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Article

Effects of Design-Based Learning Arrangements in Cross-Domain, Integrated STEM Lessons on the Intrinsic Motivation of Lower Secondary Pupils

by
Markus Reiser
1,*,
Martin Binder
2 and
Holger Weitzel
1
1
Department of Science Education, University of Education Weingarten, Kirchplatz 2, 88250 Weingarten, Germany
2
Department of Engineering Education, University of Education Weingarten, Kirchplatz 2, 88250 Weingarten, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2024, 14(6), 607; https://doi.org/10.3390/educsci14060607
Submission received: 15 March 2024 / Revised: 23 May 2024 / Accepted: 30 May 2024 / Published: 5 June 2024
(This article belongs to the Section STEM Education)
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Abstract

:
This study examines the influence of learning arrangements in which biology and technology (engineering) are taught in combination on the intrinsic motivation of lower secondary school students in STEM lessons. It is set in the context of STEM promotion to counter an observable decrease in motivation and interest during the school years. In a quasi-experimental design with 413 students (M = 12.53, SD = 0.818), we compare a design-based STEM learning program with two alternative teaching approaches (model building and reconstruction). A comparison of the intervention groups (ANOVA) reveals that the Design group shows significantly higher motivation than the alternatives. A multiple linear regression shows that the motivational starting position (=motivation in standard science/biology lessons), cognitive abilities, and participation in the design approach are significant predictors of intrinsic motivation. The results suggest that design-based, cross-domain learning arrangements can be an effective component of motivation-enhancing STEM instruction.

1. Introduction

There is a general decline in motivation over the course of schooling, which also affects STEM subjects [1,2,3,4,5,6]. This decline in motivation varies by gender, depending on the subject area [7,8,9]. The greatest differences are found in relation to engineering, which appears to be of less interest to girls [9]. For example, girls are less likely to think that engineering is particularly relevant to their lives [10]. Similarly, male students perceive science to be more relevant to them than girls. Their self-efficacy expectations regarding science subjects are also comparatively higher [11]. Both of these factors contribute to an increase in the gender interest gap and are reflected in, for example, career choices [12,13,14,15,16,17,18].
One way to counteract these trends is to develop motivational STEM learning opportunities [19,20]. In their considerations on promoting motivation, Hite et al. [21] (p. 3) assume that the age group from 11 to 14 years (middle school students) is an ideal period to promote motivation for STEM subjects.
The constructs of motivation and interest are closely connected and exhibit large overlaps. Schiefele and Schaffner [22] (p. 160) state that interest is a dispositional feature of motivation. Similarly, Renninger and Hidi [23] describe interest as a motivational process that supports learning. Based on this link between the two concepts, Harackiewicz et al. [24], referring to the four-phase model of interest development [25], state that fostering situational interest also increases learner motivation. In this context, Wigfield et al. [26] discuss that individuals who are intrinsically motivated perform actions out of interest in the action. In line with this relationship between motivation and interest, this study takes into account findings from both interest research and motivation research.
The current state-of-the-art research on promoting interest and motivation in the natural sciences shows that practical activities can have a positive influence on pupils’ interest in scientific and technical topics [27]. However, the influence of the practical activity on interest depends on the individual’s previous experience [28], the type of the activity, and how the activity is reflected upon [6]. The most interesting activities for students are those that take place in the laboratory [29,30,31] and that make use of digital media [27]. For product development in technology lessons, Guedel [32] shows that hands-on activities can promote interest in technology.
In a systematic literature review, Oliveira and Bonito [33] (p. 18) emphasize that practical work increases learners’ motivation to learn science. However, practical work alone is not enough. Wijnia et al. [34] also show that problem-based approaches can increase students’ interest development, with planning processes being more significant than practical activities. Interest- or motivation-promoting learning environments should be problem-based, contain reflective, practical activities, and take into account the basic psychological needs of students [35]. Previous research on the development of interests primarily relates to practical activities in the natural sciences. So far, less research has been conducted on cross-domain working methods. For example, biology and technology overlap in terms of the similarities in the working methods [36] but also in terms of the content in the consideration of structural and functional relationships [37]. One of these activities is the construction of prototypes, in which technical solutions are developed based on biological knowledge.

2. Theoretical Background

2.1. Design-Based Learning in STEM Education

Design-based learning (DBL) is a methodological approach in which learners design solutions to real-world problems through hands-on activities. Learners actively participate in the learning process by engaging in design challenges and designing a product. The design process is an iterative, open-ended process that goes through different phases. It begins with the clarification of the problem area and the definition of the characteristics that the desired technical solution must fulfill. To this end, ideas are collected—also from other domains—with the help of which the required elements can be technically realized. Various materials that can be used for the construction of the partial solutions are analyzed. Finally, the partial solutions are combined to form a product. This product is then tested for its function and optimized if necessary. The process can go through several iterations [37,38,39].
The design process offers the opportunity to bring knowledge into an application context [37,39,40] and thus build a bridge between different STEM domains so that they can be taught in an integrated rather than isolated way [37,39,41].

2.2. Design-Based Learning and Motivation

In addition to its domain-connecting potential, the methodology of design-based learning has various aspects that promote motivation:
  • Problem-based learning
  • Hands-on activities
  • Reflection on the procedure
  • Openness to solutions
In design-based learning, product development is set in the context of a problem, which includes aspects of problem-based learning [42,43,44,45,46], and practical activities are carried out as part of the product development. This can increase interest and motivation to engage with a topic if the experience gained during the activity is positive [27] [28] (p. 751) [47].
Positive effects on interest only become apparent when the practical activities are reflected upon in class [6] (p. 103). A reflective approach to practical activities can be introduced by dealing with a problem that can be solved as part of the practical activity.
Problem-based learning environments are based on current, authentic, and complex problems faced by learners [48]. Studies by Hasni and Potvin [49], Hmelo-Silver [50], and Wijnia et al. [34] attribute a positive effect on student motivation due to problem-based learning approaches.
Doppelt et al. [51] (p. 23) also describe that the development of products goes hand in hand with practical ways of working (=hands-on activities) in which learners actively engage with the problem, which can lead to deeper learning and an increase in motivation.
The discussion about the effects of open-ended work methods on learning, including design-based learning, is controversial. Critics argue, based on cognitive load theory, that they place too great a demand on the working memory and hinder effective learning [46,52,53,54]. Opposing voices, on the other hand, point to studies showing a positive learning effect of open-ended working methods [55,56,57]. This discussion is also being held with regard to motivational effects.
Feldon et al. [58] attribute “costs in terms of motivation” to an increased level of cognitive load. Evans et al. [59] (p. 20) conclude from the results of their study of 1274 high school students that teacher instructions reducing the cognitive load have a positive effect on intrinsic motivation.
Nevertheless, similarly open-ended working methods in the form of inquiry-based learning show that these can indeed have motivational effects [60,61,62,63].
Studies from the field of STEM education show that integrated STEM arrangements that pursue a design-based approach have a positive effect on the motivation of participants. In a systematic review, Hafiz and Ayop [38] refer to several studies that point to a motivational effect of design-based integrated STEM programs. Jia et al. [64] also present a motivation-enhancing effect of a design-based (here “making”) STEM learning environment for physics.
With regard to biology and the life sciences, there are rather few studies on the impact of design-based learning arrangements. Tipmontiane et al. [65] show a positive potential of design-based approaches in biology education but also point out that design-based processes are used less frequently in biology subjects than in other subjects. They refer to the arguments of other authors [66,67,68], who mention that the complexity of biological concepts makes it difficult to find a design-based reference to them.

2.3. DBL and the Fulfillment of Basic Needs

Deci and Ryan’s [69] self-determination theory (SDT) serves as the theoretical framework for measuring intrinsic motivation in this study.
The importance of the experience of competence for intrinsic motivation is well documented empirically. Elliot et al. [70] (p. 780) refer to a series of studies from the 1980s and 1990s that identify perceived competence as an important process variable for intrinsic motivation. A significant influence of the experience of autonomy on intrinsic motivation has been demonstrated several times in connection with allowing freedom of choice [71,72] and autonomy-promoting teaching behavior [73,74,75].
The aspect of social relatedness not only serves as an extrinsic motivational incentive but also influences the development of interests and the emergence of intrinsic motivation [22] (p. 167).
Xiang et al. [76] (p. 352) describe the need for social relatedness as an important process variable for intrinsic motivation. Applied to a learning environment, this means that arrangements that satisfy these “basic needs” of learners can have a motivating effect and encourage learners to engage more intensively with the learning content [35,77,78].
A design-based approach can go hand in hand with the fulfillment of basic needs (autonomy, experience of competence, social relatedness). It enables experiences of autonomy, as the product design approach is open-ended and leaves room for learners’ ideas. Furthermore, the design-based approach offers direct feedback on one’s actions in the product development process. Errors in a design impair its function. Therefore, they must be recognized and corrected, which enables experiences of self-efficacy and competence. It can be assumed that acting within a group to solve the same problem, as well as the discursive exchange when designing the solution, can contribute to fulfilling learners’ need for social relatedness.

2.4. Possible Synergies of Motivational Aspects of Design-Based Learning

A glance at the current state-of-the-art research shows that design-based learning overlaps greatly with motivation-enhancing approaches such as problem-based learning, practical experience, reflection, and openness to solutions. Each of these aspects shows a connection to the basic needs of self-determination theory and enables their fulfillment. These potential synergies are illustrated in Figure 1.
Based on these assumed synergies of motivation-promoting effects that are combined in a design-based learning arrangement, a question arises as to whether design-based learning arrangements are suitable for fostering the motivation of pupils. To this end, we have developed lifeworld-oriented, problem-based, and cross-domain learning opportunities in which students generate solutions via construction processes for which biological phenomena serve as a source of ideas.

3. Research Questions

Against this theoretical background, we investigated the issue of how design-based, cross-domain learning arrangements affect the intrinsic motivation of secondary school students.
  • Q1: How do cross-domain, design-based problem-solving processes affect students’ intrinsic motivation?
  • Q2: Which variables influence intrinsic motivation in cross-domain design-based approaches?

4. Research Design

The study presented here is intended to contribute to understanding the influence that a design-based approach has on pupils’ intrinsic motivation.
To this end, the research questions were investigated through a quasi-experimental research design. Three interventions on the topic of the musculoskeletal system were developed and compared with each other.
  • A “Design group” in which pupils independently construct a feeding machine whose function is inspired by the form–function relationships of locomotor systems in living organisms.
  • A “Reconstruction group” in which the pupils are also asked to build a feeding machine and work on the same biological content. However, the feeding machine is reconstructed according to a plan provided.
  • A “Biology group” in which the pupils do not build a feeding machine but investigate form and function relationships for movement in various living creatures by building models and also analyzing similarities in the principles on the blueprints of simple constructions.
The intervention groups differ in that they consider the motivational aspects of the design-based approach in different ways (Table 1).
The Design group translates the structural and functional correlations developed for movement apparatuses into technical partial solutions (joints, traction mechanisms, etc.) that are used to build a technical solution (feeding machine) from everyday materials (Tetra Pak cartons, cardboard rolls, straws, strings, etc.). The design process is explorative, iterative, and not limited to a fixed solution. It is based on an overarching problem (a product is needed) and enables hands-on activities that are reflected upon (What needs to be done to make the product work? How is this realized?). The approach is also open-ended. Direct instructions are not provided.
The Reconstruction group works on the same biological content as the Design group. These are located in the same problem (problem basement). A feeding machine is to be built. However, the Reconstruction group receives a construction plan to build the feeding machine and produces its solution according to these specifications. In this approach, the learners also use practical activities to solve a problem, but the solution is predetermined (direct instruction). In this respect, this approach differs from the Design approach in the degree of direct instruction and the cognitive load. The constructed solution (a feeding machine) is then examined for analogies with the form and function relationships in locomotor systems. The procedure for finding the solution is not reflected upon.
The Biology intervention group does not construct a product (a feeding machine) and therefore has no overarching problem. The structure and functional relationships in the development of movement are worked out using models. The teaching problem orientation is based on the individual anatomical structures that are dealt with. In addition to the movement of the forearm and the exoskeletons of insects and the hydrostatic skeletons of tentacles and trunks, which are treated identically to the other intervention groups, movement mechanisms are worked on using further biological examples (functional model of a hand, model of a spider leg, construction of a snake body model). This modeling provides hands-on activities. Analogous structural and functional relationships are also analyzed and reflected upon in various blueprints of simple constructions. The procedure itself is not reflected upon. The level of instruction in creating the models is quite high. Apart from a model of the function of the forearm, which allows exploration and a free approach, the other functional models are produced according to instructions and then analyzed.
This study analyzes the three approaches regarding their effects on the learners’ intrinsic motivation. All three interventions have the same duration (5 units of 90 min each). The pupils work on the same content relating to the anatomy and function of endoskeletons, exoskeletons, and hydrostatic skeletons. The three interventions address the same cognitive goals in the form of conceptual knowledge about the correlation between structure and function in the movement of different locomotor systems.

5. Data Collection

The data were collected in a pre–post design. For this purpose, the intrinsic motivation of the standard science/biology lessons was first surveyed prior to the intervention. The intrinsic motivation related to the respective intervention was surveyed directly after the intervention. The data were collected in paper and pencil format. In addition to intrinsic motivation, the participants’ cognitive abilities were also measured in the form of an IQ test. In parallel, the conceptual knowledge about the correlation between structure and function in the movement of extremities was collected in a pre–post–follow-up design. These results will be presented in another article [79].

6. Methods

6.1. Sample

Only fully completed questionnaires (pre and post) were used in this study. The resulting sample comprised 413 pupils, who were divided into the 3 intervention groups. The “Biology group” comprised 134 pupils (67 boys and 67 girls) aged M = 12.6, SD = 0.7. A total of 148 pupils (78 boys and 70 girls) aged M = 12.35, SD = 0.91, took part in the “Design group”, while 131 pupils (62 boys and 69 girls, age M = 12.60, SD = 0.74) participated in the “Reconstruction group”.

6.2. Instruments

6.2.1. Intrinsic Motivation

Student motivation was assessed using the Short Scale for Intrinsic Motivation (KIM), an adapted version of the Intrinsic Motivation Inventory (IMI) by Deci and Ryan [80], which was developed by Wilde et al. [81]. This instrument comprises twelve items divided into four subscales: interest/enjoyment, perceived competence, perceived autonomy, and pressure/tension, each consisting of three items.
The item constructs contain a placeholder for specific activities that can be used to concretize the scales. In the surveys presented here, the placeholder “task” was replaced by “science/biology lesson” and by the name of the intervention (Table 2).
The items of the scales were mixed to prevent the students from determining their affiliation. The items were rated on a five-point Likert scale from 0 to 4 (0 = strongly disagree; 4 = strongly agree). As the items on the pressure scale are negative predictors for the construct of motivation, they were subsequently recoded inversely. To analyze the data, the values of the 12 items were calculated as an arithmetic mean.

6.2.2. Cognitive Abilities

The CFT 20-R cognition test [82] measures general fluid intelligence, as defined by Cattell [83], across language barriers. It assesses the ability to recognize and solve figural relationships and formal logical-reasoning tasks under time pressure. The CFT 20-R is widely recognized for its diagnostic utility in measuring fluid intelligence and has been validated for the age group studied. In this study, the shortened version of the test was used, which comprises part 1, with 56 items.

6.3. Analysis

The analyses were carried out using IBM SPSS Statistics Version 29.000 software [84]. Initially, descriptive analyses of the data distribution were performed, followed by further prerequisite tests for more detailed statistical analyses. Various analyses were conducted to answer the two research questions, including descriptive statistics (intervention means), within-group comparisons of the intervention groups (unpaired samples t-test), comparisons between the standard instruction and the interventions (paired samples t-test), between-group comparisons (simple ANOVA), and multiple linear regression. The effect size statistics for the parametric tests (paired and unpaired samples) were calculated with Cohen’s d, for the ANOVA with η² and for the multiple regression with R². The significance level of the inferential statistics was set at a p-value of less than 0.05.

7. Results

7.1. Descriptive Statistic

The results of the descriptive statistics are shown in Table 3.
Based on the mean values of the motivation scale, it can be seen that the participants in all the approaches had above-average motivation (M > 2.00). An examination of the normal distribution of the data using the Shapiro–Wilk test showed that the data, with the exception of the motivation data from the post survey from the Design group (p = 0.006), were normally distributed (all p-values > 0.111). The reliability was examined by determining the internal consistency of the survey using Cronbach’s α (Table 4).

7.2. Investigation of Gender-Specific Differences within the Intervention Groups

The investigation of gender-specific differences in motivation using a t-test for the independent samples shows a significant difference between the intrinsic motivation of the genders in the “Reconstruction” intervention group, favoring of the boys (Reconstruction: t(129) = 2.108 p = 0.037, d = 0.170). There was no significant difference in the other interventions (Biology: t(132) = 1.179, p = 0.240; Design: t(146) = 0.131, p = 0.896).

7.3. Comparison between the Standard Science/Biology Lessons and the Respective Intervention

Compared to the standard science/biology lessons, the paired t-tests show significant differences with moderate effect sizes for all the intervention groups (Biology: t(133) = −4.282, p < 0.001, d = −0.370; Design: t(147) = −4.976, p < 0.001, d = −0.409; Reconstruction: t(130) = −3.347, p = 0.001, d = −0.292).

7.4. Comparison between Interventions

In the group comparison (simple ANOVA) (Figure 2), the intervention groups show significant differences in terms of intrinsic motivation: F(2, 410) = 9.849, p < 0.001, η² = 0.046 (Figure 2)
The Tukey post hoc test reveals significantly higher motivation values in the Design group compared to the other two groups (Biology: p = 0.003, Reconstruction: p < 0.001).
Separated by gender, the ANOVA shows no significant differences for the boys Fboys(2, 204) = 2.255, p = 0.107 η² = 0.022. Similarly, the Tukey post hoc test for differences between the Design group and the other groups remains insignificant (Biology: p = 0.152, Reconstruction: p = 0.182).
In contrast, the girls show significant differences Fgirls(2, 203) = 8.673 p < 0.001, η² = 0.079. The post hoc test with the Tukey adjustment shows a significant difference between the intervention groups (Biology: p = 0.013, Reconstruction: p < 0.001).

7.5. Determination of Influencing Factors

The following variables were examined to determine the factors that influence the level of intrinsic motivation: age, gender, IQ, motivation in standard science/biology classes, and participation in the intervention. The regression was performed excluding one outlier (more than three standard deviations) and corrected for ambiguous heteroscedasticity using BCa bootstrapping with 5000 samples (Table 5). The model found shows a mean goodness of fit with an R2 of 0.130 (corrected R2 = 0.117) and an F-value of F(6, 405) = 10.099, p > 0.001.
The predictors intelligence quotient (IQ), initial motivation in standard lessons, and type of intervention showed significant predictive power for the intrinsic motivation of the learners. The BCa-corrected confidence intervals excluded the value 0 for all the significant predictors.

8. Discussion

This study aimed to investigate the influence of cross-domain design-based learning arrangements on pupils’ intrinsic motivation. For this purpose, three intervention groups were compared, which differed in terms of the problem reference, the working method (designing, modeling, or reconstructing), and the degree of instruction. The internal consistency (Cronbach’s α > 0.700) of the data collected indicates sufficient reliability [85]. The descriptive statistics initially showed that all the interventions lead to above-average motivation levels among the participants. Even if the initial values (motivation in standard science/biology lessons) were above average, the t-tests for the paired samples showed that participation in all the interventions resulted in significant increases in intrinsic motivation. These results can be partly explained by the strong “hands-on” nature of all three interventions and are in line with the research literature [27,28,32,47].
Comparisons within the groups with regard to gender-specific differences revealed no significant differences in the Biology and Design intervention groups. Within the Reconstruction intervention group, however, there was a significant difference (p = 0.037) in favor of the boys. This difference has a very low effect size (d = 0.170). Nevertheless, with regard to the promotion of motivation in girls, this should be examined more closely.
The comparison of the motivation values between the groups showed significant differences. The Design approach achieved significantly higher motivation values compared to the other approaches. Separating this comparison by gender reveals an interesting effect: The motivational difference between the Design group and the other two intervention groups is due to the girls. There are significant differences here (p = 0.001), with a medium effect (η² = 0.079), while the results for the boys show no significant differences (p = 0.107, η² = 0.022).
Based on our results, research question Q1 can be answered as follows: interdisciplinary, design-based problem-solving processes lead to an increase in intrinsic motivation. The increased motivation compared to the other approaches can be attributed to the fact that the design-based approach addresses motivational aspects of PBL [34,48,49,50], hands-on activities [27,28,32,47] and the fulfillment of basic psychological needs [69,70,71,72,73,74,75,76,77,86,87] in particular.
The design-based approach essentially differs from the compared approaches in the open-endedness of the working method, which is reduced in the Reconstruction approach and the Biology approach through direct instructions (e.g., blueprints). These results are analogous to motivation research in inquiry-based approaches [60,61,62,63] and also reflect the results of studies on the impact of design-based learning approaches in STEM education [38,64,65]. To the best of our current knowledge, the observation that girls’ motivation between interventions is more discriminated than that of boys and that the Design approach, in particular, generates higher motivation levels has not yet been documented but is contradicted by results such as those reported by Su and Rounds [9], according to which engineering appears to be less interesting for girls. This observation provides a starting point for further research.
The influencing factors (Q2) were determined using multiple linear regression. The comparison of the confidence intervals and p-values indicates a robust measurement, as the confidence intervals exclude 0 for all the significant p-values. The model found shows that the factors IQ, motivational starting position (motivation in standard science/biology lessons), and participation in the intervention were significant predictors of motivation. However, gender and age were not found to be significant predictors.
Given the influence of cognitive ability (IQ), it can be assumed that this interacts with intrinsic motivation, as learners with strong cognitive ability are better at solving problems. Consequently, students with strong cognitive abilities should be more likely to experience competence. Szymanski [88] (p. 11 ff.) states in the results of her study that students with higher aptitude are also more likely to experience competence than weaker students. These are, in turn, sub-variables of the construct intrinsic motivation [69,86].
With regard to the intervention attended, the results are congruent with the calculated ANOVA. This also shows significantly lower intrinsic motivation in the participants in the two alternative interventions (biology or reconstruction). This points to the motivational effectiveness of the design-based approach, but it should be viewed in light of the different motivational starting points.
The significant role of the motivational starting situation for motivation in the intervention groups is analogous to a study by Fulmer and Frijters [89]. They showed that subject interest (defined in our study as a component of motivation) positively affects motivation to complete a task that is also located in this subject (there: a reading task). Thus, students who are already motivated in science/biology lessons would also be motivated for activities (here: modeling, designing, reconstructing) referring to those lessons.

9. Conclusions

Our study was carried out in the context of countering the decline in motivation and interest over the course of schooling in STEM lessons. To this end, we pursued an integrated STEM approach in which biology and technology are combined in the design of a biology-inspired product.
Cross-domain approaches have the potential to combine research-based and engineering-based working methods from different STEM domains in order to solve a problem [36]. In this way, they offer hands-on experiences and can lead to a deeper engagement with the learning content [36,38]. Our results suggest that a design-based approach is particularly suitable for increasing students’ motivation in an integrated STEM classroom.
Learning environments designed accordingly combine different aspects (PBL, hands-on activities, reflective approach) that promote motivation. The complex problems the pupils are confronted with serve as the starting point for such lessons. These problems encourage learners to find a solution, which corresponds to the pedagogical principle of problem-based learning [48]. A cross-domain learning arrangement that uses a design-based approach enables hands-on activities that are motivating for students [27,28,32,47]. The design process of product development is not an isolated hands-on activity but is framed in a meaningful way by problem specification and associated questions: What must the product be able to do? How can it be realized? The procedure must therefore be thought through, which combines the hands-on activity with a reflective approach, which is another aspect promoting motivation [6]. This combination of hands-on activities, PBL and a reflective approach in a cross-domain design-based learning arrangement also offers learners the possibility to fulfill their psychological needs according to the self-determination theory.
However, in order to maximize the motivational effect, it seems important that the tasks in the design process are as open-ended as possible. Tasks that prescribe a fixed solution path (blueprints) should be viewed critically in this context.
Although the motivating effect of design tasks is independent of gender, this approach seems particularly attractive to girls. Regarding approaches for promoting girls’ interest in STEM education, the results of our study indicate that design-based tasks that combine technology and biology/science could serve as a component for planning STEM interventions that promote motivation.
Immersive elements could be included as a possible further step in the development of design-based learning environments. The use of VR and AR could provide students with additional insights into functional contexts or provide digital information, thereby reducing the cognitive load [90,91] and promoting learners’ independent interaction with the learning content (= autonomy) [91]. Furthermore, immersive extensions offer the potential to overcome cultural and geographical limitations, e.g., by forming intercultural design teams [92] and enabling new learning experiences.
The results of this study already show, however, that intrinsic motivation in STEM lessons can also be promoted through analog, cross-domain design-based learning arrangements.

Author Contributions

Conceptualization, M.R.; methodology, M.R.; software, M.R.; validation M.R., H.W. and M.B.; formal analysis, M.R.; investigation, M.R; resources, M.R., H.W. and M:B.; data curation, M.R.; writing—original draft preparation, M.R; writing—review and editing, M.R, H.W. and M.B.; visualization, M.R.; supervision, H.W. and M.B.; project administration, M.R.,H.W. and M:B.; funding acquisition, M.R., H.W. and M.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by VECTOR foundation, grant number P2021-0083. Supported by Open-Access-Funds of University of Education Weingarten.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Ministry of Education, Youth and Sports (protocol code KM31-6499-3/4/3). This research is exempt from formal approval by the Ethics Committee according to the regulations of the Ethics Committee of the University of Education Weingarten and is based on a self-assessment checklist. All data were collected on an explicitly voluntary basis after obtaining parental consent (parental consent has been obtained) and were completely anonymized. Inferences about participants are not possible. Neither participation nor non-participation in the study led to disadvantages for the individuals concerned.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the ongoing study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Education 14 00607 g001
Figure 1. Possible synergies of a design-based approach.
Figure 1. Possible synergies of a design-based approach.
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Figure 2. Comparison between the intervention groups.
Figure 2. Comparison between the intervention groups.
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Table 1. Illustration of the use of motivation-enhancing aspects in the different interventions.
Table 1. Illustration of the use of motivation-enhancing aspects in the different interventions.
InterventionProblem-Based LearningHands-On
Activities		Reflective ApproachOpenness to SolutionsDirect Instruction
DesignYes
(product needed)		YesYes
(steps of procedure, function)		YesNo
ReconstructionYes
(product needed)		YesLimited to the function of the productLimited
(plan)		Yes
(plan)
BiologyYes
(function of anatomical structures)		YesLimited to the function of models and blueprintsLimited
(instruction for models)		Yes
(instruction for models)
Table 2. Exemplary representation of the changed placeholders in the test instrument.
Table 2. Exemplary representation of the changed placeholders in the test instrument.
Not True at AllPartly TrueCompletely True
I enjoyed the movement project ☐ ☐ ☐ ☐☐
I was able to perform the movement project the way I wanted to.☐☐☐☐☐
I skillfully positioned myself in the movement project.☐☐☐☐☐
Table 3. Descriptive statistics of intrinsic motivation (science/biology lesson), motivation of the intervention, and IQ.
Table 3. Descriptive statistics of intrinsic motivation (science/biology lesson), motivation of the intervention, and IQ.
ApproachMeasuresNMinMaxMeanSTD
DesignIQ14862141101.715.96
Motivation science/biology lessons1480.7542.570.60
Motivation_intervention1481.253.922.850.58
ReconstructionIQ1316213599.4413.71
Motivation_science/biology lessons1310.753.672.340.60
Motivation_intervention1310.923.922.560.64
BiologyIQ1346413399.4615.25
Motivation_science/biology lessons1340.923.922.350.53
Motivation_intervention1341.173.832.620.55
Table 4. Internal consistency of the measurement instruments.
Table 4. Internal consistency of the measurement instruments.
InterventionCFT-20 R
(56 Items)		Intrinsic Motivation_Science/Biology Lessons
(12 Items)		Intrinsic Motivation_Intervention
(12 Items)
Cronbach’s αCronbach’s αCronbach’s α
Biology0.8080.7090.742
Design0.7910.8030.798
Reconstruction0.7100.7910.784
Table 5. Results of the multiple linear regression.
Table 5. Results of the multiple linear regression.
PredictorsB aSE atp
(constant)1.929
[0.825, 3.055]		0.5503.4100.001
Gender−0.093
[−0.204, 0.018]		0.056−1.6610.097
IQ0.005
[0.001, 0.009]		0.0022.4440.015
Age−0.011
[−0.081, 0.060]		0.035−0.3140.753
Biology−0.164
[−0.299, −0.028]		0.068−2.3970.017
Reconstruction−0.205
[−0.348, −0.067]		0.072−2.9740.003
Motivation standard science/biology lesson0.241
[0.126, 0.358]		0.0584.917<0.001
a Confidence intervals and standard errors per BCa bootstrapping with 5000 samples.
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Reiser, M.; Binder, M.; Weitzel, H. Effects of Design-Based Learning Arrangements in Cross-Domain, Integrated STEM Lessons on the Intrinsic Motivation of Lower Secondary Pupils. Educ. Sci. 2024, 14, 607. https://doi.org/10.3390/educsci14060607

AMA Style

Reiser M, Binder M, Weitzel H. Effects of Design-Based Learning Arrangements in Cross-Domain, Integrated STEM Lessons on the Intrinsic Motivation of Lower Secondary Pupils. Education Sciences. 2024; 14(6):607. https://doi.org/10.3390/educsci14060607

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Reiser, Markus, Martin Binder, and Holger Weitzel. 2024. "Effects of Design-Based Learning Arrangements in Cross-Domain, Integrated STEM Lessons on the Intrinsic Motivation of Lower Secondary Pupils" Education Sciences 14, no. 6: 607. https://doi.org/10.3390/educsci14060607

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