International Journal of Technology and Design Education (2019) 29:1083–1104
https://doi.org/10.1007/s10798-018-9478-8

Pilot analysis of the impacts of soft robotics design
on high‑school student engineering perceptions
Andrew Jackson1· Nathan Mentzer1 · Rebecca Kramer‑Bottiglio2,3

Accepted: 11 October 2018 / Published online: 17 October 2018
© Springer Nature B.V. 2018

Abstract
Engineering career interest, especially that of young women, declines as they approachhigh-school graduation. We used expectancy-value theory, which emphasizes expectations
for success and subjective value of experiences as antecedent factors to choice, as a framework for investigating new 9th grade soft robot design lessons. Compared to traditional
robotics, the nature of soft robotics—materially embedded safety and an emerging technology with significant social implications—positions it to be favorable for growing students’
perceptions of success and value. To gauge the impact of the lessons following the firstyear of implementation, we use multilevel and ANOVA models to predict changes in three
student perceptions following the lessons: self-efficacy (related to expectations of success),and situational motivation and career interest (both related to subjective value). Survey
responses and demographic information were collected from 431 students, before and after
both the soft robotics treatment lessons and the traditional robotics comparison lessons.
Analysis of the results indicates that changes in perception were negligible for both lesson types and genders. These comparable findings between the lesson types indicate thefeasibility of incorporating soft robotics into high-school classrooms. While not noticeably
better, the soft robotics lessons expose students to an emerging field of engineering situatedin a socially meaningful context that is theoretically aligned with career choices. Moreover,challenges that occurred during the first-year implementation suggest possible refinements
which may improve students’ experiences and perceptions as our research continues.
Keywords Soft robotics · Engineering design · Design-based learning · Expectancy-value
theory · Career interest · Multilevel modeling* Andrew Jackson
andrewjackson@purdue.edu
1Engineering/Technology Teacher Education, Purdue University, West Lafayette, USA

2

Present Address: Mechanical Engineering and Materials Science, Yale University, New Haven,USA

3

Mechanical Engineering, Purdue University, West Lafayette, USA

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Introduction
Despite around 1,000,000 incoming high school freshmen expressing interest in science,technology, engineering, and mathematics (STEM) in the United States, by some estimates,
only 42.6% will actually maintain STEM career interest through high school (Munce andFraser 2013). However, this loss of interest is not the full picture; accounting for students
who develop an interest in STEM careers during high school, the proportion of young meninterested in STEM remains unchanged whereas the proportion of young women decreases(Sadler et al. 2012). There is also evidence that while girls often achieve at an equal or
higher level than boys, girls’ STEM interests tend to be lower and decline more rapidly
than boys’ (Brotman and Moore 2008). These motivational differences impact college
major and career trajectory. The percentage of female students is lower than male students
for STEM majors (Yoder 2016; National Center for Educational Statistics 2016), and laterin the workforce (US Bureau of Labor Statistics 2017; Ashcraft et al. 2016). Collectively,
these findings speak to the gender gap in STEM fields and demonstrate that as the gap widens during secondary education, there are lasting impacts on students’ career trajectories.
Adolescents’ interests and experiences grow to shape their career choices (Betz 2006) andtherefore this is a critical time at which we might intervene.
What factors are affecting this decline in interest? Expectancy-value theory (Eccles
et al. 1983) suggests two main factors, expectancy for success and value of the experienceengaged in, are needed to maintain interest. Toward a solution, how could these factors
be leveraged to foster students’ STEM interest? Using expectancy-value theory, we parsesome evidence of gender disparities in STEM fields and describe a 9th grade, soft robot
design curriculum which might positively impact students’ interest and choices for STEM.
The soft robot design curriculum is theoretically aligned with growing students’—especially young women’s—expectancies for success and subjective value in STEM through anaccessible approach that is culturally relevant and socially important. Using data collectedfrom the first year of high-school implementation, the present study uses multilevel models, also called mixed effects models, to answer the following research question: Do soft
robot design experiences improve engineering expectancy for success, subjective value,
and interest as compared to traditional robotics experiences? Next, we follow-up to investigate the changes in individual student perceptions following the curriculum.

Robots in education
A variety of engineering and technology activities, both formal and informal, are used tocapture students’ attention and interest as they approach career decisions; among these,robotics is a growing part of formal education. Robotics has been demonstrated as an effective vehicle for hands-on learning of technical and tinkering skills (Barker et al. 2012;
Hamner et al. 2008; Stubbs and Yanco 2009). There are a number of robotics kits andtutorials to facilitate classroom implementation (e.g., Fischertecknic or LEGO robotics).
A review of robotics curricula showed that a number of learning outcomes can be targeted,from specific STEM concepts—physics, force, or mathematics subjects—to broader competencies—communications, problem-solving, or creativity (Benitti 2012; McGrath et al.
2008). In addition to in-class robotics, informal or extracurricular experiences with robotics have bolstered the popularity of educational robotics. Competitions such as FIRST or

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LEGO robotics competitions are used to leverage the hands-on and interesting aspects ofrobotics to grow students’ interest. However, there often remains disproportional participation of boys and girls, and, when girls do participate, they are often in non-technical roles(Center for Youth and Communities 2011). Subsequently, one study noted that girls are“more likely to report impacts on attitudes related to teamwork or communication skills,
and boys [more likely to report] gains in technical skills” (Center for Youth and Communities 2011). While there is some mixed or anecdotal evidence of the success of robotics programs to influence girls’ engineering perceptions (e.g., Hendricks et al. 2012), thewidespread use of robotics and the persisting challenges of gender disparity frame robotics
as a rich context for intervention. These findings are consistent with other observations
regarding robotics and gender, all of which note that educational robotics programs do little to decrease gender gaps or increase the technical role of female participants (Ball 2005;
Hartmann et al. 2007; Milto et al. 2002; Rusk et al. 2008; Skorinko et al. 2010; Stein et al.
2004; Terry et al. 2011; Diekman et al. 2011).
In contrast to existing educational robotics experiences, we have made a paradigm shift
to design materially soft robotics, transforming the design and fabrication process, whiletargeting similar learning outcomes. The novel soft robotic design experience uses a silicone rubber, one of many material approaches for soft robotic fabrication. While silicone
may have been familiar to students, the silicone fabrication process was unfamiliar. Moreover, the design of elastically deformable soft robotic actuators with silicone was a newexperience. Several advantages of soft robots and aspects of the materials used for fabrication position soft robotics to be favorable for perceptions of success and value among students. Soft robotics is an emerging type of robotics (Bao et al. 2018) which use compliant,flexible components for construction (e.g., rubber and fabric) rather than hard, rigid materials (e.g., wood and metal). A great number of new robotics applications may be available
with this change, as different material properties are incorporated into robotics systems(Trimmer 2013). Generally, soft robot systems are more versatile (Trimmer 2013), comfortable, and safer for human interaction (Trimmer et al. 2013; Roche et al. 2017) thanrigid robots. They also offer new types of motion that are difficult to achieve in traditional
robotics design (Ilievski et al. 2011; Lipson 2014). Because the surfaces deform to interact
with objects they are able to handle delicate and irregularly shaped objects well—theserobots also embed safety at the material level (Ilievski et al. 2011; Majidi 2013). Anotheraffordance of soft robotic systems is their inspiration from biological systems—theserobots represent an opportunity to learn from and mimic natural systems (Lipson 2014).
By changing the nature of robotics design experiences, soft robotics may dispel misconceptions or stereotypes about abilities to succeed and find value in engineering practice.
Furthermore, this new robotics design experience is situated in an emerging engineeringcontext, introducing students to future career possibilities that they may find interest in.
One study by Hartmann, et al. (2007) suggested that “the materiality of robotics itself plays
an important role here i.e., the fact that it [robotics] already comes along as gendered material,” implying that a change in material itself may allow females to see robotics differently. Furthermore, another study by Terry et al. (2011) found that young girls displayed
greatly increased interest in robotics when they were able to identify a direct connection
between robotics and human benefit, such as with biomedical and surgical robots. This
argument aligns with results from Diekman et al. (2011) who suggested that content withsocietal implications is more likely to interest female learners and that STEM careers are
not typically associated with societal goals. The value of aligning learning experienceswith socially relevant and culturally situated contexts was found to be critical for engaging underrepresented populations in engineering through messaging campaigns by the13

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National Academy of Engineering in their 2008 report, Changing the Conversation: Messages for Improving Pubic Understanding of Engineering. Next, we describe expectancyvalue theory (Eccles et al. 1983), which we leveraged as a framework for our research.

Expectancy‑value theory
Achievement motivation theories suggest that specific beliefs towards the outcome andvalue of an activity are critical for choices to explore, give effort, and persist in those
activities (Wigfield and Eccles 1992). Expectancy-value theory (Eccles et al. 1983) is one
motivational theory which builds on previous theories by suggesting that these beliefs arecontextualized among other social, historical, and cultural factors for each student, and thatboth expectancy of success and value for the task are required for students’ choices, as
shown in Fig. 1 (Nagengast et al. 2011; Trautwein et al. 2012; Wigfield and Eccles 2000;
Wigfield et al. 2004). The differentiation of expectancies and values is a similarly important contribution of the theory, and both have been related to achievement and choices(Schunk et al. 2014).
According to the theory, students’ judgments on likelihood of success and task value areespecially instrumental and immediately antecedent to engagement choices. Expectancy
for success relates to the question, “Am I able to do this task?” and is complimented byperceived value, “Why should I do this task?” (Schunk et al. 2014).
One factor described in influencing a student’s achievement and choices is expectation
for success. Expectations for success may be associated with either short- or long-termgoals (Eccles and Wigfield 2002). This is a “belief about how well they will do on an
upcoming task” (Wigfield et al. 2004) and is distinct from broad beliefs about ability level.
Bandura (1977) similarly separated beliefs about ability and outcomes, noting that an ability to perform an action is separate from the consequences of the action. However, in practice it is difficult to distinguish between these levels of belief—they tend to be measured ina similar way (Eccles and Wigfield 2002). Expectancies are closely related to other conceptions of self-belief such as self-concept or self-efficacy (Trautwein et al. 2012).
The other factor is a value of the task itself. Eccles et al. (1983) distinguished different types of value that might be placed on a task: intrinsic value, attainment value, utility value, and cost. Intrinsic value, also called interest value, is enjoyment from doing the

Cultural Milieu

Socializers’ Beliefs
and Behaviors

Child’s Pereptions ofSocializer’s Beliefs,
Gender Roles, andActivity Stereotypes

Expectations ofSuccess
Child’s Goals andGeneral Self-Schemas
Achievement-Related
Choices andPerformance

Stable Child
Characteristics
Child’s Interpretationsof Experience
Previous
Achievement-RelatedExperiences

Child’s Affective
Reactions andMemories

Subjective Task Value
1. Interest-enjoyment value
2. Attainment value
3. Utility value
4. Relative cost

Fig. 1  Expectancy-value theory model from Eccles and Wigfield (2002). Shaded elements highlighted represent the expectancy-, value-, and choice-related outcomes targeted in this research

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task (Hulleman et al. 2008). This leads to engagement and persistence at the task. Intrinsic
value in expectancy-value theory is conceptually similar to intrinsic motivation, though
they come from different frameworks (Wigfield et al. 2004). Attainment value stems fromthe importance of doing well on the task. For example, a task may be important for confirming an aspect of one’s identity. Utility value is based on the usefulness of the task forcurrent and future goals, especially career goals. “It is related more to the ends of a task
than to the means” (Schunk et al. 2014); a task may be seen as instrumental for achievinga goal, and completed regardless of interest or enjoyment. Finally, costs are the negative
aspects of engaging in a task. These might include necessary effort, the tradeoffs of participating in the activity (e.g., missing out on something else), or risk of negative outcomes(e.g., embarrassment).
Expectations for success and the value placed on an activity are intuitively related.
“Even during the very early elementary grades children appear to have distinct beliefs
about what they are good at and what they value” (Wigfield and Cambria 2010). Research
has consistently demonstrated that expectancies and values develop over time (Denissen
et al. 2007; Eccles et al. 1983). These beliefs are reciprocally related in that successful
experiences may enhance intrinsic value and the value of a task may lead to students toseek further experience and proficiency (Schunk et al. 2014; Trautwein et al. 2012; Wigfield and Cambria 2010). Both expectancy beliefs and subjective values are related toachievement and choices, as outlined in the expectancy-value model, though expectancy
beliefs better predict achievement while subjective values better predict choice (Meece
et al. 1990; Nagengast et al. 2011). Still, evidence attests that both constructs interact tolead to student choices (Nagengast et al. 2011; Trautwein et al. 2012).
In the theory, each student’s expectancies for success and values for the task are also fostered by their own self-schemas and goals, affective memories, and interpretations of pastexperiences. For example, the student’s goals and aspirations—the beliefs about the kind of
person they are and want to be—will shape effort on tasks they deem instrumental and doable. Past memories of an experience will come to shape the values attributed to different
tasks: “Achievement outcomes give rise to general positive or negative emotional reactions
to the outcome. Thus children should come to value those activities they have succeededon more than the activities on which they have failed” (Wigfield and Eccles 1992). It isalso important to note that the social context of tasks is interpreted by the student. Interpretations of gender or activity stereotypes or relative definitions of success, failure, and ability are filtered through the student’s perceptions to subsequently affect their expectancies
and values for the task (Eccles et al. 1983). Collectively, these factors represent the left partof Fig. 1. As suggested by the specificity of goals and past experiences, the social contexts,
expectations for success, task values, and, ultimately, choices and achievement are domain
specific (Bong 2001; Denissen et al. 2007).

Use of expectancy‑value theory
Expectancy-value theory has been used in similar contexts previously to explain student performance. Berland and Steingut (2016) used expectancy-value theory to explain variationin student efforts towards applying math and science knowledge in engineering contexts.
Their work demonstrated attainment value was a significant predictor in student effort andexpectancy-value theory was appropriate for modeling high school student STEM integration effort. Jones et al. (2010) used expectancy-value theory to model college engineering

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freshman beliefs as predictors for achievement and career plans. Their research suggested that
expectancy more strongly predicted achievement and value more strongly predicted careerplans. Both of these studies used expectancy-value theory in contexts similar to our research
indicating it as an appropriate and viable theory to drive our investigation. Using the lens of
expectancy-value theory, the scarcity of women in STEM fields mentioned in the Introduction comes, in part, from a lack of interest (value) or lack of confidence (expectancy) regarding pursuit of those activities, or both. The theoretical implications include interventions that
address stereotypes, expectations for success, and task values of STEM activities for students(van Aalderen-Smeets and Walma van der Molen 2016).

The present research
The present research considers the efficacy of our soft robot design experience compared to a
traditional robot design experience following the first year of implementation in high-schoolclasses. Prior to lesson delivery, participating teachers received standardized materials related
to both lesson types, through the STEM Center for Teaching and Learning (STEM-CTL),
the professional development arm of the International Technology and Engineering Educators Association (ITEEA), and our research team. Teachers received a detailed lesson plan
which used the 6E model (Burke 2014) and included engagement, exploratory, explanatory,
engineering, enrichment, and evaluation components, as well as video and troubleshooting
materials for teacher preparation in soft robotics fabrication. Teachers also participated in content-focused professional development which engaged them in the curriculum and offered an
opportunity for reflection on the lesson materials. During the next school year, each teacher
received the necessary materials and implemented the soft robot curriculum in about one-halfof their classes and the traditional rigid robotics design curriculum in the other half. All participating students received surveys administered to evaluate perceptions. Reflections by teachers and researcher observations demonstrated fidelity of the implementation in both lessons—minimal changes were made on the basis of teacher’s professional judgment and classroom
needs (e.g., adapting to school interruptions).
Informed by expectancy-value theory, the underlying hypothesis of this work was that aparadigm shift to soft robotics curricula would impact engineering expectancy for success,subjective value, and interest of participating students compared to traditional robot design
experiences. Sub-components of this hypothesis were three concepts of expectancy-value
theory under investigation—expectancy for success, subjective value, and interest—and theparticular effect of this experience for young women. We hoped that participation in the soft
robot lesson might grow students’ perceptions at least as well as the traditional robotics lesson
and that the gender gap might be lessened through the experience. The next section describes
our research and assessment approach to evaluate the soft robot design experience in contrast
to a traditional robot design experience.

Methods
Participants and design
The pilot implementation of the soft robotics curriculum involved seven high-school technology and engineering teachers from four schools in a Mid-Atlantic district. Each teacher

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had at least two sections of a required 9th grade course, Foundations of Technology (sometimes called Foundations of Engineering), in the Engineering byDesign curriculum provided by the STEM-CTL. The 9th grade curriculum includes a traditional robot gripper
unit which afforded the opportunity to have a quasi-experimental design where both conditions were taught by each teacher: the control condition was the traditional robotics unit
and the treatment was the soft robotic design unit. Participating teachers reported between
4 and 25 years of teaching experience.
Students were recruited in each participating teachers’ 9th grade sections, a total of 25
sections throughout the 2016–2017 school year (13 were taught in the Fall, 4 were taught
in a yearlong format, 8 were taught in the Spring). We estimate there were about 790 total
students enrolled in the foundations courses; class sizes ranged from 19 to 35 students. We
received research responses from 431 students overall (54.56%). Because the course is astate graduation requirement we expected the gender ratio to be roughly equal. Among students reporting gender, this was the case (53.05% female, 46.95% male; see Table 1).

ProcedureAt the beginning of the course teachers informed students and parents of the study to
improve robotics educational experiences and collected parent consent forms and student assent forms. As the introductory course proceeded, teachers delivered the standardized lessons—the traditional robotics lesson (control) to approximately half of
their classes (n = 10) and the soft robotics lesson (treatment) to their remaining classes(n = 15). For each teacher, control and treatment lessons began at the same time andlasted about 10 class sessions (depending on external schedule conflicts that may have
postponed any lessons). The soft robotics lesson was also designed to meet the samelearning objectives as the traditional lesson including Next Generation Science Standards (NGSS) Engineering Design standards (NGSS Lead States 2013) and Standards forTechnological Literacy #11 and #12 related to design (International Technology Education Association 2007). Both lesson types began with participating students taking a
Table 1  Demographics for research participating students
Demographic characteristic

Traditional robotics
courseSoft robotics course

Total

62
61
30

147
124
7

209
185
37

0
0
2
44
3
104

2
3
2
112
5
154

2
3
4
156
8
258

GenderFemaleMale
Not reported
Race
American Indian or Alaska Native
AsianBlack or African American
White
More than one race
Not reported

Note: An error in administration of the electronic survey prevented a number of students from seeing thedemographics reporting questions. We recovered some of the gender information from teachers and havesince corrected the error

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pre-lesson survey, aligned with Expectancy-Value outcomes and described in the next
section. At the conclusion of the lesson students repeated the online survey.
Traditional robotics lesson The existing unit engaged students in the design of arobot made from traditional rigid materials including popsicle sticks, string, syringes,
tubing, lumber, and dowels. Their objective was to create a crane-like device that canpick up blocks and stack them in a simple pick-and-place maneuver remotely controlled
by a bank of four hydraulic syringes used to actuate the arm (see Fig. 2). The introduction to the lesson focuses on the importance of design documentation to communicate
solutions and has supplemental content on hydraulics to support student design work.
Then, students work in pairs to engage in the engineering design process while individually documenting their work in an engineering design journal. The design journals
include typical elements such as Define the Problem, Brainstorm Possible Solutions,
Research Ideas and Explore Possibilities (with citations), Specify Constraints and Identify Criteria, Consider Alternative Solutions, Select an Approach, Develop a Written
Design Proposal, Sketch the Final Design, Make a Model/Prototype, Test and Evaluate,
Refine/Improve, Create/Make Product, and Communicate Results. After designing theirrobot, the teams present their design journals as well as their solutions to the design
challenge. Assessment is based on the engineering design notebook, performance of therobot, and final presentation.
Soft robotics lesson Engineering byDesign uses standards-based lessons which support
the replacement of design content and activities while maintaining learning outcomes. The
soft robotics procedures are congruent with the same standards, although situated in a different context aligned with the expectancy-value outcomes described previously. For theintervention lesson, the design objective was to use a two-part silicone rubber to fabricate a pneumatically actuated gripper which could pick up and sort a golf ball, which was
symbolic of farm produce harvesting or sorting, without damage. The introduction of thelesson was based on the same design documentation instruction and had supplementary
content on pneumatics principles (instead of hydraulics). The activity was completed inpairs while students individually documented their design work and students presented
their final design process and solution (just like the existing lesson).

Fig. 2  Examples of traditional robotics gripper design (left) and soft robotics gripper design (right) basedon the lesson materials

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The lesson was also refined through pilot testing with middle- and high-school students.
Since the design and fabrication of silicone actuators was unfamiliar to students, eachteacher provided basic instructions on how to mix and cure the silicone using the provided
materials (e.g., mixing cups, 3D-printed molds, and toaster oven incubators; Jackson et al.
2017a). Students began by assembling the reconfigurable, 3D-printed mold based on theirdesign configuration—which might vary in configuration from other designs. After mixing
and pouring the liquid silicone into the mold, it cures in about 4 h at room temperature or
15 min at 150°F. Students used more silicone to attach an inextensible layer made from
cotton fabric, and then allowed the addition to cure. Finally, they tested the gripper throughinflation with a squeeze bulb pump (see Fig. 2).
The fabrication approach, using a two-part silicone elastomer, was chosen because it
was one of the most developed, having previously been used in a successful outreach effort
with children (Finio et al. 2013). The soft robotics lesson materials were adapted in orderto support redesign opportunities for students; we have reported on this iterative process inother work (Jackson et al. 2017b; Zhang et al. 2017). Using a developed 3D-printed mold,students were encouraged to explore design variation in their fabrication of the soft robot
gripper. Design opportunities include variation in the number, spacing, and clustering of
air chambers in the actuator, as well as the size of the actuator—each variable and theirinteraction has potential impacts on final performance of the gripper.
The initial attempts at making grippers showed that it is more complicated than it seems
to be: the variety of design variables affect the functionality of the gripper and variation in
the fabrication process can impact its actuation characteristics. In fact, inspection of a subset of student-designed grippers showed that 81% of failures were related to the fabrication
process (Zhang et al. 2017). Consequently, the unit was created with three distinct design
cycles. In the first two design experiences, students design, fabricate, test, and reflect on
the performance of just one finger (rather than the entire gripper). Students are guided by
the teacher in hypothesizing and testing which design and fabrication variables impact performance. Informed by a web search and their empirical results, student teams proceed
to design, prototype, fabricate, and test a gripper, documenting their work in their design
journals.

Measures and outcomes
The online survey, taken at the beginning and end of the lesson, used existing measuresof student perceptions, as well as asked about demographic information. We reviewed thelanguage of each scale to consider its use with high-school students and found each scale tobe appropriate. We describe each scale and how it aligns with the Expectancy-Value framework undergirding this analysis.
Self-efficacy—expectancies for success We used engineering self-efficacy as a domainspecific measure for expectancies for success. “In empirical studies, the two components
have shown very high intercorrelations, and competence and expectancy beliefs havetypically been collapsed into a single construct or used interchangeably” (Trautwein et al.
2012). Engineering self-efficacy was measured by the Engineering Skills Self-Efficacy
Scale (Mamaril et al. 2016). This contains 12 questions measured on a six-point scaleof confidence for the activity from 1 (Completely uncertain) to 6 (Completely certain).
Although the scale was developed for undergraduate engineering students, the questions
theoretically relate to self-efficacy for engineering (1) experimentation, (2) tinkering, and(3) design, which are relevant outcomes for our intervention at the high school level. The

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scale structure and alignment with other motivational constructs was verified for use withhigh school students (Jackson 2018). In both undergraduate and high-school levels, allthree self-efficacy constructs significantly correlated with intention to persist in engineering (each r > .11, p < .01) and were reliable (.79 < ωh < .93). The three skills are highly correlated and we used an average score on the pre- and post-survey to calculate students’ gain
in engineering skills self-efficacy.
Situational motivation—subjective task value We used the Situational Intrinsic Motivation Scale (SIMS; Guay et al. 2000) to represent subjective task value for the material.
The SIMS has 16 questions on four subscales aligned with forms of intrinsic and extrinsic
motivation: intrinsic motivation, identified regulation, external regulation, and amotivation.
Each question is measured on a seven-point scale according to whether the statement corresponded with the student’s reasons for participating in the activity, from 1 (Corresponds
not at all) to 7 (Corresponds exactly). The SIMS has been used in prior design activity
research (Stolk 2013). In five development and validation studies, the SIMS showed satisfactory reliability, all subscales had Chronbach’s α greater than .62, and prediction of
behavior and task interest (Guay et al. 2000).
Even though the SIMS is based on a different theoretical framework than expectancyvalue theory, the four types of subjective task value described in expectancy-value theory
are conceptually represented by the SIMS subscales (see Fig. 3). First, intrinsic value isbased on interest in the task; the Intrinsic Motivation subscale asks about similar motives
for participating in the activity (e.g., “…I think this course is pleasant” or “I feel goodwhen taking this course”). Attainment value is derived from alignment between the activity and personal identities; the Identified Regulation subscale uses similar reasons such as“doing it for my own good” and being “good for me” to justify engagement. Next, utility
value stems from usefulness for future goals as do the External Regulation questions of
the SIMS. Finally, relative cost in expectancy-value theory is represented by Amotivationon the SIMS. Both cost and amotivation are given a negative weight which corresponds
to their negative impact on choice and behavior. Expectancy-Value research and Intrinsic
Motivation instruments have also been paired in previous research (e.g., Berland and Steingut 2016).
Student gains in subjective value were based on a composite value, the self-determination index (SDI). This index incorporates all four SIMS constructs and weighs eachscale according to its relative effect on choice (Vallerand 2001). Intrinsic Motivation
and Identified Regulation are positive, self-imposed sources of motivation and weighted

Subjective Value Constructs

SIMS Instrument Constructs

Intrinsic Value
Intrinsic Motivation“This material is fascinating. I enjoy participating in it.”
Attainment Value
Identified Regulation“I see myself as a maker. Working on these projects fits who I am.”Utility Value
External Regulation“If I want to be an engineer in the future I need to do well in this material.”
Cost
Amotivation“There are other things that I would rather be doing.”

Fig. 3  Alignment between subjective value and SIMS instrument constructs with fictitious student motives

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1093+ 2 and + 1, respectively. External Regulation and Amotivation are less self-determined,
due to external pressures, and weighted − 1 and − 2, respectively. “The total scorereflects the person’s relative level of self-determined motivation” (Vallerand 2001), inother words, the relative value and importance of the activity. The total score ranges
from − 18 to + 18 with a higher score representing increasingly perceived value fromthe task; value gain was the difference between the pre- and post-lesson SDI scores.
Career interest—intrinsic value Finally, we used the STEM Career Interest Survey(STEM-CIS; Kier et al. 2013) to represent the Intrinsic Value component of expectancyvalue theory. The original development of the STEM-CIS sampled middle-school student perceptions of each STEM domain. For this research we used only the Engineering Scale of the instrument, which has 11 questions. Responses range from 1 (strongly
disagree) to 5 (strongly agree). Calculating individual scores for each content area is anappropriate use of the instrument based on the factor structure (Kier et al. 2013). The
scale developers found good internal consistency (α = .86), good model fit (χ2 = 50.27,
df = 38, p = .91, NFI = .95, CFI = .99, RMSEA = .017), and factor loadings ranging from
.33 to .78 with these questions. We calculated each student’s gain in interest value as thedifference between their average pre- and post-lesson responses.

Analysis
To compare the efficacy of the hard and soft robotics lessons, we used multilevel modeling to predict gains in student outcome expectancies, task value, and interest value.
Multilevel modeling takes into account the “nested” structure of students in classes
and allows hypothesis testing of the varying impacts that classroom characteristics
might have on individual outcomes (Raudenbush and Bryk 2002). These models arealso called hierarchical models and have been used in evaluation studies similar to ours,
where the new curriculum experience is given at the classroom level (Lachapelle et al.
2015). Model assumptions for multilevel modeling include normally distributed error at
all model levels and constant variance of the residuals and across groups. Assumptions
were found to be met through visual inspection of residual plots.
We considered the amount of variation in student outcomes that existed acrossclasses, using the intra-class coefficient (ICC), before proceeding with the multilevel
analysis. In multilevel models, the significance of predictors was determined using theWald t test statistic and Satterthwaite approximation to take into account different classsizes (Satterthwaite 1946). In cases where between-class variation was negligible (alow ICC), we reverted to ANOVA models using the F-test statistic. Statistical analysis
was conducted in R software (R Core Team 2017) using the lme4 package (Bates et al.
2015).
Following statistical analysis on the change scores for student perceptions, we useddescriptive statistics to understand the changes in student perceptions after the lessons.
These procedures complement the statistical analysis in several ways. First, in contrast to
the statistical procedures which aggregate data to look for group differences and reduces
findings to coefficients and probabilities, we could uncover insight by looking at the trajectory for each student. Next, it allowed us to adopt an exploratory approach to explain theprevious statistical findings. Finally, it was a preliminary opportunity to conjoin the selfefficacy, motivation, and interest outcomes in a single analysis. Based on the descriptive,
exploratory approach we did not consider statistical likelihood in follow-up analyses.

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Results
Seven hundred fifty-two survey responses were collected from participating students,
including pre- and post-lesson surveys from both types of robotics lessons. Responses
which were missing two or more questions on any of the measurement instruments
were removed from analysis (2.93%). Responses where we noted failure to legitimately
engage with the survey, such as improbable completion times or no variation on reversescored items, were also removed (2.19%). Once pre- and post-lesson surveys werematched per student, gain scores were calculated from the composite scores for each of
the three outcomes (described previously in the Measures and Outcomes section). Of
the 424 students with completed surveys, 290 students (68.40%) had completed both
attempts and were retained. Eleven students (3.79%) were identified as outliers becauseof the extremity of their standardized score (p < .001; Tabachnick and Fidell 2007) on
either the self-efficacy gains, motivational gains, or interest gains and were removed
from the data leaving a final 279 student scores, from 22 classes, to be used in statistical modeling. The average number of responses per class was 12.7 (SD = 6.03). The
responses retained for multilevel modeling were similar in proportions of lesson type(control or treatment) and gender to the sample of research participants (in Table 1).
Means, standard deviation, and reliability indicators for each of the measurement
instruments are reported in Table 2. Reliability was calculated using hierarchical omega(ωh; McDonald 1999) which is more appropriate than alpha reliability for multidimensional measures (Zinbarg et al. 2005). The high reliability of each set of questions supported our use of the scale composite scores for the remainder of the analysis (Raykov
and Zinbarg 2011). Student gains in all three outcomes were not highly correlated(Table 3). The lack of correlation among outcomes may demonstrate that each measure
represented a distinct perception of engineering expectancy or value. Alternatively, features of the robotics lessons may be differentially impactful for students’ perceptions.
The proportion of variation between classes differed across the three outcomes we investigated (Table 3). Based on the low ICC for self-efficacy gains and interest gains, thesetwo dependent variables were fit with two-way ANOVA models; motivation was fit witha multilevel model. Models for all three outcomes were fit with gender and lesson type(treatment vs. comparison group) and the gender-lesson type interaction as independent
variables to address the research question of whether the new lessons are helpful forstudent perceptions, especially young women’s perceptions. These self-efficacy, motivation, and interest model results are presented sequentially in the next sections.

Table 2  Psychometric properties of pre- and post-lesson outcome measures
MeasureNumber of
items

Potential range

Pre-lesson survey(n = 395)
M (SD)

Self-Efficacy Skills

12

1–6

Motivation
Interest

16
11− 18 to 18
1–5

13

Post-lesson survey(n = 315)ωh

M (SD)

ωh4.32 (1.02).92

4.26 (1.10).95− 3.55 (7.17)
3.23 (0.85)

.78
.86

− 3.86 (7.26)
3.17 (0.89)

.83
.87

1095

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Table 3  Descriptive statistics, intra-class correlation, and intercorrelations for outcome gains. (n = 279)
MeasureM (SD)ICCa

1–

1. Self-efficacy gain

0.00 (0.87)< .01

2. Motivation gain
3. Interest gain

− 0.38 (4.94)
0.00 (0.50)

.13
.05

a

.06 (p = .33)
.10 (p = .08)2

3–
.08 (p = .18)–

ICC was calculated for each outcome based on class-level variance in an unconditional model

Self‑efficacy gains
We used a two-way ANOVA model to determine whether or not students’ self-efficacy
increased differently as a result of the lesson type or student gender. Initial analysis
demonstrated a non-significant interaction effect, F(1, 275) = 0.41, p = .52, which wasremoved and the model rerun. The final model included student gender and lesson typeas predictors of self-efficacy gains following the lessons. None of the results were significant; in other words, self-efficacy gains are not different between young men andyoung women, F(1, 276) = 0.093, p = .76, or between lesson types, F(1, 276) = 2.376,
p = .12.

Motivation gains
Motivation gains did show variability by class according to the ICC. Therefore, we useda multilevel model which specified the class for each student, as well as the predictor variables of interest to the research. There was not an interaction effect for genderand lesson-type, so the term was removed and the model rerun. None of the predictors
in the final model were statistically significant in predicting motivation gains following the robotics lessons. The overall model estimate for motivation gains was roughly
even, − 0.44, and the effects of gender and lesson-type were similarly small, − 0.85 and
0.69, respectively. Each of these coefficients is interpreted as a change in motivationdifference, which had a wide initial range. Differences in motivation gain following thelessons are not related to student gender or the robotics lesson type, as indicated by thelack of significant predictors (Table 4).

Interest gains
As with self-efficacy gains, the interest gain scores did not exhibit variability acrossclasses so the model was fit using two-way ANOVA. The interest gain results imitatedthe results from self-efficacy and motivation student perceptions. The potential interaction between student gender and lesson type was not significant, F(1, 275) = .13, p = .71,
and was removed. In the final model main effects for student gender and lesson typewere also not significant, F(1, 276) = 0.23, p = .63, and F(1, 276) = 2.42, p = .12, respectively. The interest gains following participation in the robotics lessons were the samefor both genders and lesson types.
In summary, group differences for each outcome, by lesson type and gender, areshown in Fig. 4. The figure illustrates the similarity of outcomes of different groups.

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Table 4  Multilevel model parameter estimates for effects of gender and lesson type on motivation gains
df

t

pa

0.88

23.57

− 0.50

.62

0.58
1.02

274.3318.72

1.18− 0.84.24.41

Fixed effects

Coefficient

SE

Intercept− 0.44

GenderLesson type0.69
− 0.85

Random effects

Variance component

SD

Class

2.97

1.72

Residual

21.50

4.64

a

Probability values are based on the Wald t-test statistic and Satterthwaite approximation

Given the overlap of the groups, the statistical procedures are unable to associatechanges in outcomes to either the lesson type a student participated in or the students’gender.

Exploratory analysis
With the statistical procedures yielding similar results by lesson type and gender, follow-up
analysis was conducted to explore whether, and how, student scores were changing following the lessons. Descriptive statistics reported in Table 3 indicate that individual gainscores are variable even though the average change over time is negligible. Therefore, weused the standard deviation of each pre-test to code how student perceptions changed overtime: each student was coded for whether they increased by at least a standard deviation,
decreased by at least a standard deviation, or held the same perception (stayed within astandard deviation) after the lessons, on each outcome (receiving three codes in total). The
proportion of students increasing or decreasing on each measure was then considered bylesson type and gender (Table 5).

Perception Gains

Self−Efficacy

Motivation

Interest

0.2

0.0

−0.2

Student Gender
Male
Female−0.4
Traditional Robotics

Soft Robotics

Traditional Robotics

Soft Robotics

Traditional Robotics

Soft Robotics

Lesson Type

Fig. 4  Group differences in self-efficacy, motivation, and interest by lesson type and gender. Due to differences in scale of the variables, each gain score was standardized; the y-axis is in standard deviation units
and the points represent the average standardized score by group13

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Most students did not change perceptions (65.95%) following the robotics lessons. There
were also no students whose self-efficacy, motivation, and interest increased or decreased
congruently, again indicating that these are distinct perceptions. In fact, only seven students
demonstrated changes on more than one outcome (4.67%). In most cases, students increasing in their perception of engineering following the lessons were met by a similar numberof students decreasing their perceptions. Student self-efficacy perceptions were the mostvolatile, with 45 students (16.14%) reporting changes, and interest was the least changed(21 students, 7.53%). This suggests that a focus on self-efficacy supports in the curriculum
might be necessary.

Discussion
Our interest in student perceptions following the robotics lessons was to identify potential supports for students’ engineering career choices, especially for young women. The
soft robotics intervention is theoretically positioned to influence student perceptions. We
hypothesized an improvement in students’ engineering perceptions, and a decrease in the
expected gender gap. However, based on changes in three measures—self-efficacy, motivation, and interest—we did not observe significant differences between the soft robotics andtraditional robotics experiences, or between young men and young women. Also, based on
the low correlation scores among outcome gains and few students changing in more thanone, we reason that students’ trajectory on each outcome is distinct. Potential changes inself-efficacy, motivation, and interest do not seem reciprocal.
Yet, a closer look at the distribution of change scores on these outcomes showed thatstudent attitudes were malleable. This evidence is consistent with findings from Hernandez et al. (2014) which demonstrated changes in students’ STEM perceptions throughout
high-school. In their research, overall changes in student perceptions were negligible, however a further analysis showed a nearly even number of students increasing and decreasing
in STEM perception. Many students in our robotics lessons had changing perceptions, it
may be a matter of identifying the underlying student or environmental characteristics that
affect change. It is possible that student or environmental characteristics (for example, early
elements included in Fig. 1) may explain changes in student scores, especially the directionof change. The need to identify underlying factors is more salient given the contradictingchanges in students’ perceptions that we observed—for as many students as were growing
in positive perceptions of engineering, a similar size group was becoming disenchanted.
Of the three outcomes we investigated, more students’ self-efficacy perceptions (alignedwith expectancy for success) changed following the robotics design experiences, than did
students’ perceptions of motivation or interest. The hands-on nature of the robotics design
may facilitate this effect, since it generates immediate feedback as students build and test
their designs.
Given the newness of the soft robotics lesson materials to the instructors, the similarlevels of performance on self-efficacy, motivation, and interest outcomes between the two
lesson types suggests that improvements to this design context may uncover benefits in the
future. Put in context of the timeline for of our soft robotics curriculum development, in thefirst year of implementation the new soft robotics lessons yielded results similar to traditional (widespread) robotics design lessons on the measures used. The multilevel modelingand ANOVA tests showed no negative changes in self-efficacy, motivation, or interest as aresult of the experimental nature of the soft robotics lessons. Despite a lack of observable

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1 (0.36%)

1 (0.36%)
1 (0.36%)
20 (7.17%)
17 (6.09%)
10 (3.58%)

3 (1.08%)

7 (5.43%)
5 (3.88%)
5 (3.88%)
4 (3.10%)85 (65.89%)

1 (0.78%)
8 (6.20%)
10 (7.75%)
4 (3.10%)Decrease

12 (8.00%)
3 (2.00%)
3 (2.00%)
2 (1.33%)
99 (66.00%)

1 (0.67%)

1 (0.67%)

Increase

12 (8.00%)
7 (4.67%)
6 (4.00%)

1 (0.67%)

3 (2.00%)

Decrease

Female (n = 150)

4 (4.65%)
7 (8.14%)2 (2.33%)
3 (3.49%)
54 (62.79%)

Increase

1 (1.16%)
9 (10.47)
1 (1.16%)
3 (3.49%)

2 (2.33%)

Decrease

Traditional robotics(n = 86)

15 (7.77%)
1 (0.52%)
6 (3.11%)3 (1.55%)130 (67.36%)

1 (.52%)

1 (.52%)

Increase

11 (5.70%)
16 (8.28%)
7 (3.63%)

1 (.52%)

1 (.52%)

Decrease

Soft robotics (n = 193)

Entries show number and percentage of total by group (overall, by gender, and by lesson type). Blank cells represent zero students identified. Unless indicated in the variablecolumn, the outcomes did not change

19 (6.81%)
8 (2.87%)
8 (2.87%)
6 (2.15%)184 (65.95%)

1 (0.36%)

Self-efficacy and interest
Motivation and interest
Self-efficacy only
Motivation onlyInterest only
Mixed ±
No change

Increase

Increase

Decrease

Male (n = 129)

Overall (n = 279)

Self-efficacy and motivation

Variable

Table 5  Count and proportion of students changing on self-efficacy, motivation, and interest outcomes

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differences by lesson type, the curriculum, as designed, is aligned with expectancy-value
theory and expected to support student perceptions; it is possible these differences were
not detected, whether by the instruments used or in the limited timeframe of the robotics
design experience.

Implications
These reported findings have impacted our research program moving forward. The incongruity of failing to observe changes using multilevel and ANOVA procedures, yet seeing a large proportion of individual students with shifting perceptions, has caused us toreflect on the assumptions and limitations of our analytical methods. While commonly
used, ANOVA methods group participants and can mask individual differences, as evidenced here. Further research could seek to identify student characteristics which predict
individual trajectories. Furthermore, the intention of our research—to change student perceptions of engineering—leads to potential risk of response-shift bias (Howard & Dailey1979). Response-shift bias is caused when self-evaluation before and after an intervention
are conducted from different frames of reference; in our case, by attempting to broaden students’ perceptions of engineering, initial reports may not take into account the understanding and experiences realized in the design experience. This risk is especially problematicfor the soft robotic design experience since it exposes students to novel uses of silicone andapplications within engineering, compared to the familiar, traditional robotics design experience. In addition to considering retrospective reporting to alleviate this bias, our future
work will more heavily consider qualitative methods from student participants. Qualitative
strategies afford a focus on the experiences and meaning-making of individuals, as well as
a complex understanding of issues (Creswell and Poth 2017).
Beyond the difficulties of instrumentation just described, we observed several challenges
which emerged as the planned curriculum was presented in the classroom. Implementation
challenges are common for new instructional programs—the process of revising instructional materials based on data is built into “almost any instructional design model” (Dicket al. 2015)—and what is planned, even best practice, is often different than what occurs
in the classroom (Perrow 2013). However, it is likely these challenges have impacted students’ and teachers’ perceptions of success in robotics design. Specifically related to the
expectancy-value outcomes, several major revisions have been made for teaching and datacollection in the second year of implementation. These changes may address the differential trajectories of student perceptions by improving the design experience. First, robot
fabrication materials have been changed after discovering nitrile (used in gloves and rubberbands) prevented the silicone robots from curing. Changing these materials should support student success in the design experience by reducing the number of gripper failurescaused by incompletely cured silicone. Second, emphasis on iteration through the design
process has been reframed, which should also promote expectancy for success. Students’beliefs in their ability to complete a task are most strongly influenced by their perspectives
on prior experience (Britner and Pajares 2006; Usher and Pajares 2009). We have reframed
the finger design and testing, previously considered prototyping, as a research process; inthe research process failure may be more accepted, and less impactful for students’ expectancy of success (Lottero-Perdue and Parry 2015). And third, the design context has been
adapted to emphasize societal benefits of the design solution, and subsequently subjective value. Previous evidence has suggested that the among students selecting engineering

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careers, the choice is at least partially based on perceived benefit to society (Benson et al.
2013). The human-oriented nature of careers seems especially for women, who tend todemonstrate greater interest in “people” activities (Su et al. 2009).
With these refinements to the soft robot design lessons, our research is ongoing. The
teacher impacts of this research have included more than 8 h of face-to-face professional
development and teaching observations annually, classroom resources for lesson implementation, and community building with researchers and teachers for curriculum support.
Following the first year implementation we have continued to work with the participating teachers, as well as provided training for teachers in a second school district who will
deliver the material to their classes in the 2017–2018 school year. For all teachers involved,
the soft robotics lessons have provided exposure to a new context of engineering practice.
The project will analyze new data from teachers in both districts in the academic year,
which may produce similar conclusions or evidence of improvement in the engineering
perceptions of participating students.

Conclusion
This research has led to the development of new robotics design teaching material—forsoft robotics—and demonstrated its feasibility for classroom use. The lessons offer a newperspective on engineering and have performed as well as traditional robotics design experiences, based on student engineering perceptions. The curriculum materials are available
for use by practitioners and researchers and are included in the Supplemental Materialsof Zhang et al. (2017) or a Google Drive folder at http://www.tiny.cc/SRMol​ds. Based onteachers’ enthusiasm for this new design context, the soft robotics design learning experiences are being integrated into an upper level course called Advanced TechnologicalApplications offered by the STEM-CTL, released in April of 2018. The identification ofchanging student perceptions of self-efficacy, motivation, and interest also contributes tothe aim of supporting student career trajectories for engineering. While we do not yet haveevidence of positively influencing high-school engineering perceptions, we reiterate, withevidence, that student attitudes are changing, and that this is an important time for practitioners and researchers to intervene to sustain student interest.
Funding This research was supported by the National Science Foundation under Grant DRL-1513175.

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