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Engineering Design Thinking: High School Students' Performance and Knowledge

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Title: Engineering Design Thinking: High School Students' Performance and Knowledge
Language: English
Authors: Mentzer, Nathan, Becker, Kurt, Sutton, Mathias
Source: Journal of Engineering Education. Oct 2015 104(4):417-432.
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: 16
Publication Date: 2015
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: DRL0918621
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Engineering Education, Thinking Skills, Design, Problem Solving, Teaching Methods, Instructional Effectiveness, Expertise, Technical Occupations, High School Freshmen, High School Seniors
DOI: 10.1002/jee.20105
ISSN: 1069-4730
Abstract: Background: Because design is recognized as a critical element of engineering thinking, it is crucial for educators to utilize the most effective methods to teach engineering problem solving. Results from this study of students' thinking process may shape future teaching methods. Purpose: This article explores the differences in design processes between high school engineering students and expert engineers. It also examines the differences between high school freshmen who have taken one engineering course and seniors who have taken a series of engineering courses. Design/Method: Fifty-nine high school students from four states were asked to think aloud in a three-hour design task that was audio and video recorded. Verbal reports from the audio and video became source data for protocol analysis. Results from previous studies provided expert design performance data for comparisons. Results: Students and experts alike spent a large portion of their time modeling. Students spent significantly less time in the process of information gathering than experts. Freshmen spent significantly less time in the idea generation process than seniors and experts. Freshmen and seniors spent significantly less time determining the feasibility of their ideas, evaluating alternative ideas, and decision making than experts. Conclusions: High school students engage in design thinking with little understanding of the problem from the client's perspective. Students tend to become fixated on a single solution rather than comparing alternatives. By encouraging development of alternative solutions, K-12 engineering education could foster opportunities to critically evaluate students' design solutions.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1255356
Database: ERIC
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  Value: <anid>AN0110525496;6m401oct.15;2018Jun29.11:58;v2.2.500</anid> <title id="AN0110525496-1">Engineering Design Thinking: High School Students' Performance and Knowledge. </title> <p>Background: Because design is recognized as a critical element of engineering thinking, it is crucial for educators to utilize the most effective methods to teach engineering problem solving. Results from this study of students' thinking process may shape future teaching methods. Purpose: This article explores the differences in design processes between high school engineering students and expert engineers. It also examines the differences between high school freshmen who have taken one engineering course and seniors who have taken a series of engineering courses. Design/Method: Fifty‐nine high school students from four states were asked to think aloud in a three‐hour design task that was audio and video recorded. Verbal reports from the audio and video became source data for protocol analysis. Results from previous studies provided expert design performance data for comparisons. Results: Students and experts alike spent a large portion of their time modeling. Students spent significantly less time in the process of information gathering than experts. Freshmen spent significantly less time in the idea generation process than seniors and experts. Freshmen and seniors spent significantly less time determining the feasibility of their ideas, evaluating alternative ideas, and decision making than experts. Conclusions: High school students engage in design thinking with little understanding of the problem from the client's perspective. Students tend to become fixated on a single solution rather than comparing alternatives. By encouraging development of alternative solutions, K‐12 engineering education could foster opportunities to critically evaluate students' design solutions.</p> <p>engineering design; high school; engineering education</p> <p>One of the primary goals of engineering design education is to equip students with the capability of using engineering design to solve problems. To develop this capability, educators should have access to a detailed knowledge of the cognitive processes of students in science, technology, engineering, and mathematics (STEM) and of expert engineering designers. As students progress through a sequence of courses thematically linked to engineering content with design as a fundamental concept, design cognition should be on a trajectory towards design cognition exhibited by professional experts. Our research attempted to characterize this evolution in engineering learning so that we may begin to identify potentially novel pathways to approach learning from the novice to the expert in engineering education.</p> <p>According to the National Center for Technological Literacy (NCTL, [<reflink idref="bib30" id="ref1">30</reflink>] ), “While most people spend 95% of their time interacting with the technologies of the human‐made world, few know these products are made through engineering, the missing link that connects science and math with innovation” (NCTL, [<reflink idref="bib30" id="ref2">30</reflink>] , “The Missing Piece”). The Center also suggested that “The key to educating students to thrive in this competitive global economy is introducing them early to the engineering design skills and concepts that will engage them in applying their math and science knowledge to solve real problems” (NCTL, [<reflink idref="bib30" id="ref3">30</reflink>] , para. 3). A better understanding of engineering and its relationship to society can benefit all citizens, even though few will pursue engineering as a career (National Academy of Engineering, [<reflink idref="bib29" id="ref4">29</reflink>] ). Engineering employs principles of mathematics and science to create technologies, and thus serves to increase STEM literacy and workforce readiness by intensifying and diversifying student participation in STEM learning experiences.</p> <p>Efforts are currently in place to develop an understanding of engineering among high school students through formal and informal educational experiences. Developing students' understanding of engineering design is aligned with the Standards for Technological Literacy Standard 9 (International Technology and Engineering Educators Association, [<reflink idref="bib14" id="ref5">14</reflink>] ). The Next Generation Science Standards were created in an effort to develop K‐12 science standards rich in content and practice, and coherent across disciplines. The Standards indicated that engineering must be a fundamental part of science education, since it is expected that students can carry and transfer knowledge across science disciplines through modeling, planning, conducting investigations, analyzing and interpreting data, and constructing explanations to demonstrate understanding core ideas of science (NGSS Lead States, [<reflink idref="bib33" id="ref6">33</reflink>] ). Science performance expectation HS‐ETS1 is Engineering Design (NGSS Lead States, [<reflink idref="bib33" id="ref7">33</reflink>] ) and addresses the relationship between science and technology.</p> <p>Design is a critical element of engineering thinking that differentiates engineering from other problem‐solving approaches (Dym, [<reflink idref="bib10" id="ref8">10</reflink>] ; Dym, Agogino, Eris, Frey, & Leifer, [<reflink idref="bib11" id="ref9">11</reflink>] ). A primary goal of engineering design education is to equip students to think more like expert designers. Studies in other domains have shown significant differences between novice and expert cognitive processes. In engineering, there is a gap between the skills developed in universities and the skills needed in industry (Jonassen, Strobel, & Lee, [<reflink idref="bib15" id="ref10">15</reflink>] ; Patil, [<reflink idref="bib34" id="ref11">34</reflink>] ). To develop expert‐like design behavior in students, educators must understand the cognitive processes of both undergraduate students and expert design engineers. However, little is known about the design processes used by high school students, since most studies are either of college‐level student engineers or of professional engineers. The purpose of our study was to produce evidence on the differences in design processes and design stages between high school students and experts. As a result, this article may help lay the foundation to narrow these differences and improve design curriculum.</p> <hd id="AN0110525496-2">Background</hd> <p>Design thinking is a creative way of problem solving (Vande Zande, [<reflink idref="bib38" id="ref12">38</reflink>] ). It promotes development of diverse ideas, which are essential for innovation (Staw, [<reflink idref="bib36" id="ref13">36</reflink>] ). Studies show that teaching design thinking improves students' ability to learn core subjects, fosters social skills (Goldman, [<reflink idref="bib13" id="ref14">13</reflink>] ; Kolodner et al., [<reflink idref="bib21" id="ref15">21</reflink>] ), encourages students' metacognition, and teaches younger students how to work in groups (Carroll et al., [<reflink idref="bib6" id="ref16">6</reflink>] ).</p> <p>The Carnegie Foundation for the Advancement of Teaching conducted a series of studies led by Sheppard, Macatangay, Colby, and Sullivan that focused on educating engineers. The study by Sheppard et al. ([<reflink idref="bib35" id="ref17">35</reflink>] ) identified reflective judgment as an appropriate framework for understanding the cognitive development of design thinking: “As individuals develop mature reflective judgment, their epistemological assumptions and their ability to evaluate knowledge claims and evidence and to justify their claims and beliefs change” (p. 25).</p> <p>King and Kitchener ([<reflink idref="bib18" id="ref18">18</reflink>] ) have identified three clusters of reflective thinking: pre‐reflective thinking, quasi‐reflective thinking, and reflective thinking. The quasi‐reflective cluster of development is characterized by people recognizing that some problems are ill‐structured and that uncertainty requires judgment about the design problem. Individuals in the pre‐reflective thinking cluster perceive knowledge to be certain and its sources are authority or direct experience. Results of a 10‐year longitudinal study of reflective judgment suggest that juniors in high school are in a pre‐reflective cluster of cognitive development while college juniors tend to be nearing a quasi‐reflective cluster (King, [<reflink idref="bib17" id="ref19">17</reflink>] ; Kitchener, [<reflink idref="bib19" id="ref20">19</reflink>] ; Kitchener & King, [<reflink idref="bib20" id="ref21">20</reflink>] ). Design thinking studies conducted at the college level might yield different results because of the advanced cognitive development of college students as compared with high school students. These developmental differences in cognitive approach to design thinking suggest that high school student performance may differ from college student and expert performance.</p> <p>The Center for Engineering Learning and Teaching at the University of Washington has extensively explored design thinking and the cognitive processes of college engineering students (Atman, Chimka, Bursic, & Nachtmann, [<reflink idref="bib2" id="ref22">2</reflink>] ; Atman, Kilgore, & McKenna, [<reflink idref="bib3" id="ref23">3</reflink>] ; Morozov, Yasuhara, Kilgore, & Atman, [<reflink idref="bib26" id="ref24">26</reflink>] ; Mosborg et al., [<reflink idref="bib27" id="ref25">27</reflink>] ; Mosborg et al., [<reflink idref="bib28" id="ref26">28</reflink>] ). Findings in the past decade from this research rely on a variety of studies of freshmen and seniors from multiple major universities and practicing expert engineers in the field. Design problems used in the Center's studies were ill‐structured and open‐ended. Such ill‐structured and open‐ended design problems have many potential solution paths that assist researchers to better understand the cognitive behavior of the designers.</p> <p>The playground design task selected for our study has been used in the Center's studies and can be traced to Dally and Zhang ([<reflink idref="bib9" id="ref27">9</reflink>] ), who identified the need for project‐driven approaches in the freshman engineering design course. Dally and Zhang used this approach to increase student performance and retention, and to situate student learning of abstract concepts through real‐world applications in an experiential activity. Atman et al. ([<reflink idref="bib2" id="ref28">2</reflink>] ) revised the work of Dally and Zhang to create a playground design task where university engineering students were presented with a brief playground design task and access to background information upon request. Participants were provided with a maximum of three hours to develop a solution to the problem while thinking aloud. Mosborg et al. ([<reflink idref="bib27" id="ref29">27</reflink>] ) applied the playground design task using the think aloud research protocol to 19 practicing engineers who were identified as experts in the field. Mosborg et al. ([<reflink idref="bib28" id="ref30">28</reflink>] ) compared groups of freshman and senior college‐level engineering students with practicing engineers using data previously collected on the playground design task. Using data from previous studies, Atman et al. ([<reflink idref="bib3" id="ref31">3</reflink>] ) analyzed data that focused on the language of design and its relationship to design thinking as a mediator and how this internalization of design thinking relates to language acquisition. Atman et al. provided a well‐developed design task and results for comparisons between the high school student data and experts. For more details on the design task, see Atman et al. ([<reflink idref="bib2" id="ref32">2</reflink>] , [<reflink idref="bib3" id="ref33">3</reflink>] ).</p> <p>A conceptual theme of the Center's research was that performance can be placed on a continuum from novice to expert. Expert performance represents a target for a novice's development. One goal of education is to improve novice performance such that it more closely resembles expert design thinking. The goal of our study was to extend the novice–expert continuum to include high school students.</p> <hd id="AN0110525496-3">Method</hd> <hd id="AN0110525496-4">Research Design</hd> <p>In this descriptive study, we collected data without manipulating the environment to provide information about design processes of freshmen and senior high school students. Two research questions drove this inquiry:</p> <p>How do high school students who have completed a series of engineering courses compare to experts in the design process?</p> <p>How does high school student participation in a multiyear sequence of engineering courses affect their design process?</p> <p>Data were collected from a sample of 59 high school students during a span of one year. The students, who were engaged in an articulated sequence of engineering design courses, were identified from four participating high schools across the United States. Data were gathered from 30 student participants entering the sequence of courses (mainly freshmen), and in the second phase of data collection, data were gathered from 29 students exiting the sequence of courses (mainly seniors). Our study adds to design‐thinking research by extending the definition of a novice to include high school students. When educators understand the differences between high school student performance and expert performance, they can develop methods to help students progress toward expert performance.</p> <hd id="AN0110525496-5">Sample</hd> <p>A power analysis was conducted to determine required sample size. Power analysis is used to determine a degree of confidence for a specified sample. Effect sizes were calculated with data presented in previous research (Mosborg et al., [<reflink idref="bib28" id="ref34">28</reflink>] ). A power of 0.80 is considered desirable (Cohen, [<reflink idref="bib8" id="ref35">8</reflink>] ) using a two‐tailed alpha = 0.05, and is typical in educational studies such as this one. Results of the power analysis indicated that a minimum sample size of approximately 25 students would be appropriate, assuming effect sizes of the comparisons of college freshmen to expert were similar to those of high school students to experts. We decided to target 30 students in each group to account for potential mortality or data loss.</p> <p>The sample chosen for our research project specifically targeted exemplary high schools with recognized efforts in engineering design programs. Schools were identified by a collaboration of university faculty at four institutions. Four institutions were identified according to their research and engagement work with K‐12 engineering education. Faculty at each institution recommended a high school that met the sampling criteria. The criterion was an established program of study that included a sequence of courses developed in association with an engineering outreach effort as part of a university program. The teachers targeted were “technology teachers with a good understanding of science and the interactions between technology, science, and society” (National Research Council, [<reflink idref="bib31" id="ref36">31</reflink>] , p. 108). School selection included a nationally representative sample to facilitate generalizability. The high school teachers who participated in this research were principally housed in technology education programs and had experience teaching engineering design, math, and science. They were not practicing engineers nor had they been engineering faculty in higher education. Students were recruited at each of the schools and recommended by their instructors as being students who were highly engaged in the engineering program. School and community‐level demographic data are presented in Tables [NaN] and [NaN] .</p> <p>School Demographics</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="left" /><th align="center">Gender (percent)</th><th align="center">Ethnicity (percent)</th></tr><tr valign="bottom"><th align="left">School</th><th align="center">Enrollment</th><th align="center">Female</th><th align="center">Male</th><th align="center">African American</th><th align="center">American Indian</th><th align="center">Asian</th><th align="center">Caucasian</th><th align="center">Hispanic</th></tr><tr valign="top"><td align="left">1</td><td align="char" char=".">1136</td><td align="char" char=".">45</td><td align="char" char=".">55</td><td align="char" char=".">2</td><td align="char" char=".">1</td><td align="char" char=".">3</td><td align="char" char=".">65</td><td align="char" char=".">30</td></tr><tr valign="top"><td align="left">2</td><td align="char" char=".">216</td><td align="char" char=".">54</td><td align="char" char=".">46</td><td align="char" char=".">1</td><td align="char" char=".">1</td><td align="char" char=".">1</td><td align="char" char=".">76</td><td align="char" char=".">20</td></tr><tr valign="top"><td align="left">3</td><td align="char" char=".">1833</td><td align="char" char=".">47</td><td align="char" char=".">53</td><td align="char" char=".">4</td><td align="char" char=".">1</td><td align="char" char=".">1</td><td align="char" char=".">86</td><td align="char" char=".">7</td></tr><tr valign="top"><td align="left">4</td><td align="char" char=".">874</td><td align="char" char=".">55</td><td align="char" char=".">45</td><td align="char" char=".">96</td><td align="char" char=".">0</td><td align="char" char=".">1</td><td align="char" char=".">1</td><td align="char" char=".">2</td></tr></table> </ephtml> </p> <p>1 Note. Data from National Center for Education Statistics.</p> <p>Community Demographics by School</p> <p> <ephtml> <table><tr valign="bottom"><th align="left">School</th><th align="center">Community population</th><th align="center">Median household income (U.S. dollars)</th><th align="center">Ethnicity (percent)</th></tr><tr valign="bottom"><th align="center">African American</th><th align="center">American Indian</th><th align="center">Asian</th><th align="center">Caucasian</th><th align="center">Hispanic</th></tr><tr valign="top"><td align="left">1</td><td align="char" char=".">91,000</td><td align="char" char=".">45,000</td><td align="char" char=".">1.2</td><td align="char" char=".">0.5</td><td align="char" char=".">4.0</td><td align="char" char=".">88.3</td><td align="char" char=".">8.2</td></tr><tr valign="top"><td align="left">2</td><td align="char" char=".">78,000</td><td align="char" char=".">34,000</td><td align="char" char=".">2.3</td><td align="char" char=".">1.2</td><td align="char" char=".">1.4</td><td align="char" char=".">79.0</td><td align="char" char=".">23.6</td></tr><tr valign="top"><td align="left">3</td><td align="char" char=".">61,000</td><td align="char" char=".">36,000</td><td align="char" char=".">3.2</td><td align="char" char=".">0.4</td><td align="char" char=".">1.2</td><td align="char" char=".">88.9</td><td align="char" char=".">9.1</td></tr><tr valign="top"><td align="left">4</td><td align="char" char=".">>500,000</td><td align="char" char=".">59,000</td><td align="char" char=".">54.0</td><td align="char" char=".">0.4</td><td align="char" char=".">3.2</td><td align="char" char=".">40.6</td><td align="char" char=".">8.8</td></tr></table> </ephtml> </p> <p>2 Note. Data from United States Census Bureau.</p> <p>We held meetings with the identified high school engineering and technology education teachers to create an understanding of the goals of the study and their role of providing us with access to the school facilities and students. Once the teachers were familiar with the study, we made classroom visits to begin the recruitment process. During each classroom visit, we described the purpose of the study along with expectations of the students if they volunteered to be participants.</p> <p>Students interested in participating were provided a recruitment packet that included a letter to parents with a description of the study, a parental consent form, a participant information form, a student assent form, and a demographic data sheet. Fifteen students from each participating school were selected, and times were established for data collection. All data for each student were collected in one sitting. In most situations, students were able to complete the design task after school hours or on weekends. Due to the logistics of data collection, efforts spanned several days; this constraint presented the risk that a student who finished the design task could talk with another who had not yet started the task. To minimize this complication in the study, students were asked not to discuss the session with peers until all the data had been collected from that school.</p> <p>Students were placed into two target groups, freshmen and seniors, to represent engineering program beginners and completers. Engineering courses were self‐reported and included courses such as Engineering in a Global Society, Introduction to Engineering, Principles of Engineering, Engineering Graphics, Drafting, Exploring Technology, Freshmen Pre‐Engineering, Digital Electronics, Civil Engineering, Civil Engineering and Architecture, Aerospace Engineering, Computer Integrated Engineering, Biomedical Engineering, Engineering Projects in Service Learning, Robotics, Design Technology, Senior Design in Engineering, and Engineering Design and Development. Since data were collected from four schools across the country, courses taken by students varied from location to location without standardization at the course level. Most freshmen were in their first engineering‐related course when they participated in this study. Twenty‐one of the 30 freshman responded and reported enrolling in an average of 1.4 courses per student. Most students were recruited from the course they were taking and therefore had not yet completed the course. Twenty‐nine of the 30 seniors responded and reported taking an average of 4.1 courses per student. Participant demographic data are shown in Table [NaN] .</p> <p>Participant Demographics</p> <p> <ephtml> <table><tr valign="bottom"><th align="left">Self‐reported identity</th><th align="center">Male</th><th align="center">Female</th></tr><tr valign="top"><td align="left">Asian</td><td align="char" char=".">2</td><td align="char" char=".">1</td></tr><tr valign="top"><td align="left">Black or African American</td><td align="char" char=".">12</td><td align="char" char=".">0</td></tr><tr valign="top"><td align="left">Hispanic or Latino</td><td align="char" char=".">2</td><td align="char" char=".">3</td></tr><tr valign="top"><td align="left">White</td><td align="char" char=".">25</td><td align="char" char=".">7</td></tr><tr valign="top"><td align="left">More than one race</td><td align="char" char=".">4</td><td align="char" char=".">2</td></tr><tr valign="top"><td align="left">Other or unknown</td><td align="char" char=".">2</td><td align="char" char=".">0</td></tr></table> </ephtml> </p> <hd id="AN0110525496-6">Data Collection</hd> <p>The design task presented to students in our study was similar to that used in previous research (Atman et al., [<reflink idref="bib2" id="ref37">2</reflink>] , [<reflink idref="bib1" id="ref38">1</reflink>] , [<reflink idref="bib3" id="ref39">3</reflink>] ). The design task consisted of four paragraphs of instructions and is summarized here, along with a sample piece of information shown in Figure [NaN] :</p> <p>You live in a mid‐size city. A local resident has recently donated a corner lot for a playground. Since you are an engineer who lives in the neighborhood, you have been asked by the city to design a playground. Any equipment you design must be safe for the children, remain outside all year long, not cost too much, and comply with the Americans with Disabilities Act. The neighborhood does not have the time or money to buy ready‐made pieces of equipment. Your design should use materials that are available at any hardware or lumber store. The playground must be ready for use in 2 months.</p> <p>The playground design task is an effective way to demonstrate design thinking by students because it is an open‐ended, realistic, accessible, and complex problem (Mosborg et al., [<reflink idref="bib28" id="ref40">28</reflink>] ). This design task did not require domain‐specific knowledge, such as electrical, biological, or mechanical engineering, and therefore, is accessible to many student participants with a variety of backgrounds and experiences (Mosborg et al., [<reflink idref="bib28" id="ref41">28</reflink>] ). Playgrounds are familiar to students because they are common to many neighborhoods.</p> <p>Before administrating the design task, we set up equipment for the data collection in an empty classroom. First, we arranged a table to create a workspace for the students. The table had enough space to make the student comfortable while working through the design task. A calculator, ruler, a small note pad, graph paper, white 8.5″ × 11″ paper, pencil, highlighter, sticky notes, and a piece of paper identifying the design task were placed on the table before the student entered the room. An audio and video recorder captured the student working. The researcher made a judgment about the student's voice projection. Quieter students were asked to wear a lapel microphone. The recordings showed what students were looking at, reading, or writing. The documents used in administering the design task were colored to help differentiate between information (blue), problem definition (yellow), and student work (white).</p> <p>A member of the research team served as an administrator and provided the student with the design task. The design task included a description of the problem, constraints, and the method students could use to access information. Upon specific request, the administrator provided various documents containing information. Using a simple chart, the administrator made a note of what information was requested by the students, as well as the specific time the information was requested. The student was given three hours to complete the task, although most students finished before the administrator stopped the session.</p> <p>In the playground design task, students played the role of an engineer assigned to design a playground on a donated city block. The constraints included limited budget, child safety consideration, and compliance with laws or zoning. The student was also able to query the research administrator for additional specific information, for example, the lot layout, cost of materials, or neighborhood demographics. The design task required students to present a written proposal describing their design. Limitations of this design task included the lack of opportunity for students to investigate the need for a solution; it was directly presented to them. Students did not have an opportunity to construct physical models or prototypes (although one student did use folded and torn post‐it notes). Students were aware that implementation of the design task would not occur and their designs would not be built.</p> <p>During the design task, the administrator was responsible for ensuring that students were continuously thinking aloud. It was imperative for the students to verbalize their thoughts while simultaneously working through the problem. The administrator kept students talking by prompting them, using phrases such as “keep talking,” “what are you thinking,” “what are you doing,” and “what are you drawing.” The administrator continued the think‐aloud process until the students indicated they were finished (or the three‐hour session had expired). Once the students had completed a design that satisfied the problem, the administrator thanked them for participating in the study. Students were compensated for their time with a $40 check.</p> <hd id="AN0110525496-7">Data Coding</hd> <p>The playground design task coding scheme was adopted from prior studies (Atman et al., [<reflink idref="bib2" id="ref42">2</reflink>] ; Mosborg et al., [<reflink idref="bib27" id="ref43">27</reflink>] ; Mosborg et al., [<reflink idref="bib28" id="ref44">28</reflink>] ). The data were coded according to time spent in eight design processes and “other” (which accounted for time spent off‐task) as presented by Mosborg et al. ([<reflink idref="bib28" id="ref45">28</reflink>] , p. 15):</p> <p>problem definition: defining what the problem really is</p> <p>information gathering: searching for and collecting information needed to solve the problem</p> <p>idea generation: thinking up potential solutions (or parts of potential solution) to the problem</p> <p>modeling: detailing how to build the solution (or parts of the solution) to the problem</p> <p>feasibility: assessing and passing judgment on a possible or planned solution to the problem</p> <p>evaluation: comparing and contrasting two (or more) solutions to the problem on a particular dimension (or set of dimensions), such as strength or cost</p> <p>decision making: selecting one idea or solution to the problem after actively considering two or more options</p> <p>communication: communicating elements of the design in writing or with oral reports, to parties such as contractors and the community</p> <p>other: none of the above codes apply</p> <p>In addition to measuring time in the eight categories, time was calculated in three design stages. The problem‐scoping stage was a sum of problem definition and gathering information; the developing alternative solutions stage was the sum of idea generation, modeling, and feasibility and evaluation; and the project realization stage was the sum of decision making and communication.</p> <p>Two graduate student researchers were trained in the coding methods using documents shared by the University of Washington. While the coding scheme was consistent with previous studies, our technique was slightly different. Previous studies used transcription, segmenting, and coding as three separate activities in the analysis process (Atman et al., [<reflink idref="bib2" id="ref46">2</reflink>] ). Our study bypassed transcription by using NVIVO software, which presented coding analysts with synchronized video and audio feed. Codes were associated with the timeline on the audio and video tracks (rather than transcripts). Verbal reports captured in the audio and video record were collected to analyze students' utterances (Lammi & Gero, [<reflink idref="bib22" id="ref47">22</reflink>] ). The utterances became the source data for protocol analysis. Consistent with previous literature on protocol analysis (Ericsson & Simon, [<reflink idref="bib12" id="ref48">12</reflink>] ), data were first segmented and then coded as two separate activities rather than being segmented during the coding process. Segments were created by identifying breaks or pauses in student talk, which signaled potential transitions between two thoughts. The pause itself was typically associated with the utterance that immediately preceded it, because the new segment began with the start of the next verbalization. Two students coded the data to permit interrater reliability comparisons, which were computed with Cohen's kappa (Cohen, [<reflink idref="bib8" id="ref49">8</reflink>] ).</p> <hd id="AN0110525496-8">Data Analysis</hd> <p>Analysis was conducted in two phases. First, comparisons were drawn between experts and high school seniors, and second, between high school seniors and high school freshmen. Prior to analysis, data distributions were tested, and it was determined that they were significantly different from normal distribution. Nonparametric testing is appropriate for data that violate the assumptions of normality, and these data were tested with a significance level of 0.05. Expert data from previous research were published in Atman et al. ([<reflink idref="bib1" id="ref50">1</reflink>] ) as mean data. We contacted the Atman research team, and they provided median data appropriate for nonparametric comparison testing. The Wilcoxon signed‐rank test is comparable to the one‐sample t‐test and, due to the non‐normal data distribution, was used for this study to compare seniors to experts. Expert median values were used as population values in the one‐sample Wilcoxon testing. Comparisons between high school freshmen and seniors were conducted using a Mann‐Whitney U test, which is a nonparametric test comparable to the independent samples t‐test and appropriate for non‐normal data distributions.</p> <hd id="AN0110525496-9">Validity of Comparison to Expert Data</hd> <p>To improve fidelity of the comparison between our study and the previous expert study, we obtained the technical reports from Atman et al.'s ([<reflink idref="bib2" id="ref51">2</reflink>] , [<reflink idref="bib1" id="ref52">1</reflink>] , [<reflink idref="bib3" id="ref53">3</reflink>] ) research, as discussed above, and attempted to follow a comparable process of data collection and coding. We gave students the same amount of time and similar instructions on how to complete the design task. We also provided similar office supplies. Like Atman et al., we provided the same types of resource information to use during the design task, with a few exceptions. For example, price lists and rules about the Americans with Disabilities Act were updated. Atman et al. coded transcript data while watching audio and video footage, whereas we coded the audio and video data directly using NVIVO software.</p> <p>In an attempt to ensure consistency between our research and Atman et al.'s ([<reflink idref="bib2" id="ref54">2</reflink>] , [<reflink idref="bib1" id="ref55">1</reflink>] , [<reflink idref="bib3" id="ref56">3</reflink>] ), we convened an advisory board that included two researchers who were part of Atman's team at the time they collected and analyzed expert data. We pilot tested our study with 16 students and reviewed our data collection and analysis process with the advisory board. After the advisory board reviewed the pilot study methodology and implementation, we made modifications prior to data collection and analysis for our study. While differences between this study and the expert study are worth noting, we believe the similarities in data collection and analysis procedures overcome the differences.</p> <hd id="AN0110525496-10">Results</hd> <hd id="AN0110525496-11">Interrater Reliability</hd> <p>Two graduate student researchers segmented the dataset, and two undergraduate student researchers coded the dataset. Each undergraduate researcher was responsible for one half of the dataset. Each researcher also coded 25% of the other researcher's set to calculate interrater reliability. Interrater reliability data are presented for each code compared at the segment level in Table [NaN] . The average interrater reliability of.91 was comparable to previous work (Atman et al., [<reflink idref="bib1" id="ref57">1</reflink>] ).</p> <p>Cohen's Kappa for Design Processes</p> <p> <ephtml> <table><tr valign="bottom"><th align="left">Design process</th><th align="center">Cohen's kappa</th></tr><tr valign="top"><td align="left">Problem definition</td><td align="char" char=".">.91</td></tr><tr valign="top"><td align="left">Information gathering</td><td align="char" char=".">.95</td></tr><tr valign="top"><td align="left">Idea generation</td><td align="char" char=".">.90</td></tr><tr valign="top"><td align="left">Modeling</td><td align="char" char=".">.92</td></tr><tr valign="top"><td align="left">Feasibility</td><td align="char" char=".">.80</td></tr><tr valign="top"><td align="left">Evaluation</td><td align="char" char=".">.92</td></tr><tr valign="top"><td align="left">Decision making</td><td align="char" char=".">.92</td></tr><tr valign="top"><td align="left">Communication</td><td align="char" char=".">.94</td></tr><tr valign="top"><td align="left">Other</td><td align="center">na</td></tr><tr valign="top"><td align="left">Average interrater reliability</td><td align="char" char=".">.91</td></tr></table> </ephtml> </p> <hd id="AN0110525496-12">Senior‐to‐Expert Comparison</hd> <p>The median number of minutes spent by experts on the design task was 132.6, while the median number of minutes spent by high school seniors was 81.5. A Wilcoxon signed‐rank test showed that experts spent significantly more time working on the design task than did high school seniors students, W = 57.000, p <.001, r =.659. A series of Wilcoxon signed‐rank tests were run comparing high school seniors with expert data, as shown in Table [NaN] . Experts allocated a significantly higher percentage of their time to the problem‐scoping stage than did high school seniors, W = 58.000, p <.001, r =.655. The difference in this stage is primarily attributed to information gathering, where experts spent a significantly higher percentage of time than did high school seniors, W = 45.000, p <.001, r =.704. The developing alternative solutions stage was not significantly different for high school seniors as compared with experts, but a few of the design processes within this stage were significantly different. Experts spent a significantly higher percentage of time on feasibility than did high school seniors, W = 130.500, p =.036, r =.383. Evaluation, a process related to feasibility, also showed significant differences between experts and seniors, W = 124.000, p =.024, r =.412. The project realization stage was not significantly different from seniors to experts, but the percentage of time allocated to decision making was significantly different. Experts allocated a significantly higher percentage of time to decision making than did seniors, W = 23.500, p <.001, r =.770.</p> <p>Comparison of Seniors and Experts Task Time Allocation</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="center">Median percent of total design task time</th><th align="left" /></tr><tr valign="bottom"><th align="left">Design process measures</th><th align="center">High school seniors (n = 29)</th><th align="center">Experts (n = 19)</th><th align="center">p‐value</th></tr><tr valign="top"><td align="left">Problem‐scoping stage</td><td align="char" char=".">14.7</td><td align="char" char=".">25.1</td><td align="char" char="."><.001*</td></tr><tr valign="top"><td align="left">Problem definition</td><td align="char" char=".">6.0</td><td align="char" char=".">6.2</td><td align="char" char=".">.571</td></tr><tr valign="top"><td align="left">Information gathering</td><td align="char" char=".">3.6</td><td align="char" char=".">19.8</td><td align="char" char="."><.001*</td></tr><tr valign="top"><td align="left">Developing alternative solutions stage</td><td align="char" char=".">77.1</td><td align="char" char=".">70.6</td><td align="char" char=".">.658</td></tr><tr valign="top"><td align="left">Idea generation</td><td align="char" char=".">3.5</td><td align="char" char=".">3.6</td><td align="char" char=".">.910</td></tr><tr valign="top"><td align="left">Modeling</td><td align="char" char=".">61.8</td><td align="char" char=".">54.5</td><td align="char" char=".">.178</td></tr><tr valign="top"><td align="left">Feasibility analysis</td><td align="char" char=".">4.6</td><td align="char" char=".">7.8</td><td align="char" char=".">.036*</td></tr><tr valign="top"><td align="left">Evaluation</td><td align="char" char=".">0.3</td><td align="char" char=".">1.1</td><td align="char" char=".">.024*</td></tr><tr valign="top"><td align="left">Project realization stage</td><td align="char" char=".">3.1</td><td align="char" char=".">4.5</td><td align="char" char=".">.328</td></tr><tr valign="top"><td align="left">Decision making</td><td align="char" char=".">0.3</td><td align="char" char=".">1.6</td><td align="char" char="."><.001*</td></tr><tr valign="top"><td align="left">Communication</td><td align="char" char=".">1.4</td><td align="char" char=".">3.5</td><td align="char" char=".">.323</td></tr><tr valign="top"><td align="left">Other</td><td align="char" char=".">2.3</td><td align="center">na</td><td align="center">na</td></tr></table> </ephtml> </p> <ulist> <item>3 Data from Atman et al. (<reflink idref="bib1" id="ref58">1</reflink>).</item> <item>4 Data not published.</item> <item>5 p <.05.</item> </ulist> <hd id="AN0110525496-13">Freshman‐to‐Senior Comparison</hd> <p>The median number of minutes spent on the design task for high school seniors was 81.5 and 76.0 for freshmen. A Mann‐Whitney U test indicated that the total time spent on the design task was not significantly different for high school seniors than for freshmen, U = 422.500, p =.850, r =.025. A series of Mann‐Whitney U tests were run to determine differences between high school freshmen starting the sequence of engineering courses and high school seniors who had taken multiple engineering courses (see Table [NaN] ). No significant differences were detected in any of the three stages of design (problem scoping, developing alternative solutions, or project realization). However, three design processes had significant differences. High school seniors spent a significantly higher percentage of time on idea generation than did freshmen, U = 669.000, p <.001, r =.463. Seniors also spent significantly more time considering the feasibility of their ideas than did freshmen, U = 564.000, p =.050, r =.255. The percentage of time allocated to decision making was significantly higher for the seniors than for the freshmen, U = 631.000, p =.001, r =.426.</p> <p>Comparison of High School Student Task Time Allocation</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="center">Median percent of total design task time</th><th align="left" /></tr><tr valign="bottom"><th align="left">Design process measures</th><th align="center">High school freshmen (n = 30)</th><th align="center">High school seniors (n = 29)</th><th align="center">p‐value</th></tr><tr valign="top"><td align="left">Problem‐scoping stage</td><td align="char" char=".">14.0</td><td align="char" char=".">14.7</td><td align="char" char=".">.371</td></tr><tr valign="top"><td align="left">Problem definition</td><td align="char" char=".">6.4</td><td align="char" char=".">6.0</td><td align="char" char=".">1.000</td></tr><tr valign="top"><td align="left">Information gathering</td><td align="char" char=".">5.5</td><td align="char" char=".">3.6</td><td align="char" char=".">.606</td></tr><tr valign="top"><td align="left">Developing alternative solutions stage</td><td align="char" char=".">72.5</td><td align="char" char=".">77.1</td><td align="char" char=".">.844</td></tr><tr valign="top"><td align="left">Idea generation</td><td align="char" char=".">0.5</td><td align="char" char=".">3.5</td><td align="char" char="."><.001*</td></tr><tr valign="top"><td align="left">Modeling</td><td align="char" char=".">60.9</td><td align="char" char=".">61.8</td><td align="char" char=".">.133</td></tr><tr valign="top"><td align="left">Feasibility</td><td align="char" char=".">2.6</td><td align="char" char=".">4.6</td><td align="char" char=".">.050*</td></tr><tr valign="top"><td align="left">Evaluation</td><td align="char" char=".">0</td><td align="char" char=".">0.3</td><td align="char" char=".">.098</td></tr><tr valign="top"><td align="left">Project realization stage</td><td align="char" char=".">0</td><td align="char" char=".">3.1</td><td align="char" char=".">.058</td></tr><tr valign="top"><td align="left">Decision making</td><td align="char" char=".">0</td><td align="char" char=".">0.3</td><td align="char" char=".">.001*</td></tr><tr valign="top"><td align="left">Communication</td><td align="char" char=".">0</td><td align="char" char=".">1.4</td><td align="char" char=".">.309</td></tr><tr valign="top"><td align="left">Other</td><td align="char" char=".">2.3</td><td align="char" char=".">2.3</td><td align="center">na</td></tr></table> </ephtml> </p> <p>6 *p <.05.</p> <hd id="AN0110525496-14">Discussion</hd> <p>Engineering design thinking can support students' STEM learning, enhance knowledge and abilities, and build interest in STEM fields. Learning engineering design thinking is critical in creating a pathway to STEM fields, engineering professions, and more broadly, to promoting technological literacy for all students. There is a need to investigate high school student and teacher understanding of the problem‐scoping stage and to develop, test, and disseminate evidence‐based learning experiences that support student and teacher learning of engineering design processes. The summary of results in Table [NaN] compares freshmen, seniors, and experts. We offer the following discussion with the understanding that a limitation of our research is that we assume changes in design thinking are related to the design courses taken by students. However, we acknowledge that the impact of students' prior learning, learning in other courses taken concurrently, and social maturation between freshman and senior year were not measured nor controlled in this study.</p> <p>Summary Comparisons between High School Students and Experts</p> <p> <ephtml> <table><tr valign="bottom"><th align="left">Design process measures</th><th align="center">Aggregated findings*</th></tr><tr valign="top"><td align="left">Problem‐scoping stage</td><td align="center">F = S < E</td></tr><tr valign="top"><td align="left">Problem definition</td><td align="center">F = S = E</td></tr><tr valign="top"><td align="left">Information gathering</td><td align="center">F = S < E</td></tr><tr valign="top"><td align="left">Developing alternative solutions stage</td><td align="center">F = S = E</td></tr><tr valign="top"><td align="left">Idea generation</td><td align="center">F < S = E</td></tr><tr valign="top"><td align="left">Modeling</td><td align="center">F = S = E</td></tr><tr valign="top"><td align="left">Feasibility</td><td align="center">F < S < E</td></tr><tr valign="top"><td align="left">Evaluation</td><td align="center">F = S < E</td></tr><tr valign="top"><td align="left">Project realization stage</td><td align="center">F = S = E</td></tr><tr valign="top"><td align="left">Decision making</td><td align="center">F < S < E</td></tr><tr valign="top"><td align="left">Communication</td><td align="center">F = S = E</td></tr></table> </ephtml> </p> <ulist> <item>7 Note: F = high school freshmen, S = high school seniors, E = expert data from Atman et al. (<reflink idref="bib1" id="ref59">1</reflink>).</item> <item>8 *p <.05.</item> </ulist> <hd id="AN0110525496-15">Problem‐Scoping Stage</hd> <p>There was little difference in time spent in the problem‐scoping stage between students in their first engineering course and students who had completed an average of four courses. But there was a significant difference between high school students and experts, with experts allocating a much higher percentage of time to this stage. Because experts also spent more time overall in the problem‐scoping stage, they allocated more minutes to understanding the design problem before attempting a solution. Interestingly, differences in time at this stage are primarily attributed to the information‐gathering process rather than the problem definition process. Mentzer ([<reflink idref="bib23" id="ref60">23</reflink>] ) has shown that the high school senior's most frequently requested piece of information was related to material costs. Hence, while high school students spent little time on information gathering, they also gathered less information related to the problem (because the information gathered was primarily solution related). Much of the information available was problem related, including stakeholder demographics, community concerns, children's body dimensions, parents' concerns, and community demographics. Information gathering about the problem is critical to meeting the client's needs, and high school students' lack of information gathering reduces their ability to engage in authentic engineering design experiences.</p> <hd id="AN0110525496-16">Developing Alternative Solutions</hd> <p>Differences in the developing alternative solutions stage may be related to the differences in the problem‐scoping stage. Students early in their first engineering course spent significantly less time in this stage than did experts and students finishing the sequence of courses. This finding aligns with literature that suggests novice designers become fixated on the first solution they consider; this fixation creates barriers to developing alternative solutions that are based on real and perceived constraints (Toh, Miller, & Kremer, [<reflink idref="bib37" id="ref61">37</reflink>] ).</p> <p>Idea generation is one of the few processes where there was a significant difference in growth between high school freshmen and seniors. Students' abilities to develop alternative solutions are critical skills in the engineering design process and a key to the innovation that is important in STEM education. Modeling in design thinking is essential and an important differentiator between engineering ways of thinking and knowing and other problem‐solving strategies. In this study, student efforts to model included determining size, position, scale, quantity needed, shape, and location. Modeling was done visually, graphically, physically, and through mathematical representations. A more detailed investigation of high school senior modeling behaviors is described by Mentzer, Huffman, and Thayer ([<reflink idref="bib25" id="ref62">25</reflink>] ). Feasibility and evaluation are essential engineering design processes, where the designer makes judgments on the practicality and functionality of a design (feasibility) or critically compares multiple design alternatives (evaluation). Data show that seniors make significantly more feasibility judgments on their designs than do freshmen, and experts spend more time than do seniors. As designers establish an understanding of the problem, they clarify constraints, criteria, and necessary functions of their solutions. Because high school students spend significantly less time understanding the problem, they may be less prepared to consider the extent to which their ideas will satisfy the design problem. If students are unable to evaluate and determine the feasibility of their proposed solutions, they may allocate effort in developing a solution that does not meet the client's needs.</p> <hd id="AN0110525496-17">Project Realization</hd> <p>In the project realization stage we found no differences between high school freshmen, seniors, and experts. Project realization consisted of decision making and communication. Seniors spent significantly more time in decision making than did freshmen, and experts spent significantly more time than did seniors. Decision‐making efforts were coded when students specifically chose between two or more alternatives. In many cases, students demonstrated design fixation by specifying elements of design without considering alternatives.</p> <hd id="AN0110525496-18">Implications</hd> <p>The National Academy of Engineering Committee on K‐12 Engineering Education reviewed curricula in 2008 and reported that “In most of the curricula, the first step in a design activity is to pose a problem or define a test. [Yet... few curricula engaged] students in a robust analysis to identify and define the problem” (National Resource Council, [<reflink idref="bib32" id="ref63">32</reflink>] , pp. 83–84). The weakness of novice designers' problem‐scoping efforts is noted by college and high school‐level engineering design research studies (Becker, Mentzer, Park, & Huang, [<reflink idref="bib4" id="ref64">4</reflink>] ; Bogusch, Turns, & Atman, [<reflink idref="bib5" id="ref65">5</reflink>] ; Christiaans & Dorst, [<reflink idref="bib7" id="ref66">7</reflink>] ; Kilgore, Atman, Yasuhara, Barker, & Morozov, [<reflink idref="bib16" id="ref67">16</reflink>] ; Mentzer & Fosmire, in press; Mentzer et al., [<reflink idref="bib25" id="ref68">25</reflink>] ). Problem scoping, critical in design thinking, is the first stage of engineering design and sets the foundation for developing solutions. Research studies show that although design can be taught and that problem scoping is a learnable skill, it is consistently weak in novice designers. The development and dissemination of curricula to engage students in problem scoping is essential to foster design thinking in novice designers.</p> <p>As instructors work with students in problem scoping and generating multiple ideas or solutions aligned with the problem, students will be more likely to engage in decision making more frequently. Students will need to actively consider the design problem, and evaluate and choose between design solutions. Further research should explore the development, testing, and scalability of educational materials related to problem scoping and study the impact on student thinking. There are differing thoughts on how, when, and at what grade level these educational experiences should take place. Becker, Mentzer, Park, and Huang ([<reflink idref="bib4" id="ref69">4</reflink>] ) stated that understanding engineering and technological literacy is especially important to develop during the high school years because “technologically literate people should also know something about the engineering design process” (National Research Council, [<reflink idref="bib31" id="ref70">31</reflink>] , p. 18). However, why wait until high school? If one of the goals of education is to improve novice performance such that it resembles expert design thinking more closely, we should begin to model this process earlier by considering the following questions:</p> <p>What is the engineering design thinking of the very young?</p> <p>How early should we teach and foster engineering design thinking?</p> <p>What are developmentally appropriate learning progressions for engineering design?</p> <p>Our research study compared high school freshmen and senior students with expert engineers in a novice–expert continuum of design thinking. Our findings about the differences in performance between freshmen, seniors, and experts can inform efforts to improve secondary education and should guide the focus of engineering design education. These differences may also provide insight for earlier educational treatments, including the primary level, and help identify opportunities for informal education. These differences also highlight challenges and weaknesses that high school students will bring with them as freshmen in college. Collegiate‐level engineering and technology education programs should be aware of the background knowledge and skills that freshmen have so that educators and educational programs can best meet the needs of their students.</p> <hd id="AN0110525496-19">Acknowledgment</hd> <p>This material is based upon work supported by the National Science Foundation under Grant DRL‐0918621. 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="AN0110525496-20"> <title>References</title> <blist> <bibl id="bib1" idref="ref38" type="bt">1</bibl> <bibtext>Atman, C., Adams, R. S., Cardella, M., Turns, J., Mosborg, S., & Saleem, J. ( 2007 ). Engineering design processes: A comparison of students and expert practitioners. 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  Data: Engineering Design Thinking: High School Students' Performance and Knowledge
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  Data: <searchLink fieldCode="AR" term="%22Mentzer%2C+Nathan%22">Mentzer, Nathan</searchLink><br /><searchLink fieldCode="AR" term="%22Becker%2C+Kurt%22">Becker, Kurt</searchLink><br /><searchLink fieldCode="AR" term="%22Sutton%2C+Mathias%22">Sutton, Mathias</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Engineering+Education%22"><i>Journal of Engineering Education</i></searchLink>. Oct 2015 104(4):417-432.
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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
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  Data: 16
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  Data: 2015
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  Data: National Science Foundation (NSF)
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  Data: DRL0918621
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  Data: Journal Articles<br />Reports - Research
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  Label: Education Level
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  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Engineering+Education%22">Engineering Education</searchLink><br /><searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Design%22">Design</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Expertise%22">Expertise</searchLink><br /><searchLink fieldCode="DE" term="%22Technical+Occupations%22">Technical Occupations</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Freshmen%22">High School Freshmen</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Seniors%22">High School Seniors</searchLink>
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  Label: DOI
  Group: ID
  Data: 10.1002/jee.20105
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  Data: 1069-4730
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Because design is recognized as a critical element of engineering thinking, it is crucial for educators to utilize the most effective methods to teach engineering problem solving. Results from this study of students' thinking process may shape future teaching methods. Purpose: This article explores the differences in design processes between high school engineering students and expert engineers. It also examines the differences between high school freshmen who have taken one engineering course and seniors who have taken a series of engineering courses. Design/Method: Fifty-nine high school students from four states were asked to think aloud in a three-hour design task that was audio and video recorded. Verbal reports from the audio and video became source data for protocol analysis. Results from previous studies provided expert design performance data for comparisons. Results: Students and experts alike spent a large portion of their time modeling. Students spent significantly less time in the process of information gathering than experts. Freshmen spent significantly less time in the idea generation process than seniors and experts. Freshmen and seniors spent significantly less time determining the feasibility of their ideas, evaluating alternative ideas, and decision making than experts. Conclusions: High school students engage in design thinking with little understanding of the problem from the client's perspective. Students tend to become fixated on a single solution rather than comparing alternatives. By encouraging development of alternative solutions, K-12 engineering education could foster opportunities to critically evaluate students' design solutions.
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  Data: 2020
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  Data: EJ1255356
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      Pagination:
        PageCount: 16
        StartPage: 417
    Subjects:
      – SubjectFull: Engineering Education
        Type: general
      – SubjectFull: Thinking Skills
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      – SubjectFull: Design
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      – SubjectFull: Problem Solving
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      – SubjectFull: Teaching Methods
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      – SubjectFull: Instructional Effectiveness
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      – SubjectFull: Technical Occupations
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      – SubjectFull: High School Freshmen
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      – TitleFull: Engineering Design Thinking: High School Students' Performance and Knowledge
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