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Choose Your Evidence: Scientific Thinking Where It May Most Count

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Title: Choose Your Evidence: Scientific Thinking Where It May Most Count
Language: English
Authors: Kuhn, Deanna (ORCID 0000-0002-1321-3289), Modrek, Anahid S.
Source: Science & Education. Feb 2022 31(1):21-31.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 11
Publication Date: 2022
Document Type: Journal Articles
Reports - Evaluative
Education Level: Higher Education
Postsecondary Education
Descriptors: Scientific Literacy, Evidence, Science Process Skills, Data, College Students, Metacognition, Thinking Skills, Mathematics Skills, Mathematical Logic
DOI: 10.1007/s11191-021-00209-y
ISSN: 0926-7220
Abstract: Schooling traditionally affords students more experience in learning and practicing procedures than in identifying what a situation calls for. When asked to choose appropriate numerical data to support their causal claims, college students perform surprisingly poorly. In one case we describe, almost all chose limited, inconclusive data as sufficient evidence, despite having available the more comprehensive data needed to support their claim and despite their established competence to employ such data for this purpose. Our objective in highlighting this weakness is to make a case that choosing one's evidence warrants the status of an important metacognitive intellectual skill and educational objective, one central to but that extends well beyond the domains of scientific and mathematical reasoning and hence warrants greater attention both in and beyond the science curriculum. People may choose evidence to justify their assertions in an ill-considered way, with potential adverse effects in both private and public communication.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1326528
Database: ERIC
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  Value: <anid>AN0154873208;nmo01feb.22;2022Jan27.07:19;v2.2.500</anid> <title id="AN0154873208-1">Choose Your Evidence: Scientific Thinking Where It May Most Count </title> <p>Schooling traditionally affords students more experience in learning and practicing procedures than in identifying what a situation calls for. When asked to choose appropriate numerical data to support their causal claims, college students perform surprisingly poorly. In one case we describe, almost all chose limited, inconclusive data as sufficient evidence, despite having available the more comprehensive data needed to support their claim and despite their established competence to employ such data for this purpose. Our objective in highlighting this weakness is to make a case that choosing one's evidence warrants the status of an important metacognitive intellectual skill and educational objective, one central to but that extends well beyond the domains of scientific and mathematical reasoning and hence warrants greater attention both in and beyond the science curriculum. People may choose evidence to justify their assertions in an ill-considered way, with potential adverse effects in both private and public communication.</p> <p>Keywords: Judgment; Reasoning; Argument; Evidence; Discourse; Epistemology; Choice; Science education; Math education</p> <p>The original version of this article was revised: The original version of this article unfortunately contains incorrect article title due to a typesetting mistake.</p> <hd id="AN0154873208-2">Introduction</hd> <p>We live not only in a time of information proliferation (Hills, [<reflink idref="bib12" id="ref1">12</reflink>]) but a time that allows and even encourages us to "Choose your news," and, from this news, to choose your facts. The latter implies our choosing the evidence to support those facts, should we need to do so. The following discussion illustrates some common possibilities in choosing evidence to substantiate a claim.<emph>Al:</emph> The US tax system is unfair. It's rich people who are most likely to cheat on their taxes.<emph>Ben:</emph> I know. Some of us should write a letter to the editor making people aware of this.<emph>Al:</emph> Should we include anything to prove our point?<emph>Clay:</emph> I've known a lot of well-off people who cheat. We just need to point them out.<emph>Don:</emph> But there are others who don't.<emph>Al:</emph> But how many?<emph>Ben:</emph> Maybe we could show there are more that do than don't. That should do it.<emph>Clay:</emph> I think it would be more convincing to focus on the cheaters and show they are most often rich.<emph>Al:</emph> But there are lots of poor people. At least some of them must cheat.<emph>Ben:</emph> Right. And there are a lot of rich people who don't cheat. So I think that kind of disproves our point. It's not clear-cut. Maybe we'd better forget this.<emph>Don:</emph> But wait. There are probably a lot more rich cheaters and poor honest taxpayers, than there are poor cheaters and rich honest ones. So that would prove our point.<emph>Ed:</emph> Hold on, I've got a simpler idea. Why don't we just compare the percentage of rich people who cheat to the percentage of poor people who cheat?</p> <p>Evidence in the form of Ed's final suggestion is appealingly simple. The form is also the one seen very frequently in a wide variety of print or other media. Yet, each of the other forms portrayed above is not uncommon and is based on responses we observed in recent work (Kuhn & Lerman, [<reflink idref="bib27" id="ref2">27</reflink>]). We think they are important because they highlight a form of everyday scientific thinking having serious consequences. Moreover, it is a form that science educators arguably have neglected to students' disadvantage.</p> <p>In both science classrooms and in research studies of scientific thinking, the question students are most commonly posed is this: "Here are some findings; what conclusions can you draw?" Outside these contexts, in print or electronic media or in informal discourse, we all constantly encounter assertions, often unaccompanied by any data that might support them. We might ask ourselves, "Is this really true?" or we might be wrongly influenced to accept it by the number of times we have heard the claim. But rarely do we go to the trouble of asking ourselves, "What evidence would be needed to show the claim is correct?" Nor is it a hypothetical question very often asked in science classrooms. Yet, asking what data would support or falsify a new hypothesis is the first question that professional scientists typically ask in taking their work in a new direction.</p> <p>Our objective here is to make a case that choosing one's evidence warrants the status of an important metacognitive intellectual skill and educational objective of broad relevance, one that is central to but extends well beyond domains of scientific and mathematical reasoning. Our focus in making this case is the common context in which people have a free hand in choosing what serves as sufficient evidence to support their own claims or to accept others' claims—hence a context that invokes disposition (rather than only procedural or strategic cognitive competence) and serves to identify the epistemological standards an individual subscribes to (Greene, Sandoval, & Braten, [<reflink idref="bib10" id="ref3">10</reflink>]; Mills, [<reflink idref="bib41" id="ref4">41</reflink>]; Metz, Weisberg & Weisberg, [<reflink idref="bib39" id="ref5">39</reflink>]; Moshman, [<reflink idref="bib46" id="ref6">46</reflink>]).</p> <p>A rapidly expanding body of work now exists on argumentation and, most recently in particular, the role of evidence in argumentation—studies that highlight the complexities of different forms of evidence and the distinct roles they play in relation to a claim (Duncan, Chinn, & Barzilai, [<reflink idref="bib7" id="ref7">7</reflink>]; Hemberger et al., [<reflink idref="bib11" id="ref8">11</reflink>]; Iordanou & Constantinou, [<reflink idref="bib16" id="ref9">16</reflink>]; Jiménez-Aleixandre & Puig, [<reflink idref="bib17" id="ref10">17</reflink>]; Macagno, [<reflink idref="bib32" id="ref11">32</reflink>]; Macagno & Walton, [<reflink idref="bib33" id="ref12">33</reflink>]; McNeill & Berland, [<reflink idref="bib35" id="ref13">35</reflink>]; Miralda-Banda, Garcia-Mila, & Felton, [<reflink idref="bib42" id="ref14">42</reflink>]; Monteira & Jiménez-Aleixandre, [<reflink idref="bib45" id="ref15">45</reflink>]; Sampson & Clark, [<reflink idref="bib55" id="ref16">55</reflink>]; Shi, [<reflink idref="bib58" id="ref17">58</reflink>], [<reflink idref="bib59" id="ref18">59</reflink>]; Villarroel, Felton, & Garcia-Mila, [<reflink idref="bib66" id="ref19">66</reflink>]). These studies point to overall weakness in key skills of recognizing the critical role of and effectively using evidence to both support and weaken claims, both in individual written and verbal argument and in dialogic argumentation with others. Such skills do show improvement, however, with engagement and practice (Kuhn & Crowell, [<reflink idref="bib26" id="ref20">26</reflink>]; Hemberger et al., [<reflink idref="bib11" id="ref21">11</reflink>]; Iordanou, [<reflink idref="bib14" id="ref22">14</reflink>]; Iordanou & Constantinou, [<reflink idref="bib16" id="ref23">16</reflink>]; Rapanta, [<reflink idref="bib49" id="ref24">49</reflink>]; Reznitskaya & Wilkinson, [<reflink idref="bib51" id="ref25">51</reflink>]; Ryu & Sandoval, [<reflink idref="bib54" id="ref26">54</reflink>]; Shi, [<reflink idref="bib58" id="ref27">58</reflink>]). In general, progress in dialogic argument precedes that in individual argument (Kuhn, [<reflink idref="bib24" id="ref28">24</reflink>]; Kuhn & Moore, [<reflink idref="bib29" id="ref29">29</reflink>]; Mayweg-Paus & Macagno, [<reflink idref="bib34" id="ref30">34</reflink>]; Shi, [<reflink idref="bib58" id="ref31">58</reflink>]). Initially, the essays of novices consist largely of supporting arguments for a favored position. Later to appear are attention to and arguments seeking to weaken the opposing position. Only more gradually do essays begin to include mention of possible strengths of the opposing position or weaknesses of the favored position, and later still the "However" statements that serve to connect and weigh opposing arguments. Also gradually increasing is the use of evidence to weaken as well as support claims (Hemberger et al., [<reflink idref="bib11" id="ref32">11</reflink>]; Shi, [<reflink idref="bib58" id="ref33">58</reflink>]).</p> <p>In the present discussion, our focus shifts from the one common to the body of research just cited—individuals' competence in making effective use of evidence in their arguments and argumentation—to a focus on their disposition to do so. Disposition becomes critical when people have a largely free hand in deciding whether and how to bring to bear evidence to support a claim they believe is correct, a situation we have suggested is a common one in natural conversational contexts. Yet, it has been the subject of much less investigation, except perhaps less directly in research on epistemological understanding (Greene et al., [<reflink idref="bib10" id="ref34">10</reflink>]; Moshman, [<reflink idref="bib46" id="ref35">46</reflink>]), in which people are queried regarding how they know what to accept as true. Another related contemporary line of research examines how people react to "fake news" and the attributes of the source and of the receiver of such news in affecting whether such news will be accepted and whether it will be relayed to others (Barzilai, Tzadok, & Eshet-Alkalai, [<reflink idref="bib3" id="ref36">3</reflink>]; Barzilai & Zohar, [<reflink idref="bib2" id="ref37">2</reflink>]; De Keersmaecker, Dunning, Pennycook, et al., [<reflink idref="bib4" id="ref38">4</reflink>]; Lazer, Baum et al., [<reflink idref="bib30" id="ref39">30</reflink>]; Pennycook & Rand, [<reflink idref="bib48" id="ref40">48</reflink>]). In addition to the factors of source credibility and cognitive and personality characteristics of the receiver, however, is the form of the evidence one selects as bearing on the assertion and one's judgment regarding its sufficiency and strength. It is this epistemological dimension of understanding of evidence, and its significance in science education, that is our concern here.</p> <p>One of us has written at length about mature epistemological understanding as a developmental achievement that many individuals do not complete (Kuhn, [<reflink idref="bib20" id="ref41">20</reflink>], [<reflink idref="bib21" id="ref42">21</reflink>], [<reflink idref="bib25" id="ref43">25</reflink>]). Its achievement is foundational to the disposition, as distinguished from competence, to think critically or to engage in what has been coined epistemic vigilance (Sperber, Clement et al., [<reflink idref="bib62" id="ref44">62</reflink>]; Settlage & Southerland, [<reflink idref="bib57" id="ref45">57</reflink>]). Early conceptions of knowledge as reflecting an objective reality evolve toward a more correct one of knowledge as constructed by human minds (Iordanou, [<reflink idref="bib15" id="ref46">15</reflink>]). Initially, however, this construction is understood as yielding only a multiplicity of subjective opinions that knowers are free to adopt at will in the form of personal possessions. This multiplist level of understanding offers a way of making sense of adolescents' discovery of the existence of multiple, often seemingly reasonable yet diverging claims. Only at a next level do subjective and objective dimensions become coordinated in an understanding of knowledge as judgment (rather than immutable fact or unconstrained opinion), based on evaluation in a framework of alternatives and evidence. The marshaling of evidence as a means of supporting or challenging claims thus assumes its key role, as an essential tool in distinguishing among conflicting claims.</p> <hd id="AN0154873208-3">An Examination of Evidence Choice Among College Students</hd> <p>In the study we draw on here, 43 college students enrolled in the initial weeks of an introductory statistics course at a private university in the Northeast US. Their ages were mostly in the early twenties, about two-thirds female. Three-quarters identified as White, with the remainder African American, Latino/a, and Asian. The university is selective in its admission and serves a largely homogeneous, upper-middle- to upper-class population whose families can afford to pay the high fees the institution charges. They served not as a convenience sample but as a sample of a population that are widely expected to hold standards higher than those of an unselected population with respect to their exercise of care in drawing unsupported inferences. Critical thinking, after all, is regarded as among the most highly valued achievements of a college-level education.</p> <p>The main problem introduced in writing was this: <emph>A National Health Data Bank have identified children's obesity (excess weight) as a concern and wants to determine the cause. They have collected information from a broad, representative sample of 200 American four-year-olds, These are the numbers they came up with. Each card shows the children they identified who fall into the category shown on that card. The Data Bank researchers now need someone to analyze the results and to write a summary report of</emph><bold><emph>what these findings show</emph></bold><emph>about the causes of young children's being overweight</emph> (bold text in original).</p> <p>Students were presented a set of 19 cards ordered randomly, each containing a different kind of information pertaining to this sample of 200 4-year olds. They were told they could examine and organize the cards in any ways they wished as preparation for writing their reports, and the cards remained available to them while they wrote their reports. The cards fell into two categories: those that contained numerical data and those that did not.</p> <p>Of the 19 cards, 12 introduced numerical data—just a single number, identifying the number of children who did/did not possess a binary attribute and who were/were not overweight, e.g., "This card lists cases of CHILDREN WHO DO NO EXERCISE and ARE NOT OVERWEIGHT. NUMBER OF CASES: 30 [of 200 total children observed]." (See Fig. 1.) The information each card contained is summarized in Table 1, one number per card. As reflected there, the information suggests a strong association between exercise and weight, a moderate association between diet and weight, and a negligible association between parent weight and child weight at age 4. (No information was available regarding cross-classification of individuals in multiple categories, of a sort that would allow assessment of additive or interactive effects of the three identified variables on the outcome variable.)</p> <p>Graph: Fig. 1 Sample evidence card</p> <p>Table 1 Numerical values appearing on 12 individual cards (one value per card)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th /><th><p>Overweight</p></th><th><p>Not overweight</p></th></tr></thead><tbody><tr><td><p>Exercise</p></td><td><p>20</p></td><td><p>90</p></td></tr><tr><td><p>No exercise</p></td><td><p>60</p></td><td><p>30</p></td></tr><tr><td><p>Regular diet</p></td><td><p>40</p></td><td><p>80</p></td></tr><tr><td><p>High-calorie diet</p></td><td><p>40</p></td><td><p>40</p></td></tr><tr><td><p>Parent average</p></td><td><p>20</p></td><td><p>90</p></td></tr><tr><td><p>Parent overweight</p></td><td><p>60</p></td><td><p>30</p></td></tr></tbody></table> </ephtml> </p> <p>Included in the 19 cards were seven that contained no numerical information, included to assess whether statements containing no or incomplete evidence would be regarded as evidence. Four were simply assertions made by a possibly authoritative source (e.g., "Dr. B. Marcy of Beacon clinic claims that parents' body type affects children's likelihood of being overweight"). The remaining three were assertions of prevalence of an outcome variable level (e.g., "Dr. F. Prentice sees a large number of children in his practice and reports that a majority of them are overweight") or of a potentially causal variable level (e.g., "Dr. K. Lester reports a large number of children in her practice do not get enough exercise").</p> <p>Most students organized the numerical cards and began by recording the information from them on a provided blank sheet, using various formats (typically simply a list, but occasionally in a branching chart form or as a histogram). In their reports, most then showed numerical calculations followed by inferences, although some showed only calculations and others only inferences. Patterns of performance were categorized based on how many of the four cells in a cross-tabulation table (as shown in Table 1) a student utilized. A variety of patterns appear, but almost none reflect the correct comparison of two proportions. An illustration of the one that did appears in the final row of Table 2, striking in its simplicity. Descriptions of categories and their frequencies appear in Table 2 by category, with illustrations for each. As seen there, examples for each of the categories show that students in every category displayed little hesitation in advancing causal claims.</p> <p>Table 2 Patterns of performance on causal analysis problem (<emph>n</emph> = 43)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th><p>Pattern</p></th><th><p><italic>n</italic></p></th><th><p>Examples</p></th></tr></thead><tbody><tr><td><p>No data referenced but 1 or more inferences made</p></td><td><p>7</p></td><td><p>Based on the cards, obesity seems to be based on diet, exercise, and overweight parents</p></td></tr><tr><td><p>No data referenced and no inference made</p></td><td><p>2</p></td><td><p>From this data, we would be able to crunch the numbers and compare percentages because of the sample size being consistent across the board</p></td></tr><tr><td><p>1-cell reference with causal inference</p></td><td><p>6</p></td><td><p>Because exercising and not-overeating has the highest total (90/200), it is an indication that children who exercise more are at less risk of being overweight</p></td></tr><tr><td><p>1-cell reference without inference</p></td><td><p>4</p></td><td><p>This card shows the greatest number of cases who exercise and are not overweight, indicating it might be a key factor. However, no direct cause can be determined since there are multiple confounding variables that affect accuracy such as family income</p></td></tr><tr><td><p>2 adjacent cells referenced with causal inference</p></td><td><p>7</p></td><td><p>Overweight children outnumbered not-overweight children in the do-not-exercise category. This tells me that the biggest factor in becoming overweight is not exercising enough</p></td></tr><tr><td><p>2 adjacent cells referenced with non-causal inference</p></td><td><p>5</p></td><td><p>I found that a parent's weight does not play a part in a child's weight because there was a larger outcome of non-overweight children with overweight parents than overweight children with overweight parents</p></td></tr><tr><td><p>2 non-adjacent (diagonal) cells referenced without inference</p></td><td><p>3</p></td><td><p>Some say children who exercise are overweight, and some say they are not. They contradict 1 another. This shows there is no clear-cut answer to the question of factors that cause obesity</p></td></tr><tr><td><p>2 non-adjacent (diagonal) cells referenced with inference</p></td><td><p>3</p></td><td><p>Out of 200 children, 75 had a parent who was not overweight and those children were also not overweight. On the other hand, 35 children out of 200 were overweight and had an overweight parent. So, parent weight matters</p></td></tr><tr><td><p>4-cell reference with causal inference</p></td><td><p>5</p></td><td><p>Exercise matters. There are a high number of kids who exercise and are not overweight. Another high number who do not exercise are overweight. The other 2 cards have little number of cases</p></td></tr><tr><td><p>4-cell proportional reference with causal inference</p></td><td><p>1</p></td><td><p>The data show children who eat a local diet are less likely to become overweight—80 are not and 40 are. High calorie shows an equal number on each side</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>N</emph> = 43. Classification is based on the highest level displayed. Performance patterns differed little when categorizing by modal strategies, which were of course slightly lower</p> <p>The categorization system in Table 2 is an objective one, based only on how many of the four cells were utilized. Categories are listed in the order of the number of cells utilized. Each participant is counted only once, based on the highest performance level exhibited. (Performance patterns differed little when categorizing by modal strategies, which were of course slightly lower.) The majority of reports addressed two or more of the three factors. Among those students who addressed at least two factors, most—26 of 35, or 74%—used the same strategy in analyzing two or more of the three variables for which data were available.</p> <p>One might attribute such causal judgment weaknesses to limitations in skill in proportional reasoning. Long documented are the difficulties students exhibit in mastering proportional reasoning, even among those who have performed well in mathematics to this point (Tourniaire & Pulos, [<reflink idref="bib64" id="ref47">64</reflink>]; VanDooren, Vamvakoussi, & Verschaffel, [<reflink idref="bib65" id="ref48">65</reflink>]). (A weakness in employing all relevant frequencies has been referred to as neglect of base rates in the adult cognition literature; Kahneman, Slovic, & Tversky, [<reflink idref="bib19" id="ref49">19</reflink>].) A second problem administered either before or after the main problem, however, made this interpretation less likely: "Susie and Sally are setting up lemonade stands. Susie is mixing 6 cans of lemon juice and 3 cans of water in her pitcher. Sally is mixing 4 cans of lemon juice and 1 can of water in hers. Whose lemonade will taste more lemon-y and why?" All but one student solved this problem correctly and provided appropriate reasoning, despite the problem structure encouraging an erroneous conclusion. A correct response incorporated all four values into the calculation and related them in terms of a ratio (2:1 vs. 4:1) or proportion (66% vs. 80%). Only two students failed to do so, comparing only the absolute amounts of water in each pitcher ("Sally's because she's using less water") in one case or in the other using a proportional strategy but incorrectly. Thus, an inability to reason proportionally does not explain students' challenges in the main problem. Nor did administration of the lemonade problem before rather than after the main problem to half the sample serve as a prompt to improve performance.</p> <hd id="AN0154873208-4">What Might Students (and Educators) Be Missing?</hd> <p>Wherein then lies the problem? At one level, such data might be taken as a further cry of alarm regarding poor mathematics achievement of American students, up to and including students of college level. Mathematical proficiency at the level we examined extends well beyond mathematics itself. It is fundamental to science education (Hilton & Hilton, [<reflink idref="bib13" id="ref50">13</reflink>]), as well as foundational for numerous other fields within business, economics, engineering, and beyond. The findings are consistent with lines of research in developmental psychology that have demonstrated early competence yet later weaknesses that become apparent at an older age. Proportional reasoning is one such case. Denison and Xu ([<reflink idref="bib5" id="ref51">5</reflink>]) describe infants' abilities to make at least implicit judgments of proportion, and they offer the recommendation that proportion can be introduced profitably much earlier in the school curriculum, thereby weakening what has been referred to as the whole number bias that is reinforced by early mathematics instruction (Siegler, Thompson, & Schneider, [<reflink idref="bib60" id="ref52">60</reflink>]).</p> <p>The evidence described here certainly supports the recommendation of earlier emphasis on proportion in the mathematics as well as science curriculum. At the same time, it suggests that more or better instruction on proportion is not the whole answer. Students were almost all able to execute a proportional reasoning strategy in a simple numerical context. Executing the strategy, however, did not, in turn, serve as a prompt to apply the strategy in the main problem, which offered three distinct opportunities to do so (for each of the three proposed factors possibly linked to outcome). Those who encountered and successfully solved the lemonade problem immediately before encountering the main problem did no better on the main problem than did those who encountered it only after the main problem. This finding highlights the broad and ubiquitous dilemma in educational psychology of failure to achieve transfer, and more specifically in the mathematical context, it points to the need to maintain continuing interplay and coordination between procedural and conceptual knowledge (Rittle-Johnson, [<reflink idref="bib52" id="ref53">52</reflink>]).</p> <p>Both elementary causal and proportional reasoning, in any case, should have been within the competence of college students. Children in the first decade of life commonly infer causal relations based simply on co-occurrence, but by late adolescence, most have learned the control-of-variables strategy of holding other factors constant before inferring a causal relation between antecedent and outcome, even though they continue to struggle with multivariable causality involving multiple contributors to an outcome (Kuhn, [<reflink idref="bib22" id="ref54">22</reflink>], [<reflink idref="bib25" id="ref55">25</reflink>]; Lee & Wilkerson, [<reflink idref="bib31" id="ref56">31</reflink>]; Teig, Scherer, & Kjaernsli, [<reflink idref="bib63" id="ref57">63</reflink>]).</p> <p>A further factor in participants' favor in the present task is the fact that the context in which students were asked to apply these skills was a favorable one, and context means a great deal in affecting the classroom behaviors students display (Jiménez-Aleixandre, Rodriguez, & Duschl, [<reflink idref="bib18" id="ref58">18</reflink>]; Kuhn, [<reflink idref="bib20" id="ref59">20</reflink>]; Settlage & Southerland, [<reflink idref="bib57" id="ref60">57</reflink>]). The context was a statistics class in which students would have been aware that their mathematical skills were to be made use of.</p> <p>If the context was supportive and skill in proportional and causal reasoning was not at stake, what then prevented college students from applying their skills in the main problem, where it would have been not just highly useful but essential? Why did they not recognize the need to do so? The answer to this question reaches beyond the realms of mathematics or science. Our proposed answer to the question was suggested in the introduction to this work. The main task affords students an uncustomary degree of freedom in organizing their response to it. It stands in contrast to the task common in science classrooms and in science education research in which students are presented data and asked what conclusions can be drawn. In the present case, students had to choose what data to call on that in their view would serve as sufficient evidence to support an identified claim. As noted earlier, the problem thus becomes one of epistemological standards: What constitutes relevant evidence and what constitutes sufficient evidence? This challenge is not only infrequent in research tasks but infrequent in most students' educational experience as well, in science or math classes as well as elsewhere. Especially in mathematics problems, students early on become aware that the numbers that appear in the problem are the ones to use, and the task is simply to figure out how to do so.</p> <p>Students were able to choose appropriate evidence at least in the limited sense of categorizing the data points according to the potential causal variable being considered, but this achievement required little more than category-name matching. On the positive side, they did, however, largely ignore the cards that lacked numbers and contained only single-case assertions. Those who mentioned them did so only to comment that the assertion had been supported or contradicted by their data analysis. This contrasts with performance of middle-school students to whom we have given this problem, who frequently included reference to one or more of the non-numerical cards as support for one of their conclusions.</p> <p>The performance college students displayed here, in selecting from and implementing procedural knowledge in mathematics to address a real-world problem, we believe bears on long-standing debates in science and mathematics education. Education science is now advanced enough to make clear that students need to find purpose in what they seek to learn or they are unlikely to succeed. Thus, coming to appreciate how mathematics addresses real-world problems is arguably an essential dimension of modern mathematics education. Recent reports (Lee & Wilkerson, [<reflink idref="bib31" id="ref61">31</reflink>]) indicate there remains a long way to go in realizing this objective.</p> <p>A further objective arguably is development in metacognitive and epistemological realms that will allow students to exercise caution in appropriately using their strategic reasoning skills in the wide range of contexts where they apply. The college students reported on here showed little caution in making claims, almost all confirming causal (rather than non-causal) relations (and this applies across all the strategy categories identified). Only one expressed concern with potential interactions among the three variables, despite their likelihood, this student noting that the cards did not provide cross-classification information that would be necessary to assess this possibility. Nor did many show understanding of the critical distinction between correlation and causality, even though a few used such language ("I've determined exercise and diet cause obesity...parent weight is just a correlation"), with only one correctly noting that all the provided data were only correlational, limiting conclusions. Finally, almost all took the three variables for which data were available to be the total set of potential causal contributors. The three who noted the potential contribution of additional variables (named or not) most often did so to explain cases that did not conform to the (diagonal) pattern of association between antecedent and outcome variable.</p> <p>Our own further objective here has been to highlight implications beyond proficiency in statistical reasoning. The epistemological failing of subscribing to very weak evidence standards for making causal inferences that our participants exhibited leads to concern about their susceptibility to accepting without scrutiny similar unsubstantiated claims they encounter both in their academic work and outside of it. The sorts of incomplete data that these students chose as good evidence for a causal connection are ubiquitous in electronic and print media. The consequences for both personal and public discourse have of late become painfully evident.</p> <p>We began this article by highlighting the worrisome implications of adults today becoming comfortable with choosing their evidence and claims as a function only of their goals and needs (Mercier & Sperber, [<reflink idref="bib38" id="ref62">38</reflink>]). Potential remedies lead us squarely back to education. The pre-college curriculum affords students limited experience in deciding what to do, what information to consider, what additional information to look for, and when multiple possibilities are available—in short, in taking charge of their own thinking and learning, allowing them to experience epistemic agency (DiSessa, [<reflink idref="bib6" id="ref63">6</reflink>]; Elmore, [<reflink idref="bib8" id="ref64">8</reflink>], [<reflink idref="bib9" id="ref65">9</reflink>]; Rudolph, [<reflink idref="bib53" id="ref66">53</reflink>]; Miller, Manz, Russ, Stroupe, & Berland, [<reflink idref="bib40" id="ref67">40</reflink>]; Scardamalia & Bereiter, [<reflink idref="bib56" id="ref68">56</reflink>]; Sikorski & Hammer, [<reflink idref="bib61" id="ref69">61</reflink>]). In short, cognitive self-regulation is best nurtured from an early age and is predictive of positive educational outcomes (Modrek & Kuhn, [<reflink idref="bib43" id="ref70">43</reflink>]; Modrek et al., [<reflink idref="bib44" id="ref71">44</reflink>]). Its importance becomes critical in scientific and indeed all higher-order thinking, where practice is a key to its development, both with peers and in apprenticeship with more skilled others (Arvidsson & Kuhn, [<reflink idref="bib1" id="ref72">1</reflink>]; Papathomas & Kuhn, [<reflink idref="bib47" id="ref73">47</reflink>]; Kuhn, [<reflink idref="bib23" id="ref74">23</reflink>], [<reflink idref="bib24" id="ref75">24</reflink>]; Mehan & Cazden, [<reflink idref="bib36" id="ref76">36</reflink>]; Mercer & Littleton, [<reflink idref="bib37" id="ref77">37</reflink>]; Rapanta, [<reflink idref="bib49" id="ref78">49</reflink>]; Resnick, Asterhan et al., [<reflink idref="bib50" id="ref79">50</reflink>]; Reznitskaya & Wilkinson, [<reflink idref="bib51" id="ref80">51</reflink>]). Yet, individual cognitive weaknesses, of the sort highlighted by the data presented here, should not be eclipsed by exclusive focus on the study of thinking in social contexts (Kuhn & Modrek, [<reflink idref="bib28" id="ref81">28</reflink>]). In the current work with middle schoolers, we are investigating ways to develop and strengthen the individual epistemological awareness and self-regulation that will improve performance on the type of task reported on here.</p> <p>Certainly by college age, students need to be able to know what they need to know as they reason and learn. And they should be able to inhibit themselves and challenge others in advocating claims that lack the necessary evidence (Mills, [<reflink idref="bib41" id="ref82">41</reflink>]). The eventual downside of their not doing so is arguably a contributing factor to the declining standards apparent in contemporary public and private discourse. 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  Data: Choose Your Evidence: Scientific Thinking Where It May Most Count
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  Data: <searchLink fieldCode="DE" term="%22Scientific+Literacy%22">Scientific Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence%22">Evidence</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Process+Skills%22">Science Process Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Data%22">Data</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink><br /><searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Skills%22">Mathematics Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+Logic%22">Mathematical Logic</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11191-021-00209-y
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0926-7220
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Schooling traditionally affords students more experience in learning and practicing procedures than in identifying what a situation calls for. When asked to choose appropriate numerical data to support their causal claims, college students perform surprisingly poorly. In one case we describe, almost all chose limited, inconclusive data as sufficient evidence, despite having available the more comprehensive data needed to support their claim and despite their established competence to employ such data for this purpose. Our objective in highlighting this weakness is to make a case that choosing one's evidence warrants the status of an important metacognitive intellectual skill and educational objective, one central to but that extends well beyond the domains of scientific and mathematical reasoning and hence warrants greater attention both in and beyond the science curriculum. People may choose evidence to justify their assertions in an ill-considered way, with potential adverse effects in both private and public communication.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2022
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1326528
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1326528
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11191-021-00209-y
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 21
    Subjects:
      – SubjectFull: Scientific Literacy
        Type: general
      – SubjectFull: Evidence
        Type: general
      – SubjectFull: Science Process Skills
        Type: general
      – SubjectFull: Data
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Metacognition
        Type: general
      – SubjectFull: Thinking Skills
        Type: general
      – SubjectFull: Mathematics Skills
        Type: general
      – SubjectFull: Mathematical Logic
        Type: general
    Titles:
      – TitleFull: Choose Your Evidence: Scientific Thinking Where It May Most Count
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kuhn, Deanna
      – PersonEntity:
          Name:
            NameFull: Modrek, Anahid S.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 0926-7220
          Numbering:
            – Type: volume
              Value: 31
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Science & Education
              Type: main
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