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Shared Resources: Engineering Students' Emerging Group Understanding of Thermodynamic Work

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Title: Shared Resources: Engineering Students' Emerging Group Understanding of Thermodynamic Work
Language: English
Authors: Cao, Ying, Koretsky, Milo D.
Source: Journal of Engineering Education. Oct 2018 107(4):656-689.
Availability: Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 34
Publication Date: 2018
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: TUES1245482
Document Type: Journal Articles
Reports - Research
Descriptors: Engineering Education, Fundamental Concepts, Thermodynamics, Science Laboratories, Concept Formation, Cooperative Learning, Interaction, Learning Processes, Educational Technology
DOI: 10.1002/jee.20237
ISSN: 1069-4730
Abstract: Background: We have developed several interactive virtual laboratories (IVLs) based on a sequence of agent-based molecular simulations designed to target specific threshold concepts to help students learn thermodynamics. We previously analyzed learning in the IVLs from a cognitive perspective, seeking to repair students' misconceptions; however, that perspective provided limited information for iteratively improving the IVLs. Purpose: In this study, we shift to a sociocultural perspective to identify student learning resources activated during their engagement in the Thermodynamic Work IVL. We seek to identify the productive social and environmental triggers through which students develop conceptual ideas using technology in a social setting. Method: We conducted emergent lexical coding on a cohort of 187 students' textual responses as they completed the IVL in a studio setting. We then analyzed the discursive and technology interactions of four students from different groups using video recordings. Results: Coding results show distributions of students' activated resources. Almost all of the students demonstrated productive ideas, and almost all also revealed opportunities to learn more. Through the detailed studies, we illustrate the moment-by-moment interactions of students with one another and with technology to describe how they activate and share resources. These interactions are conceptualized and illustrated through the construct of shared resources. We relate shared resources to processes of knowledge co-construction and knowledge transfer, and discuss implications for instructional practice and educational technology design. Conclusions: The resources framework helps us recognize productive ideas in students' evolving understanding of thermodynamic work. Shared resources allows for elaboration of the interwoven cognitive and social aspects of learning.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1254069
Database: ERIC
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  Value: <anid>AN0133464848;6m401oct.18;2018Dec11.06:06;v2.2.500</anid> <title id="AN0133464848-1">Shared Resources: Engineering Students' Emerging Group Understanding of Thermodynamic Work </title> <p>Background: We have developed several interactive virtual laboratories (IVLs) based on a sequence of agent‐based molecular simulations designed to target specific threshold concepts to help students learn thermodynamics. We previously analyzed learning in the IVLs from a cognitive perspective, seeking to repair students' misconceptions; however, that perspective provided limited information for iteratively improving the IVLs. Purpose: In this study, we shift to a sociocultural perspective to identify student learning resources activated during their engagement in the Thermodynamic Work IVL. We seek to identify the productive social and environmental triggers through which students develop conceptual ideas using technology in a social setting. Method: We conducted emergent lexical coding on a cohort of 187 students' textual responses as they completed the IVL in a studio setting. We then analyzed the discursive and technology interactions of four students from different groups using video recordings. Results: Coding results show distributions of students' activated resources. Almost all of the students demonstrated productive ideas, and almost all also revealed opportunities to learn more. Through the detailed studies, we illustrate the moment‐by‐moment interactions of students with one another and with technology to describe how they activate and share resources. These interactions are conceptualized and illustrated through the construct of shared resources. We relate shared resources to processes of knowledge co‐construction and knowledge transfer, and discuss implications for instructional practice and educational technology design. Conclusions: The resources framework helps us recognize productive ideas in students' evolving understanding of thermodynamic work. Shared resources allows for elaboration of the interwoven cognitive and social aspects of learning.</p> <p>Keywords: conceptual learning; learning technology; social learning theory</p> <p>An ability to apply conceptual knowledge is widely believed to be an important competency for engineering practice (Vincenti, [<reflink idref="bib67" id="ref1">67</reflink>]), with recent research focusing on how educators improve and assess conceptual understanding in university engineering students (Redish & Smith, [<reflink idref="bib53" id="ref2">53</reflink>]; Streveler, Brown, Herman, & Montfort, [<reflink idref="bib63" id="ref3">63</reflink>]; Streveler, Litzinger, Miller, & Steif, [<reflink idref="bib64" id="ref4">64</reflink>]). To help students build conceptual understanding in thermodynamics, we recently developed a set of interactive virtual laboratories (IVLs) where students are guided through a sequence of approximately 15 frames prompting them to interact with simulations and relate the microscopic behavior of molecules in a system to its macroscopic phenomena (Bowen, Reid, & Koretsky, [<reflink idref="bib2" id="ref5">2</reflink>]). These IVLs were specifically constructed to target research‐identified threshold concepts (Meyer & Land, [<reflink idref="bib43" id="ref6">43</reflink>]) by drawing on student difficulties identified in the science and engineering education literature. Here, we report the results of a study of one particular IVL, Thermodynamic Work, which students completed in groups in a studio environment (interactive small‐group class sessions encouraging cooperative learning, Koretsky, [<reflink idref="bib2" id="ref7">2</reflink>]). We are interested in both the ways that the IVL provides a technology to build conceptual understanding and the ways that student interaction supports emerging understanding.</p> <p>Our original conception in creating the IVLs was based on the belief that given the "right way of thinking" through pedagogically grounded simulations of molecular behavior that make the mechanism of emergent macroscopic phenomena visible, most students would develop canonical understanding of the threshold concepts targeted. Yet, we found that only a subset of students provided responses that fully aligned with the normatively correct explanations; for example, only approximately 40% correctly answered a summative question in the Thermodynamic Work IVL, where the correct answer involves mentioning net work done to the system. Similarly, Nelson et al. ([<reflink idref="bib46" id="ref8">46</reflink>]), who used animated simulations to demonstrate the microscopic phenomena of electrons in semiconductor materials, reported normatively correct answers for only approximately 20% of the student responses. Such results indicating student learning difficulties seem to align with the claim that misconceptions are persistent and robust (Chi, [<reflink idref="bib8" id="ref9">8</reflink>]).</p> <p>However, we noticed that often when student responses did not include the correct answer, they exhibited elements of reasonable understanding of the question presented (e.g., ideas about molecular kinetics and irreversibility). In contrast, the correct answers ranged widely from only a direct answer to the question to a thoughtful elaboration on why the answer was correct, thus demonstrating a deep understanding of the subject. We found that a binary notion of right or wrong was limited in helping us understand what students <emph>did</emph> learn from the Thermodynamic Work IVL. A misconception perspective did not provide information to iteratively improve the instructional design of the technology tool.</p> <p>To address this limitation, we adopted an alternative resources framework, which posits that student knowledge and associated reasoning ability consist of small pieces of primitive conceptual understanding (diSessa, [<reflink idref="bib11" id="ref10">11</reflink>]) that can function as cognitive resources (Hammer, Elby, Scherr, & Redish, [<reflink idref="bib18" id="ref11">18</reflink>]). These resources, which are gained from such past experiences as observations in everyday life or in laboratories and reading and connecting to a textbook, can remain tacit until they are activated when applied to a particular piece of knowledge in a certain context. For example, <emph>closer means stronger</emph> is a piece of knowledge gained by the everyday observation of getting warm by sitting close to a fireplace (Hammer, [<reflink idref="bib17" id="ref12">17</reflink>]). This knowledge of closer means stronger can be reinforced when students learn that the magnetic pull (or push) is stronger when two magnets are close to each other through everyday experience, a physics laboratory experiment, or a mathematical expression calculating magnetic forces. This piece of knowledge, which is not inherently right or wrong, can remain tacit until a trigger activates it and other resources upon which understanding of a subject can be built and expressed in an external representation, such as a written answer. The resources framework allows us to categorize the fine grain elements of student understanding that we have observed in written answers submitted by students.</p> <p>We have also observed instances when student group interactions led to an emergent, more comprehensive understanding of concepts even when no one in the group had initially mastered them or provided a correct answer. Focusing on whether students' final answers were correct also did not account for such interactions. We conjecture that sense‐making elements, or resources, can be identified in student ideas and that sharing resources can account for the emergent group understanding. To investigate these social and environmental interactions, we applied the resources framework and through this study propose the construct of shared resources. Although the empirical study described in this article precipitated our conceptualization of the construct of shared resources, it is consistent with student interactions our research team has observed in previous investigations of collaborative learning environments ranging from the use of peer instruction in large lecture classes (Brooks & Koretsky, [<reflink idref="bib3" id="ref13">3</reflink>]; Koretsky, Brooks, & Higgins, [<reflink idref="bib31" id="ref14">31</reflink>]; Koretsky, Brooks, White, & Bowen, [<reflink idref="bib32" id="ref15">32</reflink>]) to guided inquiry in studio environments (Koretsky, [<reflink idref="bib28" id="ref16">28</reflink>], [<reflink idref="bib29" id="ref17">29</reflink>]; Koretsky et al., [<reflink idref="bib34" id="ref18">34</reflink>]) to more extensive project‐based learning in industrially situated capstone design projects (Hirshfield & Koretsky, [<reflink idref="bib19" id="ref19">19</reflink>]; Koretsky, Amatore, Barnes, & Kimura, [<reflink idref="bib30" id="ref20">30</reflink>]; Koretsky, Kelly, & Gummer, [<reflink idref="bib26" id="ref21">26</reflink>]).</p> <p>A shared resource is one that multiple people can draw upon and use to form ideas. According to the shared resources construct, different students activate personal resources to form ideas, sharing them with other group members. Although the ideas contributed do not have to be canonically correct, they should include elements leading to a more comprehensive understanding. Shared ideas can become a foundation on which the group constructs further understanding or makes progress beyond what any single member can accomplish alone. In this scenario, shared resources are situated in the group. However, when an individual member learns about another group member's resource through the group discussion and later activates it independently in different contexts, then the shared resource transcends the specific group setting.</p> <p>These observations and conjectures prompted the research questions explored in this study, in which we investigate student conceptual learning in the IVL from a sociocultural perspective. Taking this perspective, we focus on students' participation in learning, a change from our previous focus on the individual, sequestered mental structures they acquired. More specifically, we address the following research questions:</p> <p></p> <ulist> <item> As identified through their written explanations to conceptual questions about thermodynamic work, what resources do students demonstrate and how do resources distribute among students?</item> <p></p> <item> In what ways do students interact with one another (social) and with the technology (environment) to share resources?</item> </ulist> <hd id="AN0133464848-2">Background and Theoretical Framework</hd> <p>We approach conceptual understanding through situative learning (Greeno, [<reflink idref="bib14" id="ref22">14</reflink>]), which asserts that development of conceptual understanding in engineering students should directly connect to the ways conceptual knowledge is used in engineering practice (Bucciarelli, [<reflink idref="bib4" id="ref23">4</reflink>]; National Research Council, [<reflink idref="bib45" id="ref24">45</reflink>]; Streveler et al., [<reflink idref="bib64" id="ref25">64</reflink>]; Trevelyan, [<reflink idref="bib65" id="ref26">65</reflink>]; Vincenti, [<reflink idref="bib67" id="ref27">67</reflink>]). Professional engineers use conceptual knowledge to form intuitive expectations about how systems respond (Streveler et al., [<reflink idref="bib64" id="ref28">64</reflink>]). In addition, they use conceptual knowledge to define a problem, manage multiple factors in a system appropriately, find an initial solution path, and iteratively improve upon it (Vincenti, [<reflink idref="bib67" id="ref29">67</reflink>]). During this process, engineers commonly collaborate, bouncing their emergent conceptual ideas back and forth to arrive at improved approaches (Bucciarelli, [<reflink idref="bib4" id="ref30">4</reflink>]; Trevelyan, [<reflink idref="bib65" id="ref31">65</reflink>]). Because we are interested in instructional designs that involve technology tools and human interactions that align with situative learning, we draw on a theoretical perspective (resources) different from the commonly applied misconception perspective.</p> <hd id="AN0133464848-3">A Review of the Misconception Perspective</hd> <p>The misconception perspective stems from the understanding that expert knowledge consists of coherent structural models where concepts fit within appropriate ontological categories (Chi, [<reflink idref="bib7" id="ref32">7</reflink>]). Although novices' knowledge is also seen as structural, their structures are different from the expert's, that is, novices may hold some naïve theories (Carey, [<reflink idref="bib6" id="ref33">6</reflink>]; McCloskey, [<reflink idref="bib41" id="ref34">41</reflink>]; Vosniadou, [<reflink idref="bib68" id="ref35">68</reflink>]) or assign concepts to incorrect categories (Chi, [<reflink idref="bib7" id="ref36">7</reflink>]). When a student's conception appears different from an expert's, it is considered a misconception. This view of student knowledge as structural and possibly different from the expert leads to binary comparisons of correct and wrong conceptions.</p> <p>Much of the work on developing student conceptual understanding in engineering sciences is based on the misconception perspective. This work typically uses performance on concept inventories, psychometrically tested instruments designed to assess student understanding based on answers to a set of concept questions, to determine which concepts students know or do not know (e.g., Olds, Streveler, Miller, & Nelson, [<reflink idref="bib48" id="ref37">48</reflink>]; Prince, Vigeant, & Nottis, [<reflink idref="bib51" id="ref38">51</reflink>]; Steif & Dantzler, [<reflink idref="bib62" id="ref39">62</reflink>]). When students choose different answers from the normatively correct answers, researchers and educators often interpret the results in terms of student misconceptions.</p> <p>Based on this point of view, learning is a process that restructures knowledge from the misconception into the normatively correct mental model. Through data from concept inventories and interviews based on similar questions, student misconceptions have been identified and instructional interventions developed to "repair" these misconceptions in engineering (e.g., Hamilton, Lesh, Lester, & Brilleslyper, [<reflink idref="bib16" id="ref40">16</reflink>]; Krause, Kelly, Corkins, & Tasooji, [<reflink idref="bib36" id="ref41">36</reflink>]; Nelson et al., [<reflink idref="bib46" id="ref42">46</reflink>]), and particularly in the thermal sciences (Miller, Streveler, Yang, & Santiago Román, [<reflink idref="bib44" id="ref43">44</reflink>]; Prince, Vigeant, & Nottis, [<reflink idref="bib52" id="ref44">52</reflink>]). Posner, Strike, Hewson, and Gertzog ([<reflink idref="bib50" id="ref45">50</reflink>]) propose that misconceptions can be repaired by eliciting students' mental models, confronting the students by presenting contradictory phenomena, thereby stimulating cognitive discomfort and forcing them to revise their mental models to accommodate the new phenomena. According to this view, conceptual change happens primarily within students' minds. For example, in a canonical understanding of thermodynamics, the concept of energy describes a state whereas the concept of work describes a process. Adopting the misconception perspective, some students have difficulty with work–energy problems because they confuse the state–process categories. Students may approach work as a state, not understanding that the amount of work done depends on the specific process a system undergoes even when its initial and final states are identical. Students, thus, have a misconception that work is a state, meaning they need to shift their ontological categorization.</p> <p>We acknowledge that this approach of identifying and repairing misconceptions has led to valuable progress in engineering instruction. However, it also has limitations in explaining phenomena that show student knowledge is situated (Greeno, [<reflink idref="bib14" id="ref46">14</reflink>]; Johri & Olds, [<reflink idref="bib22" id="ref47">22</reflink>]) and context dependent (Hammer et al., [<reflink idref="bib18" id="ref48">18</reflink>]). For example, a student could demonstrate a different understanding about the same concept when solving paper and pencil problems versus when working in a laboratory or when working alone versus when discussing with other students. Among the efforts to identify students' learning difficulties in the thermal sciences, researchers have highlighted the complexity of students' thinking, emphasizing the importance of context in determining the correctness of their answers (Keeler, Ekstedt, Cao, & Koretsky, [<reflink idref="bib25" id="ref49">25</reflink>]; Nilsson & Niedderer, [<reflink idref="bib47" id="ref50">47</reflink>]). These researchers advocate for a shift of focus toward understanding the thinking processes of students (Keeler et al., [<reflink idref="bib25" id="ref51">25</reflink>]) and the need for a dialogue between teachers, researchers, and students to gain a shared, contextualized understanding of student ideas (Nilsson & Niedderer, [<reflink idref="bib47" id="ref52">47</reflink>]). Indeed there have been calls "to move beyond the identification of misconceptions" (Smith III, diSessa, & Roschelle, [<reflink idref="bib59" id="ref53">59</reflink>], p. 154) to "help practitioners develop more effective classroom practices that allow for the refinement of knowledge into more sophisticated and expert like understandings" (Bain, Moon, Mack, & Towns, [<reflink idref="bib1" id="ref54">1</reflink>], p. 331).</p> <hd id="AN0133464848-4">The Resources Perspective</hd> <p>The resources perspective posits an alternative ontology of cognitive structure from misconceptions. While misconceptions are conceptualized as a unitary cognitive entity, resources "ascribe cognitive objects [...] at a finer grain‐size than concepts or abilities as people experience them. In this view, knowledge and experience are emergent" (Hammer et al., [<reflink idref="bib18" id="ref55">18</reflink>], p. 92). According to Hammer et al. ([<reflink idref="bib18" id="ref56">18</reflink>]), students activate a set of resources to compile a locally coherent understanding of a subject. Although experts process in a similar way, they are better at coordinating multiple resources to construct globally coherent understanding. A combination of resources can be activated multiple times and become crystalized, in which case a set of resources can appear similar to a (mis)conception.</p> <p>The resources perspective, however, also explains and predicts students' variant and shifting conceptual ideas when sets of resources are not stabilized. For example, when students learn concepts related to work and energy in thermodynamics, they may have some ideas about a process and a state, such as ideas about initial, final, and something changing in between. They may also have some ideas about how work and energy relate to other system features both macroscopic (e.g., pressure, volume) and microscopic (e.g., the moving molecules). However, students do not necessarily have well‐formulated theories about how these ideas relate. Rather, resources are activated, and students construct live explications for a given problem, say gas compression. When they are given a different problem, for example gas expansion, they may construct different explanations. This activation of resources is dependent on multiple aspects of the context: the question prompt (e.g., one asks about work, and the other asks about temperature), the tool they use (e.g., a simulation or a worksheet), the specific ways they interact with the tool, and the discussion with others (students and instructors).</p> <p>According to the resources framework, learning involves building more coherent understanding through leveraging existing ideas rather than removing structured misconceptions from the student's mind. Empirical studies have examined student ideas on various topics through the resources perspective (e.g., Cao & Brizuela, [<reflink idref="bib5" id="ref57">5</reflink>]; Hammer, [<reflink idref="bib17" id="ref58">17</reflink>]; Sabo, Goodhew, & Robertson, [<reflink idref="bib55" id="ref59">55</reflink>]).</p> <hd id="AN0133464848-5">Shared Resources</hd> <p>In this study, we are interested in the interaction between the cognitive elements of a student's mind and the social and material elements of the environment, a notion that was mentioned in a footnote in the study conducted by Hammer et al. ([<reflink idref="bib18" id="ref60">18</reflink>]):</p> <p>The activation of finer‐grained cognitive resources should often depend on the social and physical [material] environment such that the resulting knowledge can coherently be attributed to the overall system (people + environment). In this way, a resources framework provides a mechanism by which elements of an individual's mind interact with elements of the social and physical environment to create knowledge that's situated or even distributed. (p. 117, Note 2)</p> <p>This social aspect of the resources framework has not yet been fully elaborated nor explored in empirical research. Individual resources become shared when students work in a group: resources are activated by individuals, expressed in ideas, and shared among learners through discursive correspondence in language and through other representations and inscriptions. When resources are shared, knowledge is co‐constructed in the group, or transferred to a different context.</p> <p>Consider a hypothetical group of students working on a problem in which they are thinking about how heating a container of gas changes the system properties. One student activates the resource <emph>heat moves things</emph> (she might have learned it from boiling water and seeing the lid pop up because of the hot air inside), forming the idea that the pressure must increase as the system is heated. A second student activates a different resource, <emph>heat expansion and cold contraction</emph>, generated from various observations in life and forms a different idea that the volume increases. Both <emph>heat moves things</emph> and <emph>heat expansion and cold contraction</emph> are potential resources to share. For various reasons (e.g., past experience, the question prompt), a particular resource is more easily activated for one student in this context than for another. Working together provides a larger set of activated resources to the group, and subsequently more ideas are generated. When both ideas in the hypothetical example are expressed in the group, the shared ideas can lead to a discussion about responses to heating, for example, a constant‐volume (isochoric) process that addresses the pressure increase versus a constant‐pressure (isobaric) process that addresses volume expansion, perhaps even leading to an equation of state involving all the variables. Therefore, the shared ideas can provide a mutual discussion foundation for emergent group understanding of thermodynamics and a chance for the students to better internalize one another's resources. Through this interaction, they can more easily activate the resources independently in different contexts to solve problems.</p> <p>In summary, we take a sociocultural perspective (Vygotsky, [<reflink idref="bib69" id="ref61">69</reflink>]) to interpret student learning in a group as articulated through the construct of shared resources. We shift perspective from approaching knowledge as an abstract entity to be acquired that is, therefore, either correct (normative) or wrong (misconceptions), to one that knowledge is emergent and iteratively constructed. Thus, knowing entails meaningful participation in activities that encourage individual and collaborative sense‐making (shared resources). Features of technology can stimulate students to activate more or different resources relevant to the learning topic, and group interactions provide opportunities for activating, coordinating, and sharing resources.</p> <hd id="AN0133464848-6">Context: Thermodynamic Work IVL and Studio</hd> <p>Thermodynamics, which is conceptually challenging for engineering and science students, has received considerable attention from education researchers (e.g., Kautz, Heron, Shaffer, & McDermott, [<reflink idref="bib24" id="ref62">24</reflink>]; Loverude, Kautz, & Heron, [<reflink idref="bib39" id="ref63">39</reflink>]; Meltzer, [<reflink idref="bib42" id="ref64">42</reflink>]; Streveler et al., [<reflink idref="bib64" id="ref65">64</reflink>]). The IVLs were developed to address student learning difficulties reported in the engineering and science education research literature. In this section, we first summarize the literature on student learning difficulties with the concept of thermodynamic work, the basis for our Thermodynamic Work IVL. We then describe the structure of the IVL, connecting it to the difficulties reported in the research. Finally, we describe the studio context where the IVL was completed by students in groups during this study.</p> <hd id="AN0133464848-7">Student Difficulty Understanding Thermodynamic Work</hd> <p>Researchers in chemistry, physics, and engineering have reported several conceptual obstacles impacting normative understanding of thermodynamic work. A synthesis of this literature resulted in four core concept areas that we addressed in the design of the Thermodynamic Work IVL. These studies primarily used questionnaires and interviews based on questions similar to those found in homework and on tests, with few employing classroom observations (Clark, Thompson, & Mountcastle, [<reflink idref="bib10" id="ref66">10</reflink>]; van Roon, Van Sprang, & Verdonk, [<reflink idref="bib66" id="ref67">66</reflink>]). The student difficulties reported, which are presented in the first column of Table 1, are summarized below.</p> <p>Difficulties Identified in the Literature and the Ways the Thermodynamics Work IVL Addresses Them</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Student difficulty identified in the literature</th><th>References</th><th>Ways this difficulty is addressed in the Thermodynamic Work IVL</th></tr></thead><tbody valign="top"><tr><td>Relationship between the macro‐ and the molecular scales</td><td>Kautz, Heron, Loverude, et al. (<xref ref-type="bibr" rid="bibr23">23</xref>), Kautz, Heron, Shaffer, et al. (<xref ref-type="bibr" rid="bibr24">24</xref>), and Loverude et al. (<xref ref-type="bibr" rid="bibr39">39</xref>)</td><td>Students first explore a single molecule simulation, then an ensemble of molecules. Students observe the molecular simulation next to the graphical representations of the macroscopic properties (p, v, T).</td></tr><tr><td>Work as a path dependent process</td><td>Clark et al. (<xref ref-type="bibr" rid="bibr10">10</xref>), Meltzer (<xref ref-type="bibr" rid="bibr42">42</xref>), Nilsson and Niedderer (<xref ref-type="bibr" rid="bibr47">47</xref>), and van Roon et al. (<xref ref-type="bibr" rid="bibr66">66</xref>)</td><td>Students calculate the unequal work in a two‐step compression /expansion process that returns the system to its initial volume and reflect on why the system does not return to its original temperature.</td></tr><tr><td>Relationship between work and energy</td><td>Kautz, Heron, Loverude, et al. (<xref ref-type="bibr" rid="bibr23">23</xref>), Loverude et al. (<xref ref-type="bibr" rid="bibr39">39</xref>), and Meltzer (<xref ref-type="bibr" rid="bibr42">42</xref>)</td><td>Students use the relationship between work and energy described by the first law of thermodynamics to calculate the values for work and temperature.</td></tr><tr><td>Reasoning</td><td>Clark et al. (<xref ref-type="bibr" rid="bibr10">10</xref>), Kautz, Heron, Loverude, et al. (<xref ref-type="bibr" rid="bibr23">23</xref>), Loverude et al. (<xref ref-type="bibr" rid="bibr39">39</xref>), Meltzer (<xref ref-type="bibr" rid="bibr42">42</xref>), Nilsson and Niedderer (<xref ref-type="bibr" rid="bibr47">47</xref>), van Roon et al. (<xref ref-type="bibr" rid="bibr66">66</xref>), and Rozier and Viennot (<xref ref-type="bibr" rid="bibr54">54</xref>)</td><td>Students explain the single molecule phenomena. They extend this reasoning to an ensemble of molecules to make sense of macroscopic data (calculations and graphs). Students relate mechanical, thermal, and mathematical aspects to interpret the simulation.</td></tr></tbody></table> </ephtml> </p> <hd id="AN0133464848-8">Relationship between the macro‐ and microscales</hd> <p>According to Loverude et al. ([<reflink idref="bib39" id="ref68">39</reflink>]), the misinterpretation of simple microscopic (molecular) models might interfere with student ability to understand macroscopic phenomena. Similarly, Kautz, Heron, Loverude & McDermott ([<reflink idref="bib23" id="ref69">23</reflink>]) and Kautz, Heron, Shaffer, et al. ([<reflink idref="bib24" id="ref70">24</reflink>]) found that many students who cannot properly interpret the macroscopic variables of pressure, temperature, and volume in an ideal gas have fundamentally flawed microscopic models, often justifying incorrect answers about the behavior of an ideal gas using such an incorrect or incomplete microscopic model.</p> <hd id="AN0133464848-9">Process of work</hd> <p>According to van Roon et al. ([<reflink idref="bib66" id="ref71">66</reflink>]), many first‐year college students consider heat as a state function, not as a path dependent function. More specifically, Meltzer ([<reflink idref="bib42" id="ref72">42</reflink>]) found a widespread and persistent tendency in students to improperly over‐generalize the state–function concept to apply to both work and heat. His results indicate that students thought the net work done or net heat absorbed by a system undergoing a cyclic process must be zero. Meltzer also found pervasive confusion in students regarding path dependent quantities, a result supported by Clark et al. ([<reflink idref="bib10" id="ref73">10</reflink>]), who also found that students thought work depends only on the end points, or is path independent. In addition, these researchers reported that students had difficulty differentiating the concept of steady state versus equilibrium. In terms of the mechanism of thermodynamic work, Nilsson and Niedderer ([<reflink idref="bib47" id="ref74">47</reflink>]) argued that students primarily described work without reference to pressure (P) and volume (V). In relation to this issue, Clark et al. ([<reflink idref="bib10" id="ref75">10</reflink>]) found that students had difficulties with canonical representations such as <emph>P–V</emph> diagrams.</p> <hd id="AN0133464848-10">Relationship between work and the first law of thermodynamics</hd> <p>Loverude et al. ([<reflink idref="bib39" id="ref76">39</reflink>]) argued that although students had studied the first law, few recognized its relevance and fewer still were able to apply the concept of work to account for a change in temperature in an adiabatic process. Furthermore, according to these researchers, students frequently failed to differentiate between the concepts of heat, temperature, work, and internal energy, a finding supported by the research conducted by Kautz, Heron, Loverude, et al. ([<reflink idref="bib23" id="ref77">23</reflink>]). Similarly, Meltzer ([<reflink idref="bib42" id="ref78">42</reflink>]) reported that only 20% or fewer of the students in his study could make effective use of the first law of thermodynamics even after instruction, and Nilsson and Niedderer ([<reflink idref="bib47" id="ref79">47</reflink>]) found students had difficulty relating the enthalpy change in a process to work.</p> <hd id="AN0133464848-11">Problematic reasoning</hd> <p>Rozier and Viennot ([<reflink idref="bib54" id="ref80">54</reflink>]) claimed that students commonly reduce the intrinsic complexity of problems, from a simple reduction in the number of variables considered to a more elaborate procedure where all the variables are considered but in a simplified way. According to Loverude et al. ([<reflink idref="bib39" id="ref81">39</reflink>]), some of the difficulties that students exhibited in applying the concept of work in a thermal process were related to their difficulties with mechanics. Similarly, van Roon et al. ([<reflink idref="bib66" id="ref82">66</reflink>]) reported that first‐year university students still see work as a mechanical concept. In addition, Kautz, Heron, Loverude, et al. ([<reflink idref="bib23" id="ref83">23</reflink>]) argued that students' difficulties with mechanics severely limited their ability to relate the ideal gas law to physical situations. Moreover, researchers have identified that some students are unable to describe the meaning of thermodynamic equations (Nilsson & Niedderer, [<reflink idref="bib47" id="ref84">47</reflink>]) or to interpret <emph>P–V</emph> diagrams (Clark et al., [<reflink idref="bib10" id="ref85">10</reflink>]), and students confuse heat, work, and internal energy because the concepts share the same units (Meltzer, [<reflink idref="bib42" id="ref86">42</reflink>]).</p> <p>The results from these studies were often accompanied by recommendations for instruction, suggestions including setting appropriate teaching goals (Rozier & Viennot, [<reflink idref="bib54" id="ref87">54</reflink>]), developing learning progressions (van Roon et al., [<reflink idref="bib66" id="ref88">66</reflink>]), making significant changes to standardized instruction (Meltzer, [<reflink idref="bib42" id="ref89">42</reflink>]), and developing tutorials to address students' serious and numerous difficulties (Kautz Heron, Loverude, et al., [<reflink idref="bib23" id="ref90">23</reflink>]; Kautz, Heron, Shaffer, et al., [<reflink idref="bib24" id="ref91">24</reflink>]). These suggestions focus on how to repair student misconceptions, which is commensurate with our initial approach as we incorporated design features to address these difficulties in the Thermodynamic Work IVL.</p> <hd id="AN0133464848-12">Interactive Virtual Laboratories</hd> <p>To address the student difficulties in learning thermodynamics, the second author of this article led the development of the IVLs, a series of agent‐based molecular simulations designed to target specific threshold concepts (Bowen et al., [<reflink idref="bib2" id="ref92">2</reflink>]; Male & Baillie, [<reflink idref="bib40" id="ref93">40</reflink>]; Meyer & Land, [<reflink idref="bib43" id="ref94">43</reflink>]). According to the literature, some concepts are difficult to learn when an observed macroscopic pattern emerges from unobservable microscopic phenomena (Chi, [<reflink idref="bib8" id="ref95">8</reflink>]; Kautz Heron, Loverude, et al., [<reflink idref="bib23" id="ref96">23</reflink>]; Kautz, Heron, Shaffer, et al., [<reflink idref="bib24" id="ref97">24</reflink>]; Loverude et al., [<reflink idref="bib39" id="ref98">39</reflink>]). Therefore, each IVL includes an agent‐based, two‐dimensional simulation of an ensemble of gas molecules, thus making both the micro‐ and macroscale phenomena observable to help students bridge the conceptual gap. Other pedagogical features of IVLs include inquiry‐based scaffolding, a sequence of simulations and questions designed to guide students in building conceptual understanding, and multiple representations such as equations, graphs, and textual descriptions. In the IVLs, students are guided through a set of frames to conduct virtual experiments and subsequently to respond to prompts asking them to predict, calculate, manipulate, and reflect on observed phenomena related to core thermodynamic concepts (Bowen et al., [<reflink idref="bib2" id="ref99">2</reflink>]). Currently, six IVLs can be accessed through the engineering education online platform, the Concept Warehouse (Koretsky, Falconer, et al., [<reflink idref="bib33" id="ref100">33</reflink>]).</p> <p>The Thermodynamic Work IVL includes simulations that engage students with a molecular model to explain why doing work on a gas‐phase system adds or removes energy and then connects the microscopic understanding to macroscopic observables (temperature, volume, and pressure of the gas system). As illustrated in the third column in Table 1, we developed specific simulations, mathematical representations, and question prompts targeting each of the research‐identified difficulties. Students are guided to learn that work adds internal energy to a system through an exchange of kinetic energy between ideal gas molecules in the system and its surroundings (e.g. a moving piston), specifically through the momentum transfer caused by the collision between the molecules and a moving boundary. The simulation uses a progressive understanding approach to assist conceptual learning. Table 2 provides a brief description of the 14 frames that guide student learning.</p> <p>Frames in the Work IVL</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Block</th><th align="left">Frame</th><th>Description</th><th>Question type</th></tr></thead><tbody valign="top"><tr><td>Review</td><td>1</td><td>Equation: PV work</td><td>Multiple choice</td></tr><tr><td>2</td><td>Equation: first law of thermodynamics</td><td>Multiple choice</td></tr><tr><td>Single molecule</td><td>3</td><td>Single molecule simulation</td><td>Written response (conceptual)</td></tr><tr><td>4</td><td>Single molecule simulation</td><td>None</td></tr><tr><td>Bulk gas</td><td>5</td><td>Gas compression: simulation, graphs</td><td>Numerical (work magnitude)</td></tr><tr><td>6</td><td>Gas compression: simulation, graphs</td><td>Numerical (temperature)</td></tr><tr><td>7</td><td>Gas compression: numbers, compare results</td><td>Written response (compare)</td></tr><tr><td>8</td><td>Graph and slopes: comparison</td><td>Numerical (representations)</td></tr><tr><td>9</td><td>Gas expansion: predict work and temperature</td><td>Numerical (work and temperature)</td></tr><tr><td>10</td><td>Gas expansion: simulation, numbers, graphs</td><td>None</td></tr><tr><td>11</td><td>Gas expansion: simulation, numbers, graphs</td><td>Written response (conceptual)</td></tr><tr><td>Reflection</td><td>12</td><td>On concepts (work and energy)</td><td>Written response (conceptual)</td></tr><tr><td>13</td><td>On the Virtual Lab</td><td>Written response</td></tr><tr><td>14</td><td>Exit</td><td>None</td></tr></tbody></table> </ephtml> </p> <p>The Work IVL begins with a review of basic concepts using multiple‐choice questions in Frames 1 and 2. It then introduces students to the idea of <emph>P–V</emph> work in the context of a single molecule (Figure 1), which is represented by a single elastic sphere in a closed container. The molecular velocity vector (both the magnitude and the direction) is shown every time the molecule collides with the piston (called a "movable wall" in the question prompt) or other stationary surfaces. Students can move the piston by clicking and dragging a slider. When students move the piston down so that it is moving when the molecule collides with it, the molecule speeds up through an exchange of momentum. Students are then asked to explain how the temperature relates to the molecular kinetic energy and why an increase in molecular speed leads to an increase in system temperature when many molecules are present.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6M4/01oct18/jee20237-fig-0001.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="jee20237-fig-0001.jpg" title="Frame 3 of the Thermodynamic Work IVL. The actual IVL image has been rendered in black and white for readability." /> </p> <p></p> <p>The simulation then progresses to a more complicated system with an ensemble of molecules (Figure 2). Students are asked to compress and then expand the system and perform numerical computations to calculate the values of work and final temperature. Students are subsequently asked to explain in Frame 11 why expansion to the system's initial volume leads to a much smaller decrease in temperature than the increase in temperature in the preceding compression, resulting in a higher final temperature than the original. Upon completing the simulation, students are asked to summarize their understanding of the concepts of work and energy (Frame 12) and then to relate what they learned from the IVL to what they have learned elsewhere in class. Table 3 summarizes the question prompts in the three frames that elicit conceptual explanations (Frames 3, 11, and 12).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6M4/01oct18/jee20237-fig-0002.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="jee20237-fig-0002.jpg" title="Frame 11 of the Thermodynamic Work IVL. The actual IVL image has been rendered in black and white for readability." /> </p> <p></p> <p>Question Prompts in the Three Conceptual Frames (<reflink idref="bib3" id="ref101">3</reflink>, 11, and 12)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Frame</th><th>Question prompt</th><th>Model answer</th></tr></thead><tbody valign="top"><tr><td>3</td><td><p><italic>nc</italic><sub>v</sub>Δ<italic>T</italic> = − <italic>P</italic><sub>ext</sub>Δ<italic>V</italic></p><p>From the equation above, we see that temperature increases as we do work by decreasing volume. Temperature is an expression of molecular kinetic energy, so as the system is compressed, the molecules must speed up. These ideal gas molecules can be thought of as perfectly elastic bouncy balls. Using the movable wall above, can you determine what event causes the molecule's speed to change? Can you explain why that would cause a temperature change in many molecules?</p></td><td><p>(Part 1) When the downward moving wall collides with the molecule, momentum (and kinetic energy) is transferred to the molecule, causing an increase in the speed of the molecule; conversely, momentum (and kinetic energy) transfer with the upward moving wall causes the speed of the molecule to decrease.</p><p>(Part 2) When the wall moves down, the speeds of the many molecules which collide with it increase. The average kinetic energy of gas molecules in the system therefore increases. Since temperature is a measure of the average kinetic energy of the gas molecules, the increase in average kinetic energy leads to a higher temperature. Similarly, when the wall moves up, the speeds of the many molecules decrease, the average kinetic energy of the gas molecules decreases and the gas system exhibits a lower temperature.</p></td></tr><tr><td>11</td><td>The system has undergone a two‐step process. First it was compressed by adding a block. Then it was expanded to its original volume by removing the block. Before the compression, the system's temperature was 200 K. After the expansion, the temperature was (student answer in previous frame) K. Why is the system temperature higher after going through this two‐step process?</td><td><p>The system's temperature is higher because the work done by the surroundings on the system is greater than the work done by the system on the surroundings. This difference can be understood by the external pressure of each process: compression is at 15 bar and expansion is at 2 bar. Since w = ‐P<sub>ext</sub> Δv, the two‐step process results in positive net work done by the surroundings on the system; therefore, the system's internal energy (and the average kinetic energy of the gas molecules) is greater in the final state than that of the initial state. The process in this problem is not reversible.</p></td></tr><tr><td>12</td><td>Now that you have completed the simulation, describe how the process of doing work adds energy to a system.</td><td>In this simulation, work leads to the transfer of energy due to a volume change caused by an external pressure (force) on the system. If the system's volume decreases, then the surroundings do work on the system, leading to an increase in the system's internal energy. If the process is adiabatic (no energy transfer from heat), the average kinetic energy of the molecules in the system also increases, and the system exhibits a higher temperature.</td></tr></tbody></table> </ephtml> </p> <p>The Work IVL has been through three improvement cycles. During the first iteration, we analyzed a set of think‐aloud protocols (Ericsson & Simon, [<reflink idref="bib13" id="ref102">13</reflink>]) and reflective interviews using a small sample of students (Bowen et al., [<reflink idref="bib2" id="ref103">2</reflink>]). We found that students exhibited two orientations of reasoning when explaining the single‐molecule phenomenon (Frame 3), equation‐based reasoning and concept‐based reasoning, the type used depending whether the students correctly answered the conceptual questions asked during the post‐interview. In the second iteration, we identified misconceptions by analyzing students' think‐aloud protocols during the IVL, finding that even when their final answers were not normatively correct, students still exhibited productive thought processes and partially correct reasoning in their responses (Keeler et al., [<reflink idref="bib25" id="ref104">25</reflink>]). This current study focuses on the third iteration, moving away from identifying what was right or wrong in student responses toward recognizing productive resources. As the IVL was delivered in a studio setting, we are naturally also interested in the role that the setting played as students formulated responses.</p> <hd id="AN0133464848-15">Studios</hd> <p>The IVLs can be completed by students individually or in a group, as homework or as a class activity. In the study reported here, students completed the IVL in a thermodynamics studio course (Koretsky, [<reflink idref="bib28" id="ref105">28</reflink>]). In this studio‐based curriculum, students in 10 required large enrollment courses (150–250 students) meet in smaller studio sections of 24–30 students interspersed between the lectures. Students in this study had typically engaged in five prior studio courses. In each studio, students form small groups and do an activity in which they are required to complete a worksheet consisting of problem scenarios and conceptual and numerical questions designed to either reinforce content from the previous lecture or foreshadow the following lecture. A graduate teaching assistant or instructor circulates around the room, interacting with the students and groups by asking facilitating questions to promote student learning. The social interactions between the students themselves and with the instructor are critical and strongly encouraged. In studio, we seek to provide a collaborative environment where students can develop and test ideas with their peers but also get intermittent support in the form of coaching from their instructors (Koretsky, [<reflink idref="bib28" id="ref106">28</reflink>]).</p> <p>When studio activities use an IVL, students individually complete it on their laptops but are encouraged to discuss the activity in their groups as they do so. This combination of studio and IVL creates a fertile learning ecology where students interact with the technology tool (e.g., simulated phenomena, multiple representations) and other learners. Using the IVL in a studio setting allowed us to probe such aspects of conceptual learning as multiple factors, shifting ideas, and working with people and technology that are especially relevant to the open‐ended and emergent nature of the use of conceptual knowledge in engineering practice (Bucciarelli, [<reflink idref="bib4" id="ref107">4</reflink>]; Streveler et al., [<reflink idref="bib64" id="ref108">64</reflink>]; Vincenti, [<reflink idref="bib67" id="ref109">67</reflink>]).</p> <hd id="AN0133464848-16">Methodology</hd> <p>The current study builds on previous research in which a small number of participants completed several IVLs in a clinical environment outside the classroom with a researcher asking them interview questions or prompting participants to think‐aloud (Bowen et al., [<reflink idref="bib2" id="ref110">2</reflink>]; Keeler et al., [<reflink idref="bib25" id="ref111">25</reflink>]). When designing the study reported here, we took a naturalistic approach (Guba, [<reflink idref="bib15" id="ref112">15</reflink>]; Lincoln & Guba, [<reflink idref="bib38" id="ref113">38</reflink>]), attending to student ideas and interaction as they appeared in a regular classroom setting.</p> <p>We identified activated student learning resources of thermodynamic work and investigated empirical examples that could demonstrate shared resources. For the former, we drew on lexical analysis methods (Lebart, Salem, & Berry, [<reflink idref="bib37" id="ref114">37</reflink>]) and conducted iterative emergent lexical coding on the written responses of the entire class (187 participants) to the conceptual questions in the Work IVL (Table 3). For the latter, we collected video data of individual students and applied progressive refinement in analyzing the videos (Engle, Conant, & Greeno, [<reflink idref="bib12" id="ref115">12</reflink>]; Stake, [<reflink idref="bib61" id="ref116">61</reflink>]). We have conceptually proposed two categories of shared resources: situated shared resources (in a single context) and transferred shared resources (to a different context). Given the boundary of our data collection in this article (one context), we investigate only the first category empirically. However, studies of the second category could be conducted using an appropriate methodology for studying transfer to other contexts.</p> <hd id="AN0133464848-17">Participants</hd> <p>Participants in this study were chemical, environmental, and biological engineering students enrolled in a junior‐level engineering thermodynamics course at a large, public university in the Northwestern United States. The primary topics of this course, which is required for all three programs, include the first and second laws of thermodynamics, equations of state, and the thermodynamic web (Koretsky, [<reflink idref="bib27" id="ref117">27</reflink>]). A total of 187 students agreed to participate and signed IRB‐approved informed consent forms. They self‐identified as 65% chemical engineering students, 21% biological engineering, 14% environmental engineering; 34% women, 66% men; and 43% White—non‐Hispanic, 35% Asian or Pacific Islander, 11% White—Hispanic, with the remaining reporting other race identities. Participants completed the Thermodynamic Work IVL in studio in week 2 of a 10‐week term. Researchers did not offer any guidance; rather they let the instructor decide whether and how to implement it to support learning in the class. In fact, it was this instructor's request to use the IVLs that led us to design this research study.</p> <hd id="AN0133464848-18">Data Collection</hd> <p>We collected responses from all 187 participants as they completed the Thermodynamic Work IVL in the Concept Warehouse and exported the data to a spreadsheet for analysis. We also recruited five students from the 187 and recorded their interactions through the built‐in microphone and screen‐capturing software in the laptop they were provided to complete the IVL. These data included audio of the student's think‐aloud verbalizations (Ericsson & Simon, [<reflink idref="bib13" id="ref118">13</reflink>]), verbal interactions with the group, and a video of all actions shown on the monitor of the computer. The first author explained the research project to and obtained consent from each student before the studio. During the class, this researcher sat in the corner of the studio classroom, conducting general field observation away from the individual studied. After the studio, the researcher conducted a brief interview with the student and collected the laptop.</p> <p>There were eight total studio sections in this course, two sections running concurrently across the hall from each other over four class periods. We intended to collect data from four students in different sections, two from each classroom denoted as Room A and B, deliberately choosing students in sections facilitated by different graduate teaching assistants. However, during Period 1, the student participant in Room A experienced technical problems and did not complete the studio. As a result, we collected data from one student in Room A during Period 2, one student in Room B during Period 3, and two students in Room B during Period 4. The two students from the same section (Room B, Period 4) were from different small discussion groups. Thus, we collected usable data from four of the five students recruited. As the architecture in the rooms was similar, we do not think this adjustment substantially changed the data collected. The four students from whom we collected video data are given the pseudonyms Parker, Thomas, Leo, and Austin. Parker completed the IVL in 45 min, Thomas in 38 min, Leo in 35 min, and Austin in 29 min.</p> <p>All four students were males (one Asian and three White) in the junior year of their programs. Austin and Thomas were chemical engineering majors, and Leo and Parker were bioengineering majors. Their performance to date in these programs varied, including one student whose grades were significantly above the cohort average (Leo), two who were around the cohort average (Austin and Parker), and one who was significantly below the cohort average (Thomas). Both bioengineering students participated in an International Baccalaureate (IB) Program, earning college credit for multiple classes during high school and, to different degrees, aspired to medical school. Both chemical engineering students were older than average, with one of them serving in the military before enrolling. The diverse characteristics in academic performance of the four participants added to the trustworthiness of the study by allowing us to reconcile the ways various participants interacted with the other students in their groups and with the technology.</p> <hd id="AN0133464848-19">Data Analysis</hd> <p></p> <hd id="AN0133464848-20">Emergent lexical coding</hd> <p>We coded student written responses iteratively according to the lexical themes that emerged from the text, viewing them as external artifacts of the activated student resources. Of the 187 sets of responses, 159 participants had valid inputs in both Frames 11 and 12. Of these 159 participants, 139 also had valid inputs in Frame 3 (the invalid inputs were either empty or appeared as "undefined"). A total of 457 responses were analyzed. First, we hand‐coded approximately 10% of the responses to each conceptual question, recognizing reflected elements of thinking such as ideas about energy, collision, and speed of the molecule. We identified theme words in these ideas and then coded the rest of the responses through an automatic search for those words. Next, we examined the individual responses that did not receive any codes through automatic coding, hand‐coding them to identify new themes or theme words for a subsequent automatic search of all responses. We conducted this process iteratively until we no longer found new theme words.</p> <p>We developed code categories by grouping emergent key words with similar meanings. For example, words referring to the interaction between the molecule and the movable wall—collision, touch, interact, hit, impact, contact, bounce, and collide—were grouped into the code <emph>collision</emph>. Tables 4, 5, 6 list the definitions of the codes for each of the frames analyzed along with example student responses. As students usually included multiple ideas in explaining a phenomenon, the codes are not mutually exclusive, with each student response often receiving multiple codes. We used the frequency of codes to identify the distribution of resources reflected in the 159 student answers (our first research question). While the activated resources associated with a given code, say energy, may vary in content, all the activated resources about energy are cognitive blocks around the prototypical idea that energy explains this thermodynamic phenomenon.</p> <p>Codes for Written Responses to Frame 3</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Code</th><th>Identifier (When student response contains)</th><th>Example</th></tr></thead><tbody valign="top"><tr><td>Collision</td><td>Collision, touch, interact, hit, impact, contact, collide, bounce</td><td><p>Student response: "the <italic>volume</italic> decrease, and the <italic>touch</italic> and the <italic>motion</italic> between the molecules increase, so the <italic>temperature</italic> increase"</p><p>Received codes: volume, collision, speed, temperature.</p></td></tr><tr><td>Kinetic energy</td><td>Kinetic energy, kinetic E</td></tr><tr><td>Speed</td><td>Speed, velocity, fast, momentum, motion</td></tr><tr><td>Temperature</td><td>Temperature</td></tr><tr><td>Volume</td><td>Volume</td></tr></tbody></table> </ephtml> </p> <p>Codes for Written Responses to Frame 11</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Code</th><th>Identifier (When student response contains)</th><th>Example</th></tr></thead><tbody valign="top"><tr><td>Work</td><td>Work</td><td><p>Student response: "It is a <italic>non‐reversible</italic> process, and the <italic>work</italic> done to the surroundings is less than the work the surroundings did to the system initially."</p><p>Received codes: work, irreversibility.</p></td></tr><tr><td>Pressure</td><td>Pressure, Pext, p(ext), p =, 15, 2, bar, area</td></tr><tr><td>Irreversibility</td><td>Irreversible, not a reversible, nonreversible, isn't a reversible, nonreversible, isn't reversible, not going through a reversible, path</td></tr><tr><td>Molecule</td><td>Molecule, particle, molecular</td></tr><tr><td>Energy</td><td>Energy</td></tr></tbody></table> </ephtml> </p> <p>Codes for Written Responses to Frame 12</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Code</th><th>Identifier (When student response contains)</th><th>Example</th></tr></thead><tbody valign="top"><tr><td>System</td><td>System, sys</td><td><p>Student response: "<italic>work</italic> done by surrounding decease the <italic>volume</italic> and it increase the <italic>kinetic energy</italic> of <italic>molecules</italic> in container and it increases <italic>temperature</italic>."</p><p>Received codes: work, energy, molecule, internal, volume, temperature</p></td></tr><tr><td>Work</td><td>Work</td></tr><tr><td>Energy</td><td>Energy</td></tr><tr><td>Molecule</td><td>Molecule, molecular, particle</td></tr><tr><td>Pressure</td><td>Pressure</td></tr><tr><td>Volume</td><td>Volume</td></tr><tr><td>Internal</td><td>Internal, kinetic</td></tr><tr><td>Heat</td><td>Heat, adiabatic</td></tr><tr><td>Temperature</td><td>Temperature</td></tr><tr><td>Irreversibility</td><td>Irreversible, reversible, path</td></tr></tbody></table> </ephtml> </p> <hd id="AN0133464848-21">Progressive refinement in video analysis</hd> <p>Studying video data allowed us to understand the working processes that led each student to their answer. A total of 147 min of video were analyzed. We conducted three rounds of video analysis, progressively refining our analytical focus. First, we transcribed the videos and did a moment‐by‐moment qualitative examination of the working processes of the four students. Understanding the process was our original intent for collecting the video data and the general orientation of our second research question. In this analysis, we flagged the interactions that did not appear in the final answer, including paths of cursor movement, the icons clicked, the previous frames revisited, the answers initially typed in but later revised, other computer applications used during the IVL, and all the utterances in the audio recordings.</p> <p>Although these four students were all asked to think aloud while completing the IVL, based on the video, they rarely verbalized what they were doing or thinking, with most of their verbalizations involving reading the question prompts or their answers. However, we noticed occasions when students initiated discussion with other group members that led the student to enter an answer with additional ideas not present during their original ideas verbalized or written in the video.</p> <p>This observation initiated a second round of analysis that focused on the moments when the group had discussions, especially when they generated more comprehensive answers. We observed that the more comprehensive answers typically included both the student's initial ideas and ideas from groupmates expressed through discussion. The lexical coding (described previously) showed abundant student ideas and various ways ideas were organized in an answer. To reconcile the video analysis with coding results, we developed the construct of <emph>shared resources</emph>, those that were activated and expressed through ideas, and shared with others. We then refined our second research question to study how students share resources.</p> <p>The final round of analysis directly addressed this revised research question, identifying instances of <emph>shared resources</emph> in the four students' answering of the conceptual questions. Based on our findings from the first research question (the ideas identified), we looked for indicators of <emph>shared resources</emph>, student ideas that (a) were not in the student's original answer (usually verbal), (b) were brought up through group interaction, and (c) were later included in the student's final answer. Finally, we summarized the conditions that led to shared resources or the possibility of sharing resources in this study.</p> <hd id="AN0133464848-22">Results</hd> <p>This section presents the results of the three conceptual frames separately, with information from the previous frame(s) providing the context for the subsequent one(s). We present <emph>frequencies</emph> and co‐occurrence <emph>patterns</emph> in codes to address our first research question on the resources identified and their distribution. We then draw on video analysis to tentatively show instances of shared resources (and no shared resources) and summarize <emph>conditions</emph> when shared resources are likely to occur, addressing our second research question on how students share resources.</p> <hd id="AN0133464848-23">Frame 3</hd> <p>Before accessing Frame 3, students answered multiple‐choice questions in which they were shown an adiabatic compression process that required them to identify the correct equations for calculating thermodynamic work (Frame 1) and for the first law of thermodynamics (Frame 2). Then in Frame 3, students were provided a single‐molecule simulation and a question prompt (see Figure 1). Students were asked to drag the slider on the side, moving the virtual piston up and down, and observe the magnitudes and directions of the velocity of the molecule while it bounced in the container. Students were then prompted to answer a two‐part question: (a) What event caused the molecule's speed to change and (b) why would it cause a temperature change in a system composed of many molecules.</p> <hd id="AN0133464848-24">RQ1: Distribution of resources</hd> <p>Coding results of student responses to Frame 3 shown in Table 7 indicate that 73% of the student responses included the idea of speed (and, therefore, demonstrated activation of resources about it), 71% collision, 65% temperature, 60% kinetic energy, and 45% volume. Some students also included additional ideas, but the percentages of these ideas were markedly lower (e.g., 18% included internal energy). Each student expressed at least one of the ideas shown in Table 7.</p> <p>Coding Results for Frame 3 (Total = 139)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Ideas</th><th align="center">Speed</th><th align="left">Collision</th><th align="left">Temperature</th><th align="left">Kinetic energy</th><th align="left">Volume</th></tr></thead><tbody valign="top"><tr><td># of students</td><td>101</td><td>98</td><td>91</td><td>83</td><td>63</td></tr><tr><td>Percentage</td><td align="right">73%</td><td align="center">71%</td><td align="center">65%</td><td align="center">60%</td><td align="center">45%</td></tr></tbody></table> </ephtml> </p> <p>Among the five theme ideas, four can be identified in the text of the question prompt for Frame 3. The idea of collision, which was not in the question prompt, was identified in 98 (71%) responses. The simulation was designed to facilitate student understanding that a moving sphere colliding elastically with a surface moving toward it rebounds with a speed greater than if the surface had been stationary. We considered students' expressing the idea of collision in their responses as evidence of a productive activation of resources. Ideas about temperature and volume reflect the system's macroscopic properties, while collision, kinetic energy, and speed are ideas about the molecular microscopic activities in the single‐molecule simulation, concepts that are directly related to the main design purpose of this frame.</p> <p>Of the total 139 students, 134 included at least one of the three microscopic ideas. (The remaining five included only macroscopic ideas.) The co‐occurrence of these three ideas is represented graphically in Figure 3 in a square‐shaped Venn Diagram, the area of each part being approximately proportional to the number of students who included the corresponding idea. As shown in this diagram, 50 students included all three microscopic ideas, while 23 included collision and speed but not kinetic energy, 13 included kinetic energy and speed but not collision, and 12 included collision and kinetic energy but not speed. Approximately 15 students included only speed, 13 only collision, and 8 only kinetic energy.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6M4/01oct18/jee20237-fig-0003.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="jee20237-fig-0003.jpg" title="A square‐shaped Venn Diagram of code co‐occurrence for Frame 3. Numbers are of students who included microscopic ideas (collision, kinetic energy, and speed) in their answers. The area of each part of the diagram is approximately proportional to the number of students who included the corresponding ideas." /> </p> <p></p> <p>All four students that we recorded included collision. In addition, Leo included all three microscopic ideas, Thomas and Parker collision and speed, and Austin collision and kinetic energy. Their "positions" in the diagram are shown by the solid shapes, and their full answers are shown in Table 8.</p> <p>Student Responses to the Conceptual Question in Frame 3</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Frame 3</th><th>Parker (circle)</th><th>Leo (square)</th><th>Thomas (triangle)</th><th>Austin (star)</th></tr></thead><tbody valign="top"><tr><td>Answer</td><td>In a smaller container (made by moving the wall closer), the particles contact the walls of the container more frequently, causing them to move faster.</td><td>Change in kinetic energy. The molecules move faster. Interact with the container more.</td><td>It is impacts with the moving piston while it is being compressed that cause the atom to increase in speed.</td><td>Compressing the gas increases the average kinetic energy of the molecules by reducing the distance that they travel to reach a wall collision.</td></tr><tr><td>Received codes</td><td>Speed, collision, volume</td><td>Speed, collision, kinetic energy</td><td>Speed, collision</td><td>Collision, kinetic energy</td></tr></tbody></table> </ephtml> </p> <hd id="AN0133464848-26">RQ 2: Sharing of resources</hd> <p>One obvious condition needed for students to share resources is some type of interaction among them. Given our video data focused on the computer screen, we could recognize student verbal interactions in the audio but not silent interactions such as gestures. For the interactions identified, we categorized the subject of the interaction (what it was about) and the ways the groupmates interacted (how it initiated and proceeded). With these actions, an event of shared resources sometimes happened, but not always. For the latter, we attempted to recognize what stopped it and thus conjectured more sufficient conditions for its occurrence.</p> <p>All four students that we recorded interacted with groupmates about Frame 3. Applying our indicator of shared resources, we identified one student who demonstrated shared resources (Parker) and three students who partially did so (Austin, Leo, and Thomas). The way that each student interacted with groupmates indicated certain conditions that could lead to shared resources. For the students who initiated interaction but did not demonstrate shared resources, we looked for phenomena that might explain why the condition was not sufficient for this sharing to occur. The following student transcripts describe the interaction process and highlight these conditions.</p> <hd id="AN0133464848-27">Austin</hd> <p>The single‐molecule simulation was constructed based on the mechanics of elastic collisions: the molecule's speed changing when it collides with a moving piston. According to the video, Austin moved and stopped the right‐side bar quickly, causing the piston to be stationary when the molecule hit it; consequently, the speed of molecule did not change. However, the question prompt indicated that the molecule's speed should change with a "movable wall." He spotted this discrepancy that led him to initiate the conversation with his groupmate shown below. We use italicized font for student speech and regular font for our description of the behaviors observed.</p> <p>Austin: <emph>Are you on the movable wall part?</emph></p> <p>Austin's neighbor: <emph>I did</emph>.</p> <p>Austin: <emph>There is no changing speed.. </emph>.</p> <p>Austin's neighbor: <emph>You'd have to move it down</emph>.</p> <p>Austin: <emph>I AM. Nothing is changing</emph>. (Austin moves the bar down, but the molecule is not hitting the piston)</p> <p>Austin's neighbor: <emph>Are you on the.. </emph>.</p> <p>Austin: <emph>Oh. Hey! Now! Now I got one</emph>. (Austin moves the bar further down, and now the molecule hits the moving piston.)</p> <p> <emph>I was moving it up and down, and I had it down to the very bottom and the speed was the same. And now the speeds are going up. So.. </emph>. (scrolls to the text box and types in answer).</p> <p>In this conversation, Austin's groupmate confirmed that moving the bar was going to change the speed. Receiving this confirmation, Austin moved the bar again, and this time, the molecule hit the moving wall and the speed increased. It is possible that if the discussion continued toward resolving the discrepancy, sharing ideas and resources could be identified. In other words, <emph>describing a discrepancy</emph> could be one possible condition toward shared resources.</p> <hd id="AN0133464848-28">Leo</hd> <p>When Leo arrives at Frame 3, he moves the piston up and down. Then, he talks to his groupmate.</p> <p>Leo: <emph>Oh! If you like bounce it upon or something?</emph></p> <p>Leo's neighbor: <emph>Yeah</emph>.</p> <p>Leo moves the piston up and down a few more times. He scrolls down to the question prompt and types while reading what he is typing.</p> <p>Change in kinetic energy. The molecules move faster.</p> <p>Pauses and then continues typing and reading.</p> <p>Interact with the container more. But they are ideal, so I guess they don't bounce around with themselves. All right? Let's see.</p> <p>Leo submits his answer.</p> <p>The simulation and question in Frame 3 seemed to activate Leo's resources about kinetic energy, molecular speed, and collision. Leo's beginning of the sentence "Oh! If you... " indicated that seeing the phenomenon and role of collision was unexpected. It was something that Leo observed, related to the question, and wanted to share to get feedback. The idea of collision was explicit and addressed to his groupmate, and Leo received a confirming "Yeah" from this groupmate. He included this idea about collision in his final answer as well as the ideas about the number of collisions (the molecule interacts with the container more) and changes in molecular kinetic energy and speed. The student interaction shows <emph>requesting feedback</emph> on an offered idea could be a condition to foster further shared resources.</p> <hd id="AN0133464848-29">Thomas</hd> <p>Thomas' groupmate asked him whether the molecule's speed remains the same value if the cylinder ("bar") is kept stationary after having been moved.</p> <p>Thomas' neighbor: <emph>Does the speed of the molecule like stays the same? Like if you don't touch the bar, say 900 m/s, and then like move the bar, and let it stay. Does it stay at 900? Does it like increase or does it go back to the.. </emph>.</p> <p>Thomas: <emph>No. I think it stays the same once at any given point. Like if the bar at start</emph> (moves the bar). <emph>Oh, it's just look like the... Yeah. When you move the bar, it changes, otherwise.. </emph>. (inaudible)</p> <p>We did not collect data from Thomas' groupmate, so we cannot say what the student meant when she asked if the molecule's speed "stays the same." If as she said that the molecule's speed would stay the same as the original value before the bar was moved (900 m/s), then it is likely that during the time she was mentioning "and then like move the bar," the molecule never collided with the bar, information that is critical (as in Austin's case). This is a good question as the student phrased it conceptually, trying to generalize the situation (don't touch, then move, then let it stay).</p> <p>Thomas first answered it with "No." He then elaborated with a full sentence, moved the bar to show his groupmate "like if the bar...," used an analogy "it just look like...," and stated a generalized answer "when you move the bar,... otherwise,... " Because Thomas did not verbalize his initial ideas before the discussion, we could not compare to determine whether his submitted answer had any additional ideas as a result of this discussion. Yet, <emph>asking a generalized question</emph> and <emph>responding to it by elaborating</emph> in this case seemed have stimulated a discussion about ideas.</p> <hd id="AN0133464848-30">Parker</hd> <p>Parker arrives at Frame 3 and pulls down the movable piston leading to the following discussion:</p> <p>Parker: <emph>Cool! So, as you move the wall down, speed increases. The molecule's ... (</emph>inaudible<emph>)</emph></p> <p>Parker moves the piston further down. The molecule moves up and down very fast between the piston and the bottom, and the velocity vectors form a zig‐zag line.</p> <p>Oh ... . Baby. Zig‐zag. Nice.</p> <p>Parker reads the question prompt, at the same time moves the piston up, and the molecule slows down after colliding with the piston. Then there is some inaudible chit‐chat in the group.</p> <p>Parker (to neighbor): <emph>Did you guys say that the molecule's speed changes because it makes more collisions per unit time?</emph> (pulling piston down while talking)</p> <p>Parker's neighbor: <emph>Essentially, yeah</emph>.</p> <p>Parker types his answer into the textbox under the prompt: "In a smaller container (made by moving the wall closer), the particles contact the walls of the container more frequently, causing them to move faster."</p> <p>In this episode, Parker first only described the relationship between the direction of the wall movement and the speed‐change in the molecule (in Turn 1). After talking to his groupmate in Turn 2, Parker rephrased (or paraphrased) his groupmate's idea about the relationship between frequency of collision and the speed‐change in the molecule, and asked for confirmation. Parker's final answer included both ideas, direction of wall‐movement and frequency of wall‐molecule collision, as reasons for the changing speed of the molecule. According to our indicators, we consider this episode could be an event of shared resources. <emph>Rephrasing/paraphrasing others' ideas</emph> seemed to be the condition for this case.</p> <hd id="AN0133464848-31">Frame 11</hd> <p>In Frame 11, students were asked to answer why the system did not cool to its original temperature when it expanded to its original volume (see Table 3 and Figure 2). This question prompted students to reflect on the two‐step process that they had just completed. Students worked with an ensemble of molecules representing the bulk gas and were asked to virtually place a block on top of the piston to compress the gas. Later, they were asked to take the block off from the piston and watch the gas expand. As the system was compressing or expanding, external pressure–volume and temperature–volume graphs were plotted in real‐time. Students were asked to read the graphs and calculate the values of work and temperature based on the phenomena they observed. The true values were revealed during experiments in the frame after students submitted their calculated answers. Students saw that the final temperature of the system was significantly higher than its original value (668 K vs. 200 K) when the system expanded back to the original volume. They were asked to give an explanation for why the temperature was higher.</p> <hd id="AN0133464848-32">RQ 1: Distribution of resources</hd> <p>Coding results of students' responses to Frame 11 are shown in Table 9. In their responses, emergent ideas included ideas about work, energy, irreversibility, pressure, and molecule. A small number of students expressed other ideas (e.g., "Because of some loss"). As the question prompt does not include any of these words, these ideas indicate resources activated by students when responding to the question.</p> <p>Coding Results for Frame 11 (Total = 159)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Ideas</th><th align="left">Work</th><th align="left">Energy</th><th align="left">Irreversibility</th><th align="left">Pressure</th><th align="left">Molecule</th><th align="left">Other</th></tr></thead><tbody valign="top"><tr><td># of students</td><td align="center">91</td><td align="center">81</td><td align="center">71</td><td align="center">25</td><td align="center">24</td><td align="center">7</td></tr><tr><td>Percentage</td><td align="center">61%</td><td>51%</td><td>45%</td><td>16%</td><td>15%</td><td>4%</td></tr></tbody></table> </ephtml> </p> <p>The co‐occurrences of these ideas are represented in the Venn Diagrams seen in Figure 4. We constructed four quadrants based on whether the ideas of work and energy were present in a student response. These two ideas are associated with conservation of energy (the first law of thermodynamics) which is how an expert would classify this type of question (Chi, Feltovich, & Glaser, [<reflink idref="bib9" id="ref119">9</reflink>]). Our data show that these two ideas were also the most prevalent among students. The area of a quadrant is proportional to the number of student responses; Quadrant I represents the 50 students who included both work and energy, while Quadrant II represents the 47 students who included work but not energy, Quadrant III the 31 students who did not include work or energy, and Quadrant IV the remaining 31 students who included energy but not work. Within each quadrant, a Venn Diagram represents the occurrences of the other three ideas (pressure, molecule, and irreversibility), with the area again proportional to the number of students who included the corresponding ideas (similar to the Venn Diagram in Figure 3).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6M4/01oct18/jee20237-fig-0004.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="jee20237-fig-0004.jpg" title="Code co‐occurrences in Frame 11. Quadrants and embedded Venn Diagrams of the five emergent student ideas. The area of a quadrant is proportional to the number of student responses that fall into the category. Quadrant I represents student answers having both work and energy. Quadrant II represents answers having work but not energy. Quadrant III represents the answers not having work nor energy. Quadrant IV represents the answers having energy but not work. Within each quadrant, a Venn Diagram represents the co‐occurrences of the other three ideas (pressure, molecule, and irreversibility) with the areas proportional to the number of students who included the corresponding ideas. Like in Figure 3, Thomas' answer is represented with a solid triangle, Parker with a circle, Leo with a square, and Austin with a star." /> </p> <p></p> <p>As shown in Figure 4, among the 159 students, only one student addressed all five ideas shown in Quadrant I. In Quadrant II where the students did not include energy, they also did not include molecule; more than half (24 of 47) of these students included the idea of irreversibility. The four students we recorded happen to fall into each of the four quadrants. Their full answers are shown in Table 10.</p> <p>Student Responses to the Conceptual Question in Frame 11</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Frame 11</th><th>Parker (circle)</th><th>Leo (square)</th><th>Thomas (triangle)</th><th>Austin (star)</th></tr></thead><tbody valign="top"><tr><td>Answer</td><td><p>The system temperature is higher because this is not a reversible process. There was a net amount of work done on the gas that causes an increase in internal energy.</p></td><td>Internal energy of the system is higher. The molecules are moving faster (kinetic energy) and their speed is higher than when they started. The process is not reversible, so you don't get back all of the energy you put in.</td><td>The order in which the actions are taken makes a difference on the final temperature. Work is still an integral and so the area under the curve will be different depending on the order of the actions. It is a non‐reversible process.</td><td>The problem statement did not state what the initial temperature was. I actually would have gotten 668 k.</td></tr><tr><td>Received codes</td><td>Work, energy, irreversibility</td><td>Energy, irreversibility, molecule</td><td>Work, irreversibility</td><td>None</td></tr></tbody></table> </ephtml> </p> <hd id="AN0133464848-34">RQ 2: Sharing of resources</hd> <p>Among the four students, Parker and Leo had interactions with groupmates about this frame and Leo demonstrated shared resources.</p> <hd id="AN0133464848-35">Parker</hd> <p>Parker had a conceptual discussion with his groupmates about the idea of irreversibility that occurred during Frame 8, which asks about the relationship between temperature and volume (whether it is linear). As Parker's two groupmates were talking about it, one student said she did not know but would use the equation to find the slope; Parker jumped in.</p> <p>Parker's neighbor: I don't know. I am using the equation. Delta...</p> <p>Parker: I think it should be linear, because you are adding the pressure all the same time.</p> <p>Parker's neighbor: <emph>Yeah</emph>.</p> <p>Parker: <emph>So, like the pressure's added over a long period of time, which would be where you'd see if it were a reversible process</emph>.</p> <p>During this conversation, Parker listened to his neighbor and offered a conceptual explanation of why the temperature–volume relationship should be linear. He included the reason that pressure is constant (pressure is added all the same time) and pointed out the relationship between the way pressure is added and the reversibility of the process, although not very clearly articulated (when pressure is added over a long period of time, he believes the process is reversible). He was sharing conceptual ideas with groupmates. Whether Parker's ideas became shared was not evident. However, Parker did <emph>make his idea public</emph>, creating a possible condition for the ideas to be shared.</p> <hd id="AN0133464848-36">Leo</hd> <p>Leo originally talked about his ideas about energy (internal energy and kinetic energy) to answer the question. While typing his answer, he asked his groupmate about it:</p> <p>Leo (talks to his group while he starts to type in his answer): <emph>Because internal energy of the system is higher. Right? Like kinetic energy.. </emph>.</p> <p>Leo's neighbor: <emph>Oh yeah, yeah. I guess it's... Coz it's like, not reversible. The energy that you added in isn't any energy that you get back</emph>.</p> <p>Leo: <emph>Oh. Okay... left in there... the temperature... (</emph>inaudible<emph>)</emph></p> <p>(Leo is typing additional information in his answer.)</p> <p>In this discussion, Leo's groupmate used the idea of irreversibility to describe the process. Leo later included this idea in his final response. According to our indicators, we identify that this episode could be an event of shared resources. <emph>Requesting feedback</emph> and <emph>providing an alternative idea</emph> seemed to be the condition for shared resources in this case.</p> <hd id="AN0133464848-37">Frame 12</hd> <p></p> <hd id="AN0133464848-38">RQ 1 Distribution of resources</hd> <p>Frame 12 asked students to summarize their understanding of work and energy at the completion of the IVL. The question is more open‐ended and is not tied to a specific observation. Students included a variety of ideas as shown in the coding results in Table 11. All 159 students' responses were coded by the 10 ideas in Table 11.</p> <p>Coding Results for Frame 12 (Total = 159)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Ideas</th><th align="left">Energy</th><th align="left">Work</th><th align="left">System</th><th align="left">Internal</th><th align="left">Molecule</th><th align="left">Temperature</th><th align="left">Volume</th><th align="left">Pressure</th><th align="left">Heat</th><th align="left">Irreversibility</th></tr></thead><tbody valign="top"><tr><td># of students</td><td align="center">141</td><td align="center">128</td><td align="center">119</td><td align="center">87</td><td align="center">81</td><td align="center">49</td><td align="center">35</td><td align="center">29</td><td align="center">21</td><td align="center">13</td></tr><tr><td>Percentage</td><td>89%</td><td>81%</td><td>75%</td><td>55%</td><td>51%</td><td>31%</td><td>22%</td><td>18%</td><td>13%</td><td>8%</td></tr></tbody></table> </ephtml> </p> <p>Energy, work, and system were the most common ideas identified in the student responses. Theme words relevant to these three ideas were present in the question prompt in Frame 12, while the remaining seven ideas in Table 11 were not present as words in this prompt. Students summarized their ideas about what they learned about work and energy in this IVL. More than half of the students related their understanding to internal or kinetic energy, and more than half also addressed the molecular features of the gaseous system. Approximately one‐third included temperature, and approximately one‐fifth of the students made statements about pressure and volume, both of which are related to the expression for thermodynamic work. Last, heat (or adiabatic) was included by 13% of the students and irreversibility by 8%. Table 12 presents the four students' answers to Frame 12.</p> <p>Student Responses to the Conceptual Question in Frame 12</p> <p> <ephtml> <table><thead valign="bottom"><tr><th>Frame 12</th><th>Parker (circle)</th><th>Leo (square)</th><th>Thomas (triangle)</th><th>Austin (star)</th></tr></thead><tbody valign="top"><tr><td>Answer</td><td>Adding work increases internal energy (assuming that heat does not escape), so the temperature of the system increases. If the system is returned to its original state, there is still a net work done on the gas, which remains with the gas. Change in work is not a state function.</td><td>It (work) changes the internal energy; increases collisions with the container, which will impact kinetic energy of molecules and thus temperature.</td><td>Doing work on a system adds energy by increasing in internal energy of the molecules in the system. Compressing the gas causes the atoms of the gas to move faster which is felt as higher temperature.</td><td>By doing work on a system the energy is used to increase the kinetic energy of the molecules within the system. Increasing the kinetic energy of the particles increases the systems temperature. This therefore shows that by doing work on a system the internal energy increases.</td></tr><tr><td>Received codes</td><td>System, work, energy, internal, heat, temperature</td><td>Work, energy, molecule, internal, temperature</td><td>System, work, energy, molecule, internal, temperature</td><td>System, work, energy, molecule, internal, temperature</td></tr></tbody></table> </ephtml> </p> <p>Parker's answer described the relationship between work and internal energy. He concluded that work was not a state function. Parker also pointed out that the system's temperature increases after the two‐step process and there was "net work done on the gas." Leo's answer described the relationship between work and internal energy. He also described the relationship between the microscopic features (collisions between gas molecules and the container and the kinetic energy of the molecules) and the resultant macroscopic phenomenon (change in the temperature of the system). Thomas stated a coherent relationship between the process of doing work and the change in the system's internal energy. He also attributed the compression of gas to the increase in the speed of the gas molecules, relating their higher speed to the system's higher temperature. Austin related the process of doing work to the transfer of energy, resulting in the molecules' higher kinetic energy. In addition, he related this higher kinetic energy to the system's higher temperature. In the end, he concluded that doing work increases the system's internal energy, a statement that is consistent with the first law of thermodynamics.</p> <hd id="AN0133464848-39">RQ 2: Sharing of resources</hd> <p>When working on Frame 12, most students were typing their summative answers quietly. There were not any verbal interactions in the video recordings, so we did not identify events of shared resources in Frame 12.</p> <hd id="AN0133464848-40">Discussion</hd> <p>In this article, we used the resources framework (Hammer et al., [<reflink idref="bib18" id="ref120">18</reflink>]) to explore engineering student responses to conceptual questions in thermodynamics. This framework allows us to see what is fruitful in student answers and to emphasize what aspects students bring to learning rather than what they lack. It provides an alternative interpretation of some observed student difficulties in learning challenging concepts: students have difficulties not because they have robust misconceptions that need repair but because there are cognitive resources that have yet to be activated.</p> <p>Our data indicate that student answers include productive ideas that can build understanding toward overcoming the difficulties in learning the concept of thermodynamic work that are reported in the literature (see the first column in Table 1). For example, in response to Frame 12, Parker exhibited an understanding of the irreversibility of work, which is a reported difficulty in the literature (Clark et al., [<reflink idref="bib10" id="ref121">10</reflink>]; Meltzer, [<reflink idref="bib42" id="ref122">42</reflink>]; van Roon et al., [<reflink idref="bib66" id="ref123">66</reflink>]), and Leo, Thomas, and Austin showed understanding of the micro–macro relationship, also seen as difficult for students (Kautz, Heron, Loverude, et al., [<reflink idref="bib23" id="ref124">23</reflink>]; Loverude et al., [<reflink idref="bib39" id="ref125">39</reflink>]). Further, all four students related change of internal energy to the process of doing work, another reported learning difficulty (Kautz, Heron, Loverude, et al., [<reflink idref="bib23" id="ref126">23</reflink>]). They all showed a more general understanding of the relationship between system and surroundings, and between work and energy, both identified as challenging for students (Loverude et al., [<reflink idref="bib39" id="ref127">39</reflink>]; van Roon et al., [<reflink idref="bib66" id="ref128">66</reflink>]). In their responses to previous frames, Parker and Thomas showed an understanding of work in relation to pressure and volume, another identified difficulty (Nilsson & Niedderer, [<reflink idref="bib47" id="ref129">47</reflink>]). Throughout the IVL, the students demonstrated their ability to relate multiple variables and articulate relationships in a complex thermodynamic situation, a reported issue in student reasoning (Rozier & Viennot, [<reflink idref="bib54" id="ref130">54</reflink>]). As shown in Tables 7, 9, and 11, and in Figures 3 and 4, these ideas are widely distributed in the entire class's written responses, with some being very common among the students (e.g., those frequencies greater than 75%).</p> <p>The distribution of ideas showed the diverse ways that they were organized in student answers, a diversity of thinking that provided a foundation for students to have conversations, make contributions, and develop a group understanding of the topic. In the video analysis, we identified events when students initiated discussion and shared ideas (resources) with groupmates. We categorized conditions when sharing resources is likely to happen. We found interaction is necessary, and facilitating conditions included describing a discrepancy; requesting feedback; asking a general question; responding to a question by elaborating, rephrasing, or paraphrasing others' ideas; making ideas public; and providing an alternative idea.</p> <p>Our study has several limitations. We examined only one cohort of students in one environment using one technology. The video analysis examined four students, all male. As we recorded one student in a group, we sometimes had evidence of only one student's part of the interaction. Student interactions were brief and seemed incidental. The conditions identified for shared resources were closely contingent to the specific students we observed. To identify and categorize conditions more accurately and meaningfully, studies examining student interactions in different contexts are needed.</p> <p>One main finding from this study is that every student brought some ideas to share but few, if any, articulated the entire set of possible ideas. The students who participated in the video analysis allowed us to identify potential opportunities for sharing. However, the demographics of the four students did not match the class. Further research is needed to identify structures that inhibit populations from freely sharing ideas, and based on those findings, strategies need to be developed to provide all students opportunity to share resources. The varied backgrounds and histories of the students then become an asset to the learning environment.</p> <hd id="AN0133464848-41">Shared Resources Framework</hd> <p>We arrived at theoretical conjectures that incorporate our work beyond this study and propose the construct of <emph>shared resources</emph>. The construct of shared resources specifically addresses the social and environmental interactions and attends to the social and environmental interactions and attends to both cognitive (<emph>resources</emph>) and social (<emph>shared</emph>) aspects of learning. Shared resources resonates with other sociocultural theories such as distributed cognition (Salomon, [<reflink idref="bib56" id="ref131">56</reflink>]); however, it places primacy on the value of a student's contributions in a social setting. The observation of shared resources events in this study is consistent with the idea of co‐construction in learning (Jacoby & Ochs, [<reflink idref="bib21" id="ref132">21</reflink>]; Koretsky, Nolen, Gilbuena, Tierney, & Volet, [<reflink idref="bib35" id="ref133">35</reflink>]; Singh, [<reflink idref="bib58" id="ref134">58</reflink>]; Smith et al., [<reflink idref="bib60" id="ref135">60</reflink>]). We project another way that personal resources can become shared: when a student independently activates a resource in a different context that another student introduced in previous discussion. In this situation, shared resources aligns with ideas of transfer (Pellegrino & Hilton, [<reflink idref="bib49" id="ref136">49</reflink>]; Schwartz, Bransford, & Sears, [<reflink idref="bib57" id="ref137">57</reflink>]). Empirical studies connecting shared resources to transfer are needed.</p> <p>In summary, we infer two ways of interest to engineering educators by which resources can be shared to develop conceptual understanding:</p> <p></p> <ulist> <item> In <emph>co‐construction</emph>, where resources from multiple people are activated and ideas are presented and generatively lead to new understanding that would be unlikely for individuals alone to develop</item> <p></p> <item> In <emph>transfer</emph>, where a student takes up a resource introduced earlier by another student such that she activates it to formulate ideas in a new situation.</item> </ulist> <p>This perspective has important implications for social learning and educational technology design.</p> <hd id="AN0133464848-42">Implications for Student Social Interaction</hd> <p>If we conceive of learning simply from the binary of correct and wrong answers (e.g., the misconception perspective), then interacting with peers might be considered a waste of time if a student already "knows" the answer. At best, we adapt a "tutor" model where the more capable students become teachers of the less capable students. Here, learning is primarily in one direction with a less capable student gaining benefit and a more capable student being responsible to teach. However, if we look at the results from Frame 11 (Figure 4), we see that 158 of the 159 student answers do not contain all five salient ideas present in the class responses. Therefore, from the multifaceted resources perspective, almost every student has the opportunity to learn as there are aspects to their explanations that were not articulated. Meanwhile, every student wrote some ideas and had resources to contribute. As individuals activate resources and share ideas, the resources become common tools of a learning community that can lead to knowledge co‐construction and transfer.</p> <p>The shared resources construct gives credit to student's prior ideas rather than focusing on identifying deficiencies and criticizing them. This construct provides instructors with actionable ways for designing learning activities and responding to students. While instructors still need to develop active learning activities, equal value should be placed on instructional practices that lead to sharing resources when students are engaged in these activities, for example, making student thinking visible, noticing and re‐voicing student ideas, and facilitating group interactions to include all students (Horn, [<reflink idref="bib20" id="ref138">20</reflink>]; Windschitl & Calabrese Barton, [<reflink idref="bib70" id="ref139">70</reflink>]).</p> <hd id="AN0133464848-43">Implication for Educational Technology Design</hd> <p>The framework of shared resources also shifts fundamentally the ways we approach the design and implementation of technology‐based learning environments. Instructors using technology should look outside the technology itself and consider how it is deployed in learning environments to position learners to share resources. We argue that attributing student learning or their misconceptions to features of a simulation tool leads educators to place too much reliance on the technology itself. Therefore, less than satisfactory learning gains naturally lead to conclusions that we need to invest in the costly development of more effective technologies (e.g., Nelson et al., [<reflink idref="bib46" id="ref140">46</reflink>]). Rather, in our study, we recognize a class environment in which an existing technology can be leveraged to become more effective. The social structure of groups in the studio setting was critical to allow for the activated resources of individual students to be shared with others. The resources theoretical lens allows us to recognize the role of social interactions that lead to the progressive co‐construction of conceptual knowledge. In other words, we shift the perspective from one where learning occurs <emph>through</emph> the technology to one where the technology is a useful epistemic tool <emph>in</emph> a collaborative learning environment.</p> <p>Importantly, the resources framework shifts the orientation of how to develop technology systems for learning. A more conventional view might cast conceptual knowledge as an acquired entity. This proposition naturally positions technology designers to build systems that provide students with adaptive responses that steer them to the right answer. Such a view of "adaptive learning" asks the computer to do the work of assessing and responding. Alternatively, we can view learning as a socially mediated process where the learner activates and shares resources to iteratively develop more globally coherent conceptual understanding. Technology designers then seek to identify the appropriate resources associated with a difficult concept and look for ways that technology experiences can trigger students to activate resources. In the case of IVLs, we would identify (as we did in this study) how features in simulations, representations, and textual descriptions activate resources in different students and encourage students to share resources. We would also look for ways for instructors to further facilitate resource activation and sharing, as technology and human factors are entangled in the system. Rather than fixating only on potential technology improvements, we now think more holistically about the learning environment, incorporating supportive instructional practices as we use the innovative technologies.</p> <hd id="AN0133464848-44">Acknowledgment</hd> <p>We gratefully acknowledge support from the National Science Foundation under the grant TUES 1245482. We also thank the instructor and students involved for their cooperation in this research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.</p> <ref id="AN0133464848-45"> <title> References </title> <blist> <bibl id="bib1" idref="ref54" type="bt">1</bibl> <bibtext> Bain, K., Moon, A., Mack, M. R., & Towns, M. H. (2014). A review of research on the teaching and learning of thermodynamics at the university level. Chemistry Education Research and Practice, 15 (3), 320 – 335. https://doi.org/10.1039/C4RP00011K</bibtext> </blist> <blist> <bibl id="bib2" idref="ref5" type="bt">2</bibl> <bibtext> Bowen, A., Reid, D., & Koretsky, M. (2015). Development of interactive virtual laboratories to help students learn difficult concepts in thermodynamics. Chemical Engineering Education, 49 (4), 229 – 238. <ulink href="http://journals.fcla.edu/cee/article/view/87186">http://journals.fcla.edu/cee/article/view/87186</ulink></bibtext> </blist> <blist> <bibl id="bib3" idref="ref13" type="bt">3</bibl> <bibtext> Brooks, B. J., & Koretsky, M. D. (2011). The influence of group discussion on students' responses and confidence during peer instruction. Journal of Chemical Education, 88 (11), 1477 – 1484. https://doi.org/10.1021/ed101066x</bibtext> </blist> <blist> <bibl id="bib4" idref="ref23" type="bt">4</bibl> <bibtext> Bucciarelli, L. L. (1988). An ethnographic perspective on engineering design. Design Studies, 9 (3), 159 – 168. https://doi.org/10.1016/0142-694X(88)90045-2</bibtext> </blist> <blist> <bibl id="bib5" idref="ref57" type="bt">5</bibl> <bibtext> Cao, Y., & Brizuela, B. M. (2016). High school students' representations and understandings of electric fields. Physical Review Physics Education Research, 12 (2), 020102. https://doi.org/10.1103/PhysRevPhysEducRes.12.020102</bibtext> </blist> <blist> <bibl id="bib6" idref="ref33" type="bt">6</bibl> <bibtext> Carey, S. (1985). Conceptual change in childhood. Cambridge, MA : MIT Press.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref32" type="bt">7</bibl> <bibtext> Chi, M. (1992). Conceptual change within and across ontological categories: Examples from learning and discovery in science. In R. N. Giere (Ed.), Cognitive models of science: Minnesota studies in the philosophy of science (pp. 129 – 186). Minneapolis, MN : University of Minnesota Press.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref9" type="bt">8</bibl> <bibtext> Chi, M. T. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. The Journal of the Learning Sciences, 14 (2), 161 – 199. https://doi.org/10.1207/s15327809jls1402_1</bibtext> </blist> <blist> <bibl id="bib9" idref="ref119" type="bt">9</bibl> <bibtext> Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5 (2), 121 – 152. https://doi.org/10.1207/s15516709cog0502_2</bibtext> </blist> <blist> <bibtext> Clark, J. W., Thompson, J. R., & Mountcastle, D. B. (2014). Investigating student conceptual difficulties in thermodynamics across multiple disciplines: The first law and PV diagrams. Paper presented at the ASEE Annual Conference & Exposition, Indianapolis, IN. Abstract retrieved from https://peer.asee.org/20713</bibtext> </blist> <blist> <bibtext> diSessa, A. A. (1988). Knowledge in pieces. In G. Forman & P. B. Pufall (Eds.), Constructivism in the computer age (pp. 49 – 70). Hillsdale, NJ : Lawrence Erlbaum Associates.</bibtext> </blist> <blist> <bibtext> Engle, R. A., Conant, F. R., & Greeno, J. G. (2007). Progressive refinement of hypotheses in video‐supported research. In R. Goldman, R. Pea, B. Barron, & S. J. Derry (Eds.), Video research in the learning sciences (pp. 239 – 254). Mahwah, NJ : Lawrence Erlbaum Associates.</bibtext> </blist> <blist> <bibtext> Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis. Cambridge, MA : MIT Press.</bibtext> </blist> <blist> <bibtext> Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53 (1), 5 – 26. https://doi.org/10.1037/0003-066X.53.1.5</bibtext> </blist> <blist> <bibtext> Guba, E. G. (1978). Toward a methodology of naturalistic inquiry in educational evaluation (CSE Monograph 8). Los Angeles : University of California, Los Angeles, Center for the Study of Evaluation.</bibtext> </blist> <blist> <bibtext> Hamilton, E., Lesh, R., Lester, F., & Brilleslyper, M. (2008). Model‐eliciting activities (MEAs) as a bridge between engineering education research and mathematics education research. Advances in Engineering Education, 1 (2), 1 – 25. <ulink href="http://advances.asee.org/wp-content/uploads/vol01/issue02/papers/aee-vol01-issue02-p06.pdf">http://advances.asee.org/wp-content/uploads/vol01/issue02/papers/aee-vol01-issue02-p06.pdf</ulink></bibtext> </blist> <blist> <bibtext> Hammer, D. (2000). Student resources for learning introductory physics. American Journal of Physics, 68 (S1), S52 – S59. https://doi.org/10.1119/1.19520</bibtext> </blist> <blist> <bibtext> Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89 – 120). Greenwich, CT : Information Age Publishing.</bibtext> </blist> <blist> <bibtext> Hirshfield, L., & Koretsky, M. D. (2018). Gender and participation in an engineering problem‐based learning environment. Interdisciplinary Journal of Problem‐based Learning, 12 (1), 1 – 20. https://doi.org/10.7771/1541-5015.1651</bibtext> </blist> <blist> <bibtext> Horn, I. S. (2012). Strength in numbers: Collaborative learning in secondary mathematics. Reston, VA : National Council of Teachers of Mathematics.</bibtext> </blist> <blist> <bibtext> Jacoby, S., & Ochs, E. (1995). Co‐construction: An introduction. Research on Language and Social Interaction, 28 (3), 171 – 184. https://doi.org/10.1207/s15327973rlsi2803_1</bibtext> </blist> <blist> <bibtext> Johri, A., & Olds, B. M. (2011). Situated engineering learning: Bridging engineering education research and the learning sciences. Journal of Engineering Education, 100 (1), 151 – 185. https://doi.org/10.1002/j.2168-9830.2011.tb00007.x</bibtext> </blist> <blist> <bibtext> Kautz, C. H., Heron, P. R., Loverude, M. E., & McDermott, L. C. (2005). Student understanding of the ideal gas law, part I: A macroscopic perspective. American Journal of Physics, 73 (11), 1055 – 1063. https://doi.org/10.1119/1.2049286</bibtext> </blist> <blist> <bibtext> Kautz, C. H., Heron, P. R., Shaffer, P. S., & McDermott, L. C. (2005). Student understanding of the ideal gas law, part II: A microscopic perspective. American Journal of Physics, 73 (11), 1064 – 1071. https://doi.org/10.1119/1.2060715</bibtext> </blist> <blist> <bibtext> Keeler, J., Ekstedt, T., Cao, Y., & Koretsky, M. D. (2016). Data analytics for interactive virtual laboratories. Paper presented at the ASEE Annual Conference & Exposition, New Orleans, LA. Abstract retrieved from https://<ulink href="http://www.asee.org/public/conferences/64/papers/16806/view">www.asee.org/public/conferences/64/papers/16806/view</ulink></bibtext> </blist> <blist> <bibtext> Koretsky, M., Kelly, C., & Gummer, E. (2011). Student perceptions of learning in the laboratory: Comparison of industrially situated virtual laboratories to capstone physical laboratories. Journal of Engineering Education, 100 (3), 540 – 573. https://doi.org/10.1002/j.2168-9830.2011.tb00026.x</bibtext> </blist> <blist> <bibtext> Koretsky, M. D. (2013). Engineering and chemical thermodynamics (2nd ed.). Hoboken, New Jersey : John Wiley & Sons.</bibtext> </blist> <blist> <bibtext> Koretsky, M. D. (2015). Program level curriculum reform at scale: Using studios to flip the classroom. Chemical Engineering Education, 49 (1), 47 – 57. <ulink href="http://journals.fcla.edu/cee/article/view/84260">http://journals.fcla.edu/cee/article/view/84260</ulink></bibtext> </blist> <blist> <bibtext> Koretsky, M. D. (2017). Cognitive and social aspects of engagement in active learning. Chemical Engineering Education, 51 (4), 198 – 204. <ulink href="http://journals.fcla.edu/cee/article/view/104870">http://journals.fcla.edu/cee/article/view/104870</ulink></bibtext> </blist> <blist> <bibtext> Koretsky, M. D., Amatore, D., Barnes, C., & Kimura, S. (2008). Enhancement of student learning in experimental design using a virtual laboratory. IEEE Transactions on Education, 51 (1), 76 – 85. https://doi.org/10.1109/TE.2007.906894</bibtext> </blist> <blist> <bibtext> Koretsky, M. D., Brooks, B. J., & Higgins, A. Z. (2016). Written justifications to multiple‐choice concept questions during active learning in class. International Journal of Science Education, 38 (11), 1747 – 1765. https://doi.org/10.1080/09500693.2016.1214303</bibtext> </blist> <blist> <bibtext> Koretsky, M. D., Brooks, B. J., White, R. M., & Bowen, A. S. (2016). Querying the questions: Student responses and reasoning in an active learning class. Journal of Engineering Education, 105 (2), 219 – 244. https://doi.org/10.1002/jee.20116</bibtext> </blist> <blist> <bibtext> Koretsky, M. D., Falconer, J. L., Brooks, B. J., Gilbuena, D. M., Silverstein, D. L., Smith, C., & Miletic, M. (2014). The AIChE Concept Warehouse: A web‐based tool to promote concept‐based instruction. Advances in Engineering Education, 4 (1), 1 – 27. https://files.eric.ed.gov/fulltext/EJ1076138.pdf</bibtext> </blist> <blist> <bibtext> Koretsky, M. D., Montfort, D., Nolen, S. B., Bothwell, M., Davis, S., & Sweeney, J. (2018). Towards a stronger covalent bond: Pedagogical change for inclusivity and equity. Chemical Engineering Education, 52 (2), 117 – 127. <ulink href="http://journals.fcla.edu/cee/article/view/104870105859">http://journals.fcla.edu/cee/article/view/104870105859</ulink></bibtext> </blist> <blist> <bibtext> Koretsky, M. D., Nolen, S. B., Gilbuena, D. M., Tierney, G., & Volet, S. E. (2014). Productively engaging student teams in engineering: The interplay between doing and thinking. Proceedings of the Frontiers in Education Conference, Madrid, Spain.</bibtext> </blist> <blist> <bibtext> Krause, S., Kelly, J., Corkins, J., & Tasooji, A. (2009). The role of prior knowledge on the origin and repair of misconceptions in an introductory class on materials science and engineering. Proceedings of the Research in Engineering Education Symposium, Palm Cove, Queensland, Australia. Abstract retrieved from <ulink href="http://rees2009.pbworks.com/f/rees2009%5fsubmission%5f47.pdf">http://rees2009.pbworks.com/f/rees2009%5fsubmission%5f47.pdf</ulink></bibtext> </blist> <blist> <bibtext> Lebart, L., Salem, A., & Berry, L. (1997). Exploring textual data. Dordrecht, The Netherlands : Kluwer Academic Publisher.</bibtext> </blist> <blist> <bibtext> Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA : Sage.</bibtext> </blist> <blist> <bibtext> Loverude, M. E., Kautz, C. H., & Heron, P. R. (2002). Student understanding of the first law of thermodynamics: Relating work to the adiabatic compression of an ideal gas. American Journal of Physics, 70 (2), 137 – 148. https://doi.org/10.1119/1.1417532</bibtext> </blist> <blist> <bibtext> Male, S. A. and C.A. Baillie. (2011). Threshold capabilities: An emerging methodology to locate curricula thresholds. Proceedings of the Research in Engineering Education Symposium, Madrid, Spain.</bibtext> </blist> <blist> <bibtext> McCloskey, M. (1983). Naive theories of motion. In D. Gentner & A. L. Stevens (Eds.), Mental models (pp. 299 – 323). Hillsdale, NJ : Lawrence Erlbaum Associates. https://files.eric.ed.gov/fulltext/ED223417.pdf</bibtext> </blist> <blist> <bibtext> Meltzer, D. E. (2004). Investigation of students' reasoning regarding heat, work, and the first law of thermodynamics in an introductory calculus‐based general physics course. American Journal of Physics, 72 (11), 1432 – 1446. https://doi.org/10.1119/1.1789161</bibtext> </blist> <blist> <bibtext> Meyer, J., & Land, R. (2003). Threshold concepts and troublesome knowledge: Linkages to ways of thinking and practicing within the disciplines. In C. Rust (Ed.), Improving student learning: Improving student learning theory and practice – Ten years on (pp. 412 – 424). Oxford : Oxford Centre for Staff and Learning Development.</bibtext> </blist> <blist> <bibtext> Miller, R. L., Streveler, R. A., Yang, D., & Santiago Román, A. I. (2011). Identifying and repairing student misconceptions in thermal and transport science: Concept inventories and schema training studies. Chemical Engineering Education, 45 (3), 203 – 210.</bibtext> </blist> <blist> <bibtext> National Research Council (2000). In J. D. Bransford, A. Brown, & R. Cocking (Eds.), How people learn: Brain, mind, experience, and school: Expanded edition. Washington, DC : National Academies Press.</bibtext> </blist> <blist> <bibtext> Nelson, K. G., McKenna, A. F., Brem, S. K., Hilpert, J., Husman, J., & Pettinato, E. (2017). Students' misconceptions about semiconductors and use of knowledge in simulations. Journal of Engineering Education, 106 (2), 218 – 244. https://doi.org/10.1002/jee.20163</bibtext> </blist> <blist> <bibtext> Nilsson, T., & Niedderer, H. (2012). An analytical tool to determine undergraduate students' use of volume and pressure when describing expansion work and technical work. Chemistry Education Research and Practice, 13 (3), 348 – 356. https://doi.org/10.1039/c2rp20007d</bibtext> </blist> <blist> <bibtext> Olds, B. M., Streveler, R. A., Miller, R. L., & Nelson, M. A. (2004). Preliminary results from the development of a concept inventory in thermal and transport science. Paper presented at the ASEE Annual Conference & Exposition, Salt Lake City, UT. Abstract retrieved from https://peer.asee.org/12983</bibtext> </blist> <blist> <bibtext> Pellegrino, J. W., & Hilton, M. L. (Eds.). (2013). Education for life and work: Developing transferable knowledge and skills in the 21st century. Washington, DC : National Academies Press. https://doi.org/10.17226/13398</bibtext> </blist> <blist> <bibtext> Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66 (2), 211 – 227. https://doi.org/10.1002/sce.3730660207</bibtext> </blist> <blist> <bibtext> Prince, M., Vigeant, M., & Nottis, K. (2012). Development of the heat and energy concept inventory: Preliminary results on the prevalence and persistence of engineering students' misconceptions. Journal of Engineering Education, 101 (3), 412 – 438. https://doi.org/10.1002/j.2168-9830.2012.tb00056.x</bibtext> </blist> <blist> <bibtext> Prince, M., Vigeant, M., & Nottis, K. (2012). Using inquiry‐based activities to repair student misconceptions related to heat, energy and temperature. Proceedings of the Frontiers in Education Conference, Seattle, WA. Abstract retrieved from https://doi.org/10.1109/FIE.2012.6462344</bibtext> </blist> <blist> <bibtext> Redish, E. F., & Smith, K. A. (2008). Looking beyond content: Skill development for engineers. Journal of Engineering Education, 97 (3), 295 – 307. https://doi.org/10.1002/j.2168-9830.2008.tb00980.x</bibtext> </blist> <blist> <bibtext> Rozier, S., & Viennot, L. (1991). Students' reasonings in thermodynamics. International Journal of Science Education, 13 (2), 159 – 170. https://doi.org/10.1080/0950069910130203</bibtext> </blist> <blist> <bibtext> Sabo, H. C., Goodhew, L. M., & Robertson, A. D. (2016). University student conceptual resources for understanding energy. Physical Review Physics Education Research, 12 (1), 010126. <ulink href="http://doi.org/10.1103/PhysRevPhysEducRes.12.010126">http://doi.org/10.1103/PhysRevPhysEducRes.12.010126</ulink></bibtext> </blist> <blist> <bibtext> Salomon, G. (Ed.). (1993). Distributed cognitions: Psychological and educational considerations. Cambridge, UK : Cambridge University Press.</bibtext> </blist> <blist> <bibtext> Schwartz, D. L., Bransford, J. D., & Sears, D. (2005). Efficiency and innovation in transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 1 – 51). Greenwich, CT : Information Age Publishing.</bibtext> </blist> <blist> <bibtext> Singh, C. (2005). Impact of peer interaction on conceptual test performance. American Journal of Physics, 73 (5), 446 – 451. https://doi.org/10.1119/1.1858450</bibtext> </blist> <blist> <bibtext> Smith, J. P., III, diSessa, A. A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. The Journal of the Learning Sciences, 3 (2), 115 – 163. https://doi.org/10.1207/s15327809jls0302_1</bibtext> </blist> <blist> <bibtext> Smith, M. K., Wood, W. B., Adams, W. K., Wieman, C., Knight, J. K., Guild, N., & Su, T. T. (2009). Why peer discussion improves student performance on in‐class concept questions. Science, 323 (5910), 122 – 124. <ulink href="http://science.sciencemag.org/content/323/5910/122">http://science.sciencemag.org/content/323/5910/122</ulink></bibtext> </blist> <blist> <bibtext> Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA : Sage.</bibtext> </blist> <blist> <bibtext> Steif, P. S., & Dantzler, J. A. (2005). A statics concept inventory: Development and psychometric analysis. Journal of Engineering Education, 94 (4), 363 – 371. https://doi.org/10.1002/j.2168-9830.2005.tb00864.x</bibtext> </blist> <blist> <bibtext> Streveler, R., Brown, S., Herman, G. L., & Montfort, D. (2014). Chapter 5: Conceptual change and misconceptions in engineering education: Curriculum, measurement, and theory‐focused approaches. In A. Johri & B. Olds (Eds.), Cambridge handbook of engineering education research (pp. 83 – 102). Cambridge, UK : Cambridge University Press.</bibtext> </blist> <blist> <bibtext> Streveler, R. A., Litzinger, T. A., Miller, R. L., & Steif, P. S. (2008). Learning conceptual knowledge in the engineering sciences: Overview and future research directions. Journal of Engineering Education, 97 (3), 279 – 294. https://doi.org/10.1002/j.2168-9830.2008.tb00979.x</bibtext> </blist> <blist> <bibtext> Trevelyan, J. (2014). The making of an expert engineer. London, UK : CRC Press/Balkema.</bibtext> </blist> <blist> <bibtext> van Roon, P. H., Van Sprang, H. F., & Verdonk, A. H. (1994). 'Work' and 'heat': On a road towards thermodynamics. International Journal of Science Education, 16 (2), 131 – 144. https://doi.org/10.1080/0950069940160203</bibtext> </blist> <blist> <bibtext> Vincenti, W. G. (1990). What engineers know and how they know it: Analytical studies from aeronautical history. Baltimore : Johns Hopkins University Press.</bibtext> </blist> <blist> <bibtext> Vosniadou, S. (2002). On the nature of naive physics. In M. Limon & L. Mason (Eds.), Reconsidering the processes of conceptual change: Issues in theory and practice (pp. 61 – 76). Dordrecht, The Netherlands : Kluwer. https://doi.org/10.1007/0-306-47637-1_3</bibtext> </blist> <blist> <bibtext> Vygotsky, L. S. (1980). Mind in society: The development of higher psychological processes. Cambridge, MA : Harvard University Press.</bibtext> </blist> <blist> <bibtext> Windschitl, M., & Calabrese Barton, A. (2016). Rigor and equity by design: Seeking a core of practices for the science education community. In D. H. Gitomer & C. A. Bell (Eds.), AERA handbook of research on teaching (5th ed., pp. 1099 – 1158). Washington, D.C. : AERA Press. https://doi.org/10.3102/978-0-935302-48-6_18</bibtext> </blist> </ref> <aug> <p>By Ying Cao and Milo D. Koretsky</p> <p></p> <p>Ying Cao is a Postdoctoral Scholar in the School of Chemical, Biological, and Environmental Engineering at Oregon State University, Corvallis, Oregon 97331;.</p> <p>Milo D. Koretsky is a Professor in the School of Chemical, Biological, and Environmental Engineering at Oregon State University, Corvallis, Oregon 97331;.</p> </aug> <nolink nlid="nl1" bibid="bib67" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib53" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib63" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib64" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib43" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib46" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib11" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib18" firstref="ref11"></nolink> <nolink nlid="nl9" bibid="bib17" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib31" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib32" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib28" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib29" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib34" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib19" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib30" firstref="ref20"></nolink> <nolink nlid="nl17" bibid="bib26" firstref="ref21"></nolink> <nolink nlid="nl18" bibid="bib14" firstref="ref22"></nolink> <nolink nlid="nl19" bibid="bib45" firstref="ref24"></nolink> <nolink nlid="nl20" bibid="bib65" firstref="ref26"></nolink> <nolink nlid="nl21" bibid="bib41" firstref="ref34"></nolink> <nolink nlid="nl22" bibid="bib68" firstref="ref35"></nolink> <nolink nlid="nl23" bibid="bib48" firstref="ref37"></nolink> <nolink nlid="nl24" bibid="bib51" firstref="ref38"></nolink> <nolink nlid="nl25" bibid="bib62" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib16" firstref="ref40"></nolink> <nolink nlid="nl27" bibid="bib36" firstref="ref41"></nolink> <nolink nlid="nl28" bibid="bib44" firstref="ref43"></nolink> <nolink nlid="nl29" bibid="bib52" firstref="ref44"></nolink> <nolink nlid="nl30" bibid="bib50" firstref="ref45"></nolink> <nolink nlid="nl31" bibid="bib22" firstref="ref47"></nolink> <nolink nlid="nl32" bibid="bib25" firstref="ref49"></nolink> <nolink nlid="nl33" bibid="bib47" firstref="ref50"></nolink> <nolink nlid="nl34" bibid="bib59" firstref="ref53"></nolink> <nolink nlid="nl35" bibid="bib55" firstref="ref59"></nolink> <nolink nlid="nl36" bibid="bib69" firstref="ref61"></nolink> <nolink nlid="nl37" bibid="bib24" firstref="ref62"></nolink> <nolink nlid="nl38" bibid="bib39" firstref="ref63"></nolink> <nolink nlid="nl39" bibid="bib42" firstref="ref64"></nolink> <nolink nlid="nl40" bibid="bib10" firstref="ref66"></nolink> <nolink nlid="nl41" bibid="bib66" firstref="ref67"></nolink> <nolink nlid="nl42" bibid="bib23" firstref="ref69"></nolink> <nolink nlid="nl43" bibid="bib54" firstref="ref80"></nolink> <nolink nlid="nl44" bibid="bib40" firstref="ref93"></nolink> <nolink nlid="nl45" bibid="bib33" firstref="ref100"></nolink> <nolink nlid="nl46" bibid="bib13" firstref="ref102"></nolink> <nolink nlid="nl47" bibid="bib15" firstref="ref112"></nolink> <nolink nlid="nl48" bibid="bib38" firstref="ref113"></nolink> <nolink nlid="nl49" bibid="bib37" firstref="ref114"></nolink> <nolink nlid="nl50" bibid="bib12" firstref="ref115"></nolink> <nolink nlid="nl51" bibid="bib61" firstref="ref116"></nolink> <nolink nlid="nl52" bibid="bib27" firstref="ref117"></nolink> <nolink nlid="nl53" bibid="bib56" firstref="ref131"></nolink> <nolink nlid="nl54" bibid="bib21" firstref="ref132"></nolink> <nolink nlid="nl55" bibid="bib35" firstref="ref133"></nolink> <nolink nlid="nl56" bibid="bib58" firstref="ref134"></nolink> <nolink nlid="nl57" bibid="bib60" firstref="ref135"></nolink> <nolink nlid="nl58" bibid="bib49" firstref="ref136"></nolink> <nolink nlid="nl59" bibid="bib57" firstref="ref137"></nolink> <nolink nlid="nl60" bibid="bib20" firstref="ref138"></nolink> <nolink nlid="nl61" bibid="bib70" firstref="ref139"></nolink>
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  Label: Title
  Group: Ti
  Data: Shared Resources: Engineering Students' Emerging Group Understanding of Thermodynamic Work
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Cao%2C+Ying%22">Cao, Ying</searchLink><br /><searchLink fieldCode="AR" term="%22Koretsky%2C+Milo+D%2E%22">Koretsky, Milo D.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Engineering+Education%22"><i>Journal of Engineering Education</i></searchLink>. Oct 2018 107(4):656-689.
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  Label: Availability
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  Data: Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 34
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2018
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Science Foundation (NSF)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: TUES1245482
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Engineering+Education%22">Engineering Education</searchLink><br /><searchLink fieldCode="DE" term="%22Fundamental+Concepts%22">Fundamental Concepts</searchLink><br /><searchLink fieldCode="DE" term="%22Thermodynamics%22">Thermodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Laboratories%22">Science Laboratories</searchLink><br /><searchLink fieldCode="DE" term="%22Concept+Formation%22">Concept Formation</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+Learning%22">Cooperative Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Interaction%22">Interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/jee.20237
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1069-4730
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: We have developed several interactive virtual laboratories (IVLs) based on a sequence of agent-based molecular simulations designed to target specific threshold concepts to help students learn thermodynamics. We previously analyzed learning in the IVLs from a cognitive perspective, seeking to repair students' misconceptions; however, that perspective provided limited information for iteratively improving the IVLs. Purpose: In this study, we shift to a sociocultural perspective to identify student learning resources activated during their engagement in the Thermodynamic Work IVL. We seek to identify the productive social and environmental triggers through which students develop conceptual ideas using technology in a social setting. Method: We conducted emergent lexical coding on a cohort of 187 students' textual responses as they completed the IVL in a studio setting. We then analyzed the discursive and technology interactions of four students from different groups using video recordings. Results: Coding results show distributions of students' activated resources. Almost all of the students demonstrated productive ideas, and almost all also revealed opportunities to learn more. Through the detailed studies, we illustrate the moment-by-moment interactions of students with one another and with technology to describe how they activate and share resources. These interactions are conceptualized and illustrated through the construct of shared resources. We relate shared resources to processes of knowledge co-construction and knowledge transfer, and discuss implications for instructional practice and educational technology design. Conclusions: The resources framework helps us recognize productive ideas in students' evolving understanding of thermodynamic work. Shared resources allows for elaboration of the interwoven cognitive and social aspects of learning.
– Name: AbstractInfo
  Label: Abstractor
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  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2020
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1254069
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        Value: 10.1002/jee.20237
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 34
        StartPage: 656
    Subjects:
      – SubjectFull: Engineering Education
        Type: general
      – SubjectFull: Fundamental Concepts
        Type: general
      – SubjectFull: Thermodynamics
        Type: general
      – SubjectFull: Science Laboratories
        Type: general
      – SubjectFull: Concept Formation
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      – SubjectFull: Cooperative Learning
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      – SubjectFull: Interaction
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      – SubjectFull: Learning Processes
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      – SubjectFull: Educational Technology
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      – TitleFull: Shared Resources: Engineering Students' Emerging Group Understanding of Thermodynamic Work
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            NameFull: Cao, Ying
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            NameFull: Koretsky, Milo D.
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