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Thinking Processes Associated with Undergraduate Chemistry Students’ Success at Applying a Molecular-Level Model in a New Context Melonie A. Teichert,† Lydia T. Tien,‡ Lisa Dysleski,† and Dawn Rickey*,† †

Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States Department of Chemistry and Geosciences, Monroe Community College, Rochester, New York 14623, United States

‡

S Supporting Information *

ABSTRACT: This study investigated relationships between the thinking processes that 28 undergraduate chemistry students engaged in during guided discovery and their subsequent success at reasoning through a transfer problem during an end-of-semester interview. During a guided-discovery laboratory module, students were prompted to use words, pictures, and symbols to make their mental models of chemical compounds added to water explicit, both prior to the start (initial model) and at the end (refined model) of the module. Based on their responses to these model assignments, we characterized students’ knowledge and thinking processes, including the extent to which individual students engaged in (a) constructing molecular-level models that were consistent with experimental evidence; (b) constructing molecular-level models that progressed toward scientific accuracy; (c) constructing molecular-level models that were scientifically correct; (d) making connections between laboratory observations and the molecular-level behavior of particles; (e) accurate metacognitive monitoring of how their molecular-level models changed; and (f) using evidence to justify model refinements. Analyses revealed three thinking processes that were strongly associated with correct reasoning in the transfer context during an end-of-semester interview: constructing molecular-level models that were consistent with experimental evidence, engaging in accurate metacognitive monitoring, and using evidence to justify model refinements. The extent of student engagement in these three key thinking processes predicted correct reasoning in a new context better than the scientific correctness of a student’s content knowledge prior to instruction. Although we did not explore causal relationships, these results suggest that integrating activities that promote the key thinking processes identified into instruction may improve students’ understanding and success at transfer. KEYWORDS: First-Year Undergraduate/General, Chemical Education Research, Laboratory Instruction, Inquiry-Based/Discovery Learning, Analogies/Transfer, Problem Solving/Decision Making, Aqueous Solution Chemistry FEATURE: Chemical Education Research

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appropriately in new situations, rendering this knowledge practically useless to them for all but the most mundane tasks. Thus, research designed to elucidate how learners can attain sufficiently deep understanding to allow them to apply scientific models in new contexts is a high priority.7,8,19−21 As we endeavor to educate students who can solve novel and complex problems, identifying conditions that facilitate transfer is of interest in chemistry education and across science, technology, engineering, and mathematics (STEM) disciplines. This work seeks to understand the types of cognitive engagement that are associated with successful transfer. Specifically, we explore relationships between the thinking processes undergraduate chemistry students engage in during a guided-discovery

acilitating learning with a depth of understanding that empowers students to apply what they have learned effectively in new and different contexts, an achievement often referred to as transfer of learning,1−3 is one of the most important goals of contemporary science education. Understanding how to promote transfer through instruction has been a high priority for all areas of education for over a century, yet it continues to be an elusive goal.4−6 Transfer is a particular challenge for learning in complex and abstract domains such as science and mathematics, and a recent report highlighted transfer as an important emerging field in discipline-based education research (DBER).7,8 Research indicates that science learning via traditional instructional methods is typically shallow: Students often memorize facts and algorithms rather than construct robust understandings of scientific principles and models.9−18 In such cases, students are unable to apply the instructed scientific models © XXXX American Chemical Society and Division of Chemical Education, Inc.

Received: October 14, 2016 Revised: June 9, 2017

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DOI: 10.1021/acs.jchemed.6b00762 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

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To elucidate specific thinking processes that may be linked to transfer success in chemistry, we examined the knowledge and thinking processes of individual students as they participated in an instructional sequence that included a guided-discovery laboratory module followed by direct instruction on the topic. Specifically, we investigated the relationships between the students’ knowledge and thinking while participating in a guided-discovery laboratory module at the beginning of a semester and their success at applying the relevant molecularlevel model in the new context of boiling point elevation at the end of the semester.

laboratory module in which they construct and refine molecularlevel models of aqueous solutions and their subsequent transfer problem-solving success in the new context of the boiling point elevation of solutions.

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BACKGROUND Theoretical frameworks for transfer of learning, which vary in their foci and the aspects of transfer they seek to explain, range from classical cognitive views to resources-based accounts to situative perspectives.4,6,22−31 We frame this study in terms of similarity transfer32−34 involving the application of underlying principles (a molecular-level model) in a new context. Similarity transfer frameworks propose that, to reason correctly about a problem involving transfer, a student must have wellformed prior knowledge (in this study, the molecular-level models), recognize that the knowledge is relevant to the novel problem at hand, and create an effective mapping to the new problem.35 Transfer frameworks also vary with respect to whether knowledge and reasoning are viewed as unitary constructs that can be transferred intact (the typical focus in similarity transfer) or as being composed of many finer-grained cognitive resources that may or may not be activated in a particular context.23,25,29 From the resources perspective, successful transfer requires well-developed cognitive resources, and the ability to activate and apply the appropriate resources when faced with a novel problem. Our previous research has documented connections between specific problem contexts and students’ activation of fine-grained, molecular-level ideas.36−38 Thus, the framework we adapt for this study is also informed by resources-based accounts of transfer25 that are consistent with similarity transfer for cases involving the application of well-formed principles (students’ previously constructed, molecular-level models). Although there are different models of transfer that vary in their foci, there is good consensus that transfer success depends on prior knowledge and experiences, and is facilitated by deep and robust prior learning.1,19,25,39 Empirical investigations addressing how to promote student learning with a depth of understanding that enables transfer often compare the transfer success rates of groups of students taught via different instructional methods. Such studies have contributed that transfer may be facilitated when learners compare and contrast multiple examples of (or contexts for) the content to be learned, and generalize the common deep structure.32,33,40−45 In addition, some of these studies demonstrated that providing an expert explanation to students after they attempted to describe the deep structure of contrasting examples promoted higher rates of transfer success compared with other instructional methods.40,43,44 Finally, instructional approaches that prompt metacognition (monitoring and regulation of one’s own thinking and learning46−48) have been shown to improve students’ transfer success in a variety of domains, including reading, mathematics, physics, and chemistry.41,42,49−51 While the aforementioned empirical work provides important information regarding instructional conditions that may improve transfer success rates of groups, students experiencing the same instructional conditions exhibit different levels of success. Thus, identifying the thinking processes that individual students engage in is important for understanding the cognitive mechanisms that support transfer. Yet few studies, especially for chemistry at the undergraduate level, have investigated relationships between student engagement in specific thinking processes and transfer success.

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INSTRUCTIONAL CONTEXT

The instructional context for this study was a first-semester undergraduate general chemistry course including laboratory and lecture components that were taught by the same instructor. The laboratory component of the course employed a guided-discovery pedagogy called the Model−Observe−Reflect−Explain (MORE) Thinking Frame, which is designed to facilitate student construction of evidence-based, molecularlevel explanations,52 and has been shown to improve students’ transfer success when compared with standard approaches to laboratory instruction.41,42 The “What Happens when Chemical Compounds are Added to Water?” module,53 subsequently referred to as the “dissolution module”, provides the context for the current study. Facilitating Thinking Processes That May Support Transfer

When participating in MORE laboratory instruction, students are prompted to use words, pictures, and symbols to make their internal representations, or mental models,54,55 of how chemical systems function explicit. Students begin by describing their ideas about the chemical system they will study from macroscopic and molecular-level perspectives in a written laboratory assignment (initial model). Then, the students conduct a set of laboratory experiments (observe) that involve comparing and contrasting multiple examples of phenomena of interest, and are explicitly prompted to ref lect upon the implications of their empirical observations as they relate to their initial model ideas. Finally, based on the evidence they collect and their reflections upon it, students refine their models, and explain how and why (using evidence) they revised their molecular-level ideas compared with their initial models. After completing one iteration of MORE, students apply MORE to a subsequent set of laboratory activities, which provides additional opportunities for them to refine their personal models and generalize the common deep structure. The MORE Thinking Frame was designed to engage students in thinking processes that research has found to be effective for developing robust understandings and thus potentially supporting transfer,41,56 and recent work has provided additional support for MORE’s original design principles. First, throughout the MORE model-construction process, students are prompted to make connections between macroscopic observations and evidence and their molecular-level models. Chemistry experts easily transition between and integrate macroscopic observations, molecular-level behavior, and the corresponding symbolic representations,57−59 but novices have difficulty connecting, or even understanding, the three aspects.60 Thus, chemistry educators have suggested that instruction should emphasize the connections among these three aspects to improve student understanding.61 B



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Second, students are encouraged to test and revise their personal models for consistency with experimental evidence. Students are prompted to compare their ideas with the data and observations they collect, to reflect upon whether their ideas are consistent with these data, and, finally, to refine their ideas to account for any inconsistencies. In this way, students use evidence to revise, build upon, generalize, and extend their models. Optimally, the nature of the evidence that students collect in the laboratory facilitates student construction of more scientifically accurate models. Work in mathematics education found that students who were able to support and justify their knowledge generalizations had more sophisticated knowledge structures,24 and other work suggests that the construction of high-quality scientific explanations that relate evidence to a claim may be related to improved student performance.62 Thus, the ability to use evidence as justification for model refinement may be important for developing more robust understandings. Finally, the MORE Thinking Frame prompts students to metacognitively monitor their own understanding of the models they construct and refine. Individuals who more effectively monitor their own ideas and thinking are more able and ̈ ideas in the face of contradictory more likely to refine naive experimental results. Thus, as part of their model assignments, students are asked to explain how and why (using specific experimental evidence) their models have changed over the course of a laboratory module. We refer to this part of the refined model assignment as the student’s “metacognitive reflection”.

Box 1. Excerpts from Morgan’s Models for Sugar Dissolved in Water A. Excerpts f rom Morgan’s Initial Model: Presenting Her Initial Model It is tiny, white granules. It feels sticky and coarse. The water is clear, wet and odorless. When the C12H22O11 is added to the water it sinks to the bottom of the water. When it is stirred the C12H22O11 is dissolved in the water and the mixture is clear. It looks the same as the plain water. As you continue to add C12H22O11 to the mixture it will stop dissolving and settle to the bottom of the container... Like the NaCl, when C12H22O11 is added to water the bonds are broken. The H+ ions are attracted to the oxygen part of the water molecule and the C− and O− are attracted to the hydrogen part of the water molecule. B. Excerpts f rom Morgan’s Ref ined Model: Presenting Her Refined Model Two grams of C12H22O11, colorless, odorless, rectangular crystals, were added to 20 mL of H2O. When the mixture is stirred, the C12H22O11 dissolved in the H2O and became a clear solution. The conductivity was measured at 0 μS/cm. The solution was not conductive... C12H22O11 is a molecular compound. When C12H22O11 is added to water it breaks down into molecules that are neutral. Because these molecules are neutral the solution does not conduct.

Excerpts from One Student’s MORE Models

C12H 22O11 → C6H12O5 and C6H10O6

To illustrate the context of our study and the nature of the student work we analyzed for evidence of content knowledge and thinking processes, we present excerpts from one student’s written models for the dissolution laboratory module, which was the first laboratory module of the semester (Box 1). This student was chosen as a criterion case63 because her work exemplifies the key thinking processes that will be discussed in detail. Because we will return to this case throughout the paper, we call this student Morgan, a pseudonym. For the initial model (prelaboratory assignment, Box 1A), each student was asked to describe his or her understanding of what happens when salt (NaCl) and sugar (C12H22O11) are added (separately) to water from both macroscopic and molecular-level perspectives. Students’ initial models were handwritten and ranged from one-half to four pages in length, averaging about one page in length. After participating in a whole-class discussion, during which students shared their initial ideas, students carried out several

C. Excerpts f rom Morgan’s Ref ined Model: Explaining Her Metacognitive Reflection My molecular level model for C12H22O11 was incorrect. I thought that, similar to the NaCl, the sugar molecules would break down into charged ions. As we determined from this experiment that did not happen. The C12H22O11 molecules broke down into neutral molecules when added to the H2O because the solution did not conduct. If it had broken down into charged ions the solution would have been conductive. sets of experiments designed to inform their models of aqueous solutions. (Students submit their initial models to their instructor prior to the whole-class discussion, but retain a copy in their laboratory notebooks.) In the set of experiments most relevant to this report, students added several differ-

Figure 1. Schematic representing the research question: What are the associations between student thinking during instruction and later transfer success? C



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and thinking processes during the guided-discovery portion of instruction, we analyzed students’ written initial and refined models for the dissolution module. Students submitted their initial and refined models at the beginning of the week 3 and week 5 laboratory sessions, respectively. End-of-Semester Interviews. At the end of the semester (weeks 14−15), a researcher interviewed students to assess their abilities to apply their molecular-level models of aqueous solutions in a new context. Participants volunteered to take part in 1 h interviews for modest payment. The interviews consisted of two parts described in detail elsewhere,37 with part I focused on isomorphic questions and part II on transfer questions. Part I asked students to predict and explain the conductivity of various mixtures, including salt (NaCl) added to water (observed during the dissolution module) and additional isomorphic cases. For part II (transfer), students first read a short passage explaining boiling point elevation, and then were asked to predict and explain the relative boiling points of pairs of aqueous solutions, including 0.020 M glucose (C6H12O6) compared with 0.020 M sodium chloride (NaCl). Boiling point elevation had not been discussed in the students’ chemistry class, and correct explanations of the relative boiling points of solutions required students to activate and apply their molecular-level models of aqueous solutions in the new context. Students were also asked to draw molecular-level pictures for each solution they discussed in both parts I and II of the interview.

ent compounds, including salt and sugar, to distilled water; made observations; measured the electrical conductivities of the mixtures; and compared and contrasted these examples. A description of all dissolution module activities is published in an earlier report.53 Students collected macroscopic evidence (conductivity measurements) that directly related to their molecular-level models of aqueous solutions (presence and quantities of ions in solution). For each set of experiments, students considered “reflection questions” provided in their laboratory manuals, related their observations and measurements to their initial models, and were prompted to refine their molecular-level models to be consistent with the experimental evidence. As part of their postlaboratory assignment, students formally refined their models. In their refined models, in addition to presenting their revised ideas (Box 1B), students were prompted to engage in metacognitive reflection, specifically to explain how and why their models had changed (Box 1C). Students’ refined models ranged from less than one page to four pages, averaging about 1.5 pages in length. Students’ written models provided the primary data for characterizing students’ understanding and thinking processes during the dissolution module. We describe how we coded these models in a subsequent section of this article.

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RESEARCH QUESTION This work explores whether and how engagement in particular thinking processes during a guided-discovery chemistry laboratory module is associated with transfer success. As illustrated in Figure 1, the central research question we addressed was, “What are the relationships between knowledge and thinking processes that individual students engaged in during the dissolution module and their success at applying their molecular-level model of aqueous solutions appropriately in a new context during an end-of-semester interview?” Based on of the previous research on transfer and the development of deep understanding of molecular-level chemistry models described above, we identified and characterized potentially important aspects of students’ knowledge and thinking during the dissolution laboratory module, and then determined which of those aspects distinguished the students who successfully applied their molecular-level models in the new context of boiling point elevation from those who did not.

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Analyses of Student Models

We analyzed students’ written models of aqueous salt and sugar solutions to identify the extent to which individual students engaged in several aspects of thinking for which the written models could provide evidence. We identified these aspects as potentially important based on the previous research on transfer and the development of a deep understanding of molecular-level chemistry models described in the introduction. Specifically, we characterized the extent to which individual students engaged in (a) constructing refined molecular-level models that were consistent with experimental evidence (Conceptions coding scheme65); (b) constructing refined molecular-level models that progressed toward scientific accuracy relative to their initial models (Conceptions coding scheme65); (c) constructing initial and refined molecular-level models that were scientifically correct (Conceptions coding scheme65); (d) making connections between observations and measurements from the laboratory and the molecular-level behavior of particles (Connecting Evidence to the Molecular Level coding scheme, Figure 2); (e) accurate metacognitive monitoring of how their molecular-level models changed (Metacognitive Reflections coding scheme, Figure 3B); and (f) using evidence to justify personal model refinements as part of metacognitive reflection (Metacognitive Reflections coding scheme, Figure 3C). Aspects a−c describe knowledge and thinking processes related to the understandings that students develop regarding molecular-level models of aqueous solutions. Aspect d is a chemistry-specific thinking process for developing such understanding via guided discovery, and aspects e and f assess the quality of students’ metacognitive monitoring as a promising thinking process for developing robust understandings and enhancing transfer.

METHODS

Setting and Participants

The 28 students (15 women and 13 men) who participated in the study were recruited from a first-semester general chemistry course for science majors at a community college, 11 students from one semester and 17 students from a subsequent semester. The same instructor taught both semesters, and there were no statistically significant differences at the p < 0.10 level between the student samples with respect to gender distribution (Fisher’s exact test) or preinstructional knowledge as assessed by a standardized mathematics and chemistry pretest64 (two-tailed, unpaired t test). All students included in the study attended the related laboratory sessions during the third and fourth weeks of the semester, completed all associated laboratory assignments, and completed the chemistry course. Data Sources

Written Laboratory Assignments: Initial and Refined Models. To characterize aspects of each individual’s knowledge D



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Figure 2. Connecting Evidence to the Molecular Level coding scheme.

whether or not students’ initial and refined models were consistent with the experimental evidence they collected, and whether or not students’ models were scientifically correct. Our requirements for correctness were strict: Students had to clearly state the scientifically correct conception, and could not express any incorrect conceptions. For example, if a student wrote that sodium chloride separated into ions in solution, but drew an associated molecular-level picture that did not include charges on the particles, this was coded as incorrect. Prior work on students’ conceptions about the nature of dissolved particles in aqueous solutions has shown that students’ verbal explanations and accompanying illustrations are often inconsistent with each other.37,65,67 We therefore considered all aspects of student work in our coding to obtain as complete a picture of students’ conceptions as possible. Because many students progressed in their understanding from their initial models to their refined models, but did not satisfy our strict requirements for correctness, we also characterized the extent to which students’ models progressed toward scientific accuracy. In this way, we captured students’ productive model revisions, such as incorporating new ideas that were consistent with evidence, even if they did not necessarily lead to a fully correct model. It is important to note that the instructor did not present the correct ideas to students before they submitted their final written models, but instead facilitated student discussions of how the evidence they collected related to their molecular-level models. Connecting Evidence to the Molecular Level Coding. Although the Conceptions coding characterized students’ mental models and allowed us to determine the extent to which students’ models were consistent with experimental evidence, it did not capture the extent to which students explicitly used evidence as they refined their models. This distinction is important since students may revise their models to be

To characterize the extent to which students engaged in aspects a−c, we applied the Conceptions coding scheme that we developed previously to categorize students’ molecular-level ideas about what happens when salt and sugar are added to water.65 In the previous work, the same researchers who performed the Conceptions coding for this study obtained an intercoder reliability of 88% for a subset of models. To characterize aspects d−f, we developed two new coding schemes (Figures 2 and 3) using qualitative content analysis.66 In the development of these coding schemes, the main categories were structured based on our framework of thinking processes that potentially contribute to developing robust understandings and thus successful transfer. The initial subcategories were also concept-driven, and then additional subcategories were generated based on the content of students’ models. Figure 2 presents the Connecting Evidence to the Molecular Level coding scheme developed to characterize aspect d, and Figure 3 presents the Metacognitive Reflections coding scheme used to characterize aspects e and f. Using 81 sets of initial and refined models from multiple institutions, two researchers obtained intercoder reliabilities of 80% for Connecting Evidence to the Molecular Level (Figure 2) and 70% for Metacognitive Reflections on how molecular-level models changed (Figure 3). The same two researchers independently coded each set of student models for this study, and brought in a third researcher to discuss any differences in coding and reach consensus on the final codes assigned. Models were coded blind to the identities of the students and without any information regarding the students’ end-of-semester interview performance. Conceptions Coding. Conceptions coding characterized the progression of student conceptions from their initial models to their refined models, and is described in an earlier report.65 We used the results of the Conceptions coding to determine E



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Figure 3. Metacognitive Reflections coding scheme.

Morgan wrote that “when an ionic compound, salts, [sic] are added to water the compound is broken down into cations and anions. These ions are charged and cause the solution to be conductive.” Morgan explicitly made a correct connection between the conductivity measurements and her molecularlevel salt solution model. Metacognitive Reflections Coding. We developed the Metacognitive Reflections coding scheme (Figure 3) to capture the quality of students’ metacognitive monitoring and reflections about how their understanding progressed over the course of a laboratory module. Students’ molecular-level models of salt and sugar solutions were coded separately. We coded all statements in each student’s refined model that referred to the initial model. Example excerpts of students’ metacognitive reflections are shown in Table 1, with details of the coding discussed in the sections below. Students’ Perceptions of How Their Models Changed. In the refined model assignment for the dissolution module, students were asked to compare their refined models to their

consistent with the evidence without making explicit macroscopic−molecular connections. The Connecting Evidence to the Molecular Level coding scheme (Figure 2) was developed to characterize the ways in which students made connections between the macroscopic evidence they collected in the laboratory and their molecular-level models. First, we coded for whether or not students mentioned particular types of experimental evidence (conductivity data and visual appearances of mixtures) (Figure 2A). Then, we coded for whether or not students explicitly linked each type of macroscopic evidence they mentioned to their molecular-level models, and for any explicit macroscopic−molecular connections students made, we coded for the correctness of those connections (Figure 2B). Using the Connecting Evidence to the Molecular Level coding scheme to analyze students’ models of salt and sugar solutions, students were characterized as making correct, incorrect, or no macroscopic-molecular connections for conductivity measurements and for appearance of mixtures. For instance, F



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Table 1. Examples of Metacognitive Reflections Coding 3A Perception of How Model Changed

Excerpts from Students’ Refined Model Reflections Student 16: “My initial model and revised model are the same.”

No change

3B Accuracy of Metacognitive Monitoring

3C Use of Evidence To Justify Model Refinements

Accurate

No mention

Student 33: did not mention her initial molecular-level models in her refined model

No mention

No mention

No mention

Student 42: “The refined model of this experiment supports our initial model and offers further detail of the characteristics of compounds in solution. Both substances dissolved completely after agitation, and our tests for conductivity support the hypothesis for how molecules/ions act in solution. Ions seperate [sic] in solution, and therefore conduct electricity. Since the separation of ionic pieces and number of ions present contribute to conductivity, we can say that the sugar/covalently bound substances stay bound, and do not form any ions.”

No change

Accurate

Explicit

Student 68: “This model has changed from the initial model due to a better understanding of the properties of ionic and covalently bonded compounds, how they react in water, and the factors that affect conductivity.”

Change

Incomplete/ vague

Vague

Morgan: see Box 1C for reflection on molecular-level sugar solution

Change

Accurate

Explicit

as being incomplete/vague because he did not articulate the specific aspects of his molecular-level model that changed (see excerpt in Table 1). Use of Evidence To Justify Model Refinements. Finally, based on of its perception category (Figure 3A), each metacognitive reflection was coded for the student’s use of experimental evidence as part of his or her metacognitive reflection to justify why the student did or did not refine the model (Figure 3C). Such use of evidence was coded as explicit use of evidence, vague use of evidence, or no mention of evidence. Examples of each of these codes are presented in Table 1. While student 16 does not mention experimental evidence (no mention) and student 68 only mentions “factors that affect conductivity” (vague), student 42 provides an explanation of how conductivity measurements indicated the presence or absence of ions (explicit). As illustrated by the refined model excerpts from Morgan (Box 1C) and student 42 (Table 1), accurate metacognitive reflections that explicitly cite evidence can be succinct. In contrast, while student 68’s metacognitive reflection (Table 1) was also coded as perceiving a change in his model, his reflection is incomplete/vague regarding what about his model changed and vague regarding the evidence that prompted the revisions. It is important to note that coding students’ use of evidence to justify model refinements is different from the previously described coding of macroscopic− molecular connections (Figure 2B) in that macroscopic− molecular connections are coded any time students make such connections in their models, whereas this coding only applies when students make macroscopic−molecular connections to justify their model refinements as part of their metacognitive reflection on how their model has changed from initial to refined.

initial models, and to explain any changes (or lack of changes) using experimental evidence. Thus, the refined models were first coded according to students’ perceptions of how aspects of their personal models changed (or did not change) from initial to refined (Figure 3A). Table 1 presents excerpts from students’ refined model reflections and how they were coded. Accuracy of Students’ Metacognitive Monitoring. Next, based on its perception category, each student’s metacognitive reflection was coded according to its accuracy as assessed by the researchers’ interpretation of how the student’s model changed from initial to refined. Based on the criteria described in Figure 3B, the accuracy of each student’s metacognitive monitoring was classified as accurate, somewhat inaccurate, incomplete/vague, or no mention/comparison. (Note that a student could engage in accurate metacognitive monitoring of how their model changed even if the student’s conceptions were incorrect.) Assigning a metacognitive monitoring code of accurate versus somewhat inaccurate was not always straightforward. Because students’ models often contained many different ideas and details, it was important to consider the different aspects of students’ initial and refined models to determine accuracy. Our criteria for earning a code of accurate were strict. If a student indicated his or her model was unchanged, but a key aspect of the model was judged to have changed by the researchers, the student was somewhat inaccurate. For this aspect of Metacognitive Reflections coding, the results of the Conceptions coding of students’ initial and refined models were used to help determine whether key ideas in students’ models changed. For example, students 16 and 42 articulated correct molecular-level ideas in their initial models, made no changes in their refined models, and were coded as being accurate with respect to their metacognitive monitoring (see excerpts in Table 1). Morgan’s model excerpt in Box 1C provides another example of accurate metacognitive monitoring, in this case for how her molecular-level sugar model changed. Student 68’s initial model indicated that he thought that both NaCl and C12H22O11 become ions (NaCl− and C12H22O11−) when dissolved in water, and his refined model explained that NaCl breaks apart into Na+ ions and Cl− ions while C12H22O11 breaks apart into glucose molecules. These refinements of the student’s ideas were captured by different Conceptions codes for the student’s initial and refined molecular-level salt and sugar models. In the expert analysis, the student revised his ideas to be consistent with the experimental evidence. With respect to his metacognitive monitoring, student 68 was coded

Analyses of Student Interviews

The student interview responses were analyzed by the same researcher using the same methods reported previously. In the previous work, a second researcher analyzed a subset of interviews with perfect (100%) intercoder reliability.37 All interviews were coded blind to the identity of the students and without knowledge of what the students wrote in their dissolution models. Based on these analyses, each student’s response to part II of the interview was characterized as containing correct reasoning or incorrect reasoning. Quantitative Analyses

To guide us in finding patterns in the model and interview coding results, we explored associations between the student model characteristics we coded and student success at reasonG



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Of the 28 students, 23 made correct connections between their molecular-level salt models and the conductivity evidence, indicating that a solution of salt water conducts electricity due to the presence of separated ions in solution. Only 4 students made an incorrect macroscopic−molecular connection for salt solutions. Fifteen students correctly related the absence of conductivity to the absence of ions in sugar solutions, and only 2 students made an incorrect macroscopic−molecular connection for sugar solutions. Twelve students did not explicitly connect their conductivity data with their molecular-level sugar models. (Since few students made explicit macroscopic− molecular connections regarding the appearance of the salt and sugar solutions, they are not discussed here nor included in Table 2.) Only 13 of the 28 students exhibited accurate metacognitive reflections regarding how their molecular-level models for salt or sugar solutions changed from initial to refined. Thus, although students were explicitly asked to explain how their models changed, many either chose not to do this or had difficulty with this aspect of the assignment. The least frequently achieved aspect coded in the models was students’ use of evidence to support an initial molecular-level model (if the initial model was unchanged) or model refinements (if the model changed) as part of the student’s metacognitive reflection. Of the 28 students, 15 neglected to mention any evidence in support of how their ideas progressed, while 9 students invoked explicit evidence (such as conductivity data) for at least one of their molecular-level models as part of their metacognitive reflection. Many students had difficulty engaging in metacognitive reflection describing how their models changed, but they seemed to have even greater difficulty with evidencebased justifications of why their models did or did not change.

ing through the transfer problem in the end-of-semester interview. We established binary variables based on the coding of students’ knowledge and thinking processes in categories a−f described above. For example, one variable indicated whether or not a student engaged in accurate metacognitive monitoring with respect to their molecular-level model of either the salt solution or the sugar solution. Because all variables explored were binary and our sample size was small, the strengths of the associations between each variable and success at reasoning through the transfer problem were determined by calculating phi (ϕ) coefficients, which were calculated in SPSS via a chi-square (χ2) test for independence.68 The literature varies somewhat, but taking a conservative approach, ϕ coefficients below 0.40 indicate negligible-to-moderate associations, and coefficients above 0.40 indicate relatively strong-to-very-strong associations.69,70 Thus, we used the criterion of ϕ > 0.40 and simply refer to these as “strong” associations, with larger coefficients indicating stronger associations. (See Supporting Information for more information and a complete list of variables tested.)

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RESULTS

Results of Model Coding

Table 2 summarizes key results of our coding of students’ dissolution laboratory models. Students were generally more Table 2. Summary of Results of Student Model Analyses Number of Studentsa (%) Who Exhibited Knowledge or Thinking Process Salt

Sugar

Either Salt or Sugar

Both Salt and Sugar

Molecular Model Consistent with Evidence 11 (39) 12 (43) 19 (68) 4 (14) 24 (86) 21 (75) 26 (93) 19 (68) Molecular Model Progressed in Accuracy Refined model 25 (89) 20 (71) 25 (89) 20 (71) Molecular Model Scientifically Correct Initial model 11 (39) 10 (36) 17 (61) 4 (14) Refined model 23 (82) 12 (43) 24 (86) 11 (39) Macroscopic−Molecular Conductivity Connections (Refined Model) Correct connection(s) 23 (82) 15 (54) 24 (86) 14 (50) Incorrect connection(s) 4 (14) 2 (7) 5 (18) 1 (4) Only correct 21 (75) 14 (50) 21 (75) 13 (46) connection(s) Accuracy of Metacognitive Monitoring (Refined Model) Accurate monitoring 11 (39) 9 (32) 13 (46) 7 (25) Somewhat inaccurate 3 (11) 3 (11) 5 (18) 1 (4) monitoring Incomplete/vague 7 (25) 8 (29) 8 (29) 7 (25) monitoring No mention/ 7 (25) 8 (29) 8 (29) 7 (25) comparison Use of Evidence To Justify Model Refinements Explicit use of evidence 7 (25) 6 (21) 9 (32) 4 (14) Vague use of evidence 3 (11) 4 (14) 4 (14) 3 (11) No mention of evidence 18 (64) 18 (64) 21 (75) 15 (54)

Student Success at Reasoning through the Transfer Interview Problem

Initial model Refined model

a

Of 28 students interviewed, 12 students (43%) reasoned correctly on the transfer question that a sodium chloride solution would have twice the boiling point elevation of an equimolar glucose solution. Although the vast majority of students constructed refined molecular-level models for aqueous salt solutions that were both consistent with experimental evidence and scientifically correct, the interviews indicated that many had difficulty activating and applying these models appropriately in a new context. Specifically, during part I of the interview (isomorphic questions), 26 students (93%) correctly represented aqueous NaCl as separated ions to explain why the solution would conduct electricity. However, in part II (transfer question), when asked to draw a molecular-level picture of aqueous NaCl in the context of boiling point elevation, only 17 of these students indicated separated ions without interviewer prompting. Thus, 9 (32%) of the interviewed students answered what a chemist would consider to be the same question differently just minutes apart.37 Although the interview problems from parts I and II shared substantial cognitive elements, the level of transfer success observed in part II was low, indicating that many students did not activate the appropriate cognitive resources in the new context. After interviewer prompting to compare their representations from the different parts of the interview, 24 students (86%) correctly represented aqueous NaCl as separated ions. Nevertheless, only half of these students ultimately applied their molecular-level model to make a correct boiling point elevation prediction. Excerpts of transcripts illustrating reasoning during the interviews for

N = 28.

successful with their models of salt added to water, compared with their models of sugar added to water, with respect to consistency with evidence, progression toward accuracy, scientific correctness, and macroscopic−molecular conductivity connections. H



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(ϕ = 0.059). Here, one of the more striking results is that the correctness of students’ initial models was not a predictor of transfer success. Having correct initial molecular-level models for both salt and sugar prior to participating in the dissolution module had a negligible association with success at reasoning through the transfer problem at the end of the semester (ϕ = 0.059). In fact, this was the weakest association of the variables we tested. Thus, it appears that the thinking processes that students engaged in during the dissolution module played a more important role in subsequent transfer problem-solving success than students’ initial knowledge of the relevant chemical systems. Similarly, while the consistency of students’ refined models with the laboratory evidence they collected was strongly associated with transfer success (ϕ = 0.596), the consistency of students’ initial models with evidence was not (ϕ = 0.059). This is perhaps expected since, when writing their initial models, students had not yet collected evidence to relate to their models. However, this result again illustrates that engaging in key thinking processes during instruction was much more strongly associated with transfer success than characteristics of students’ initial ideas. Further analyses (see Supporting Information) also indicate that the key thinking processes identified are not proxies for general chemistry proficiency; in other words, it is not the case that students who engaged in the three key thinking processes above were simply the high-achieving students in the chemistry course.

students 16, 33, and 42 are provided in Supporting Information. The 43% overall success rate of students reasoning through the transfer interview problem allowed us to investigate the relationships between success at applying the model in the new context and the knowledge and thinking processes that individual students engaged in during the dissolution module two months earlier. Thinking Processes Strongly Associated with Transfer Success

To guide us in finding patterns in the model and interview coding results, we determined which of the student knowledge and thinking process variables were most strongly associated with interview transfer success as described by phi (ϕ) coefficients. These analyses revealed strong associations (ϕ > 0.40) between student engagement in six aspects of thinking during the dissolution module at the beginning of the semester and successful reasoning in the transfer context at the end of the semester (see Table 3). Ultimately, we focused on the three Table 3. Aspects of Students’ Thinking Strongly Associated with Correct Reasoning in the Transfer Context Aspect of Students’ Knowledge or Thinking Explicitly used evidence to justify molecular-level model refinements for at least one solution (salt or sugar) Explicitly engaged in accurate metacognitive monitoring of how molecular-level model changed for at least one solution (salt or sugar) Constructed refined molecular-level models that are consistent with experimental evidence for both solutions (salt and sugar) Progressed toward scientific accuracy from initial to refined for molecular-level models (salt and sugar) Made only correct connections between conductivity measurements and the molecular-level behavior of particles in solution (salt and sugar) Explicitly used evidence to justify molecular-level model refinements for both solutions (salt and sugar)

ϕ Valuesa 0.795b 0.641c 0.596c 0.548

Patterns of Students’ Cognitive Engagement During Instruction and Transfer Success

d

Guided by the results of these statistical analyses, we examined the data to discern the aggregate patterns of students’ cognitive engagement during the dissolution module that predicted successful transfer. Figure 4 shows that, while no student who failed to construct molecular-level models of aqueous salt and sugar solutions that were consistent with the experimental evidence was successful in reasoning through the transfer interview problem (red boxes in Figure 4), all students who engaged in all three key thinking processes during the dissolution module were successful in reasoning through the end-of-semester interview transfer problem (green boxes in Figure 4). The distribution of students across the categories in Figure 4 was similar for the samples of students who took the general chemistry course in different semesters (with 4, 4, and 3 from one semester and 5, 6, and 6 from the subsequent semester categorized in the green, yellow, and red boxes, respectively). The excerpts from Morgan’s sugar models (Box 1) illustrate engagement in all three key thinking processes (following the path that ends with the green boxes in Figure 4). (Her salt model was also consistent with the experimental evidence.) During the dissolution module, Morgan revised her model from sugar breaking apart into ions to sugar breaking apart into molecules when added to water to be consistent with the conductivity evidence she obtained, and she explicitly engaged in accurate metacognitive monitoring of how her ideas had changed, citing the lack of conductivity of a sugar solution to justify the change in her ideas. (Note that her refined model was not fully correct as she indicated that sucrose would break apart into two smaller, neutral molecules.) Students who constructed molecular-level models of aqueous solutions that were consistent with the evidence collected during the dissolution module, but did not both explicitly

0.500d 0.471d

a The p values were determined via two-tailed Fisher’s exact tests using the method of summing small p values (N = 28) (see ref 68). Values of ϕ > 0.40 indicate a strong association (see refs 69 and 70). b p < 0.0005. cp < 0.005. dp < 0.05.

student thinking processes with the strongest associations in our further analyses: • constructing molecular-level models of aqueous solutions that are consistent with experimental evidence collected during the dissolution module for both the salt solution and the sugar solution, ϕ = 0.596; • explicitly engaging in accurate metacognitive monitoring of how their molecular-level models changed relative to their initial ideas for either the salt solution or the sugar solution, ϕ = 0.641; • explicitly using evidence to justif y molecular-level model refinements (or to support initial model ideas if unchanged) as part of the metacognitive reflection for either the salt solution or the sugar solution, ϕ = 0.795. The other three variables that were less strongly associated with transfer success (ϕ = 0.40−0.60) are each closely related to one of the three key variables above, and these relationships are described more fully in the Supporting Information. Several other variables tested were not strongly associated with transfer success, including correctness of initial (ϕ = 0.059) and final (ϕ = 0.190) molecular-level models, and consistency of initial molecular-level models with laboratory evidence I



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Figure 4. Relationships between student engagement in three key aspects of thinking during the MORE dissolution laboratory module at the beginning of the semester and success at reasoning through a transfer problem during an end-of-semester interview.

evidence to justify personal model refinements. Our findings suggest that engaging in these three key thinking processes may contribute to student construction of sufficiently deep understandings of scientific models to facilitate the activation and application of appropriate cognitive resources in new problem contexts. In the context of a guided-discovery−direct-instruction sequence, transfer problem-solving success was much more strongly associated with constructing models (during guided discovery) that were consistent with evidence (ϕ = 0.596) than constructing models that were fully correct, which was only weakly associated with transfer success (ϕ = 0.190). Constructing a correct initial or refined model was not sufficient for transfer success during the end-of-semester interview as only 55% of students whose refined models were fully correct were successful at applying those ideas effectively in the new context. Furthermore, constructing models that were consistent with experimental evidence, but not fully correct, during the guideddiscovery portion of instruction did not prevent students from solving the transfer interview problem at the end of the semester. Specifically, although all students who correctly reasoned through the transfer question constructed correct molecular-level models for the salt solution, six of these students (including Morgan) constructed consistent but incorrect models for the sugar solution during the guideddiscovery laboratory module. (Correct models were presented to students after they completed the laboratory module, prior to the end-of-semester interviews, presumably facilitating further refinement of models.) To activate and apply resources effectively in new problem contexts, students need, ultimately, to develop correct models from which to reason. However, in our study, constructing models that were consistent with the evidence collected during the guided-discovery portion of instruction, especially in conjunction with engaging in accurate metacognitive monitoring that integrated evidence, was a better predictor of future transfer success than having correct initial models or constructing fully correct models. Making correct macroscopic−molecular connections facilitates the process of refining molecular-level models to be consistent with that evidence, and was also strongly associated with transfer success.

engage in accurate metacognitive monitoring and use evidence to justify their personal model refinements (following the paths that end with the yellow boxes in Figure 4), averaged a 30% success rate in reasoning through the transfer interview problem. In these cases, it is possible that students who successfully reasoned through the transfer problem engaged in the key thinking processes at some point, but did not do so explicitly in the written models that we analyzed. As an example of a student following the path that ends with the lower set of yellow boxes in Figure 4, student 57 made correct connections between conductivity data and molecularlevel models for both salt and sugar, and constructed scientifically correct (and thus consistent) refined models for the salt and sugar solutions. However, his refined models were coded as no mention/comparison for both metacognitive monitoring (Figure 3B) and use of evidence as part of that reflection (Figure 3C), indicating no explicit attempt at metacognitive reflection in his refined models. During the end-of-semester interview, this student correctly represented both salt and sugar solutions on the molecular level in both interview contexts (conductivity and boiling point elevation) without interviewer prompting. In the transfer context, however, the student incorrectly reasoned that the sugar solution would have a higher boiling point than the salt solution because a sugar molecule contains more atoms compared with the two ions present in a formula unit of the salt NaCl.

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DISCUSSION Similar to instructional methods in other domains that seek to promote deep understanding to facilitate transfer,49−51 our previous work comparing outcomes for groups of students demonstrated that MORE instruction improved students’ transfer success when compared with standard approaches to chemistry laboratory instruction.41,42 However, as is typical, only a subset of students participating in MORE modules was successful at transfer. The work reported here builds on the previous studies by contributing new knowledge regarding specific thinking processes that are associated with transfer success for individual students, including constructing molecular-level mental models that are consistent with evidence, engaging in accurate metacognitive monitoring, and using J



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Thus, engaging in accurate and complete reflection on the progression of one’s own molecular-level ideas appears to contribute to the development of the deep understandings that facilitate the activation and application of molecular-level models in relevant situations. This work also contributes that the use of evidence to explain the refinement of one’s ideas during the process of metacognitive reflection may be related to transfer success in some contexts.

One concern that instructors sometimes express about guided-discovery teaching methods is that students may construct ideas that are incorrect. In this work, however, as long as students constructed molecular-level models that were consistent with the laboratory evidence they collected, constructing an incorrect model during guided discovery did not prevent them from refining that model further (presumably during subsequent direct instruction) and ultimately constructing a robust scientific model that facilitated transfer. Similarly, previous work has shown that “productive failure” can actually enhance student learning.71 In another study, researchers found that groups of students that generated a greater number of what the authors call representations and solution methods (RSMs) performed better on a post-test, regardless of whether the RSMs generated were correct or not.72 Furthermore, Schwartz, Sears, and Chang73 and Schwartz et al.40 proposed that it was the process of students inventing their own solutions, not the correctness of their ideas, that was linked to higher levels of transfer after participating in a guideddiscovery−direct-instruction sequence. As illustrated in Figure 4, of the students who developed refined molecular-level models of salt and sugar solutions that were consistent with the experimental evidence, those who reasoned through the transfer problem correctly were distinguished by the presence and quality of their reflections on how and why their molecular-level models changed compared with their initial models (i.e., a specific form of metacognitive monitoring), including whether or not they incorporated experimental evidence to justify changes (or to support aspects that had not changed). For example, consider three students (16, 33, and 42) whose initial and refined molecular-level models were fully correct, and thus consistent with the experimental evidence, throughout the dissolution laboratory module. (Excerpts from these students’ metacognitive reflections, and the codes assigned to them, are shown in Table 1.) Student 33 did not mention his initial models in his refined model, and thus, we have no evidence that he engaged in metacognitive reflection. Student 16 exhibited accurate metacognitive monitoring when he indicated that his models did not change from initial to refined, but he did not use evidence to support making no changes to his model. Like the other two students, student 42 made no changes to his correct initial models but, in contrast to the other two students, not only engaged in accurate metacognitive monitoring, but also used evidence to explain how his models were consistent with the evidence collected and thus justify making no changes to the models. Of these three students who all constructed correct and consistent models initially, only student 42 reasoned through the end-of-semester transfer interview problem correctly. (See interview excerpts in Supporting Information.) Compared with the other two students, student 42 more clearly articulated a coherent molecular-level view of particles in solution, and activated and applied his molecular-level models in a productive way in the context of the boiling-point elevation transfer problem. In a handful of studies mentioned in the Background section, instructional methods that prompt student metacognition in various forms were shown to facilitate higher transfer rates in groups of students compared with traditional instruction.41,42,49−51 This study further contributes that individual students who explicitly engaged in a specific type of metacognitive monitoring (i.e., reflecting on how and why their molecular-level ideas changed) were more likely to be successful at transfer.

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IMPLICATIONS AND LIMITATIONS The results of this research have important potential implications for chemistry education. Current chemistry instruction and assessment at the college level tend to focus on teaching students the scientifically accepted models through direct instruction, and rarely encourage students to construct and reflect upon their own ideas about molecular-level behavior and how and why (with reference to evidence) these ideas change over time. Our results suggest that students’ engagement in these thinking processes may play an important role in promoting the robust conceptual understandings that support students’ application of learned scientific models in new contexts. Although the molecular-level aspects of our analyses of student models are chemistry-specific, the three key thinking processes identified have the potential to be generalized for promotion of deep understanding in other areas of STEM education by encouraging students to test and revise their models for consistency with experimental evidence; to reflect upon how and why their models have changed; and to use evidence to justify model refinements. While we did not investigate causal relationships between the thinking processes we identified and transfer success, the results suggest that exploring the integration of activities that promote the key thinking process identified here into chemistry courses is warranted. Such activities could be incorporated into instruction not only via laboratory courses, as illustrated here, but also through in-class or homework assignments that prompt students to engage in these thinking processes. Although this study was carried out in a Model−Observe−Reflect−Explain (MORE) classroom, our focus was not on the effectiveness of this instructional model, but on the identification of thinking processes that can be facilitated in a variety of instructional contexts. However, it is possible that the findings are specific to the samples of students who participated in these studies and/ or aspects of the particular instructional context employed. Further studies in different learning environments, in different chemistry content areas, and with larger cohorts of students are warranted to investigate the potential benefits of integrating the identified thinking processes in varied settings. In summary, by examining individual students’ knowledge and thinking in depth, we have distinguished the learning of students who experienced the same curriculum and instruction, and revealed three thinking processes that are strongly associated with later success at applying a molecular-level model effectively in a new context. Although prior work on the MORE Thinking Frame found that MORE students outperformed control students on examination questions requiring transfer,41,42 the present study indicates that student engagement in specific thinking processes is key, not simply the participation in MORE curriculum and instruction. The extent of student engagement in these three key aspects of thinking was much more strongly associated with transfer success than the nature of a student’s content knowledge prior to instruction. While comparing the learning outcomes of groups experiencing K



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(4) Barnett, S. M.; Ceci, S. J. When and Where Do We Apply What We Learn? A Taxonomy for Far Transfer. Psych. Bull. 2002, 128 (4), 612−637. (5) Detterman, D. K. The Case for the Prosecution: Transfer as an Epiphenomenon. In Transfer on Trial: Intelligence, Cognition, and Instruction; Detterman, D. K., Sternberg, R. J., Eds.; Ablex: Norwood, NJ, 1993; pp 1−24. (6) Dori, Y. J.; Sasson, I. A Three-Attribute Transfer Skills Framework - Part I: Establishing the Model and Its Relation to Chemical Education. Chem. Educ. Res. Pract. 2013, 14, 363−375. (7) Singer, S. R.; Nielsen, N. R.; Schweingruber, H. A. DisciplineBased Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering; National Research Council: Washington, DC, 2012. (8) Singer, S. R. Advancing Research on Undergraduate Science Learning. J. Res. Sci. Teach. 2013, 50 (6), 768−772. (9) National Research Council. Improving Undergraduate Instruction in Science, Technology, Engineering, and Mathematics; National Academies Press: Washington, DC, 2003. (10) National Research Council. Evaluating and Improving Undergraduate Teaching in Science, Technology, Engineering, and Mathematics; National Academies Press: Washington, DC, 2003. (11) Bodner, G. M. I Have Found You an Argument: The Conceptual Knowledge of Beginning Chemistry Graduate Students. J. Chem. Educ. 1991, 68 (5), 385−388. (12) Duit, R. Bibliography: Students’ and Teachers’ Conceptions and Science Education. Retrieved from IPN Leibniz Institute for Science and Mathematics Education website:http://www.ipn.uni-kiel.de/ aktuell/stcse/ (accessed May 2017). (13) Hake, R. R. Interactive-Engagement Versus Traditional Methods: A Six-thousand-Student Survey of Mechanics Test Data for Introductory Physics Courses. Am. J. Phys. 1998, 66 (1), 64−74. (14) Perkins, D. N.; Simmons, R. Patterns of Misunderstanding: An Integrative Model for Science, Math, and Programming. Rev. Educ. Res. 1988, 58, 303−326. (15) Pfundt, H.; Duit, R. Bibliography: Students’ Alternative Frameworks and Science Education, 4th ed.; University of Kiel Institute for Science Education (Institut fur die Padagogik der Naturwissenschaften): Keil, Germany, 1994. (16) Bretz, S. L.; McClary, L. Students’ Understanding of Acid Strength: How Meaningful Is Reliability When Measuring Alternative Conceptions? J. Chem. Educ. 2015, 92 (2), 212−219. (17) Luxford, C. J.; Bretz, S. L. Development of the Bonding Representations Inventory To Identify Student Misconceptions about Covalent and Ionic Bonding Representations. J. Chem. Educ. 2014, 91 (3), 312−320. (18) Becker, N. M.; Cooper, M. M. College Chemistry Students’ Understanding of Potential Energy in the Context of AtomicMolecular Interactions. J. Res. Sci. Teach. 2014, 51 (6), 789−808. (19) Mestre, J. Transfer of Learning: Issues and Research Agenda. Report of a workshop held at the National Science Foundation. http://www.nsf.gov/pubs/2003/nsf03212/nsf03212.pdf (accessed May 2017). (20) Schoenfeld, A. H. Looking Toward the 21st Century: Challenges of Educational Theory and Practice. Educ. Res. 1999, 28 (7), 4−14. (21) Towns, M. H.; Kraft, A. Review and Synthesis of Research in Chemical Education from 2000−2010; Paper presented at the Second Committee Meeting on the Status, Contributions, and Future Directions of Discipline-Based Education Research, 2011. http:// sites.nationalacademies.org/DBASSE/BOSE/DBASSE_080124 (accessed May 2017). (22) Carraher, D.; Schliemann, A. D. The Transfer Dilemma. J. Learn. Sci. 2002, 11 (1), 1−24. (23) diSessa, A. A.; Wagner, J. F. What Coordination Has to Say about Transfer. In Transfer From a Modern Multidisciplinary Perpective; Mestre, J., Ed.; Information Age Publishing: Greenwich, CT, 2005; pp 121−154.

distinct instructional conditions elucidates conditions that promote desired outcomes, characterizing individual student thinking and linking it to outcomes appears crucial for understanding the complex mechanisms that underlie these outcomes. This is particularly important when considering the implementation of pedagogies in varied instructional settings. Investigating ways to maximize student engagement in key thinking processes and to establish causal relationships are important areas for future research.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.6b00762. More detail regarding methods and excerpts from interviews (PDF, DOCX)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Melonie A. Teichert: 0000-0002-9892-6631 Lydia T. Tien: 0000-0002-4189-611X Lisa Dysleski: 0000-0002-4653-3174 Dawn Rickey: 0000-0001-9624-2239 Present Address

Melonie Teichert’s current affiliation is the Department of Chemistry, United States Naval Academy, Annapolis, MD, 21402. Lisa Dysleski’s current affiliation is the College of Natural Sciences, Colorado State University, Fort Collins, CO, 80521. Dawn Rickey’s current affiliation is the National Science Foundation, Arlington, VA, 22230. Notes

Findings, conclusions, and recommendations expressed herein are those of the authors, and do not necessarily reflect the views of the NSF or ONR. The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This material is based upon work supported by the National Science Foundation (NSF) under award numbers 0208029 and 0618829. In addition, portions of this manuscript were written while author D.R. was serving at the NSF, and include NSF support through her Independent Research and Development plan. M.T. was also partially supported by research funds from the Office of Naval Research (ONR). The authors also acknowledge and thank the general chemistry students who participated in the study, Seth Anthony and Thomas Kim for their contributions, and anonymous reviewers for their valuable comments.

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REFERENCES

(1) Bransford, J. D.; Brown, A. L.; Cocking, R. R. Learning and Transfer. In How People Learn: Brain, Mind, Experience, and School; The National Academies Press: Washington, DC, 2000; pp 51−78. (2) Mestre, J. Transfer of Learning from a Modern Multidisciplinary Perspective; Information Age Publishing: Greenwich, CT, 2005. (3) Thorndike, E. L.; Woodworth, R. S. The Influence of Improvement in One Mental Function upon the Efficiency of Other Functions (I). Psych. Rev. 1901, 8 (3), 247−261. L



Article

(24) Ellis, A. B. A Taxonomy for Categorizing Generalizations: Generalizing Actions and Reflection Generalizations. J. the Learn. Sci. 2007, 16 (2), 221−262. (25) Hammer, D.; Elby, A.; Scherr, R. E.; Redish, E. F. Resources, Framing, and Transfer. In Transfer of Learning from a Modern Multidisciplinary Perspective; Mestre, J., Ed.; Information Age Publishing: Greenwich, CT, 2005; pp 89−119. (26) Lobato, J. Alternative perspectives on the transfer of learning: History, issues, and challenges for future research. J. Learn. Scien. 2006, 15 (4), 431−449. (27) Marton, F. Sameness and Difference in Transfer. J. Learn. Sci. 2006, 15 (4), 499−535. (28) Schwartz, D. L.; Bransford, J. D.; Sears, D. Efficiency and Innovation in Transfer. In Transfer of Learning from a Modern Multidisciplinary Perspective; Mestre, J., Ed.; Information Age Publishing: Greenwich, CT, 2005; pp 1−51. (29) Wagner, J. F. Transfer in Pieces. Cog. and Inst. 2006, 24 (1), 1− 71. (30) Wagner, J. F. A Transfer-in-Pieces Consideration of the Perception of Structure in the Transfer of Learning. J. Learn. Sci. 2010, 19 (4), 443−479. (31) Engle, R. A. Framing Interactions to Foster Generative Learning: A Situative Explanation of Transfer in a Community of Learners Classroom. J. Learn. Sci. 2006, 15 (4), 451−498. (32) Gick, M. L.; Holyoak, K. J. Schema Induction and Analogical Transfer. Cog. Psych. 1983, 15 (1), 1−38. (33) Brown, A. L.; Kane, M. J. Preschool Children Can Learn to Transfer: Learning to Learn and Learning from Example. Cog. Psych. 1988, 20 (4), 493−523. (34) Gentner, D.; Markman, A. B. Structure Mapping in Analogy and Similarity. Am. Psychol. 1997, 52, 45−56. (35) Martin, L.; Schwartz, D. L. Conceptual Innovation and Transfer. In International Handbook of Research on Conceptual Change, 2nd ed.; Vosniadou, S., Ed.; Taylor & Francis: New York, 2013; pp 447−465. (36) Teichert, M. A. Ph.D. Dissertation, University of California, Berkeley, CA, 1999. (37) Teichert, M. A.; Tien, L. T.; Anthony, S.; Rickey, D. Effects of Context on Students’ Molecular-level Ideas. Int. J. Sci. Educ. 2008, 30 (8), 1095−1114. (38) Teichert, M. A.; Stacy, A. M. Promoting Understanding of Chemical Bonding and Spontaneity Through Student Explanation and Integration of Ideas. J. Res. Sci. Teach. 2002, 39 (6), 464−496. (39) Engle, R. A.; Lam, D. P.; Meyer, X. S.; Nix, S. E. How Does Expansive Framing Promote Transfer? Several Proposed Explanations and a Research Agenda for Investigating Them. Educ. Psych. 2012, 47 (3), 215−231. (40) Schwartz, D. L.; Chase, C. C.; Oppezzo, M. A.; Chin, D. B. Practicing versus Inventing with Contrasting Cases: The Effects of Telling First on Learning and Transfer. J. Educ. Psych. 2011, 103 (4), 759−775. (41) Rickey, D. Ph.D. Dissertation, University of California, Berkeley, CA, 1999. (42) Rickey, D.; Tien, L. T. Research on Learning in the Chemistry Laboratory: A Trajectory Connecting Student Outcomes to Thinking Processes. In Trajectories of Chemical Education Innovation and Reform; Holme, T., Cooper, M., Varma-Nelson, P., Eds.; Oxford University Press: Washington, DC, 2013; pp 31−45. (43) Schwartz, D. L.; Bransford, J. D. A Time for Telling. Cog. and Inst. 1998, 16 (4), 475−522. (44) Schwartz, D. L.; Martin, T. Inventing to Prepare for Future Learning: The Hidden Efficiency of Encouraging Original Student Production in Statistics Instruction. Cog. and Inst. 2004, 22 (2), 129− 184. (45) Gick, M. L.; Paterson, K. Do Contrasting Examples Facilitate Schema Acquisition and Analogical Transfer? Can. J. Psych. 1992, 46 (4), 539−550. (46) Flavell, J. H. Metacognitive Aspects of Problem Solving. In The Nature of Intelligence; Resnick, L. B., Ed.; Lawrence Erlbaum Associates, Inc.: Hillsdale, NJ, 1976; pp 231−235.

(47) Rickey, D.; Stacy, A. M. The Role of Metacognition in Learning Chemistry. J. Chem. Educ. 2000, 77 (7), 915−920. (48) White, R. T. Metacognition. In Educational Research, Methodology and Measurement: An International Handbook; Keeves, J. P., Ed.; Pergamon: Oxford, 1988; pp 70−75. (49) White, B. Y.; Frederiksen, J. R. Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students. Cog. and Inst. 1998, 16 (1), 3−118. (50) Palincsar, A. S.; Brown, A. L. Reciprocal Teaching of Comprehension Monitoring Activities. Cog. and Inst. 1984, 1, 117− 175. (51) Schoenfeld, A. H. Learning to Think Mathematically: Problem Solving, Metacognition, and Sense Making in Mathematics. In Handbook of Research on Mathematics Teaching and Learning; Grouws, D., Ed.; Macmillan: New York, NY, 1992; pp 334−370. (52) Tien, L. T.; Rickey, D.; Stacy, A. M. The MORE Thinking Frame: Guiding Students’ Thinking in the Laboratory. J. Coll. Sci. Teach. 1999, 18 (5), 318−324. (53) Mattox, A. C.; Reisner, B. A.; Rickey, D. What Happens When Chemical Compounds are Added to Water: An Introduction to the Model-Observe-Reflect-Explain Thinking Frame. J. Chem. Educ. 2006, 83 (4), 622−624. (54) Johnson-Laird, P. N. Mental Models; Harvard University Press: Cambridge, MA, 1983. (55) Craik, K. The Nature of Explanation; Cambridge University Press: Cambridge, MA, 1943. (56) Tien, L. T. Ph.D. Dissertation, University of California, Berkeley, CA, 1998. (57) Gabel, D. L. Enhancing Students’ Conceptual Understanding of Chemistry Through Integrating the Macroscopic, Particle, and Symbolic Representations of Matter. In Chemists’ Guide to Effective Teaching; Pienta, N. J., Cooper, M. M., Greenbowe, T. J., Eds.; Pearson Education, Inc.: Upper Saddle River, NJ, 2005; pp 77−88. (58) Johnstone, A. H. Why is Science Difficult to Learn? Things are Seldom What They Seem. J. Comp. Assist. Learn 1991, 7 (2), 75−83. (59) Johnstone, A. H. The Development of Chemistry Teaching: A Changing Response to Changing Demand. J. Chem. Educ. 1993, 70 (9), 701−705. (60) Kozma, R.; Russell, J. Multimedia and Understanding: Expert and Novice Responses to Different Representations of Chemical Phenomena. J. Res. Sci. Teach. 1997, 34 (9), 949−968. (61) Treagust, D. F.; Chittleborough, G. Chemistry: A Matter of Understanding Representations. In Subject-Specific Instructional Methods and Activities; Brophy, J. E., Ed.; Elsevier: London, 2001; pp 239− 267. (62) Ruiz-Primo, M. A.; Li, M.; Tsai, S.-P.; Schneider, J. Testing One Premise of Scientific Inquiry in Science Classrooms: Examining Students’ Scientific Explanations and Student Learning. J. Res. Sci. Teach. 2010, 47 (5), 583−608. (63) Patton, M. Q. Qualitative Research and Evaluation Methods, 3rd ed.; Sage: Thousand Oaks, CA, 2002. (64) The 1998 Toledo Examination written by the American Chemical Society’s Examinations Institute. (65) Tien, L. T.; Teichert, M. A.; Rickey, D. Effectiveness of a MORE Laboratory Module in Prompting Students to Revise Their MolecularLevel Ideas About Solutions. J. Chem. Educ. 2007, 84 (1), 175−181. (66) Schreier, M. Qualitative Content Analysis. In The SAGE Handbook of Qualitative Data Analysis; Flick, U., Ed.; SAGE Publications Limited: Thousand Oaks, CA, 2013. (67) Kelly, R. M.; Jones, L. J. Exploring How Different Features of Animations of Sodium Chloride Dissolution Affect Students’ Explanations. J. Sci. Educ. Technol. 2007, 16, 413−429. (68) Agresti, A. An Introduction to Categorical Data Analysis; John Wiley & Sons, Inc.: New York, 1996. (69) Cohen, J. A Power Primer. Psych. Bull. 1992, 112 (1), 155−159. (70) Rea, L. M.; Parker, R. A. Designing and Conducting Survey Research; Jossey-Boss: San Francisco, 2014. (71) Kapur, M. Productive Failure. Cog. and Inst. 2008, 26 (3), 379− 424. M



Article

(72) Kapur, M.; Bielaczyc, K. Designing for Productive Failure. J. Learn. Sci. 2012, 21 (45), 45−83. (73) Schwartz, D. L.; Sears, D.; Chang, J. Reconsidering Prior Knowledge. In Thinking with Data; Lovett, M. C., Shah, P., Eds.; Lawrence Erlbaum Associates: New York, 2007; pp 319−344.

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