Social and Tactile Mixed Reality Increases Student Engagement in

Article pubs.acs.org/jchemeduc

Cite This: J. Chem. Educ. XXXX, XXX, XXX−XXX

Social and Tactile Mixed Reality Increases Student Engagement in Undergraduate Lab Activities Rainier Barrett,† Heta A. Gandhi,† Anusha Naganathan,‡ Danielle Daniels,‡ Yang Zhang,‡ Chibueze Onwunaka,‡ April Luehmann,‡ and Andrew D. White*,† †

Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States Warner School of Education, University of Rochester, Rochester, New York 14627, United States

‡

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S Supporting Information *

ABSTRACT: Undergraduate lab sessions play a crucial role in building and reinforcing conceptual understanding in STEM education. In third and fourth year higher education, lab sessions can be challenging to incorporate into the curriculum due to cost, safety, or difficulty in realizing abstract concepts. Mixed reality (MR) systems provide a novel solution due to their ability to nurture collaboration and tactile interaction. In this work, an MR system designed for use in chemical kinetics undergraduate curriculum is described. This system combines the principles of student-driven, investigative learning with tactile feedback and simulation-based teaching. A small-scale study was conducted to explore students’ use of a prototype MR system as compared to a traditional “pen-and-paper” cooperative learning activity. Differences in student engagement and learning outcomes were analyzed. Results indicate that students working with MR demonstrated slightly more accurate and nuanced conceptual understandings, conducted faster and more cycles of inquiry, expressed more clarity when articulating thoughts, and engaged in less risk aversion when presenting their ideas as compared to their peers in the control condition. KEYWORDS: First-Year Undergraduate/General, Chemical Engineering, Computer-Based Learning, Hands-On Learning/Manipulatives, Inquiry-Based/Discovery Learning, Computational Chemistry, Industrial Chemistry, Kinetics

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INTRODUCTION Undergraduate STEM lab sessions are an integral part of higher education because of their ability to develop student conceptual understanding, teach inquiry processes, and generate enthusiasm for a topic.1,2 However, it is difficult to design lab sessions that maintain tactile interaction for topics like quantum chemistry and chemical reaction engineering. This difficulty arises from factors like accessibility, cost, and safety of lab materials, as well as scaffolding complex and abstract concepts. Mixed reality (MR), a type of merging of the real and virtual worlds, is flexible in how content is represented, which enables potential lab activities that would not be feasible by traditional means. MR can also foster collaboration and invite tactile interactivity through the use of props and simulation. In 1994, Milgram et al.3 defined a comprehensive “reality− virtuality continuum” encompassing every type of virtualization between reality and virtual reality. On this continuum, display technology with entirely virtual elements is at one end, and the real environment lies on the other. MR technology is defined here as “a particular subclass of VR related technologies that involve the merging of real and virtual worlds”. The most common type of MR technology is augmented reality (AR), which “supplements the real world with virtual (computer© XXXX American Chemical Society and Division of Chemical Education, Inc.

generated) objects that appear to coexist in the same space as the real world.”4,5 A comprehensive review of AR technology and its history may be found in Billinghurst et al.6 This work arguably does not constitute an AR technology because the virtual overlay is 2D instead of 3D. It uses real objects to represent simulated objects and displays information related to the simulation. Thus, since this system emulates another reality with some virtual and some real elements, we use the broader term “MR” to describe it. Past examples of MR in education have utilized location tracking, enriching printed content with digital overlays,7 and information overlays on objects.8,9 In the informal science learning setting, Yoon et al.10,11 have developed tactile and collaborative AR museum exhibits. In Tanner et al.8 students were able to study fluid dynamics in system geometries measured and created with tablets by walking around and interacting with their environment. However, the students had to manually create the simulation parameters and wait minutes to see the results. This kind of delay means that the lab teaches computational fluid dynamics, rather than builds intuition Received: March 24, 2018 Revised: August 8, 2018

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DOI: 10.1021/acs.jchemed.8b00212 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

Article

Figure 1. Schematic diagram of the MR system’s design: (a) side view, (b) front view, and (c) top-down view of the (d) table. A shelf is mounted on one wall, seen in parts a and c, to hold the local desktop (not depicted). The projector is mounted inside the access door as shown in part b. The webcam is mounted to a cross-beam on the bottom of the frame as shown in part c. For local control, mouse, keyboard, and monitor can be mounted externally.

about how actual fluids behave. There is merit to these computational-style laboratories,12 but they cannot compete with laboratories that provide real-time or near real-time feedback for supporting students’ developing intuition. In a recent example of AR applied to chemical education, Tee et al.13 created an AR smartphone/tablet app to simulate a calorimetric titration lab activity, but each user’s experience was localized on their device. They later used the same technology to create a simulated redox lab activity whose conditions were shared between several viewers.14 These examples demonstrate near real-time feedback with AR technology, but both lacked tactile elements, removing the “hands-on” aspect of lab activities. This study explored an MR-enhanced chemical engineering lab. Undergraduate students of chemical kinetics can have difficulty connecting macroscale observations to fundamental concepts in kinetics, particularly rate laws and equilibrium.15−17 Approaches to address these challenges include increased interactivity,18 investigative learning,19,20 and tactile interactive experiences.21 These added learning designs have been shown to improve both conceptual understanding and student motivation. Rutten et al.22 found that integrating simulations with traditional instruction methods also bolsters student engagement and understanding. Therefore, a simulation-based, interactive, tactile learning activity is likely to enhance student learning outcomes. An MR interface is ideally suited for combining these concepts into a single chemical engineering classroom activity.

table surface is not a touch-screen. The AR system is designed to develop students’ intuitive understandings of rate laws, the role of temperature, and effect of reactor types, without the time and cost investment of traditional lab activities. We apply this MR technology to dynamically simulate a system of chemical reactors in near real-time, with hands-on interaction via physical “totems” that represent the reactors. Students can use these totems and prop “pipes” to connect or disconnect the reactors in the simulation, allowing for investigative, studentdriven exploration of complex chemical systems via tactile manipulation of the system. This creates a literal “hands-on” interaction with the reactors simulated by this system, as opposed to a “point-and-click” touch-screen-based approach. Using MR for this lab activity was designed to capitalize on the advantages of increased safety, increased accessibility, decreased cost, and increased speed of feedback as compared to its real counterparts. Because the reactions are simulated, hazardous materials and reactions with prohibitively expensive components can be explored without risk or excessive financial cost. Students attending schools without access to a wet chemistry lab can still interact with this system. This can also enable lab experiences that are prohibitively expensive, or prohibitively large, for a typical undergraduate curriculum, such as an industrial chemical plant. The MR system described here features tactile interactivity and nurtures collaboration. The large dimensions of this table (see Figure 1) make it difficult for a single student to perform all the necessary actions to interact with the simulation. This design is a deliberate choice. The large size of the table allows it to easily accommodate groups of between three and five students. Combined with the required use of the totems to interact with the simulation, this scale factor induces student

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THE MR SYSTEM The MR system used in this study consists of a glass-topped table, a short-throw projector, props, and a computer. The B



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module.31 The controller application was written with the Electron application framework.34 It displays plots and text, and controls the overall program flow. The kinetics simulation was also written as a Python module. Due to the distributed nature of this software, calculations can be done either locally or remotely with additional computing resources. The software used with this device is freely available in the ur-whitelab GitHub page.35 The computer vision software is capable of recognizing a collection of pretrained objects, which can be given any desired label. Object recognition is achieved via image segmentation and keypoint matching. Image segmentation is done by removing a saved background and applying a series of image filters, followed by contour finding. Opencv’s implementation of the border following algorithm for topological analysis of a binary image, written by Suzuki and Abe,36 is used to find the contours and derive the segmentation. After segmentation, the AKAZE algorithm37 generates a set of keypoints for each segment. Common keypoint “hot spots” are corners, edges, and sharp color or brightness transitions. Once keypoints are generated, they are compared geometrically against the set of keypoints from a list of known, catalogued images. If sufficient keypoints match a catalogued image, the object is flagged as a match. An interpretation of what the camera views is distributed as a mathematical graph along with x,y coordinates to the different parts of the software. Nodes are the recognized objects, and the connections between them are sent as an edge list. Any system that can be represented by a small, planar graph is possible to simulate with this platform. Note that the entire system is dynamic, so that it responds as objects are moved, added or removed, or connected. Users interact with the table entirely through computer vision and object recognition, but the software must be launched and managed via keyboard and mouse through the dashboard. Figure S1 shows a screenshot of the dashboard user interface during a simulation.

cooperation, collaboration, and discussion naturally via a shared space.23 Reaction kinetics and reactor design lie at the very heart of chemical engineering. For a given process, a chemical engineer is required to select the types of reactors, the reactor system configuration, and operating conditions for these reactors that maximize the yield of desired products.24,25 This otherwise impractical MR lab task emulated these real-world challenges. The problem required students to explore and optimize a number of competing factors simultaneously to find the “best” yield. Ill-defined problems such as this one represent the real challenges that face chemical engineers in industry and, thus, need to be included in the curriculum for undergraduate STEM education.26,27 Though this is the reality for which we need to prepare future engineers, higher education often creates scaffolded lab experiences that can remove much of the complexity, leaving the learner with an algorithmic “plug and chug” challenge of ensuring mathematical precision, often relying on computer programming.28 These skills are important for engineers, but algorithmic learning is insufficient to help learners develop systems-level intuition of the variables at play.

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METHODS

Hardware

Figure 1 details the design of the wooden frame of the MR table. The hole in the top is covered with beveled tempered glass with a rear-projection film coating on the underside. The working surface around the glass is painted with a “whiteboard” paint that allows dry-erase markers to be used. The entire system is on casters and can be moved easily. Holes have been drilled throughout the table to allow better airflow. The MR system hardware incorporates a desktop computer within the base of the table. This local computer sends video output to the projector and is used to control the system via direct input (keyboard and mouse) or remote desktop. A webcam is mounted inside the base of the table such that it can capture video of the inward-facing side of the glass. This is how object recognition is achieved. The glass is coated with a rearprojection film, which scatters the light of the projector to display the simulation visuals, and protect viewers from looking directly at the projector bulb. The projector is mounted offcenter on a cross-beam underneath the tabletop and is a shortthrow digital processing projector. A monitor can be attached to the table top to display additional simulation information, such as plots or raw data. For hardware technical specifications, see the Supporting Information (SI).

Lab Activity Design

The chemical simulations must be programmed prior to operation. For each simulation, the software requires a training session, during which the totems are presented to the camera one at a time, and labeled. Labeling a totem involves creating a bounding polygon and associating it with that label. Labeling six totems for this lab activity takes about 15 min. Multiple totems may share labels if desired, but each totem can have only one label. Totems must have visually distinct patterns so the computer vision library can distinguish between them, and track their motion. QR codes, a type of matrix barcodes, have proven to be mutually distinct and well-suited for recognition. After a totem is trained once, it is saved for all future simulations as a JPEG paired with a serialized object file encoding the set of keypoints. This chemical reactor system simulation allows students to dynamically explore staging two types of chemical reactors in a variety of sequences, with an equilibrium-limited two-reactant, two-product first order reaction. There are two types of reactors available for use (plug flow and continuously stirred), but there is no limit on the number of totems that may be trained. For example, a distillation totem could be integrated into the simulation. Unity is equipped to render these reactors and displays pie charts representing mole fractions surrounding the corresponding totems, as well as connections between them.

Software Architecture

Communication is done asynchronously, using the ZeroMQ library (ZMQ)29 as the message passing method, with encoding via Google protocol buffers.30 ZMQ was chosen because it is fast and distributed, which minimizes communication overhead. The asynchronous scheduling is handled through the Python asyncio module.31 Asynchronous behavior is desired because the software is driven by input/ output events from user interactions with the table. The projected visuals are rendered with the Unity engine,32 a game development platform with a flexible structure that allows C# scripting. Projected visuals are generated on the basis of information received from the computer vision software. The computer vision software was implemented with the opensource computer vision library OpenCV33 as a Python C



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Low-light conditions are optimal for both visibility and computer vision accuracy. A room with no lights on and shaded windows is sufficient. The computer vision software requires calibration prior to operation, which takes between five and 20 min, depending on the accuracy desired. This is achieved by finding the homography transform that aligns projected and viewed points with those from a known list of coordinates. During this process, a set of 20 randomly distributed coordinates will be generated, and the projector displays an image at each coordinate in turn. The computer vision software records the coordinates it observes, and then generates a new homography transform from the two sets of points, comparing the goodness of fit at each iteration. Three iterations are typically sufficient for good spatial calibration. Participants may add and remove reactors and connections between them throughout the activity. Pretrained reactors will be recognized, and a Unity object will be projected in the location of the totem. Reactor temperatures can be chosen by adjusting an external programmable USB dial. To connect two reactors, participants can place a straight, narrow prop (such as a 1 inch flat wooden stick), or draw a straight, bold line with 4 dry-erase marker. Connections will persist until a reactor object is removed, at which point the reactor and all connections to it are removed from the display. The simulation will restart whenever the number of connections in the graph changes. The simulation takes only a few seconds to go to completion, but continues to display the final conversion in each reactor until it is reset.

Figure 2. Example final configuration showing interconnected CSTR and PFR reactor totems and the simulation projection. Photo subjects were volunteers unaffiliated with the study.

and investigations, to address the problem. The learning objectives for the activity were to (i) understand the difference between exothermic and endothermic reactions; (ii) determine the effect of changing temperature on forward and backward reactions; (iii) explore regions of temperature where kinetics or thermodynamics limit conversion; (iv) identify which reactor type, PFR or CSTR, maximizes conversion; (v) see the ability of multiple CSTRs to approximate a PFR; and (vi) analyze the difference between parallel and serial configurations of reactors. All participants were asked to individually fill out a written assessment sheet with questions about the activity and relevant course material. Near the end of the task, these written assessments were collected. Following the task, focus groups were conducted with the control and experimental groups separately to explore subjects’ perceptions of their experiences and their understandings of the content. Audio recordings of task interactions, focus groups, and project staff debriefs were recorded and transcribed. Three questions guided the analyses of these data: How do subjects differ in their physical engagement with accessible material tools (i.e., manipulatives, computers, digital interfaces) in a collaborative challenge? How do subjects differ in their procedural engagement with the lab challenge? What evidence of reasoning and understanding exists during the interactions and in the subject-constructed artifacts?

Study Design

To demonstrate the capabilities of this MR system, a lab activity was designed to help undergraduate chemical engineering students better understand reactor engineering and kinetics. The reaction conditions for this activity, other than temperature, were fixed. Students were provided with six reactor totems: three continuously stirred tank reactors (CSTR) and three plug flow reactors (PFR) of equal volume. Students were asked to stage them together in the sequence that is optimal, determine whether the reaction is endothermic or exothermic, and make statements regarding the equilibrium constant, keq, and how it relates to temperature in this system. Complete instructions and the assessment sheet given to participants during the activity are included in the Supporting Information. An accelerated simulation reported final results for the students to analyze. After investigating different configurations, students were asked to interpret why the optimal configuration they found is best. Figure 2 shows an example final configuration after students interacted with the system. Eight participants were recruited for the study. Eligible subjects were students who had successfully completed a junior-level chemical reactor design course and were identified by the instructor as having been competent in the course. On the day of the study, four subjects were randomly assigned to the control and experimental (MR) groups, located in two different rooms. Subjects in each group were asked to work collaboratively to determine an optimal setup for a given chemical reaction under the following constraints: can include up to three of each of two types of reactors (PFR and CSTR), can adjust temperature at 5 K intervals in the range 200−800 K, and must complete the investigation in 45 min. In both groups, subjects needed to use prior knowledge and skills from the chemical reactor design course, combined with reasoning

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RESULTS Figure 3 shows the postactivity assessment scores of individuals in both the MR and control groups. The assessment was designed to gauge student understanding of the effects of temperature, kinetics, thermodynamics, reactor type choice, and configuration of reactors. It consisted of four short-answer questions which are listed in the Supporting Information. Answers were graded according to a rubric designed by instructors of the graduate and undergraduate reaction engineering courses offered by the University of Rochester chemical engineering department. The rubric is given in the Supporting Information. Though this sample size is too low to draw any broad conclusions, note that the MR group’s scores were more consistent and slightly better than those of the control group. This may indicate a higher degree of conceptual understanding generated by the ease of use of D



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Figure 3. Scores on postactivity assessment by both the MR and control groups. Both groups did a postactivity assessment which was used to assess the influence of the MR Table on student engagement in inquiry-based approaches. The maximum possible score was 40 points. Median scores for each group are shown as a horizontal line segment.

the MR table over traditional media, and warrants more study. In the following section, the results of the analysis of the dialogue and behaviors of the two groups are summarized.

Figure 4. Plot of the cycles of inquiry completed by the MR group and control group. Note the different scale in the time axis. The MR group completed 17 full cycles of inquiry and 34 partial cycles of inquiry. See Figure S2 to view partial cycles. The control group conducted four complete cycles of inquiry and six partial cycles of inquiry. One complete cycle of inquiry consists of initiation (task clarification, inquiry proposal, and refinement), exploration (task execution and observations), and interpretation (analysis of outcomes). On average, cycles of inquiry for the control group were longer in duration compared to the MR group. New cycles were coded as starting immediately after the previous ones. Thus, there was negligible “down time” between cycles.

Cycles of Inquiry

The quality and count of “cycles of inquiry” in both the MR and control conditions were identified using definitions developed from literature.38 One full cycle of inquiry included each of three distinct phases: (1) initiation, (2) observation, and (3) interpretation. The transcript and the video were coded collaboratively and then scored to determine the cycles of inquiry. Cycles of inquiry analysis became the frame used to describe physical engagement, procedural engagement, and evidence of reasoning. The scoring methodology for cycles of inquiry afforded measurement of not only the number of inquiry-based trials, but also the quality and the depth of these trials. Overall, the MR experimental group initiated more full cycles of inquiry (consisting of initiation, observation and interpretation) than the control group. The MR group engaged in 17 full cycles of inquiry and 34 additional partial cycles, totaling 51 different explorations. The control group, on the other hand, conducted only 4 full cycles of inquiry and 6 partial cycles, totaling 10 explorations, in the same amount of time (see Figure 4). In terms of the phases of the inquiry, the plots in Figure 4 indicate that both of the groups spent the majority of their time on task initiation. Task initiation included clarifying as well as making, questioning, refining, or reacting to a proposal for an investigation. In addition, initiation sometimes involved usability work, such as getting one’s computer to work (control group) or learning how to use the MR Table (experimental group). The MR group spent a significant amount of time (nearly 110 s) figuring out how to use the table and interpreting the task at the onset of the task (cycle 1) and not as much for the rest of the cycles, while the control group spent a large amount of time on initiation both at the onset (cycle 1, 367 s) and again at later phases of the task (cycle 6, 225 s, and cycle 8, 275 s). A closer examination of the transcript revealed that while the control group did more background knowledge retrieval than the MR group and relied on common procedures to solve the problems, the MR group used that time to familiarize themselves with the technology. Though both groups started the first cycle with usability work, the control group had a longer first cycle (400 s) and moved to the next without finishing it, while the MR group completed theirs (230 s). This

means that the MR group discovered how to use their available tools to solve the problem more quickly than the control group did. Motivations for Cycles of Inquiry

Note: all student names in this document have been changed to ensure conf identiality. All students received the same background information at the time of the task. It was of interest to know if the MR table provided an environment for increased exploration and investigation. It was evaluated whether engaging with the MR table motivated students to take more risks and initiate more inquiry cycles. During the task, were students using classroom knowledge to make decisions, or were students trying something, “just because”? All the instances where students proposed an inquiry were coded and characterized, regardless of whether or not that proposal led to a cycle of inquiry. Thus, the number of proposals and number of cycles differ. The differences between the control and MR groups were compared. The in vivo code “just try” was used when students proposed an inquiry without any justification for it, often using that exact phrase. Examples of “just try” proposals (without verbalized justification) are given in Table 1. The total number of proposals was higher in the MR group (38 in MR and 12 in the control), even though both groups were given the same amount of time to complete the task. In the MR group, students provided no justification (just try) for 17 out of 38 proposals, whereas in the control it was 2 out of 12. This suggests a higher level of exploratory behavior in the MR group. The code “justify” was used when students followed their proposal to begin an inquiry with a verbalized justification or reason. The control group provided justification for 10 out of 12 proposals, while the MR group provided justification for E


Not all proposals correspond to a cycle of inquiry, and not every cycle began with a proposal. b“Just try” indicates a lack of verbalized justification. c“Scholastic” indicates verbalized justification based on class experience. d“Conceptual” indicates verbalized justification based on a deeper understanding of underlying concepts. eFraction of all proposals in each category for the respective group.

Proposal Countse

Proposal Countse MR Group Examples (N = 4 Students)

21 out of 38 proposals. A trend was observed in the control group. In order to justify a trial, students often used phrases like “Cause, typically we did that. Right?” referring to what they did in the relevant course the year prior. The goal of this analysis was to determine to what extent students relied on scholastic practices in the MR and the control group. The “justify” coded statements were further distinguished using two categories: (1) scholastic, where students justified their proposal by recollecting and drawing upon what they did in class, and (2) conceptual reasoning, where students described the scientific rationale for their proposal. As shown in Table 1, the control group appeared to rely on familiar methods used in the classroom as heuristics for problem solving more than the MR group did. Conceptual reasoning was used as the main justification to start an inquiry in both the MR and control groups (20/38 in MR and 7/12 in control). However, the MR group’s justification was scholastic in only 1/38 total proposals, compared to 3/12 total proposals in the control. The three proposals in the control group that were justified in a scholastic manner demonstrated the need to rely on shared experiences from the class and external support to solve the problem. Examples are provided in Table 1. Compared to MR, the control group participants justified a larger fraction of proposals in a scholastic manner, indicating reduced exploratory behavior in the control group. On the basis of this study, using the MR table seemed to decrease reliance on the simplifications introduced in the course for addressing illdefined problems, and allowed for more risk-taking (starting trials without justification). Low-cost investments like those afforded by the MR table invite learners’ exploration. Fear of failure is greatly reduced, because the resource cost (money, time, ego) of running a trial is low. “Failure”, defined as doing the investigation wrong or getting the wrong answer, lacks meaning in the context of cycles of inquiry with the MR Table. Iterations of trials simply provide new and more data with which to think. Martinez and Stager39 who study classroombased maker spaces warn against confusing “iteration with failure when in fact any iterative design cycle is about continuous improvement, keeping what works, and improving what “doesn’t”. They argue, “This is learning, not failure.” Transitions between Cycles of Inquiry

Transitions between cycles of inquiry offer insight into how students were reasoning through the concepts in this activity. The MR group was expected to demonstrate more exploratory behavior and show an overall increase in the total number of cycles of inquiry. In order to gain a deeper understanding of these transitions, the transitions were coded for whether they were made suddenly or randomly, or if they used results from the previous inquiry as reasoning. The extent to which each transition was smooth (follows logically from the previous findings) or abrupt (lacking a clear connection to the cycle that was just completed) was also measured. Compared to the control group, students in the MR group progressed from one cycle of inquiry to another smoothly and had clear connections between cycles. MR group members would often discuss the results of earlier trials as justification for a new setup, or express interest in exploring variations of a configuration: “So now you want to see temperature effect?” (Allyson). Unconnected transitions in the MR group occurred when participants thought they had exhausted a line of reasoning and wanted to explore something new: “I don’t think

a

“Basically we want to get it to the highest point it can actually function at, right?” “Why don’t we go back to CSTR and crank up the heat on each one?” 20/38

7/12 “What if we just said 4 PFR? I feel like parallel never worksor not, works, but it’s never ideal.” “What if we tryno, it doesn’t make sense for the PFR and the CSTR.”

“Okay, with that in mindwe can choose our temperature” “We can choose a temperature that gives us the highest...” “...we wanna find the reaction rate at a lower temperature... If we use this k1 with the lower temperature, we can get the reaction rate, right?” “That’s kind of defining FA0.” “So we need to find a conversion for FA.”

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“We could change it a half second and see what “Cause, we typically did thatRight?” happens.” “I am just gonna plug 2 into that.” “cause we never used that stuff, we were just given...” “...he wants us to look at how to get 99.99% conversion...” 2/12 3/12 ‘I feel like we could just try something and see “That’s like all those temperature profiles we how it goes.” used to have to make in Python.” “Should we add another one and see what it does?” “Sure, why not?” (Example of physical input.) Garvin puts a mug on table: “We will do this.” 17/38 1/38 Control Group Examples (N = 4 Students)

Scholastic “Just Try” Proposals Group

b

Table 1. Comparison of Proposalsa Made by Students During the Pilot Study

c

Proposals with Justification

Conceptuald


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it matters what the firstwhat it is. See, what we could do because we could just do something else” (Garvin). In these cases, even if there are not direct connections between participants’ conclusions and the start of a new trial, their line of reasoning could be followed. More than half of the time, the ending of one cycle transitioned into the justification of a new cycle in the MR group: Genny: Well, why is it better? Allyson: Because it has better conversion. Garvin: Well, I think it is because it is reversible, and the PFR keeps the concentration of the products lower than the CSTR. Allyson: Yeah. That makes sense. So it minimizes the reverse reaction. Garvin: Yeah. Uh huh. Allyson: Cool. Okay. We decided that it is endothermic. (Reading from the task sheet): “Consider the role the temperature profile of various reactants will affect this change in temperature and then the conversion. Why do you think this is the case?” I mean theoretically, if it is endothermic, a higher temperature should be better, but we did it at higher temperatures, and it did not necessarily make any difference. Garvin: We will try it. Let us see. Let us see if anything happens. Crank this up to 500. The beginning of this exchange highlights the discussion that is often present during the interpretation phase of a cycle of inquiry. However, halfway through Allyson’s dialogue, she transitions from a conclusion (interpretation phase) to referencing the task sheet, and then reflecting on what they had done earlier in their interaction. Garvin initiates a new inquiry with his encouragement to try a higher temperature. As is demonstrated in this exchange, the transitions between the cycles in the MR group happen smoothly and naturally as they engage with the table and with one another. In the control group this transition from the interpretation phase of one cycle of inquiry to the initiation phase of a new one appeared to be more abrupt. It is unclear if the basis for transition was not visible because of limits related to the data available or because there was no basis for transition. The control group seemed to have a harder time connecting their conclusions to what they should do next: “I’m just hung up on how this makes any sense. You have a low temperature it’s going to be−react to completion?” (Edgar). The control group’s transitions were more abrupt than the MR group. Two of these transitions included a pause that lasted about 10 s. Harlie: So FASo, All right. let us say FA0 is Ashlea: Are we sure Edgar: I [fading voice]. Harlie: I cannot remember if it is Edgar: I think it was molar, if I remember correctly. That is why Harlie: There was theThey all relate to. [pause 12:26−12:36] Edgar: I’m gonna just type in the PFR. Harlie: Yeah. In comparison with the exchange in the MR group, the control group’s dialogue is more choppy and uneven. As they abruptly transition out of the confusion that they face in the interpretation phase after the pause, Edgar’s proposal to “just

type in the PFR” in the initiation phase of the new cycle appears random.

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CONCLUSION This study demonstrated that MR has the potential to play a positive role in developing students’ conceptual understanding by increasing the opportunities for collaboration and risktaking. The large working space of the MR table gave the students in the experimental condition a common work space that all could see, allowing all to contribute proposals, observations, and interpretations. The assessments also showed slight improvement in the MR group relative to the control group in conceptual understanding, but due to the very small sample size, we cannot draw any definitive conclusions from these results. This warrants further studies of this system and its benefits. The near real-time feedback of the MR table provided students with low-risk opportunities to engage and explore. In the study, the MR group members were heard saying “try” more often than the control group. Because of the low risk related to time and energy use, the participants in the MR group also initiated more cycles of inquiry that usually started with the students saying “try”, which afterward led to more exploration and interpretation activities. Successive exploration and interpretation of this sort improve understanding of the behavior of different types of reactors and build intuition in students, which helps them tackle ill-defined engineering problems. In future iterations of this experiment, we plan to further probe these consequences and how they relate to student understanding of class content. MR can provide a useful replication of traditional undergraduate STEM lab sessions. When combined with simulation, MR can provide collaborative, tactile, inquiry-based learning, and enhance the conceptual understanding of students. This early work supports the idea that the use of MR can lead to the type of collaborative iterative cycles of inquiry important for cementing conceptual understanding, and that MR leads to better student engagement and risk-taking. Further study of this system with larger groups of students is warranted in order to fully comprehend the impact and benefits of such MR technology in the undergraduate curriculum.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.8b00212. Task instructions, assessment questions, Figures S1 and S2, materials list, and list of affiliated source code links

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(PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Rainier Barrett: 0000-0002-5728-9074 Andrew D. White: 0000-0002-6647-3965 Notes

The authors declare no competing financial interest. G



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(17) Cakmakci, G. Identifying Alternative Conceptions of Chemical Kinetics among Secondary School and Undergraduate Students in Turkey. J. Chem. Educ. 2010, 87, 449−455. (18) Stieff, M.; Wilensky, U. Connected Chemistry: Incorporating Interactive Simulations into the Chemistry Classroom. J. Sci. Educ. Technol. 2003, 12, 285−302. (19) Chairam, S.; Somsook, E.; Coll, R. K. Enhancing Thai Students’ Learning of Chemical Kinetics. Res. Sci. Technol. Educ. 2009, 27, 95− 115. (20) 4th International Conference on New Horizons in Education. Bayram, Z.; Oskay, Ö . Ö .; Erdem, E.; Ö zgür, S. D.; Ş en, Ş . Effect of Inquiry Based Learning Method on Students Motivation. Procedia Soc. Behav. Sci. 2013, 106, 988−996. (21) Yoon, S. A.; Elinich, K.; Wang, J.; Steinmeier, C.; Tucker, S. Using Augmented Reality and Knowledge-Building Scaffolds to Improve Learning in a Science Museum. Int. J. Comput. Supported Collab. Learn. 2012, 7, 519−541. (22) Rutten, N.; van Joolingen, W. R.; van der Veen, J. T. The Learning Effects of Computer Simulations in Science Education. Comput. Educ. 2012, 58, 136−153. (23) Scott, S. D.; Carpendale, S.; Inkpen, K. M. Territoriality in collaborative tabletop workspaces. Proc. 2004 ACM Conf. Comput. Support. Coop. Work - CSCW ’04. New York; New York, 2004; p 294. (24) Hill, C.; Root, T. Introduction to Chemical Engineering Kinetics and Reactor Design; Wiley: Hoboken, NJ, 2014. (25) Fogler, H.S.; Gurmen, M.N. CRE−Chapter OneIndustial Reactors; 2007. http://umich.edu/~essen/html/01chap/html/ industrial.htm (accessed August 18, 2018). (26) Mills, J.; Treagust, D. Engineering Education, Is Problem-Based or Project-Based Learning the Answer. Aust J. Eng. Educ 2003, 3, 2− 16. (27) Ogilvie, C. A. Changes in students’ problem-solving strategies in a course that includes context-rich, multifaceted problems. Phys. Rev. Spec. Top. - Phys. Educ. Res. 2009, 5, 020102. (28) Douglas, E. P.; Koro-Ljungberg, M.; McNeill, N. J.; Malcolm, Z. T.; Therriault, D. J. Moving beyond formulas and fixations: solving open-ended engineering problems. Eur. J. Eng. Educ. 2012, 37, 627− 651. (29) Hintjens, P. ZeroMQ: The Guide; 2010. http://zeromq.org (accessed July 2018). (30) Google. Protocol Buffers. 2017. https://developers.google.com/ protocol-buffers/ (accessed July 2018). (31) Sanner, M. F. Python: A Programming Language for Software Integration and Development. J. Mol. Graph Model 1999, 17, 57−61. (32) Unity Technologies. Unity Game Engine; 2017. http://unity3d. com (accessed July 2018). (33) Itseez. Open Source Computer Vision Library; 2015. https:// github.com/itseez/opencv (accessed July 2018). (34) GitHub Inc. Electron Software Framework; 2017. https:// electron.atom.io/ (accessed July 2018). (35) White, A. D.; Barrett, R. UR Whitelab Github Page; 2018. https://github.com/ur-whitelab (accessed July 2018). (36) Suzuki, S.; Abe, K. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing 1985, 30, 32−46. (37) Alcantarilla, P. F.; Nuevo, J.; Bartoli, A. Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Proc. Brit. Mach. Vis. Conf. Bristol, UK; 2013; pp 1−11. (38) Pedaste, M.; Mäeots, M.; Siiman, L.; De Jong, T.; Van Riesen, S. A. N.; Kamp, E. T.; Zacharia, Z. C.; Manoli, C. C.; Tsourlidaki, E. Phases of inquiry-based learning: definitions and the inquiry cycle Phases of inquiry-based learning: definitions and the inquiry cycle Phases of inquiry-based learning: Definitions and the inquiry cycle. Educ. Res. Rev. 2015, 14, 47−61. (39) Martinez, S. L.; Stager, G. Invent To Learn: Making, Tinkering, and Engineering in the Classroom; Constructing Modern Knowledge Press: Torrance, CA, 2013.

ACKNOWLEDGMENTS The authors would like to thank Dr. Marc Porosoff and Dr. Wyatt Tenhaeff for their assistance in design of the pilot study lab activity, and recruitment of student participants. They would also like to thank Bob Marcotte for providing photography services, Jim Alkins for facilitating the construction of the hardware and assisting with technical design, Ric Burrell for assistance in projector design and selection, and Hilary Mogul for contributions to the computer vision source code. This work was supported by a pilot funding award from Arts, Sciences, and Engineering at the University of Rochester.

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REFERENCES

(1) Hofstein, A.; Lunetta, V. N. The Laboratory in Science Education: Foundations for the Twenty-First Century. Sci. Educ. 2004, 88, 28−54. (2) Heise, D. Asserting the inherent benefits of hands-on laboratory projects vs. computer simulations. Journal of Computing Sciences in Colleges 2006, 21, 104−110. (3) Milgram, P.; Takemura, H.; Utsumi, A.; Kishino, F. Augmented reality: a class of displays on the reality-virtuality continuum. In Proceedings of SPIE 2351, Telemanipulator and Telepresence Technologies; SPIE, 1995; pp 282−292. (4) Azuma, R. T. A Survey of Augmented Reality. Presence Teleoperators Virtual Environ 1997, 6, 355−385. (5) Azuma, R.; Baillot, Y.; Behringer, R.; Feiner, S.; Julier, S.; MacIntyre, B. Recent advances in augmented reality. IEEE Comput. Graph. Appl. 2001, 21, 34−47. (6) Billinghurst, M.; Clark, A.; Lee, G. A Survey of Augmented Reality. FNT in Human−Computer Interaction 2015, 8, 73−272. (7) Martin-Gutierrez, J.; Fabiani, P.; Benesova, W.; Meneses, M. D.; Mora, C. E. Augmented reality to promote collaborative and autonomous learning in higher education. Computers in Human Behavior 2015, 51, 752−761. (8) Tanner, E.; Savadatti, S.; Manning, B.; Johnsen, K. Usability and cognitive benefits of a mobile tracked display in virtual laboratories for engineering education. 2016 IEEE Symposium on 3D User Interfaces, 3DUI 2016Proceedings; IEEE, 2016; pp 269−270. (9) Woods, T. L.; Reed, S.; Hsi, S.; Woods, J. A.; Woods, M. R. Pilot Study Using the Augmented Reality Sandbox to Teach Topographic Maps and Surficial Processes in Introductory Geology Labs. Journal of Geoscience Education 2016, 64, 199−214. (10) Yoon, S. A.; Wang, J. Making the Invisible Visible in Science Museums Through Augmented Reality Devices. TechTrends 2014, 58, 49−55. (11) Yoon, S. A.; Elinich, K.; Wang, J.; Steinmeier, C.; Tucker, S. Using augmented reality and knowledge-building scaffolds to improve learning in a science museum. International Journal of ComputerSupported Collaborative Learning 2012, 7, 519−541. (12) Adair, D.; Bakenov, Z.; Jaeger, M. Building on a traditional chemical engineering curriculum using computational fluid dynamics. Education for Chemical Engineers 2014, 9, e85−e93. (13) Tee, N. Y. K.; Gan, H. S.; Li, J.; Cheong, B. H.-P.; Tan, H. Y.; Liew, O. W.; Ng, T. W. Developing and Demonstrating an Augmented Reality Colorimetric Titration Tool. J. Chem. Educ. 2018, 95, 393−399. (14) Gan, H. S.; Tee, N. Y. K.; Bin Mamtaz, M. R.; Xiao, K.; Cheong, B. H.-P.; Liew, O. W.; Ng, T. W. Augmented reality experimentation on oxygen gas generation from hydrogen peroxide and bleach reaction. Biochem. Mol. Biol. Educ. 2018, 46, 245. (15) Van Driel, J. H. Students’ Corpuscular Conceptions in the Context of Chemical Equilibrium and Chemical Kinetics. Chem. Educ. Res. Pract. 2002, 3, 201−213. (16) Banerjee, A. C. Misconceptions of Students and Teachers in Chemical Equilibrium. Int. J. Sci. Educ. 1991, 13, 487−494. H


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