose that, as a result, these students struggled to reconcile both types of resources, leading them to incorrectly reason that random motion only "takes over" after the system reaches equilibrium.Students' limited use of "random motion" resources in their reasoning may reflect deeper challenges with understanding randomness itself. Prior studies have shown that students struggle to reason about randomness and probability across domains, including mathematics, chemistry, and biology (10–13). Diffusion has been suggested to be difficult because it is an emergent and unobservable process: students must reason about how random interactions at the molecular scale produce a predictable net outcome at the macroscopic level (<reflink idref="bib7" id="ref25">7</reflink>, 14). The appearance of directed movement from high to low concentration may reinforce deterministic or goal-directed interpretations, especially when instruction emphasizes these outcomes without explicitly addressing the underlying stochastic mechanism.Random motion is noted, but not emphasized, in national frameworks as the mechanism underlying diffusion. The BioCore Guide (<reflink idref="bib35" id="ref26">35</reflink>), which elaborates the <emph>Vision and Change</emph> core concepts into a more detailed framework for curricular development, explicitly states that students should learn "intracellular and intercellular movement of molecules occurs via <emph>1</emph>) energy-demanding transport processes and (<emph>2</emph>) random motion," and notes that a molecule's movement is affected by its thermal energy, size, electrochemical gradient, and biochemical properties. The discipline of physiology has identified 14 core concepts for undergraduate physiology instruction (<reflink idref="bib36" id="ref27">36</reflink>). One core concept, "flow down gradients" emphasizes passive movement down energy gradients, e.g., concentration or partial pressure gradients, but does not explicitly reference the molecular mechanism of random motion (<reflink idref="bib37" id="ref28">37</reflink>). However, the related core concept of "physics/chemistry" states that physiological processes follow the laws of physics and chemistry and indirectly implies random molecular motion as the causal mechanism for diffusion (<reflink idref="bib36" id="ref29">36</reflink>).This minimal emphasis is reflected in how diffusion and random motion are typically presented in introductory biology and physiology textbooks. Introductory textbooks typically describe diffusion as the net result of molecules moving randomly, emphasizing the absence of any active driving force (38–42). Textbook sections on this topic are often limited to mentioning Brownian motion by name or conceptually, while the main focus tends to be on the outcome, i.e., net movement down a gradient. Introductory biology and physiology courses may rely on a chemistry prerequisite to introduce the students to the kinetic molecular theory underlying the mechanism of diffusion. Prior work by Cooper and colleagues (43–45) has shown that students often struggle to transfer knowledge from chemistry to biology, particularly when instructional emphasis differs. Without explicit instructional support, students may default to familiar deterministic resources and fail to coordinate their understanding with less intuitive ideas of random molecular motion with diffusion phenomena in biological systems.Another source of instructional material would be course learning objectives. In 2024, Hennessey and Freeman (<reflink idref="bib46" id="ref30">46</reflink>) developed a nationally endorsed set of lesson-level learning objectives for a year-long introductory biology course for majors. To create this set of learning objectives, they collected 3,000 introductory biology learning objectives from 63 faculty members currently teaching introductory biology from across the United States. Through a rigorous iterative process of revision, reorganization, and feedback involving over 800 biology instructors, they produced a final list of 163 recommended learning objectives. While this set includes multiple references to randomness and probability in the context of genetics and evolution, they do not include random motion in relation to molecular movement. This absence strongly suggests that, in practice, the majority of introductory biology instruction on molecular movement emphasizes the outcome, movement from high to low concentration, rather than engaging students in mechanistic reasoning about the stochastic basis of diffusion.</p> <hd id="AN0190644494-20">Teaching Implications</hd> <p>Prior research on students' reasoning about diffusion has focused on identifying and proposing ways to correct or replace student misconceptions on this topic (<reflink idref="bib5" id="ref31">5</reflink>, 8, 47). The resources perspective allows us to view students' ideas from an asset-based rather than a deficit-based perspective (<reflink idref="bib15" id="ref32">15</reflink>). Instead of "correcting" or "confronting" students' misconceptions, instructional strategies should aim to create opportunities for students to explore and integrate random motion knowledge resources (<reflink idref="bib16" id="ref33">16</reflink>). Instructors might use computational simulations to model random particle motion and guide students to observe how the emergent pattern of "High to Low" arises. For example, Meir et al. (<reflink idref="bib48" id="ref34">48</reflink>) demonstrated that allowing students to observe virtual molecules moving randomly in a simulation helped some revise their thinking. After viewing the simulation, fewer students drew unidirectional arrows from high to low when asked to depict diffusion. This approach allows students to connect their intuitive understanding of movement from high to low concentration with the mechanistic understanding of randomness at the particle level. By bridging these scales, students can develop a more robust and transferable understanding of diffusion as an emergent process.Sometimes it may be productive for students to reason about diffusion as a directed process, substances moving from high to low concentration, because this framing supports accurate predictions in many contexts. Whether the high-to-low heuristic is sufficient depends on the specific phenomenon students are trying to understand. In some cases, understanding diffusion as an emergent process driven by random molecular motion is essential (see examples in the introduction). A useful analogy in thinking about this comes from Gupta et al. (<reflink idref="bib49" id="ref35">49</reflink>), who contrast emergent and direct processes using the example of blood flow. On a macroscopic level, blood flow appears to be directly caused by the heart's pumping action and is typically taught that way. Yet at the molecular level, blood flow is also emergent; it arises from the random motion and interactions of particles under pressure gradients and thermodynamic constraints. This analogy highlights how both a directed and emergent understanding can be valid and instructionally useful, depending on scale and purpose. Supporting students in navigating these multiple understandings is key to developing flexible, mechanistic reasoning and knowing when a mechanistic unpacking is needed.In addition to using simulations, another instructional strategy that can help students effectively activate and coordinate different knowledge resources is repeated and focused practice with feedback (<reflink idref="bib50" id="ref36">50</reflink>). Without that focused practice, resources may not be coordinated properly, leading to "incorrect" answers. However, despite this lack of complete coordination, these resources are still useful. Some students who used "High to Low Then Random" reasoning activated and coordinated both the "things move from high to low" and "random motion" knowledge resources. They just did not recognize the role of random motion throughout the entire diffusion process. However, their activation of random motion resources after equilibrium was achieved indicates they had productive prior knowledge. With different coordination, this knowledge could have supported more accurate reasoning. Similarly, students who used "High to Low to Do Its Job" reasoning activated and coordinated both "things move from high to low" and "needs" resources. While needs-based reasoning is not as productive for predicting particle movement, "things move from high to low" is a good first step but should not be the final step.As instructors, we decide which mechanisms to "black box" or unpack when reasoning about phenomena (<reflink idref="bib51" id="ref37">51</reflink>, 52). While it may not be necessary to unpack random motion in every diffusion-related context, students still benefit from understanding what lies inside the black box. Developing a transferable understanding of diffusion's underlying mechanism gives students the capacity to decide when unpacking is warranted, particularly in novel or mechanistically complex scenarios. By making the mechanism visible, we empower students to use it strategically, rather than needing to default to heuristics.</p> <hd id="AN0190644494-21">Limitations</hd> <p>The homework items that we provided students for this research study were short-answer questions. Most students only answered these questions in a sentence or two, and some only in a few words. We realize that these short responses may not be fully representative of the knowledge resources that the students are using to reason. In future research, using think-aloud interviews would allow us to further probe students in the case that their answers are contradictory, confusing, or not complete.We did not include students from other institutions in this study, and thus our student population is limited. All of the students who participated in this study were from an R1 research institution, which may not accurately represent responses that we may have gotten if our sample included other institution types. In the future, expanding our sample to include more diversity of student backgrounds will be important for further investigating how students reason about diffusion.</p> <hd id="AN0190644494-22">Conclusions</hd> <p>In this paper, we present the first biology/physiology education study that investigates students' understanding of diffusion using a resources framework. By leveraging this framework, we found that students most frequently used "High to Low" and related knowledge resources and rarely coordinated them with "random motion" resources. We also found that when students are given a question that shows labeled concentrations in separate compartments, regardless of the context, they are more likely to activate "High to Low" resources and that most students were consistent in their reasoning. We argue that these results support the claim that "High to Low" and related resources have a very high cueing priority for most students. While "High to Low" resources are productive and often bring students to satisfying answers, we recommend that instructors help students unpack the random motion "black box." When students are better able to coordinate "random motion" resources, they now have a tool they can use to develop a more nuanced understanding of complex physiological phenomena.</p> <hd id="AN0190644494-23">DATA AVAILABILITY</hd> <p>Data will be made available upon reasonable request.</p> <hd id="AN0190644494-24">ACKNOWLEDGMENTS</hd> <p>We thank the students who participated in this research. We thank the Doherty and Masani Labs at Michigan State University and the University of Washington Biology Education Research Group, especially Casey Self.</p> <hd id="AN0190644494-25">GRANTS</hd> <p>This work was funded in part by National Science Foundation Grant 1661263.</p> <hd id="AN0190644494-26">DISCLAIMERS</hd> <p>The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation.</p> <hd id="AN0190644494-27">DISCLOSURES</hd> <p>No conflicts of interest, financial or otherwise, are declared by the authors.</p> <hd id="AN0190644494-28">AUTHOR CONTRIBUTIONS</hd> <p>E.E.S., M.P.W., and J.H.D. conceived and designed research; E.E.S. and J.H.D. performed experiments; A.L.V., E.E.S., Z.A.K., J.D.P., M.B.S., J.V., and J.H.D. analyzed data; A.L.V., E.E.S., M.P.W., Z.A.K., J.D.P., M.B.S., J.V., and J.H.D. interpreted results of experiments; A.L.V. and J.H.D. prepared figures; A.L.V. and J.H.D. drafted manuscript; A.L.V., E.E.S., M.P.W., and J.H.D. edited and revised manuscript; A.L.V., E.E.S., M.P.W., Z.A.K., J.D.P., M.B.S., J.V., and J.H.D. approved final version of manuscript.</p> <ref id="AN0190644494-29"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Michael JA, McFarland J. 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  Data: Directed or Random? Student Reasoning about Diffusion across Contexts
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Aeryn+L%2E+VanDerSlik%22">Aeryn L. VanDerSlik</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0007-6590-2602">0009-0007-6590-2602</externalLink>)<br /><searchLink fieldCode="AR" term="%22Emily+E%2E+Scott%22">Emily E. Scott</searchLink><br /><searchLink fieldCode="AR" term="%22Mary+Pat+Wenderoth%22">Mary Pat Wenderoth</searchLink><br /><searchLink fieldCode="AR" term="%22Zachary+A%2E+Kam%22">Zachary A. Kam</searchLink><br /><searchLink fieldCode="AR" term="%22Jasmine+D%2E+Parker%22">Jasmine D. Parker</searchLink><br /><searchLink fieldCode="AR" term="%22Maya+B%2E+Shah%22">Maya B. Shah</searchLink><br /><searchLink fieldCode="AR" term="%22Joseph+Vieregge%22">Joseph Vieregge</searchLink><br /><searchLink fieldCode="AR" term="%22Jennifer+H%2E+Doherty%22">Jennifer H. Doherty</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2333-1692">0000-0002-2333-1692</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Advances+in+Physiology+Education%22"><i>Advances in Physiology Education</i></searchLink>. 2025 49(4):1014-1025.
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  Data: American Physiological Society. 9650 Rockville Pike, Bethesda, MD 20814-3991. Tel: 301-634-7164; Fax: 301-634-7241; e-mail: webmaster@the-aps.org; Web site: https://www.physiology.org/journal/advances
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  Data: 12
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  Data: 2025
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  Data: National Science Foundation (NSF)
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  Data: 1661263
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Physiology%22">Physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+Concepts%22">Scientific Concepts</searchLink><br /><searchLink fieldCode="DE" term="%22Motion%22">Motion</searchLink><br /><searchLink fieldCode="DE" term="%22Context+Effect%22">Context Effect</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability%22">Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Public+Colleges%22">Public Colleges</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Universities%22">Research Universities</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Education%22">Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+Level%22">Knowledge Level</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink>
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  Label: DOI
  Group: ID
  Data: 10.1152/advan.00185.2025
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  Label: ISSN
  Group: ISSN
  Data: 1043-4046<br />1522-1229
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Diffusion is a critical component of the Physiology Core Concept of flow down gradients and is fundamental to understanding how ions, gases, or signaling molecules travel short distances in the body. When asked about diffusion, students often reason successfully using the "things move from areas of high to low concentration" heuristic but struggle to understand that random motion underlies this movement. We investigated the different knowledge resources students use when reasoning about diffusion across different contexts. Additionally, we determined if item context impacted the resources students activated and how consistent students were in their reasoning. We gave students a pair of questions from three contexts (plant, animal, and nonliving) that asked them to predict and explain where a molecule of gas would be located before and after equilibrium. Using the resources framework, we identified 14 common knowledge resources and 6 different patterns in resource activation. "High to low" and related resources were used in 73% of responses. Only 23% of responses included at least one "random motion" resource, and the vast majority of these responses described random particle motion starting only after equilibrium is reached. Item context did not significantly affect the resources students used. Students were also mostly consistent in their reasoning, with 76% using similar resources across the two items. These findings indicate that "high to low" and related resources have a high cueing priority for many students and that instructors should help students unpack random motion as the mechanism underlying diffusion instead of leaving it "black boxed."
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  Data: 2025
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  Data: EJ1490867
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    Subjects:
      – SubjectFull: Thinking Skills
        Type: general
      – SubjectFull: Physiology
        Type: general
      – SubjectFull: Scientific Concepts
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      – SubjectFull: Motion
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      – SubjectFull: Context Effect
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      – SubjectFull: Science Education
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      – SubjectFull: Knowledge Level
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      – SubjectFull: Undergraduate Students
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      – TitleFull: Directed or Random? Student Reasoning about Diffusion across Contexts
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