View in EDS HTML Full Text PDF Full Text

Engineering Students' Perceptions of Problem Solving and Their Future

Saved in:
Bibliographic Details
Title: Engineering Students' Perceptions of Problem Solving and Their Future
Language: English
Authors: Kirn, Adam, Benson, Lisa
Source: Journal of Engineering Education. Jan 2018 107(1):87-112.
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: 26
Publication Date: 2018
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: EEC1055950
Document Type: Journal Articles
Reports - Research
Descriptors: Engineering Education, Student Attitudes, Problem Solving, Student Motivation, Long Range Planning, Goal Orientation, Task Analysis
DOI: 10.1002/jee.20190
ISSN: 1069-4730
Abstract: Background: Preparing students to solve complex problems is an identified area of need in engineering education. Despite the documented influence of motivation on learning, little research exists that examines how motivation and problem solving in engineering interconnect. Purpose: This study explores how engineering students perceive problem solving tasks, the future, and the connections between the two. Methods: Interviews with engineering students (n = 9) about engineering problems, problem solving processes, their futures, and interactions between their futures and problem solving tasks were analyzed using interpretative phenomenological analysis (IPA). Analysis of the resulting transcripts identified and clustered units of meaning into themes, first by participant and then across participants. Results: Three themes emerged from the IPA: participants perceived engineering as being primarily a problem solving process; participants reported using different problem solving processes depending on how well the task aligned with their future goals; and participants' perceptions of their future drove the problem solving processes they report using. Conclusions: This work supports and extends engineering problem solving literature by making explicit the connections students describe between their problem solving processes and their future-oriented motivations. Additionally, this work furthers our understanding of how future-oriented motivations are conceptualized by engineering students.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1254059
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwGvcbyPFoeyBGhpBCFToOssAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDF2J3IdangwZbXLC6QIBEICBm4BWz0-nnNswNmrrAszT465Xgrz9kDfy6wFv80iCF-TAvDCqW9fAgZis4m_RUNsbwIdfb8ogTBkv_8qPHnv85qdu8lQUdWvtYuOFCZdoJvRMV2s1KPAYes9ydDdesL0iYs86D6D50wggKh3J7c1nUVgNBzNyFN1OUoD9svlR6dohkYobdiK3fewP0vwJEqJYRDJx9QvBLMD2Fq5s
Text:
  Availability: 1
  Value: <anid>AN0128731066;6m401jan.18;2018Mar29.14:36;v2.2.500</anid> <title id="AN0128731066-1">Engineering Students' Perceptions of Problem Solving and Their Future </title> <p>Abstract: Background: Preparing students to solve complex problems is an identified area of need in engineering education. Despite the documented influence of motivation on learning, little research exists that examines how motivation and problem solving in engineering interconnect. Purpose: This study explores how engineering students perceive problem solving tasks, the future, and the connections between the two. Methods: Interviews with engineering students (<italic>n</italic> = 9) about engineering problems, problem solving processes, their futures, and interactions between their futures and problem solving tasks were analyzed using interpretative phenomenological analysis (IPA). Analysis of the resulting transcripts identified and clustered units of meaning into themes, first by participant and then across participants. Results: Three themes emerged from the IPA: participants perceived engineering as being primarily a problem solving process; participants reported using different problem solving processes depending on how well the task aligned with their future goals; and participants' perceptions of their future drove the problem solving processes they report using. Conclusions: This work supports and extends engineering problem solving literature by making explicit the connections students describe between their problem solving processes and their future‐oriented motivations. Additionally, this work furthers our understanding of how future‐oriented motivations are conceptualized by engineering students.</p> <p>motivation; problem solving; interpretative phenomenological analysis; undergraduate; qualitative</p> <hd id="AN0128731066-2">Introduction</hd> <p>Why is it that some people, when they are faced with problems, get clever ideas, make inventions, and discoveries? What happens, what are the processes that lead to such solutions? What can be done to help people to be creative when they are faced with problems? (Luchins and Luchins 1970, p. 1, as cited in Mayer & Wittrock, [<reflink idref="bib45" id="ref1">45</reflink>] , p. 47)</p> <p>Researchers have sought answers to Luchins and Luchins' questions by investigating how students solve engineering problems across contexts and problem types (e.g., Cooper & Sandi‐Urena, [<reflink idref="bib8" id="ref2">8</reflink>] ; Kirn, Faber, & Benson, [<reflink idref="bib32" id="ref3">32</reflink>] ; McCracken & Newstetter, [<reflink idref="bib46" id="ref4">46</reflink>] ). However, this problem solving research in engineering has focused on the development of skills (Atman et al., [<reflink idref="bib1" id="ref5">1</reflink>] ; Baillie, Ko, Newstetter, & Radcliffe, [<reflink idref="bib2" id="ref6">2</reflink>] ; Borrego & Newswander, [<reflink idref="bib5" id="ref7">5</reflink>] ; Steif, Lobue, Kara, & Fay, [<reflink idref="bib69" id="ref8">69</reflink>] ) without accounting for the influence of personal and situational contexts that can drive student motivation and performance during problem solving (Mayer, [<reflink idref="bib44" id="ref9">44</reflink>] ). Such factors internal to the problem solver, both cognitive and affective, influence the development of problem solving skills (Jonassen, [<reflink idref="bib26" id="ref10">26</reflink>] ). In fact, when Jonassen identified key topics for future research in problem solving, he posed such questions as, “How do we support the integration of alternative perspectives by students?” that speak directly to the need for understanding students' perceptions of problem solving (Jonassen, [<reflink idref="bib28" id="ref11">28</reflink>] , p. 13). This work examines Jonassen's question by explicitly considering the role of student perspectives through the lens of motivation.</p> <hd id="AN0128731066-3">The Influence of Motivation on Problem Solving</hd> <p>Student motivation, which can serve as a driver for choice, persistence, and performance in engineering (Kirn et al., [<reflink idref="bib31" id="ref12">31</reflink>] ; Koh et al., [<reflink idref="bib34" id="ref13">34</reflink>] ; Matusovich, Streveler, & Miller, [<reflink idref="bib43" id="ref14">43</reflink>] ), is often studied at one of two‐time scales: motivation toward short‐term tasks and motivation toward long‐term goals. Task‐specific motivation seeks to understand student motivation, such as self‐efficacy (Bandura, [<reflink idref="bib3" id="ref15">3</reflink>] ), for performing and completing a specific task, such as solving a problem (Hutchison, Follman, Sumpter, & Bodner, [<reflink idref="bib24" id="ref16">24</reflink>] ). Specifically, increased engineering self‐efficacy beliefs are predictive of improved learning and understanding in introductory engineering courses (Hutchison et al., [<reflink idref="bib24" id="ref17">24</reflink>] ). Students' long‐term motivation focuses on goals such as passing a course or graduating with an engineering degree. Expectancy‐value theory work in engineering has shown that students who have higher expectations for success in their courses have significantly higher grade point averages (Jones, Paretti, Hein, & Knott, [<reflink idref="bib29" id="ref18">29</reflink>] ; Matusovich et al., [<reflink idref="bib43" id="ref19">43</reflink>] ).</p> <p>The importance of both task‐specific and long‐term scales of motivation is highlighted by a wide body of work in engineering education (e.g., Brown, Hershock, Finelli, & O'Neal, [<reflink idref="bib6" id="ref20">6</reflink>] ; Marra, Rodgers, Shen, & Bogue, [<reflink idref="bib41" id="ref21">41</reflink>] ; Matusovich, Streveler, Loshbaugh, Miller, & Olds, [<reflink idref="bib42" id="ref22">42</reflink>] ), with some studies proposing that these different time scales of motivation are connected and influence one another (DeShon & Gillespie, [<reflink idref="bib11" id="ref23">11</reflink>] ; Husman, Hilpert & Brem, [<reflink idref="bib21" id="ref24">21</reflink>] ). This connection is seen in the study of cognitive, metacognitive, and motivational aspects of problem solving in which “the will to learn depends partly on how the problem solver interprets the problem solving situation” (Mayer, [<reflink idref="bib44" id="ref25">44</reflink>] , p. 56).</p> <hd id="AN0128731066-4">Background</hd> <p>Post‐secondary engineering students construct goals that often extend beyond graduation to some point in the future. In the motivational goal setting literature, these conceptualizations of future goals are described as future time perspectives (FTPs; e.g., Husman & Lens, [<reflink idref="bib22" id="ref26">22</reflink>] ), a perspective that is defined by a set of psychological factors that examine the degree to which perceptions of the future are integrated into present motivational goal setting processes (Husman & Lens, [<reflink idref="bib22" id="ref27">22</reflink>] ). More specifically, the constructs measuring FTPs examine differences in planning for the future, delaying gratification, and making responsible choices. Prior research with engineering students shows that elements of FTPs influence self‐regulated learning profiles (Nelson, Shell, Husman, Fishman, & Soh, [<reflink idref="bib50" id="ref28">50</reflink>] ) and predict student use of knowledge building strategies (Hilpert et al., [<reflink idref="bib17" id="ref29">17</reflink>] ; Husman et al., [<reflink idref="bib21" id="ref30">21</reflink>] ).</p> <p>Our prior work examining connections between student motivation and problem solving indicated that student FTPs predict actions undertaken when solving engineering problems (Kirn, Grigg, & Benson, [<reflink idref="bib33" id="ref31">33</reflink>] ). However, this work and the work of others rely on the assumption that previously established FTP constructs developed with college student populations from a broad range of disciplines fully encompass relevant FTP constructs for undergraduate engineering students. While some of these constructs apply to engineering students (Hilpert et al., [<reflink idref="bib17" id="ref32">17</reflink>] ), there is a gap in the literature on how engineering students express and conceptualize their FTPs. Given the growing body of literature about the importance of FTP with respect to student learning and academic achievement (Husman & Shell, [<reflink idref="bib23" id="ref33">23</reflink>] ; Shell & Husman, [<reflink idref="bib65" id="ref34">65</reflink>] ; Simons, Vansteenkiste, Lens, & Lacante, [<reflink idref="bib66" id="ref35">66</reflink>] ; Nelson et al., [<reflink idref="bib50" id="ref36">50</reflink>] ; Husman et al., [<reflink idref="bib21" id="ref37">21</reflink>] ), we utilize our conceptual understanding of FTP to initiate discussions with engineering students about their motivations and the connections they make between motivations and problem solving tasks. The theoretical frameworks utilized in this study serve to elicit descriptions of engineering students' FTPs in their own words.</p> <p>Traditionally, FTP is defined by a variable sequence that influences present behavior (Hilpert et al., [<reflink idref="bib17" id="ref38">17</reflink>] ) in which four primary factors are discussed throughout engineering education (e.g., Hilpert et al., [<reflink idref="bib17" id="ref39">17</reflink>] ; Nelson et al., [<reflink idref="bib50" id="ref40">50</reflink>] ): perceived instrumentality, temporal distance, speed, and connectedness. Perceived instrumentality examines how students view present tasks as useful for their future selves. These tasks can be viewed by students with either exogenous instrumentality (e.g., not connected to an emerging future identity) or endogenous instrumentality (e.g., connected to an emerging future identity) (Nelson et al., [<reflink idref="bib50" id="ref41">50</reflink>] ). Instrumentality is similar to utility value (i.e., perceived usefulness of tasks) in the expectancy‐value literature (Wigfield & Eccles, [<reflink idref="bib72" id="ref42">72</reflink>] ), with the addition of an explicit consideration of time (Husman, Derryberry, Crowson, & Lomax, [<reflink idref="bib20" id="ref43">20</reflink>] ). Distance examines how far into the future individuals project future selves (Hilpert et al., [<reflink idref="bib17" id="ref44">17</reflink>] ) and speed, the perception of how quickly events in a student's perceived future approach the present (Gjesme, [<reflink idref="bib14" id="ref45">14</reflink>] ). The cognitive connections made between the present and future by students are termed connectedness, which can be a predictor of student achievement in post‐secondary classrooms (Husman et al., [<reflink idref="bib21" id="ref46">21</reflink>] ; Shell & Husman, [<reflink idref="bib65" id="ref47">65</reflink>] ).</p> <hd id="AN0128731066-5">The Student Perspective on Problem Solving</hd> <p>For this work, we take the approach of a student‐driven model, or understanding problem solving from the student perspective, to avoid prescribing the problem solving process. We seek to investigate how students perceive problem solving, with the ultimate goal of creating problem solving tasks and related pedagogies that leverage and are consistent with students' perceptions. The nature of our research points to an interpretative, qualitative approach to fully capture the experience of problem solving from the student's perspective. Interpretative research methods provide rich accounts from a small number of participants, with the research questions in this approach being framed by both the researcher and the participants in the examination of how the phenomenon manifests and is experienced by students (Huberman & Miles, [<reflink idref="bib18" id="ref48">18</reflink>] ). A student‐driven approach is an appropriate way to answer our research questions as it entails a deep and thorough examination of the perceptions of a small number of students.</p> <hd id="AN0128731066-6">Research Questions</hd> <p>As the purpose of our research is to investigate the connection between engineering students' perceptions of problem solving and their motivations, we set out to answer the following research questions:By addressing these research questions through interpretative qualitative methods, we can further the body of knowledge concerning the complexities of engineering student problem solving and the influence of motivation on students' perceptions of problem solving tasks and processes.</p> <p>How do undergraduate engineering students perceive the experience of engineering problem solving and the processes required to solve engineering problems?</p> <p>How do students' motivations related to present tasks and future goals in engineering influence their perceptions of engineering problem solving processes?</p> <hd id="AN0128731066-7">Methods</hd> <hd id="AN0128731066-8">Methodological Foundation</hd> <p>We employed an interpretative phenomenological analysis (IPA) approach, and according to Smith et al. ([<reflink idref="bib67" id="ref49">67</reflink>] ), IPA is concerned with human lived experience and posits that experience can be understood via an examination of the meaning people impress upon it. These meanings, in turn, may illuminate the embodied, cognitive‐affective, and existential domains of psychology.</p> <p>When faced with significant experiences, an individual has a multidimensional response, which can be either be a first‐order activity (e.g., changes in time spent at work due to becoming a single parent) or second‐order affective and mental responses to the activity (e.g., the emotional response to a terminal cancer diagnosis). IPA is an appropriate method for studying such responses, as it is concerned with examining the subjective experience: the individual's experience of “something” (Smith et al., [<reflink idref="bib67" id="ref50">67</reflink>] ). Additionally, IPA relies on the use of advanced theoretical knowledge and posits that the subjectivity of the researcher cannot be removed from the analysis (Lopez & Willis, [<reflink idref="bib38" id="ref51">38</reflink>] ). In this study, we leveraged our theoretical understanding of problem solving and motivation to examine a second‐order experience of students' perceptions of engineering problem solving as influenced by their motivations across time scales.</p> <p>Students' perceptions of problem solving and motivations are a constant “lived experience.” The goal for college is not to remain in school but rather to reach a goal beyond college; as such, students' ideas of long‐term goals permeate all aspects of their lives from conversations with family to determining which engineering problem will be the focus of their time and attention. While this lived experience does not manifest in a physical form because it is a second‐order experience, the subsequent motivations for short‐term tasks and actions taken by students are driven by their long‐term motivations (DeShon & Gillespie, [<reflink idref="bib11" id="ref52">11</reflink>] ). In this study, the specific phenomenon of interest, the connection between students' perceptions of engineering problem solving and students' motivations, was longitudinal in nature, thus differing from traditional examples of phenomena that are more instantaneous, and appropriate for IPA as its goal is to find deeper meaning and connections in the data that participants may not make on their own.</p> <p>In light of the fact that IPA is evolving and contending with criticism across fields (DeWitt & Ploeg, [<reflink idref="bib9" id="ref53">9</reflink>] ; Pringle, Drummond, McLafferty, & Hendry, [<reflink idref="bib58" id="ref54">58</reflink>] ), the methodological outline below (participant selection, interview protocol, analytic process, dynamic bracketing and methodological quality) was adapted from the work of Smith, Flowers, and Larkin ([<reflink idref="bib67" id="ref55">67</reflink>] ). Their established methodology addresses expressed criticisms through the explicit discussion of the philosophical underpinnings that guide all steps of the methodological process and is strongly rooted in the traditions of phenomenology, hermeneutics, and idiography. This approach to IPA is widely accepted in nursing and health science (Smith et al., [<reflink idref="bib67" id="ref56">67</reflink>] ), education (De Witt & Ploeg, [<reflink idref="bib9" id="ref57">9</reflink>] ) and, more recently, engineering education (Huff et al., [<reflink idref="bib19" id="ref58">19</reflink>] ). A discussion of the philosophical underpinnings of IPA and a comparison to other similar methods (e.g., descriptive phenomenology, phenomenography) can be found in Smith, Flowers, and Larkin ([<reflink idref="bib67" id="ref59">67</reflink>] ).</p> <hd id="AN0128731066-9">Participants</hd> <p>Nine students from a southeastern land‐grant institution participated in this study, with five selected based on their responses to a survey about their perceptions of the future and motivation for problem solving tasks and an additional four through snowball sampling (Biernacki & Waldorf, [<reflink idref="bib4" id="ref60">4</reflink>] ). Second‐year and beginning third‐year students were selected because of their shared engineering problem solving experiences, allowing us to explore participants' perceptions of problem solving and any connections they make to their FTPs.</p> <p>Participant inclusion in this study was designed with the goal of creating a purposeful homogeneous sample to allow for a rich exploration of the phenomenon of interest (Smith et al., [<reflink idref="bib67" id="ref61">67</reflink>] ). Our previous quantitative results in which we examined differences between students' FTPs and approaches to problem solving indicated few significant differences between engineering majors, genders, races/ethnicities (Kirn, [<reflink idref="bib31" id="ref62">31</reflink>] ; Kirn et al., [<reflink idref="bib33" id="ref63">33</reflink>] ; Kirn et al., [<reflink idref="bib31" id="ref64">31</reflink>] ). Given the limited differences seen across traditional demographic lines for the variables of interest, participants were selected with an eye toward homogeneity of attitudes, values, and beliefs relevant to their motivations. Additionally, the participants selected had similar course requirements until their junior year, and those recruited via snowball sampling were from the same peer group, were enrolled in the same courses and studied together, allowing for increased homogeneity of the sample.</p> <p>Interview participants included two male mechanical engineering majors, a female mechanical engineering major, a female materials science and engineering major, two female biomedical engineering majors, and three male biomedical engineering majors. Eight of the nine participants were White, and one Black; in addition, two of the nine students were international. Racial and ethnic status is not attached to specific participants to prevent reidentification, and all names used for participants are pseudonyms. The pseudonyms used and participant majors are listed in Table .</p> <p>Participant Pseudonyms and Majors</p> <p> <ephtml> <table border="1" cellpadding="3"><tr><th>Pseudonym</th><th>Major</th><th>Sex</th></tr><tr><td>Bonnie</td><td>Biomedical Engineering</td><td>Female</td></tr><tr><td>Caroline</td><td>Mechanical Engineering</td><td>Female</td></tr><tr><td>Damon</td><td>Mechanical Engineering</td><td>Male</td></tr><tr><td>Jeremy</td><td>Biomedical Engineering</td><td>Male</td></tr><tr><td>Katerina</td><td>Materials Science and Engineering</td><td>Female</td></tr><tr><td>Katherine</td><td>Biomedical Engineering</td><td>Female</td></tr><tr><td>Matt</td><td>Biomedical Engineering</td><td>Male</td></tr><tr><td>Silas</td><td>Biomedical Engineering</td><td>Male</td></tr><tr><td>Stefan</td><td>Mechanical Engineering</td><td>Male</td></tr></table> </ephtml> </p> <p>Participants were interviewed once between the last 2 weeks of the spring semester or the first 2 weeks of the following fall semester. Conducting interviews over an extended period allowed for an initial examination of participant responses and revisions of interview questions to increase clarity and depth of response. However, the time gap between interviews allowed for the potential introduction of new experiences for later participants such as internships; we accounted for this situation by asking participants about their experiences in engineering environments, with their responses reflecting experiences including but not limited to research opportunities, internships, and cooperative education in industry environments. More specifically, Bonnie, Caroline, Jeremy, Matt, and Stefan all had engineering internships prior to participating in their interviews, while Caroline, Jeremy, Katerina, Katherine, Silas, and Stefan all had worked in research environments on or off campus through paid, volunteer, and course‐credit driven experiences prior to their interviews. Of the nine participants, four (Bonnie, Jeremy, Matt, and Silas) were interviewed during the fall semester. These students' experiences in engineering environments were no different in scope nor duration from those participants interviewed during the previous spring semester.</p> <hd id="AN0128731066-10">Interview Protocol</hd> <p>Semistructured interview questions focused on three areas: participants' perceptions of engineering problems, future goals, and beliefs about the interconnectedness of future goals and perceptions of problem solving. The interview protocol was designed to prompt discussions related to the guiding research questions while allowing each participant to be the focus and guide of the interview. Specific questions included:</p> <olist> <item> What are your goals for the future?</item> <item> What parts of your education do you see as relevant to your future?</item> <item> To you, what is an engineering problem?</item> <item> How do the problems you solve relate to your future goals?</item> <item> How do your future goals affect how you approach the problems you solve?</item> </olist> <p>The protocol was piloted with the lead researcher interviewing two undergraduate engineering students who shared similar engineering experiences with the student population of the study, although they were in their last semester in engineering and had already arranged permanent employment after graduation, thus shifting their FTPs. The perceptions and feedback from these upper division students were utilized to refine the protocol and as such were not included in the analysis. Additionally, the interview protocol was reviewed by experts in student motivation and problem solving after the pilot period. This expert review led to refining interview prompts to better align with student perceptions of problem solving and to improve the clarity of the questions.</p> <p>Progression through the interview was guided by participant responses. Interviewers prompted participants with additional questions to guide discussions from surface level descriptions to detailed and specific responses. The researchers explained the interview, data collection and analytic processes, and prompted a general conversation related to a recent campus event (e.g., the athletic event last weekend, plans for the week) to prime participants to lead the conversation during the interview. In keeping with IPA traditions, we refer to the dialogue throughout the course of the interview as a conversation even when one‐sided. Interviews ranged from 39 to 95 min in length; these varying lengths reflect differences in the participants' willingness or ability to discuss the questions in the interview protocol. The interviewers prompted all students for additional details based on their responses to maintain a similar level of depth across interviews. Recordings of interviews were professionally transcribed, and transcriptions were reviewed while listening to the audio recording by the interviewer to create familiarity with participants' voices and ensure accuracy of transcription prior to analysis.</p> <hd id="AN0128731066-11">Analytic Process</hd> <p>Data analysis leveraged an analytic induction approach to coding in which each participant's case was treated individually and a general hypothesis was developed prior to analyzing each subsequent case. The analytic process began by examining each case in detail (individual analysis), followed by examining similarities and differences across cases. The goal of individual analysis was to produce highly detailed accounts of participants' perceptions of problem solving processes and motivations related to engineering. Individual analysis was conducted by reading, re‐reading, and re‐listening to cases to increase familiarity with the perspective and position of the participant. After establishing familiarity with the participants, descriptive (marking for comments of interest), linguistic (examining patterns in language), and interpretative (applying researcher interpretation to participant responses) coding of the interviews was conducted. Codes were organized into emergent themes, with the final themes being generated by collapsing emergent themes from each case based on similar definitions. Researchers discussed these emergent themes for clarity of understanding and appropriate depth of interpretation. Prior to analysis of the next case, initial hypotheses were revised based on the themes generated from the previous case.</p> <p>Once each case was analyzed, comparisons across individuals were made to create meaning of the shared experience of participants' engineering problem solving as influenced by their motivations. The data from this study presented both the shared themes across participants and the distinctive voices of and variations on those themes (Smith et al., [<reflink idref="bib67" id="ref65">67</reflink>] ). All levels of analysis and data presentation were conducted with the goal of keeping the participants' voices as the focus of the analysis. While this description of our methodology reads as a prescriptive list of steps, IPA provides the flexibility for the researchers to repeat steps within the process to reach deep psychological themes (Huff et al., [<reflink idref="bib19" id="ref66">19</reflink>] ).</p> <p>The interpretative phase of this work required a deviation from IPA traditions. IPA takes a stance of questioning that seeks balance between understanding the views of the participants in their own words and the use of theoretical perspectives to shed light on the phenomenon (Smith et al., [<reflink idref="bib67" id="ref67">67</reflink>] ). Understanding participants' views is referred to as hermeneutics of empathy, while examining the participant through the lens of theory is termed hermeneutics of critique (Langdridge, [<reflink idref="bib36" id="ref68">36</reflink>] ). An increased use of hermeneutics of critique was required by the lead researcher because guiding participants to speak in depth about problem solving and motivation would necessitate lengthy and intellectually dense interviews that were not feasible. Thus, the choice was made to preserve the quality of responses of the participants and place a larger burden of interpretation on the researcher. This decision allowed students to more deeply reflect on their experiences related to problem solving and motivation without having to understand deep theoretical concepts. This decision also meant the voice of the researcher is stronger when generating themes from student data. As such, there were more opportunities for biasing the data. To address this concern and ensure sound interpretation of the data, dynamic bracketing and steps for ensuring quality in interpretative research were employed.</p> <hd id="AN0128731066-12">Dynamic Bracketing</hd> <p>Dynamic bracketing, or positionality, was used to document researcher‐led interpretations of participant voices during the writing of the interview questions, data collection, and analysis (Smith et al., [<reflink idref="bib67" id="ref69">67</reflink>] ). Dynamic bracketing occurs throughout the process of conducting an IPA. It differs from bracketing in descriptive phenomenology, which requires complete documentation and setting aside of the researcher's influence, whereas IPA views the researcher as a central figure in interpreting meaning (Moustakas, [<reflink idref="bib48" id="ref70">48</reflink>] ; Smith et al., [<reflink idref="bib67" id="ref71">67</reflink>] ). Bracketing in IPA requires researchers to constantly re‐examine their experiences and interpretative lens based on the emergence of themes from the participants' data. The researcher's perspective is bracketed off to focus on the participant and is reintegrated during data analysis, specifically during the creation of themes (Smith et al., [<reflink idref="bib67" id="ref72">67</reflink>] ).</p> <hd id="AN0128731066-13">Positionality of the Researchers</hd> <p>Both researchers were previously trained in biomedical engineering. This disciplinary training led them to hypothesize about the differences in engineering FTPs based on student choice of major. In addition, the lead researcher had formal training in engineering education research, while the second author entered the engineering education research community through workshops, training and mentoring by experts in the field. Both researchers were mentored by field experts in motivation and problem solving, allowing them to develop theoretical conceptualizations that were tightly bound to data rather than theoretical tradition. Previously, this research team conducted multiple projects related to student FTPs and problem solving to explore differences across majors, genders, and race/ethnicities. Results from this work indicated limited differences between engineering majors, but the researchers were able to show connections between FTPs and problem solving tasks undertaken by students when solving typical engineering problems (Kirn et al., [<reflink idref="bib33" id="ref73">33</reflink>] ). This previous work led the researchers to hypothesize that FTPs would affect student perceptions of problem solving in insightful ways. Furthermore, conceptualizations of FTP in the literature were not congruent with results from their findings. Thus, they questioned existing FTP frameworks when exploring student interpretations of their lived experiences.</p> <hd id="AN0128731066-14">Methodological Quality</hd> <p>The discussion of quality in phenomenology and IPA is currently undergoing a transformation in communities ranging from nursing (Lopez & Willis, [<reflink idref="bib38" id="ref74">38</reflink>] ) to education (De Witt & Ploeg, [<reflink idref="bib9" id="ref75">9</reflink>] ; Smith et al., [<reflink idref="bib67" id="ref76">67</reflink>] ). Issues surrounding methodological quality emerge from the mismatch between generic methods of ensuring quality in qualitative research and the philosophical underpinnings of IPA. Many of the methods traditionally used to evaluate qualitative work (i.e., transferability and trustworthiness) are based on ideals that seek to verify that which is reported as the “truth” (Denzin & Lincoln, [<reflink idref="bib10" id="ref77">10</reflink>] ). In contrast, the philosophical underpinnings of IPA define the methodology around a participant's lived or subjective experience in the world, and as such, discussions around transferability and trustworthiness are methodologically mismatched (Smith et al., [<reflink idref="bib67" id="ref78">67</reflink>] ). For this work we focused on accurately reflecting participants' perceptions rather than the “truth.”</p> <p>To ensure quality, this study examined sensitivity to context, commitment to the participants, thoroughness of the study, transparency, and coherence (Smith et al., [<reflink idref="bib67" id="ref79">67</reflink>] ). Interviews were conducted by the lead author in comfortable and inviting spaces designed to reduce power differentials between researchers and participants. Conducting these interviews based on participant schedules and in spaces they were familiar with (i.e., an academic building within an engineering college) allowed students familiarity and control of the study context. This consideration of context mirrors ethical validation within the interpretative research quality framework developed by Walther, Sochacka, and Kellam ([<reflink idref="bib70" id="ref80">70</reflink>] ). Ethical validation takes into account the human aspects of the research, or the ways in which research is conducted with respect to a community that includes participants, researchers, and readers of the study.</p> <p>The choice of the IPA as a methodology was based on the goals of clearly presenting the voices of the participants and the interpretations of the researchers. Similar to the aspect of procedural validation in Walther et al.'s quality framework, this methodology provided a description of the process and sought to create a level of transparency in the analytic process. The coherence of the study, like theoretical validation in Walther et al.'s work, focused on how well the social reality observed (namely, students' perceptions of engineering problem solving as influenced by their motivations) agreed with the findings generated and how well the findings converged on an acceptable interpretation of the social reality. In IPA, coherence of the study is determined by the readers of the study as well as the research team. To further ensure quality, the analytic procedures were documented, allowing the entire research process to be audited by the second author and other members of the research team during and after analysis. These steps reflect process reliability (Walther et al., [<reflink idref="bib70" id="ref81">70</reflink>] ), which was also employed when our work was audited by methodological experts in post‐secondary STEM (Science, Technology, Engineering, and Mathematics) education research. These audits ensured that the account produced was credible.</p> <p>In line with IPA, the word conclusion was avoided as it implies a level of finality that cannot be drawn from this work (Hycner, [<reflink idref="bib25" id="ref82">25</reflink>] ; Lopez & Willis, [<reflink idref="bib38" id="ref83">38</reflink>] ; Moustakas, [<reflink idref="bib48" id="ref84">48</reflink>] ; Smith et al., [<reflink idref="bib67" id="ref85">67</reflink>] ). The history of IPA requires a triple‐hermeneutic turn, or passes through the cycle of interpretation, with the first being the participants' descriptions of their lived experiences and the second being the researchers' interpretations. In the third, readers must draw their own interpretations from the written work (this manuscript). While the written record of the analysis up to this point may be complete, the analysis of the lived experiences of the individuals in this study is still ongoing through active interpretations by the reader.</p> <hd id="AN0128731066-15">Results</hd> <p>The results are presented in the order of the research questions asked in this study: (<reflink idref="bib1" id="ref86">1</reflink>) How do undergraduate engineering students perceive the experience of engineering problem solving and the processes required to solve engineering problems? and (<reflink idref="bib2" id="ref87">2</reflink>) How do students' motivations related to present tasks and future goals in engineering influence their perceptions of engineering problem solving processes? Within themes, the structure of the results is framed around the idiosyncratic voices of the participants by examining descriptive and linguistic patterns. The results then move from the individual details to the themes and the interpretation of student experiences.</p> <hd id="AN0128731066-16">Consistent Approaches to Engineering Problem Solving</hd> <hd id="AN0128731066-17">Engineering is problem solving</hd> <p>Anything [is an engineering problem]. To me the definition of engineering is problem solving so it can pretty much be anything. (Caroline)</p> <p>This account from Caroline represents our attempts to meet students where they were in terms of their experiences, definitions, and understanding of engineering problem solving. The researchers' preconceived notions of problem solving from existing literature (Jonassen, [<reflink idref="bib26" id="ref88">26</reflink>] [<reflink idref="bib27" id="ref89">27</reflink>] [<reflink idref="bib28" id="ref90">28</reflink>] ; Schoenfeld, [<reflink idref="bib61" id="ref91">61</reflink>] [<reflink idref="bib62" id="ref92">62</reflink>] [<reflink idref="bib63" id="ref93">63</reflink>] ) and the dominant cultural trends around problem solving in engineering (Litzinger et al., [<reflink idref="bib37" id="ref94">37</reflink>] ) were bracketed prior to our interactions with the participants. Participants were asked to describe, “What, to you, is an engineering problem?,” thus centering conversation between the researchers and the participants firmly around the perspectives of the latter. Responses indicated that engineering problems were not complex systems or higher order mathematical concepts, but rather anything that required solving through a specific approach, namely using engineering to solve problems. This definition of an engineering problem, which was tightly shared across participants, was expressed through a shared language. Damon represents this shared language for discussing an engineering problem as any problem that is solved in a specific way:</p> <p>Essentially any problem can be looked at in an engineering way. It's more about how you go about solving the problem instead of defining what the problem is. (Damon)</p> <p>Additionally, students did not bound their definitions of engineering problem solving to traditional engineering contexts (e.g., bridges, cars, circuits, etc.) or the contexts in which they were studying engineering (e.g., their declared major) but rather expressed a broadness in what could constitute the context of an engineering problem. The only limitation on the range of definitions of engineering problems was the approaches used in solving them:</p> <p>It's finding a new approach to solve a problem that may already have an answer but just not the best one. It's modifying something so that I guess it works better and easier for the future. (Katerina)</p> <p>Participants' focus on a problem solving approach rather than context demonstrated that these students view engineering based on process rather than the actual solution to a problem. Participants' construction of engineering problem solving as an approach that can address a wide range of problems shows a shared understanding of what makes something an engineering problem. Their use of similar language and scope further indicates that engineering problem solving has a shared and common definition across the participants.</p> <hd id="AN0128731066-18">Engineering problem solving is a process</hd> <p>I spend a while thinking about the problem before I actually start it. I think about how I'm going to approach it and if that's going to end up getting me the answer that I want. I think about the sequencing steps that I'll go through. We would read the problem statement and then usually I would draw a picture, to figure out what's actually happening in the problem. Then I would look at what's being asked and then look at the information you have and then see how you can manipulate that information into an answer. (Katherine)</p> <p>Participants described specific problem solving steps as central to the ways in which engineers approach and solve problems. In addition, they discussed thinking about the solution and examining the given information, then breaking the problem into parts and identifying what additional information was needed to solve it. The next steps in problem solving that participants mentioned were drawing a picture or a diagram to aid in understanding the contexts of the problem and manipulating the information given and transferred from other sources. This entire process for students was bounded by the goal of trying to determine the desired or correct answer:</p> <p>It's just being able to look at a problem and being able to pick out what you know, what you don't know, and then use your knowledge of or understanding of the problem to be able to solve it and get an answer. (Jeremy)</p> <p>[To solve an engineering problem] sometimes means pulling formulas or pulling concepts from other problems and applying it to that one. (Stefan)</p> <p>In spite of differences between participants' backgrounds and experiences, they described a shared set of cognitive processes (e.g., identifying knowns and unknowns, drawing a picture) that can manifest during engineering problem solving, regardless of their academic characteristics. This consistency contrasted with the researchers' expectations of differences in students' perceptions of problem solving processes based on cultural differences in their majors (Godfrey & Parker, [<reflink idref="bib15" id="ref95">15</reflink>] ). Additionally, the tightness of participant interpretations of engineering problems and problem solving steps indicated limited use of unique interpretive stances by these participants. It is likely that because participants in this study were successful (i.e., in good academic standing) in their majors at the same institution, the processes they described may reflect the adapted processes needed for success in the problem‐based engineering courses that typify the curricula in the first two years at their institution.</p> <hd id="AN0128731066-19">Variation in Problem Solving Persistence</hd> <p>If I'm looking at calculus… I hate calculus. I'm looking at an issue like this and I have to solve a calculus problem and I run into an issue–I have no clue what to do. I'll probably get frustrated, and come back to it in like ten minutes. Whereas if I'm working on a problem where I know that this is something that I'm going to do [as part of my career], like it is directly, like I can tell, I know that I'm going to have to do this, if I do the work and get to a problem point where I'm like, “Damn, I did this wrong,” I'll work it out and do it again. I'd just say I don't give up as easy. Put a little more effort in. (Matt)</p> <p>When describing what he thought he would do during the problem solving process, Matt discussed the varying degree he would persist through issues. When working on problems that he thought would be useful (i.e., related to biomedical engineering), he would work through the difficulties. For problems related to topics he disliked, he did not persist but rather stepped away from them. The language of “know[ing] that I am really going to have to do this” shows up again in the responses of Katerina and Stefan when examining potential applications in their engineering major and future as engineers:</p> <p>I definitely do judge [engineering problems] based on if I think this will apply later on in life. Do I need to actually understand it? Or is it just something I need to get done, in which case I just get it done and not put as much time into trying to understand it if it doesn't click right away. So probably just like, how much effort and focus into understanding the current problem I put into and depending on if I see it applicable later on in life. (Katerina)</p> <p>In Statics and Dynamics, I really try to learn everything. I really enjoy the material, and I thought it was great. So things that are obviously related to my major–I probably shouldn't think like this–but in classes like that I really want to learn the material. And then there are classes like physics. It's still related but I just memorize a lot of stuff. Probably a week after the test I couldn't tell you what was on it. (Stefan)</p> <p>When connections to the future were obvious to participants, they discussed a willingness to “work out [the problem] again,” “[put forth] effort and focus,” and “really want to learn the material.” When Matt, Katerina, and Stefan perceived tasks as something they “hate,” “to get done,” or as “less related,” their practices reflected their short‐term task‐level motivations, shifting from persistence and effort to procrastination or avoidance of the task, just getting the task done, and/or memorizing and forgetting. The conversations provided by participants in their interviews demonstrated connections between their futures and present tasks. To further understand how participants utilized their perceptions of the future to interpret those tasks, we must understand how students construct their futures in engineering.</p> <hd id="AN0128731066-20">Perceptions of Future Selves and Interactions with the Present</hd> <p>I'm going to stick with the undergraduate [Biomedical Engineering] program, pursue a master's, and then my goal is to ultimately work for a medical device company in research and design. So, yeah, that'll be the ultimate goal. Probably a PhD after I start working, too. (Jeremy)</p> <p>Seeing connections made by participants between present tasks and their future led us to understand how students conceptualized their future goals. Jeremy discussed having an ultimate goal of working for a medical device company conducting research and design, reiterating to the interviewer that this was his ultimate goal. This ultimate goal is a reflection of Jeremy's ideal future, or a hoped‐for self that can be attained in the future. The conversations around an ideal future are mirrored in Katherine's comments about knowing what she wants to do in the future:</p> <p>I plan to do the five‐year Master's program here. And then I'm thinking about med school. I've taken the practice [Medical College Admission Test] a couple of times, but I'm not sure that's really something I want to do. But I know that I'm very interested in the imaging, bioimaging type things. I really want to work with the equipment but also people. Which is why I thought that it would be – if I was going to be a doctor I would be a radiologist. (Katherine)</p> <p>Participants' constructions of engineering future selves (i.e., emerging identities in engineering careers) demonstrated that they were future‐oriented. Instead of constructing goals that relive the past or that are focused on optimizing the experiences of the present, participants were focused on the future. They constructed these positive future selves at different time points (i.e., depth or temporal distance), varying from graduation day for some to many years into the future for others:</p> <p>I might end up getting a mechanical engineering degree and not even using it for engineering…. I'm not really sure what I want to do with engineering yet. Sometimes it seems to focus on automotive a lot. I don't find that particularly interesting. (Caroline)</p> <p>If I was at [Major Automotive Firm] or maybe another company, I'll probably stay there five or ten years. Ten years [in the future] is pretty hard to describe. (Stefan)</p> <p>Caroline's construction of a future self‐reflected her transition at the beginning of college from seeing herself as becoming an artist to becoming an engineer, while Stefan's description of his future self in engineering reflected his experiences in internships and his views of an uncertain job market. The depth or distance into the future of participants' future selves was driven by individual understandings of and experiences in the field. In addition to constructing selves that participants wanted to be in the future, they described selves that they wished to avoid. Conversations about avoided futures centered around specific careers and tasks related to those careers:</p> <p>I don't want to be a garbage man or sort of a standard factory worker. I don't want to necessarily just work at a desk all day. I definitely don't want to be an electrical engineer after this last class. (Damon)</p> <p>However, participant conversations quickly became more nuanced, differentiating between ideal and avoided selves, which were closely related. Silas expressed, “Ideally I think the anesthesiologist assistant or any sort of person in, you know, a hospital setting would be a goal. I've never really wanted to be a surgeon or a doctor.” Bonnie made a similar distinction between closely related future selves when she discussed her desire to become a doctor and avoid becoming a surgeon due to a history of arthritis in her family. The construction of detailed avoided selves demonstrates that participants had specific ideas about working toward a future self while also trying to avoid others.</p> <p>Participants then went on to refer to these future selves throughout the course of the interview to describe paths of contingent goals to get to their future ones. Jeremy and Katherine (quotes in this section above) both discussed pursuing master's degrees after completing their undergraduate degrees in biomedical engineering and then moving on to the next phase of their career, medical device design or bioimaging, respectively. Jeremy's contingent path continues beyond medical device design to earning his PhD to further position himself in a research and design company. The contingent paths to the future constructed by participants contained goals dependent on one another, through which participants defined their future selves and goals.</p> <p>In contrast, Caroline, who is not sure what she will do in engineering, constructed a set of goals to help her achieve her future self by developing skills relevant to engineering (i.e., problem solving), but these goals were not contingent on or directly related to one another. A discussion of working through a series of goals to reach her future self was notably absent from Caroline compared to peers with more highly defined futures. While Caroline discussed a need for certain skills in her future, she did not describe easily measurable accomplishments when compared to her peers. Having or lacking a set of contingent goals has the potential to shift how students view present tasks. Students with tightly defined goal paths may struggle to find relevance in engineering tasks that fall outside of their defined paths, while students with limited or non‐existent goal paths may struggle to prioritize tasks that will be most beneficial to their development as engineers.</p> <hd id="AN0128731066-21">Perceptions of Present Tasks as Shaping Desired Future Outcomes</hd> <p>I wouldn't want to be stuck doing just one tiny part. I'm a big picture person so it would be really fun to be involved in all aspects of something. (Caroline)</p> <p>When constructing future selves, participants described characteristics of day‐to‐day work they wished for their future selves. The ideas of working with the big picture, being part of the whole process, and having fun were characteristics described by Caroline when working to construct her future self. Caroline's desire for big picture experiences was, in part, guided by her past experiences working on a single automotive part re‐design during her previous internship experience. Similarly, ideas related to career characteristics were expressed by Silas when discussing working on a team that involved group‐level communication:</p> <p>I just want to be involved with both coworkers and people that you're working on or working with things…. I like to work through things as a group or have that communication. (Silas)</p> <p>Participants' conversations about future working environments were based on tasks they enjoyed undertaking in the present. In comments about how they would want to go about their day‐to‐day lives in the future, participants provided increased specificity or clarification of their future self. These comments show that students are motivated not only by their future career but also by the ways they want to approach tasks in their future.</p> <p>In addition to defining characteristics of their future careers, students constructed outcomes for their future careers that extended beyond the boundaries of their interests. Katerina's understanding of her future self was used to express the desired outcome of developing a new drug delivery method to help large groups of people as a materials engineer:</p> <p>I just want to help cure people. But I feel like if I come up with a cure, let's say for a new drug delivery method, that will cure so many more people than I could being a doctor. Like let's say you saved three lives a week, even if you're really lucky, but if you find a way to deliver drugs you can save thousands of people a day if you were lucky enough. (Katerina)</p> <p>This conversation came after Katerina had discussed wanting to be challenged, to work on open‐ended problems, and to be on the leading edge of engineering, reflecting her home life where discussions of research and development were part of regular conversations. Jeremy described his dream of helping people abroad through being a CEO of his own company and providing services and implants to help those communities:This conversation developed from Jeremy's strong connections to a specific community abroad and the role those connections played in his development as an engineer.</p> <p>[I want to own my own business because] I have dreams of helping people abroad. So being able to reach out in that aspect, even providing services for people overseas or being able to send implants, or whatever is needed over there. (Jeremy)</p> <p>The conversations with Caroline, Silas, Jeremy, and Katerina show them utilizing their present selves to craft desired future experiences. Caroline and Silas utilize previous experiences in problem solving scenarios through an internship and coursework, respectively, to describe their desired day‐to‐day work lives. Jeremy and Katerina pushed their descriptions of their future selves forward toward outcomes of their work and how they will influence larger communities. These conversations, which emerged from unique participant perspectives and cultural upbringings, are captured in how students perceived themselves at the time of the interview.</p> <hd id="AN0128731066-22">The Future as Driver for Variation in Perceptions of Present Actions</hd> <p>Because [working in teams is] not my preferred thing to do, and I always find that I like different ways of thinking about things than other people, but basically every class forces you to do at least one group project throughout the semester. And I realize that both as an engineer and as someone in the medical field, you're going to have to work in groups on projects and with people. (Bonnie)</p> <p>While expressing the use of desired skills in future work, participants also described adjusting their attitudes toward present tasks when the importance of the task to their future is clear. Bonnie describes her preferences not to work in teams, contrasted with her realization that in her desired future she will need to work in teams, and as a result, she is actively working to develop her teaming skills. Similarly, as other participants described their career goals for their future selves, they were able to find further clarity and definition in present actions:</p> <p>It's funny because once again going to my co‐op, nobody's using anything from classes that I have learned. But [my classes] are all relevant because you have to be able to think critically. And how I think about things has definitely changed while I have been in engineering. (Stefan)</p> <p>Stefan's conversation about his shift in thinking is driven by his co‐op experience at a major automotive manufacturer, which connects directly to his views of himself in the future. Using his views of the future, he has realized that while small level detail in engineering courses may not be important, the shifts in critical thinking he is developing are fundamental to his future success. Even participants who did not have clear perceptions of their future sought to create additional value for present tasks in hopes that they would be helpful in the future:</p> <p>I don't really know what my future goals are yet, so kind of whatever experiences I can get now will hopefully help me in the future. (Caroline)</p> <p>Caroline and other participants described their perceived value of present tasks in different ways based on their described perceptions of their future selves. They considered both the operating conditions of their future and potential outcome spaces of their future through descriptions of career characteristics (e.g., working in groups, big picture work, etc.) and career outcomes (e.g., helping people). Participants drew their conceptualizations of career characteristics from their work in engineering environments (e.g., Caroline's internship at an automotive parts manufacturer focused on the redevelopment of one small feature of a part with no explicit connection to the larger functionality of a car) and a range of problem solving tasks (e.g., group work, specific assignments or lab activities). Participants' definitions of career characteristics based on present tasks reflect the exposure they have had to intentionally designed problem solving scenarios in their courses of study. In contrast, limited connections between career outcomes and the present show the limited experience participants have in problem solving scenarios that explicitly discuss and evaluate the broader impacts of engineering practice.</p> <p>The connections made by participants between the cognitive effort they put into present tasks and their future goals demonstrated that they are looking for or discovering value in their present tasks, and are expressly considering the time‐based value of these tasks. In other words, participants wanted tasks to be valuable for their future selves.</p> <hd id="AN0128731066-23">Summary of Results</hd> <p>The three themes that emerged from the analysis are summarized in Table . The essence of each theme is captured with a definition and sample quotes from participants. These themes demonstrate consistent perceptions of the engineering problem solving process, where actions taken during problem solving are directed by participants' perceptions of their future goals. Student conceptualizations of their future are shaped by past experiences, contingent goal paths, and future selves. Here these conceptualizations are defined by future goals, connections between the future and present, and how they characterize their activities in the future.</p> <p>Themes Identified from Participants' Perceptions of Engineering Problems, Future Goals, and Beliefs About the Interconnectedness of Future Goals and Perceptions of Problem Solving</p> <p> <ephtml> <table border="1" cellpadding="3"><tr><th>Theme</th><th>Description</th><th>Example quotes</th></tr><tr><td>Consistent steps undertaken when solving an engineering problem</td><td>Participants described universal ways of approaching problem solving in engineering. Some students perceived problem solving and engineering to be one and the same (i.e., an overall engineering problem solving approach); others perceived engineering problem solving as a set of discreet steps.</td><td>It's more about how you go about solving the problem instead of defining what the problem is.It's just being able to look at a problem and being able to pick out what you know, what you don't know, and then use your knowledge of or understanding of the problem to be able to solve it and get an answer.</td></tr><tr><td>Variation in problem solving persistence</td><td>Participants put forth effort or procrastinated problem solving tasks based on their interest and perceptions of how useful the tasks might be to their future goals.</td><td>Whereas if I'm working on a problem where I know that this is something that I'm going to do [as part of my career]…, I'll work it out and do it again. I'd just say I don't give up as easy. Put a little more effort in.</td></tr><tr><td>Perceptions of future selves and interactions with the present</td><td>Participants differentiated between ideal and avoided future selves, which were informed in part through perceptions of present experiences. Perceptions of future selves in turn affected the value they placed on tasks in the present.</td><td>[My classes] are all relevant [to my future] because you have to be able to think critically. And how I think about things has definitely changed while I have been in engineering.</td></tr></table> </ephtml> </p> <hd id="AN0128731066-24">Connections to Theory</hd> <p>Up to this point, our interpretative findings were focused on the participants' descriptions of their experiences, which is the first hermeneutic turn in IPA: understanding the experience in the participants' own terms (Huff et al., [<reflink idref="bib19" id="ref96">19</reflink>] ). The next hermeneutic turn entails the researchers interpreting and explaining findings for our audience in terms of relevant aspects of foundational theories and current literature. Thus, the purpose of this section is to describe the lived experiences of participants in relation to established theories of motivation and problem solving processes.</p> <hd id="AN0128731066-25">Problem Solving Processes</hd> <p>Participants suggested that they would solve engineering problems in a stepwise fashion, which aligns with previous literature (Litzinger et al., [<reflink idref="bib37" id="ref97">37</reflink>] ; McCracken & Newstetter, [<reflink idref="bib46" id="ref98">46</reflink>] ). Students who solve problems in this manner are more successful in their introductory engineering courses than students who do not use stepwise processes (Grigg, [<reflink idref="bib16" id="ref99">16</reflink>] ). As students who have reached the end of their sophomore year, our participants are likely to understand what it takes to be successful in engineering and know that stepwise problem solving practices may lead to further development as an engineer. Additionally, given that participants described engineering as problem solving, identifying a clear process for their problem solving practices and seeing value in solving engineering problems may further indicate their progression toward becoming an engineer.</p> <p>All participants persisted through their first two years in engineering, potentially through the acquisition of the culturally driven language of those whom they view as engineers. For well‐defined, concept‐specific problems, knowing a set of steps for solving and attaining a solution can lead to increased academic success for students. However, knowledge developed while solving well‐defined problems does not necessarily help students address the broad and often ill‐defined nature of engineering problems that the participants described (Jonassen, [<reflink idref="bib26" id="ref100">26</reflink>] ). Additionally, participants all outlined steps they would follow to solve problems, which reflect metacognitive processes, moving from the planning stages of metacognition to control, monitoring and evaluation, followed by mathematical manipulation (Whitebread et al., [<reflink idref="bib71" id="ref101">71</reflink>] ). When discussing beneficial problems, participants mentioned that context is important to them. Context served as a driver for participants' utilization of problem solving processes and task persistence.</p> <hd id="AN0128731066-26">Future Time Perspective</hd> <p>When describing solving engineering problems, participants discussed the role of the future in directing their problem solving task performance. Specifically, participants who viewed tasks as relevant to their future were more likely to persist and work toward developing an understanding. When tasks were not relevant to participants' futures, they worked to get through these tasks quickly and did not persist when they encountered difficulties. The implications of the future on present actions are captured by perceived instrumentality from FTP theory (Husman & Lens, [<reflink idref="bib22" id="ref102">22</reflink>] ; Husman & Shell, [<reflink idref="bib23" id="ref103">23</reflink>] ; Miller, DeBacker, & Greene, [<reflink idref="bib47" id="ref104">47</reflink>] ).</p> <p>Participants described the perceived instrumentality of tasks within their disciplines based on what they perceived to be important to their future as seen in quotations from participants in the Variation in Problem Solving Persistence section. Stefan worked to limit which tasks he views with exogenous instrumentality. He did not feel that specific information learned in his engineering courses is necessarily relevant to his future; rather, he perceived the cognitive and metacognitive processes he is learning as relevant. Increases in perceived instrumentality of tasks lead students to perceive themselves using specific strategies similar to those used by self‐regulated learners (Pintrich, [<reflink idref="bib56" id="ref105">56</reflink>] ). Additionally, students' valuing of a task and selecting of a technique to complete the task begins to explain how students with similar levels of preparation may reach different levels of understanding in the same course (Hilpert et al., [<reflink idref="bib17" id="ref106">17</reflink>] ; Malka & Covington, [<reflink idref="bib39" id="ref107">39</reflink>] ; Nelson et al., [<reflink idref="bib50" id="ref108">50</reflink>] ).</p> <p>While perceived instrumentality describes how participants create value judgments based on their future, it does not explain how participants conceptualize their futures and what elements of their futures are important in making value judgments related to engineering problem solving tasks. During the interview process, participants constructed conceptualizations of themselves in the future through conversations related to distance into the future, possible selves, and the creation of stepwise paths to desired futures. Students who project their goals into the future have shown increased satisfaction on present tasks and are more likely to persist toward their goals (Husman & Lens, [<reflink idref="bib22" id="ref109">22</reflink>] ). This interaction of the future and present is seen in the participants' evaluations of their perceived level of effort on problem solving tasks based on the tasks' relevance to their future goals. Participants who projected their goals into the future perceived future‐oriented events as closely connected to one another with little temporal distance between them. An example of this perception is Jeremy's description of the steps he plans to take on a continuum through his undergraduate and graduate degrees into industry and beyond. This description mirrors similar results showing that students who project their goals more deeply into the future view tasks as more closely connected than those with proximal constructions of depth into the future (Husman & Lens, [<reflink idref="bib22" id="ref110">22</reflink>] ).</p> <p>Notably missing in the participants' conversations about their future is the discussion of speed, or how participants perceive their ability to manage upcoming events (Hilpert et al., [<reflink idref="bib17" id="ref111">17</reflink>] ). The participants in this study did not explicitly describe being unable to manage upcoming future events; their descriptions of the future did not include feelings of stress or concern about the approach of those events. These participants are currently situated in highly structured engineering programs that provide strong guidance and future direction through explicit curricular requirements (Godfrey & Parker, [<reflink idref="bib15" id="ref112">15</reflink>] ) and implicit cultural values about what they will do after graduation (i.e., working in industry or going to graduate school). Previous research has shown that engineering students' perceptions of speed are a significant predictor of the use of knowledge building strategies (Hilpert et al., [<reflink idref="bib17" id="ref113">17</reflink>] ). However, further work is needed to determine the role of speed in influencing student perceptions of problem solving.</p> <hd id="AN0128731066-27">Future Possible Selves</hd> <p>In conversations about themselves in the future, participants constructed wished or hoped for future selves alongside realistic and avoided selves. This conceptualization extends this work beyond the traditional bounds of FTP literature in terms of future goals (Husman & Lens, [<reflink idref="bib22" id="ref114">22</reflink>] ; Husman & Shell, [<reflink idref="bib23" id="ref115">23</reflink>] ) to include students' dreams or desired futures. Within participants' descriptions of their future selves are future dreams of helping people or solving big problems, both of which are perceived as realistic and attainable through their path into engineering. Future possible selves theory posits that there are three elements to how individuals describe their future selves: ideal, attainable, and avoided selves (Markus & Nurius, [<reflink idref="bib40" id="ref116">40</reflink>] ). The level of distinction students make between their ideal/realistic and avoided selves gives them the ability to create balanced possible selves (a positive hoped‐for self and a negative avoided self). Establishing balanced possible selves, which is correlated to increased positive behavioral strategies for learning such as goal setting, elaboration and regulation of present tasks (Markus & Nurius, [<reflink idref="bib40" id="ref117">40</reflink>] ), is more effective motivationally than unbalanced possible selves (Oyserman, [<reflink idref="bib51" id="ref118">51</reflink>] ; Oyserman, Brickman, & Rhodes, [<reflink idref="bib52" id="ref119">52</reflink>] ). In addition, thinking about the future or working to develop future possible selves can increase interest and efficacy (short‐term motivation) to succeed in school (Oyserman et al., [<reflink idref="bib52" id="ref120">52</reflink>] ) and self‐regulatory action (Oyserman, [<reflink idref="bib51" id="ref121">51</reflink>] ). Students who develop ideal selves are more likely to persist when faced with challenges or difficulties in their lives (Pizzolato, [<reflink idref="bib57" id="ref122">57</reflink>] ).</p> <hd id="AN0128731066-28">Contingent Goal Paths</hd> <p>Upon developing future possible selves, participants then outlined a series of steps or goals for reaching those future selves. These paths contain goals that are dependent on one another. Based on literature related to perceived instrumentality, the contingency of these goals on one another leads to increased valuing of present tasks, such as problem solving, due to the implications for future goals (Raynor, [<reflink idref="bib59" id="ref123">59</reflink>] ). Conversely, tasks that are not viewed as part of the contingent path will be viewed as less valuable, or of exogenous instrumentality (Raynor, [<reflink idref="bib59" id="ref124">59</reflink>] ). Participants in this study use the future to evaluate present tasks explicitly when they use distant future goals to construct contingent goals such as getting a master's degree or helping their home community. In addition, participants have developed future possible selves that they use in constructing future goals and creating a temporal order to these goals. If participants construct these future goals, they create a personal time space, or temporal habitus (Husman & Lens, [<reflink idref="bib22" id="ref125">22</reflink>] ), and if they can connect goals to this space, then their motivated action increases; if they cannot make this connection, then they devalue that task.</p> <hd id="AN0128731066-29">Identity‐Based Motivation</hd> <p>Participants in this study provided rich and detailed descriptions of their future selves and then utilized these selves when gauging the value of present tasks or how well they fit with their future identities. The judgment of fit to a future identity or future possible self directs participant action on problem solving tasks. Tasks that fit participants' future selves are likely to engender higher levels of persistence and motivation, even in the face of difficulty. The increased levels of motivation and persistence parallels previous trends in Identity‐Based Motivation showing that if tasks are congruent to one's future self, they are more likely to motivate action in the present (Oyserman & Destin, [<reflink idref="bib53" id="ref126">53</reflink>] ; Oyserman, Destin, & Novin [<reflink idref="bib54" id="ref127">54</reflink>] ; Landau, Oyserman, Keefer, & Smith [<reflink idref="bib35" id="ref128">35</reflink>] ).</p> <hd id="AN0128731066-30">Connections Between Motivation Theories Across Time Scales</hd> <p>Participants in this study present a view of motivation that does not relate to a single time scale of motivation but rather a blend of scales that influences the tasks students undertake when solving problems. For example, congruence between a task and an individual's identity may be moderating students' grit or willingness to persist on short‐term engineering tasks (Duckworth, Peterson, Matthews, & Kelly, [<reflink idref="bib12" id="ref129">12</reflink>] ). Grit, which examines an individual's sustained interest and willingness to work through setbacks, can be a positive predictor of student academic success and goal attainment (Duckworth et al., [<reflink idref="bib12" id="ref130">12</reflink>] ). In this study, grit manifests during discussions of smaller or short‐term problem solving tasks. Grit is moderated by students' perceived instrumentality of a problem solving task, effectively moving the conversation of grit from one that is more global to one that is domain and task specific. Thus, our study frames grit as a short‐term motivation. This interpretation of grit in engineering students challenges the theoretical underpinnings of grit (e.g., Rimfeld, Kovas, Dale, & Plomin, [<reflink idref="bib60" id="ref131">60</reflink>] ; Secules, Gupta, Elby, & Turpen, [<reflink idref="bib64" id="ref132">64</reflink>] ) and supports work that cautions against moving theoretical frameworks across cultures without considering how constructs may manifest in an environment (e.g., Pietkiewicz, & Smith, [<reflink idref="bib55" id="ref133">55</reflink>] ; Zusho & Clayton, [<reflink idref="bib73" id="ref134">73</reflink>] ). Interconnections between the different time scales of motivation are illustrated in this study by participants' moderation of grit in the present based on their FTPs (e.g., connections between a task and their future selves). Participants' expression of these multiple facets of motivation indicates a data‐driven need for closely examining how motivation frameworks are implemented and integrated in engineering.</p> <hd id="AN0128731066-31">Implications</hd> <p>From an instructional perspective, with the understanding that participants evaluated a number of different present tasks in terms of their future‐oriented motivations, discussions of careers and tasks performed by engineers should be incorporated throughout the course of students' educational experiences. All participants in this study had a positive view of the future, discussing the positive influence of the present on it. As such, instructors' discussions of the future with students may help them develop increased future‐oriented and task‐specific motivations (Kauffman & Husman, [<reflink idref="bib30" id="ref135">30</reflink>] ).</p> <p>The variable use of problem solving processes extends previous findings in engineering problem solving to show that there is more involved than students' understanding of how to solve engineering problems (Nelson et al., [<reflink idref="bib50" id="ref136">50</reflink>] ). While problems across engineering disciplines may explore similar concepts and theories, students may not perceive the value of a theoretical understanding when compared to an applied understanding. This result would argue that creating problems with relevant contexts to students' future goals would better help them see the instrumentality of the problems they are solving. The creation of contextually bound problems may be especially meaningful for students in introductory engineering courses who are just beginning their journeys as engineers.</p> <p>In conversations about engineering problems, our participants described engineering as problem solving. They did not reflect the traditional dualistic tension in which the social and technological aspects of engineering coexist (Faulkner, [<reflink idref="bib13" id="ref137">13</reflink>] ). In their minds, acquiring technical skills was a seamless part of achieving their socially relevant future goals. The participants' departure from defining engineering as the application of math and science to defining it as problem solving suggests shifting the ways in which educators could discuss engineering and the problems addressed by engineers. Shifting the discussion of engineering to a field that can help people and provide better solutions may help increase enrollment and retention of nontraditional engineering populations (Camacho & Lord, [<reflink idref="bib7" id="ref138">7</reflink>] ). Participants in this study fit a number of demographic measures that are often considered nontraditional in engineering in terms of gender (four of nine are female), ethnicity (two were of international origin), and transfer status (one transferred into engineering). They conceptualize engineering as a field that solves problems for broader societal purposes rather the traditional technocratic view of engineering presented in the media (Sochacka, Walther, Wilson, & Brewer, [<reflink idref="bib68" id="ref139">68</reflink>] ) and engineering classrooms (National Academy of Engineering, [<reflink idref="bib49" id="ref140">49</reflink>] ).</p> <p>From a research perspective, this work indicates a data‐driven argument for the exploration of combining motivation variables. While the combination of time‐scales was proposed in educational psychology spaces (DeShon & Gillespie, [<reflink idref="bib11" id="ref141">11</reflink>] ), this conversation has yet to advance into engineering beyond a few notable examples using person‐center or cluster analysis techniques (e.g., Nelson et al., [<reflink idref="bib50" id="ref142">50</reflink>] ). The combination of multiple motivation frameworks displayed by students when solving engineering problems begins to elucidate the measures of motivation salient in engineering contexts. Here, future‐oriented motivations (identity‐based motivation and FTP) and task or short‐term motivations (grit as conceptualized by the participants in this study) combine to describe a rich picture of motivation in engineering. While this work makes connections between these three frameworks, additional work is needed to examine the salient motivation frameworks in engineering.</p> <hd id="AN0128731066-32">Limitations and Future Work</hd> <p>The phenomenon of students' perceptions of engineering problem solving and the connections made between students' future oriented motivations and problem solving is longitudinal in nature. For this work, we have taken only a snapshot of students' interactions with this phenomenon and cannot speak to the ways in which students' perceptions of problem solving and future‐oriented motivations change over time; we can only speak to the descriptions of connections in the moment. In her interview, Katerina described a process by which she worked to develop her perceptions of the future:This description outlines the need for future work exploring the longitudinal shifts in student FTP development and the ways in which these perspectives influence student perceptions of problem solving.</p> <p>Every day I kind of think of something else that will be cool or not cool and I try to fit that into what I think I want to do in the future, and I guess eventually it keeps evolving into a different future based on stuff that I add to it. And how concrete any of that is depends on how well all these pieces fit together and hopefully they actually end up being some form of research that I want to do. (Katerina)</p> <p>In addition, the steps students actually undertake during problem solving merit further exploration as self‐reported metacognitive practices can be inaccurate (Whitebread et al., [<reflink idref="bib71" id="ref143">71</reflink>] ). Training received by students on how to solve problems from instructors and educational support centers (i.e., tutoring centers) may create responses that do not accurately reflect students' actual practices but instead reflect their knowledge of how engineers should solve problems. Future work should examine actual student practices used when solving engineering problems and explore how these observed practices are influenced by students' FTPs.</p> <p>The participant voices in this study represent upper‐division engineering students. While the conversations with participants provide depth and nuance to the connections between motivation and problem solving, they represent a very tightly bound set of discussions driven by our purposeful sampling procedures that may limit transferability to students at other time points in engineering such as first‐year or graduate programs. Additionally, the sample selected represents a reflection of social‐cultural practices from a single institution. While we have connected this cultural reflection to broader conversations in the literature, the voices in this study may not reflect those that would be found, for example, at historically Black colleges and universities or Hispanic serving institutions. Similarly, our participants were in good academic standing and had progressed in their majors at the same institution; thus, their perceptions of problem solving are likely influenced by those practices they have observed as being necessary for success in the problem‐based engineering courses they had completed. Future studies should include students in other types of educational settings, such as project‐based engineering programs.</p> <hd id="AN0128731066-33">Summary of Findings</hd> <p>Our findings provide an important understanding of how engineering student perceptions of solving engineering problems are shaped by their future‐oriented motivations. Additionally, the results of this work begin to elucidate how existing theory is lived and expressed by engineering students in engineering contexts. Participants of this study displayed a consistent understanding of engineering problem solving and problem solving processes. Despite this consistent understanding, students discussed varied tasks and effort levels they would take when solving engineering problems. This variation was based on participants' perceived abilities to make direct connections between the problem solving task and their FTPs. Participants noted that the depth into the future to which they could perceive their future possible selves, the characteristics of their future possible selves, and their contingent goals to reach a desired future influenced their perceived instrumentality (value) judgments of present tasks.</p> <p>Additional work is needed to examine the longitudinal nature of FTP development for engineering students and the connections to actual steps taken by students when solving problems. Once these processes and connections are better understood by researchers, educators can adapt pedagogical practices to help students make connections between present tasks and their desired futures. Overall, our findings indicate that students' conceptualizations of the future influence their perceptions of present problem solving tasks, providing evidence for engineering educators of ways to tailor their courses and problem solving tasks to encourage students to make connections between these tasks and their futures.</p> <hd id="AN0128731066-34">Acknowledgments</hd> <p>This research was supported by a grant from the National Science Foundation (EEC‐1055950). Any opinions, findings, conclusions, or recommendations expressed in the material are those of the authors and do not necessarily reflect those of the National Science Foundation. Additionally, the authors would like to thank Courtney Faber and Catherine McGough for their assistance in data collection and review of findings for this project, and Jenefer Husman and Katherine Nelson for their mentoring and expertise. Finally, we would like to thank our participants for their willingness to share their stories and time with the research team.</p> <ref id="AN0128731066-35"> <title>References</title> <blist> <bibl id="bib1" idref="ref5" type="bt">1</bibl> <bibtext>Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379. doi.org/10.1002/j.21689830.2007.tb00945.x </bibtext> </blist> <blist> <bibl id="bib2" idref="ref6" type="bt">2</bibl> <bibtext>Baillie, C., Ko, E., Newstetter, W., & Radcliffe, D. F. (2011). Advancing diverse and inclusive engineering education practices through interdisciplinary research and scholarship. Journal of Engineering Education, 100(1), 6–13. doi.org/10.1002/j.2168-9830.2011.tb00002.x </bibtext> </blist> <blist> <bibl id="bib3" idref="ref15" type="bt">3</bibl> <bibtext>Bandura, A. (2006). Guide for constructing self‐efficacy scales. In F. Pajares & T. Urdan (Eds.), Self‐efficacy beliefs of adolescents (pp. 307–337). Greenwich, CT: Information Age Publishing. </bibtext> </blist> <blist> <bibl id="bib4" idref="ref60" type="bt">4</bibl> <bibtext>Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10(2), 141–163. </bibtext> </blist> <blist> <bibl id="bib5" idref="ref7" type="bt">5</bibl> <bibtext>Borrego, M., & Newswander, L. K. (2008). Characteristics of successful cross‐disciplinary engineering education collaborations. Journal of Engineering Education, 97(2), 123–134. doi.org/10.1002/j.2168-9830.2008.tb00962.x </bibtext> </blist> <blist> <bibl id="bib6" idref="ref20" type="bt">6</bibl> <bibtext>Brown, M. K., Hershock, C., Finelli, C. J., & O'Neal, C. (2009). Teaching for retention in science, engineering, and math disciplines: A guide for faculty. CRLT Occasional Paper No. 25. Ann Arbor, MI: Center for Research on Learning and Teaching, University of Michigan. </bibtext> </blist> <blist> <bibl id="bib7" idref="ref138" type="bt">7</bibl> <bibtext>Camacho, M. M., & Lord, S. M. (2013). The borderlands of education: Latinas in engineering. Boulder, CO: Lexington Books. </bibtext> </blist> <blist> <bibl id="bib8" idref="ref2" type="bt">8</bibl> <bibtext>Cooper, M. M., & Sandi‐Urena, S. (2009). Design and validation of an instrument to assess metacognitive skillfulness in chemistry problem solving. Journal of Chemical Education, 86(2), 240. doi.org/10.1021/ed086p240 </bibtext> </blist> <blist> <bibl id="bib9" idref="ref53" type="bt">9</bibl> <bibtext>De Witt, L., & Ploeg, J. (2006). Critical appraisal of rigour in interpretive phenomenological nursing research. Journal of Advanced Nursing, 55(2), 215–229. doi.org/10.1111/j.1365-2648.2006.03898.x </bibtext> </blist> <blist> <bibl id="bib10" idref="ref77" type="bt">10</bibl> <bibtext>Denzin, N. K., & Lincoln, Y. S. (2011). The SAGE handbook of qualitative research. Thousand Oaks, CA: Sage. </bibtext> </blist> <blist> <bibl id="bib11" idref="ref23" type="bt">11</bibl> <bibtext>DeShon, R. P., & Gillespie, J. Z. (2005). A motivated action theory account of goal orientation. The Journal of Applied Psychology, 90(6), 1096–1127. doi.org/10.1037/0021-9010.90.6.1096 </bibtext> </blist> <blist> <bibl id="bib12" idref="ref129" type="bt">12</bibl> <bibtext>Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long‐term goals. Journal of personality and social psychology, 92(6), 1087. doi.org/10.1037/0022-3514.92.6.1087 </bibtext> </blist> <blist> <bibl id="bib13" idref="ref137" type="bt">13</bibl> <bibtext>Faulkner, W. (2000). Dualisms, hierarchies and gender in engineering. Social Studies of Science, 30(5), 759–792. doi.org/10.1177/0306312706059745 </bibtext> </blist> <blist> <bibl id="bib14" idref="ref45" type="bt">14</bibl> <bibtext>Gjesme, T. (1979). Future time orientation as a function of achievement motives, ability, delay of gratification, and sex. Journal of Psychology, 101, 173–188. doi.org/10.1080/00223980.1979.9915069 </bibtext> </blist> <blist> <bibl id="bib15" idref="ref95" type="bt">15</bibl> <bibtext>Godfrey, E., & Parker, L. (2010). Mapping the cultural landscape in engineering education. Journal of Engineering Education, 99, 5–22. doi.org/10.1002/j.2168-9830.2010.tb01038.x </bibtext> </blist> <blist> <bibl id="bib16" idref="ref99" type="bt">16</bibl> <bibtext>Grigg, S. J. (2012). A process analysis of engineering problem solving and assessment of problem solving skills. Dissertation. Clemson University, Clemson, SC. </bibtext> </blist> <blist> <bibl id="bib17" idref="ref29" type="bt">17</bibl> <bibtext>Hilpert, J. C., Husman, J., Stump, G. S., Kim, W., Chung, W. T., & Duggan, M. A. (2012). Examining students' future time perspective: Pathways to knowledge building. Japanese Psychological Research, 54(3), 229–240. doi.org/10.1111/j.1468-5884.2012.00525.x </bibtext> </blist> <blist> <bibl id="bib18" idref="ref48" type="bt">18</bibl> <bibtext>Huberman, M., & Miles, M. (2002). The qualitative researcher's companion. Thousand Oaks, CA: Sage. </bibtext> </blist> <blist> <bibl id="bib19" idref="ref58" type="bt">19</bibl> <bibtext>Huff, J. L., Smith, J. A., Jesiek, B. K., Zoltowski, C. B., Graziano, W. G., & Oakes, W. C. (2014). From methods to methodology: Reflection on keeping the philosophical commitments of interpretative phenomenological analysis. Proceedings of the 2015 Frontiers in Education (FIE) Conference, El Paso, TX. </bibtext> </blist> <blist> <bibl id="bib20" idref="ref43" type="bt">20</bibl> <bibtext>Husman, J., Derryberry, W. P., Crowson, H. M., & Lomax, R. (2004). Instrumentality, task value, and intrinsic motivation: Making sense of their independent interdependence. Contemporary Educational Psychology, 29(1), 63–76. doi.org/10.1016/S0361-476X(03)00019-5 </bibtext> </blist> <blist> <bibl id="bib21" idref="ref24" type="bt">21</bibl> <bibtext>Husman, J., Hilpert, J. C., & Brem, S. K., (2016). Future time perspective connectedness to a career: The contextual effects of classroom knowledge building. Psychologica Belgica, 56(3), 210–225. doi.org/10.5334/pb.282 </bibtext> </blist> <blist> <bibl id="bib22" idref="ref26" type="bt">22</bibl> <bibtext>Husman, J., & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34(2), 113–125. doi.org/10.1207/s15326985ep3402_4 </bibtext> </blist> <blist> <bibl id="bib23" idref="ref33" type="bt">23</bibl> <bibtext>Husman, J., & Shell, D. F. (2008). Beliefs and perceptions about the future: A measurement of future time perspective. Learning and Individual Differences, 18(2), 166–175. doi.org/10.1016/j.lindif.2007.08.001 </bibtext> </blist> <blist> <bibl id="bib24" idref="ref16" type="bt">24</bibl> <bibtext>Hutchison, M. A., Follman, D. K., Sumpter, M., & Bodner, G. (2006). Factors influencing the self‐efficacy beliefs of first‐year engineering students. Journal of Engineering Education, 95(1), 39. doi.org/10.1002/j.2168-9830.2006.tb00876.x </bibtext> </blist> <blist> <bibl id="bib25" idref="ref82" type="bt">25</bibl> <bibtext>Hycner, R. H. (1985). Some guidelines for the phenomenological analysis. Human Studies, 8, 279–303. doi.org/10.1007/BF00142995 </bibtext> </blist> <blist> <bibl id="bib26" idref="ref10" type="bt">26</bibl> <bibtext>Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. doi.org/10.1007/BF02300500 </bibtext> </blist> <blist> <bibl id="bib27" idref="ref89" type="bt">27</bibl> <bibtext>Jonassen, D. H. (2004). Learning to solve problems: An instructional design guide. (Vol. 6). San Francisco, CA: John Wiley & Sons. </bibtext> </blist> <blist> <bibl id="bib28" idref="ref11" type="bt">28</bibl> <bibtext>Jonassen, D. H. (2010). Learning to solve problems: A handbook for designing problem‐solving learning environments. New York, NY: Routledge. </bibtext> </blist> <blist> <bibl id="bib29" idref="ref18" type="bt">29</bibl> <bibtext>Jones, B. D., Paretti, M. C., Hein, S. F., & Knott, T. W. (2010). An analysis of motivation constructs with first‐year engineering students: Relationships among expectancies, values, achievement, and career plans. Journal of Engineering Education, 99(4), 319–336. doi.org/10.1002/j.2168-9830.2010.tb01066.x </bibtext> </blist> <blist> <bibl id="bib30" idref="ref135" type="bt">30</bibl> <bibtext>Kauffman, D. F., & Husman, J. (2004). Effects of time perspective on student motivation: Introduction to a special issue. Educational Psychology Review, 16, 1. doi.org/10.1023/B:EDPR.0000012342.37854.58 </bibtext> </blist> <blist> <bibl id="bib31" idref="ref12" type="bt">31</bibl> <bibtext>Kirn, A. (2014). The influences of engineering student motivation on short‐term tasks and long‐term goals. (Dissertation). Clemson University, Clemson, SC. </bibtext> </blist> <blist> <bibl id="bib32" idref="ref3" type="bt">32</bibl> <bibtext>Kirn, A., Faber, C. J., & Benson, L. C. (2014). Engineering students' perceptions of the future. Proceedings of the American Society for Engineering Education 2014 Annual Conference, Indianapolis, IN. </bibtext> </blist> <blist> <bibl id="bib33" idref="ref31" type="bt">33</bibl> <bibtext>Kirn, A., Grigg, S., & Benson, L. (2013) Relationships between engineering student motivation and problem solving performance. American Education Research Association Annual Conference, San Francisco, CA. </bibtext> </blist> <blist> <bibl id="bib34" idref="ref13" type="bt">34</bibl> <bibtext>Koh, C., Tan, H. S., Tan, K. C., Fang, L., Fong, F. M., Kan, D., … Wee, M. L. (2010). Investigating the effect of 3d simulation based learning on the motivation and performance of engineering students. Journal of Engineering Education, 99(3), 237–251. doi.org/10.1002/j.2168-9830.2010.tb01059.x </bibtext> </blist> <blist> <bibl id="bib35" idref="ref128" type="bt">35</bibl> <bibtext>Landau, M. J., Oyserman, D., Keefer, L. A., & Smith, G. C. (2014). The college journey and academic engagement: How metaphor use enhances identity‐based motivation. Journal of Personality and Social Psychology, 106(5), 679. doi.org/10.1037/a0036414 </bibtext> </blist> <blist> <bibl id="bib36" idref="ref68" type="bt">36</bibl> <bibtext>Langdridge, D. (2004). The hermeneutic phenomenology of Paul Ricoeur. Existential analysis: Journal of the Society for Existential Analysis, 15(2), 243–255. </bibtext> </blist> <blist> <bibl id="bib37" idref="ref94" type="bt">37</bibl> <bibtext>Litzinger, T. A., Meter, P. V., Firetto, C. M., Passmore, L. J., Masters, C. B., Turns, S. R., … Zappe, S. E. (2010). A cognitive study of problem solving in statics. Journal of Engineering Education, 99, 337–353. doi.org/10.1002/j.2168-9830.2010.tb01067.x </bibtext> </blist> <blist> <bibl id="bib38" idref="ref51" type="bt">38</bibl> <bibtext>Lopez, K. A., & Willis, D. G. (2004). Descriptive versus interpretive phenomenology: Their contributions to nursing knowledge. Qualitative Health Research, 14(5), 726–735. doi.org/10.1177/1049732304263638 </bibtext> </blist> <blist> <bibl id="bib39" idref="ref107" type="bt">39</bibl> <bibtext>Malka, A., & Covington, M. V. (2005). Perceiving school performance as instrumental to future goal attainment: Effects on graded performance. Contemporary Educational Psychology, 30(1), 60–80. doi.org/10.1016/j.cedpsych.2004.04.001 </bibtext> </blist> <blist> <bibl id="bib40" idref="ref116" type="bt">40</bibl> <bibtext>Markus, H., & Nurius, P. (1986). Possible selves. American Psychologist, 41(9), 954–969. doi.org/10.1037//0003-066X.41.9.954 </bibtext> </blist> <blist> <bibl id="bib41" idref="ref21" type="bt">41</bibl> <bibtext>Marra, R. M., Rodgers, K. A, Shen, D., & Bogue, B. (2009). Women engineering students and self‐efficacy: A multi‐year, multi‐institution study of women engineering student self‐efficacy. Journal of Engineering Education, 98, 27–38. doi.org/10.1002/j.2168-9830.2009.tb01003.x </bibtext> </blist> <blist> <bibl id="bib42" idref="ref22" type="bt">42</bibl> <bibtext>Matusovich, H., Streveler, R. A., Loshbaugh, H., Miller, R., & Olds, B. (2008). Will I succeed in engineering?: Using expectancy‐value theory in a longitudinal investigation of students' beliefs. Proceedings of the American Society for Engineering Education 2008 Annual Conference, Pittsburgh, PA. </bibtext> </blist> <blist> <bibl id="bib43" idref="ref14" type="bt">43</bibl> <bibtext>Matusovich, H., Streveler, R. A., & Miller, R. (2010). Why do students choose engineering? A qualitative, longitudinal investigation of students' motivational values. Journal of Engineering Education, 99(4), 289–303. doi.org/10.1002/j.2168-9830.2010.tb01064.x </bibtext> </blist> <blist> <bibl id="bib44" idref="ref9" type="bt">44</bibl> <bibtext>Mayer, R. E. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science, 26(1), 49–63. doi.org/10.1023/A:1003088013286 </bibtext> </blist> <blist> <bibl id="bib45" idref="ref1" type="bt">45</bibl> <bibtext>Mayer, R. E., & Wittrock, M. C. (2013). Problem‐solving transfer. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of Educational Psychology (pp. 47–62). New York, NY: Macmillan. </bibtext> </blist> <blist> <bibl id="bib46" idref="ref4" type="bt">46</bibl> <bibtext>McCracken, W. M., & Newstetter, W. C. (2001). Text to diagram to symbol: Representational transformations in problem‐solving session F2G. American Society of Engineering Education and Frontiers in Education Conference (pp. 13–17). doi.org/10.1109/FIE.2001.963721 </bibtext> </blist> <blist> <bibl id="bib47" idref="ref104" type="bt">47</bibl> <bibtext>Miller, R. B., DeBacker, T. K., & Greene, B. A. (1999). Perceived instrumentality and academics: The link to task valuing. Journal of Instructional Psychology, 26(4), 250. </bibtext> </blist> <blist> <bibl id="bib48" idref="ref70" type="bt">48</bibl> <bibtext>Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage Publications. </bibtext> </blist> <blist> <bibl id="bib49" idref="ref140" type="bt">49</bibl> <bibtext>National Academy of Engineering. (2008). Changing the conversation: Messages for Improving public understanding of engineering. Washington, DC: The National Academies Press. </bibtext> </blist> <blist> <bibl id="bib50" idref="ref28" type="bt">50</bibl> <bibtext>Nelson, K. G., Shell, D. F., Husman, J., Fishman, E. J., & Soh, L. K. (2015). Motivational and self‐regulated learning profiles of students taking a foundational engineering course. Journal of Engineering Education, 104(1), 74–100. doi.org/10.1002/jee.20066 </bibtext> </blist> <blist> <bibl id="bib51" idref="ref118" type="bt">51</bibl> <bibtext>Oyserman, D. (2004). Possible selves as roadmaps. Journal of Research in Personality, 38(2), 130–149. doi.org/10.1016/S0092-6566(03)00057-6 </bibtext> </blist> <blist> <bibl id="bib52" idref="ref119" type="bt">52</bibl> <bibtext>Oyserman, D., Brickman, D., & Rhodes, M. (2007). School success, possible selves, and parent school involvement. Family Relations, 56, 479–489. doi.org/10.1111/j.1741-3729.2007.00475.x </bibtext> </blist> <blist> <bibl id="bib53" idref="ref126" type="bt">53</bibl> <bibtext>Oyserman, D., & Destin, M. (2010). Identity‐based motivation: Implications for intervention. The Counseling Psychologist, 38(7), 1001–1043. doi.org/10.1177/0011000010374775 </bibtext> </blist> <blist> <bibl id="bib54" idref="ref127" type="bt">54</bibl> <bibtext>Oyserman, D., Destin, M., & Novin, S. (2015). The context‐sensitive future self: Possible selves motivate in context, not otherwise. Self and Identity, 14(2), 173–188. doi.org/10.1080/15298868.2014.965733 </bibtext> </blist> <blist> <bibl id="bib55" idref="ref133" type="bt">55</bibl> <bibtext>Pietkiewicz, I., & Smith, J. A. (2014). A practical guide to using interpretative phenomenological analysis in qualitative research psychology. Psychological Journal, 20(1), 7–14. doi.org/10.14691/CPPJ.20.1.7 </bibtext> </blist> <blist> <bibl id="bib56" idref="ref105" type="bt">56</bibl> <bibtext>Pintrich, P. (1999). The role of motivation in promoting and sustaining self‐regulated learning. International Journal of Educational Research, 31(6), 459–470. doi.org/10.1016/S0883-0355(99)00015-4 </bibtext> </blist> <blist> <bibl id="bib57" idref="ref122" type="bt">57</bibl> <bibtext>Pizzolato, J. E. (2006). Achieving college student possible selves: Navigating the space between commitment and achievement of long‐term identity goals. Cultural Diversity & Ethnic Minority Psychology, 12(1), 57–69. doi.org/10.1037/1099-9809.12.1.57 </bibtext> </blist> <blist> <bibl id="bib58" idref="ref54" type="bt">58</bibl> <bibtext>Pringle, J., Drummond, J., McLafferty, E., & Hendry, C. (2011). Interpretative phenomenological analysis: A discussion and critique. Nurse Researcher, 18(3), 20–24. </bibtext> </blist> <blist> <bibl id="bib59" idref="ref123" type="bt">59</bibl> <bibtext>Raynor, J. (1969). Future orientation and motivation of immediate activity: An elaboration of the theory of achievement motivation. Psychological Review, 76(6), 606–610. doi.org/10.1037/h0028291 </bibtext> </blist> <blist> <bibl id="bib60" idref="ref131" type="bt">60</bibl> <bibtext>Rimfeld, K., Kovas, Y., Dale, P. S., & Plomin, R. (2016). True grit and genetics: Predicting academic achievement from personality. Journal of Personality and Social Psychology, 111(5):780–789. doi.org/10.1037/pspp0000089 </bibtext> </blist> <blist> <bibl id="bib61" idref="ref91" type="bt">61</bibl> <bibtext>Schoenfeld, A. (1985). Mathematical problem solving. Orlando, FL: Academic Press. </bibtext> </blist> <blist> <bibl id="bib62" idref="ref92" type="bt">62</bibl> <bibtext>Schoenfeld, A. (1992). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. In D. Grouws (Ed.), Handbook for Research on Mathematics Teaching and Learning (pp. 334–370). New York, NY: MacMillan. </bibtext> </blist> <blist> <bibl id="bib63" idref="ref93" type="bt">63</bibl> <bibtext>Schoenfeld, A. (2008). Problem solving in the United States, 1970–2008: Research and theory, practice and politics. ZDM Mathematics Education, 39(5–6), 537–551. doi.org/10.1007/s11858-007-0038-z </bibtext> </blist> <blist> <bibl id="bib64" idref="ref132" type="bt">64</bibl> <bibtext>Secules, S., Gupta, A., Elby, A., & Turpen, C. (2016, April). “Turning Away” from an undergraduate programming student: Revealing culture in the construction of engineering ability. Paper presented at the American Education Research Association Annual Meeting. </bibtext> </blist> <blist> <bibl id="bib65" idref="ref34" type="bt">65</bibl> <bibtext>Shell, D. F., & Husman, J. (2001). The multivariate dimensionality of personal control and future time perspective beliefs in achievement and self‐regulation. Contemporary Educational Psychology, 26(4), 481–506. doi.org/10.1006/ceps.2000.1073 </bibtext> </blist> <blist> <bibl id="bib66" idref="ref35" type="bt">66</bibl> <bibtext>Simons, J., Vansteenkiste, M., Lens, W., & Lacante, M. (2004). Placing motivation and future time perspective theory in a temporal perspective. Educational Psychology Review, 16(2), 121–139. doi.org/10.1023/B:EDPR.0000026609.94841.2f </bibtext> </blist> <blist> <bibl id="bib67" idref="ref49" type="bt">67</bibl> <bibtext>Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: Theory, method, and research (1st ed.). London: Sage Publications, Inc. </bibtext> </blist> <blist> <bibl id="bib68" idref="ref139" type="bt">68</bibl> <bibtext>Sochacka, N., Walther, J., Wilson, J., & Brewer, M. (2014). Stories “Told” about engineering in the media: Implications for attracting diverse groups to the profession. Proceedings of Frontiers in Education Conference, FIE. Madrid, Spain. doi.org/10.1109/FIE.2014.7044009 </bibtext> </blist> <blist> <bibl id="bib69" idref="ref8" type="bt">69</bibl> <bibtext>Steif, P. S., Lobue, J. M., Kara, L. B., & Fay, A. L. (2010). Improving problem solving performance by inducing talk about salient problem features. Journal of Engineering Education, 99, 135–142. doi.org/10.1002/j.2168-9830.2010.tb01050.x </bibtext> </blist> <blist> <bibl id="bib70" idref="ref80" type="bt">70</bibl> <bibtext>Walther, J., Sochacka, N. W., & Kellam, N. N. (2013). Quality in interpretive engineering education research: Reflections on an example study. Journal of Engineering Education, 102(4), 626–659. doi.org/10.1002/jee.20029 </bibtext> </blist> <blist> <bibl id="bib71" idref="ref101" type="bt">71</bibl> <bibtext>Whitebread, D., Coltman, P., Pasternak, D. P., Sangster, C., Grau, V., Bingham, S., … Demetriou, D. (2009). The development of two observational tools for assessing metacognition and self‐regulated learning in young children. Metacognition and Learning, 4(1), 63–85. doi.org/10.1007/s11409-008-9033-1 </bibtext> </blist> <blist> <bibl id="bib72" idref="ref42" type="bt">72</bibl> <bibtext>Wigfield, A., & Eccles, J. S. (2000). Expectancy‐value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. doi.org/10.1006/ceps.1999.1015 </bibtext> </blist> <blist> <bibl id="bib73" idref="ref134" type="bt">73</bibl> <bibtext>Zusho, A., & Clayton, K. (2011). Culturalizing achievement goal theory and research. Educational Psychologist, 46(4), 239–260. doi.org/10.1080/00461520.2011.614526 </bibtext> </blist> </ref> <aug> <p>By Adam Kirn and Lisa Benson</p> <p></p> <p>Adam Kirn is an assistant professor of engineering education at the University of Nevada, Reno, 1664 N. Virginia, Reno, NV 89503‐0256; akirn@unr.edu.</p> <p>Lisa Benson is a professor of engineering and science education at Clemson University, Holtzendorff Hall, Clemson, SC 29643; lbenson@clemson.edu.</p> </aug>
Header DbId: eric
DbLabel: ERIC
An: EJ1254059
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Engineering Students' Perceptions of Problem Solving and Their Future
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kirn%2C+Adam%22">Kirn, Adam</searchLink><br /><searchLink fieldCode="AR" term="%22Benson%2C+Lisa%22">Benson, Lisa</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Engineering+Education%22"><i>Journal of Engineering Education</i></searchLink>. Jan 2018 107(1):87-112.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 26
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2018
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Science Foundation (NSF)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: EEC1055950
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Engineering+Education%22">Engineering Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Long+Range+Planning%22">Long Range Planning</searchLink><br /><searchLink fieldCode="DE" term="%22Goal+Orientation%22">Goal Orientation</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/jee.20190
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1069-4730
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Preparing students to solve complex problems is an identified area of need in engineering education. Despite the documented influence of motivation on learning, little research exists that examines how motivation and problem solving in engineering interconnect. Purpose: This study explores how engineering students perceive problem solving tasks, the future, and the connections between the two. Methods: Interviews with engineering students (n = 9) about engineering problems, problem solving processes, their futures, and interactions between their futures and problem solving tasks were analyzed using interpretative phenomenological analysis (IPA). Analysis of the resulting transcripts identified and clustered units of meaning into themes, first by participant and then across participants. Results: Three themes emerged from the IPA: participants perceived engineering as being primarily a problem solving process; participants reported using different problem solving processes depending on how well the task aligned with their future goals; and participants' perceptions of their future drove the problem solving processes they report using. Conclusions: This work supports and extends engineering problem solving literature by making explicit the connections students describe between their problem solving processes and their future-oriented motivations. Additionally, this work furthers our understanding of how future-oriented motivations are conceptualized by engineering students.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2020
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1254059
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1254059
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/jee.20190
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 87
    Subjects:
      – SubjectFull: Engineering Education
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Problem Solving
        Type: general
      – SubjectFull: Student Motivation
        Type: general
      – SubjectFull: Long Range Planning
        Type: general
      – SubjectFull: Goal Orientation
        Type: general
      – SubjectFull: Task Analysis
        Type: general
    Titles:
      – TitleFull: Engineering Students' Perceptions of Problem Solving and Their Future
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kirn, Adam
      – PersonEntity:
          Name:
            NameFull: Benson, Lisa
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 1069-4730
          Numbering:
            – Type: volume
              Value: 107
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Engineering Education
              Type: main
ResultId 1