Content area
Background
A significant challenge in school STEM education is making connections to real-life contexts beyond the school setting that are relevant and meaningful for students. Another important consideration is how out-of-school learning settings can be integrated to offer students a broad spectrum of self-regulated learning experiences across diverse contexts. Cooperation between in-school and out-of-school learning environments can foster mutual support of STEM learning, enhancing educational experiences within and beyond the classroom. In the present study, we conducted a questionnaire study using a pretest-posttest-follow-up design. We analyzed the predictive power of general learner characteristics influenced by the school environment and the integration of a museum visit into the classroom on the learning processes and learning effects of 409 10th-grade students.
Results
Findings indicate that students’ knowledge of metacognitive and motivational learning strategies significantly predicts learning process characteristics such as basic needs, perceived content relevance, and engagement. The intensity of this relationship decreases when prior individual interest is considered. Regarding medium-term learning effects three months after the museum visit, follow-up activities in the classroom and students’ knowledge of metacognitive and motivational learning strategies are predictive factors for self-perceived and objective knowledge, while individual interest is only predicted by prior individual interest.
Conclusions
The study highlights the close relationship between in-school and out-of-school STEM education. The key contribution of the study is to provide detailed insights into the different but interconnected facets of the relationship between STEM education in-school and out-of-school. It emphasizes that schools should equip learners for out-of-school learning by teaching them effective learning strategies to enhance the learning processes and outcomes of STEM museum visits. In addition, by shaping students’ prior knowledge and interest, schools contribute significantly to preparing learners for meaningful STEM experiences in out-of-school settings. Furthermore, it is important to follow up on the museum visits in the classroom to reinforce learning.
A significant challenge in school STEM (Science, Technology, Engineering, and Mathematics) education is making connections to real-life contexts beyond the school setting that feel meaningful to students. One approach to enriching formal STEM education is the inclusion of out-of-school learning environments. They can enhance school STEM education by providing diverse opportunities for students to explore STEM topics that are not easily accessible in school. This is especially true for complex topics, such as socio-scientific issues, encompassing scientific and social aspects (Kellberg et al., 2024). Furthermore, integrating diverse learning settings beyond the school environment appears to be beneficial as they provide students with a wide range of self-regulated learning experiences in various contexts (Mujtaba et al., 2018). However, the specific characteristics of out-of-school learning environments can also pose challenges for students' learning. Here, cooperation between school and out-of-school learning environments can foster mutual support of STEM learning, enhancing educational experiences within and beyond the classroom (Dillon & Wong, 2025). The different facets of this relationship are crucial for STEM education but remain under-researched. In the present paper, we focus on science museums and investigate the links between school and out-of-school STEM education in museums from two different perspectives. The first perspective addresses general learner characteristics primarily influenced by school, such as students' knowledge of learning strategies, and analyzes their impact on the learning process during the museum visit and its learning effects. The second perspective investigates how the school preparation for the topic and the follow-up activities in school after the museum visit influence the learning process during the visit and its learning effects. In this way, opportunities for improving cooperation between in-school and out-of-school STEM education can be identified.
Specifics of out-of-school learning environments like museums and their relation to STEM education
Out-of-school learning environments such as natural history and science museums differ from formal STEM educational environments such as schools (Hohenstein & Moussouri, 2017; Schwan et al., 2014): They can be characterized by a variety of simultaneously presented original objects or models that are supplemented by media and different exhibits (e.g., interactive and hands-on installations, images, videos, audio islands, and texts), displayed across a spacious exhibition area. Natural history and science museums offer a wide range of options for action and individual learning processes. The high authenticity of the original objects and their staging, which illustrate scientific contexts and possible applications, can make their relevance or practical usefulness tangible. The integration of playful elements allows for social interaction. Furthermore, museums, which are not restricted by the ‘disciplinary boundaries that characterize school subjects’ (Evans & Achiam, 2021, p. 9), increasingly thematize ‘wicked problems’ or socio- scientific issues in their exhibitions (Dillon & Wong, 2025; Kellberg et al., 2023; Kollmann et al., 2013; Yun et al., 2020). They have the multidisciplinary expertise to prepare exhibitions addressing socio-scientific issues from different perspectives (e.g., individual, social, environmental, economic, scientific; Kellberg et al., 2023; Pedretti & Iannini, 2018). This can also lead to the experience of discrepancy or surprise, which in turn provides valuable opportunities for STEM learning (Brod et al., 2018).
Effects of school visits to museums—learning processes and outcomes
Due to the above-mentioned characteristics, museums offer various opportunities to achieve a wide range of educational experiences, both at the level of learning processes (during school museum visits) and learning effects (after museum visits).
Learning processes during school museum visits
From a learning theory perspective, specifically through the lens of self-regulated learning, the mentioned characteristics of museums reveal a high potential for various self-regulated learning activities. According to most models, self-regulated learning can be defined as learners' autonomous efforts to initiate, maintain, and adapt cognitions, motivations, and behaviors to achieve their learning goals (Boekaerts et al., 2005; Schunk & Zimmerman, 2012; Winne & Hadwin, 2010). Following Daumiller and Dresel (2018) self-regulated learning comprises cognitive strategies to regulate the information processing, meta-cognitive strategies to plan, regulate, and adapt the cognitive strategies, and motivational regulation strategies to activate and maintain the learning motivation. One of the primary goals of school education is to enable students to apply these learning strategies in a self-regulated manner, both in school and in out-of-school learning settings such as museums (Bamberger & Tal, 2007). The diverse opportunities for action and learning in museums foster highly individualized learning processes, typical of self-regulated out-of-school learning, and thus contribute to knowledge acquisition (e.g., Gutwill & Allen, 2012). Furthermore, according to Lepper (1988), learners can experience the functionality of the learning activity and the learning object within a context that closely resembles the real world, to varying degrees. This contextualization of knowledge acquisition promotes a more profound understanding and counteracts inert knowledge. An indicator of self-regulated learning in museums is the (intended) interaction with exhibits and media-presented information, as well as the associated interaction with other learners. This learner engagement is the core element of the learning process in museums (cf. Ben-Eliyahu et al., 2018; Cahill et al., 2011). It encompasses three dimensions: behavioral, cognitive, and affective engagement (e.g., Ben-Eliyahu et al., 2018; Martin et al., 2016). Behavioral engagement refers to active participation in (learning) activities, such as operating interactive installations or discussing a topic/exhibit. Cognitive engagement describes the extent to which one exerts effort and focuses on the exhibit or activity to understand the presented information and ideas. Affective engagement includes positive and negative feelings, orientations, and impacts related to the learning process during interaction with the exhibit and/or fellow learners (Ben-Eliyahu et al., 2018; Martin et al., 2016). The engagement-level of learners influences their learning and understanding, thereby affecting their cognitive and motivational learning outcomes (e.g., Barriault & Pearson, 2010). Engaged learners are more actively involved in the learning process, ask questions, seek answers, and reflect on what they have seen, which leads to deeper understanding, greater knowledge gain, and increased motivation (Ainley et al., 2002; Dancu, 2005).
Furthermore, the learner’s perception of content relevance during the learning process plays a central role in fostering cognitive processes such as deep learning and engagement (cf. Brophy, 2008; Martin et al., 2016; Mayer, 1996), as well as in fostering motivational aspects of the learning process, such as situational interest as well as self-determined learning motivation (Falk & Dierking, 2018; Krapp et al., 2014). When learners are able to connect presented content with their own ideas, interests, prior knowledge, and everyday experiences, they are more likely to perceive it as relevant and to process it through deep learning strategies (e.g., Mayer, 1996), which in turn fosters better understanding and greater learning success. In addition, from a motivational perspective, specifically through the lens of self-determination theory (Ryan, 2017) and interest theory (Krapp et al., 2014; Renninger & Hidi, 2016), the mentioned characteristics of museums reveal a high potential for enhancing these qualities of motivation (e.g., Falk & Dierking, 2018; Neubauer, 2015; Neubauer & Lewalter, 2018) as well as the experience of basic psychological needs (autonomy, competence, and relatedness). A huge body of research, including studies in museum contexts, has shown that the fulfillment of basic needs is crucial for fostering self-determined motivation and situational interest (Ryan & Deci, 2017; Renninger & Hidi, 2016; e.g., Geyer, 2008; Lewalter & Geyer, 2009; Neubauer, 2015; Neubauer et al., 2014). While individual interest describes a more stable content- or object-related intrinsic quality of motivation (Krapp et al., 2014; Renninger & Hidi, 2016), situational interest is triggered by object- or situation-specific external stimuli in a current learning context (Knogler et al., 2015; Mitchell, 1993; Renninger & Hidi, 2011, 2016). Self-determined motivation and situational interest, in turn, enhance learners’ persistence, attention, and strategic use of learning resources, promoting engagement and the effective acquisition and processing of new information, and ultimately supporting improved learning outcomes (Wu et al., 2024).
Studies also show that situational interest can support individual interest and knowledge gain during a school museum visit (e.g., Geyer, 2008) and is thus a central aspect of learning processes in museums.
Learning effects of school museum visits
Studies on learning effectiveness generally indicate that school visits to museums provide motivational, social, and cognitive benefits, serving as a valuable complement to formal education (cf. Behrendt & Franklin, 2014; DeWitt & Storksdieck, 2008; Martin et al., 2016; Mujtaba et al., 2018). The success of school museum visits is often measured by cognitive effects such as knowledge gain, acquisition of skills, or deeper understanding (e.g., Bamberger & Tal, 2008; Gutwill & Allen, 2012; Whitesell, 2016). In the absence of a binding curriculum, it is important to cover knowledge acquisition within out-of-school learning more broadly, as in schools, which often focus primarily on acquiring conceptual knowledge, mainly based on skills in individual subjects (OECD, 2019). This broader view meets in particular the goal of school education to equip students with competencies for learning and decision-making outside of school to meet the challenges of the twenty-first century (Reimers Arias & Chung, 2016). This requires a cross-disciplinary use of domain-specific knowledge. Particularly in out-of-school learning environments with great scope for self-regulated learning (Krombaß & Harms, 2006), and individual choices regarding content engagement, subjective measurement instruments are especially well-suited to comprehensively capture individual learning effects during the visit (cf. e.g., Gibbs et al., 2006), while an objective knowledge test can additionally serve as a meaningful complement to make cognitive effects more comparable. Thus, besides objective knowledge measurement, self-perceived knowledge gain is also suitable for capturing changes in knowledge acquisition, as museums tackle a topic from different perspectives (Clasen, 2010). Further, it should be noted that newly acquired knowledge in an out-of-school context is often retrievable only after a phase of restructuring, refinement, and further processing through unconsciously occurring reflections. Therefore, the actual learning effects from a museum visit often become apparent only in time-delayed follow-up surveys (cf. Falk & Dierking, 1998; Mujtaba et al., 2018).
Motivational learning outcomes such as the development of curiosity, motivation, and individual interest, as well as appreciation for learning content (e.g., Krombaß et al., 2007; Martin et al., 2016), are increasingly being used as indicators for measuring the effectiveness of school visits to museums (e.g., Falk & Dierking, 2018; Wilde & Urhahne, 2008). The findings predominantly indicate a motivation-enhancing effect of school visits to museums (e.g., Falk & Dierking, 1998; Geyer, 2008; Neubauer, 2015).
The influence of learner characteristics on learning processes and outcomes in museums
Research in many fields of education has shown that the learning process and learning outcomes are influenced not only by the learning environment but also by characteristics of the learners (cf. Ben-Eliyahu et al., 2018; Cahill et al., 2011; DeWitt & Storcksdieck, 2008; Falk & Dierking, 2018; Kellberg et al., 2023; King & Datu, 2017; Liem et al., 2008; Quast, 2011; Renninger & Bachrach, 2015). Particularly important are students’ prior knowledge and prior individual interest for the motivational and cognitive processes and effects of school museum visits (e.g., DeWitt & Storcksdieck, 2008; Falk & Dierking, 2018; Falk & Storksdieck, 2005; Stavrova & Urhahne, 2010). The same applies to students' knowledge of learning strategies. However, little is known about this connection during school class visits to museums. Although students mainly gain knowledge of learning strategies at school, their prior knowledge and individual interests are influenced both by school experiences and, to some extent, by experiences outside of school.
In the context of museums, research has shown that learners’ prior knowledge can enhance a thorough understanding and meaningful engagement with the information presented by an exhibit (e.g., Corredor, 2006; Kellberg et al., 2024; Pecore et al., 2017; Stavrova & Urhahne, 2010). Regarding motivation, Ben-Eliyahu and colleagues (2018) emphasize that without a certain level of motivation or the desire to learn, there will be no engagement and thus no learning performance. Similarly, research demonstrates that individual interest in a topic is associated with increased cognitive and/or behavioral engagement, as well as increased attention and concentration during learning (Cahill et al., 2011; King & Datu, 2017; Patall et al., 2016; Renninger & Bachrach, 2015). Motivated learners ask more questions, observe and discuss more intensively, incorporate their prior knowledge and experiences to a greater extent, or think more deeply about a topic (Harackiewicz et al., 2016). Furthermore, a certain level of metacognitive and motivational knowledge of learning strategies is required for learners to independently and effectively engage with new learning content (cf. Corredor, 2006; Karatas & Arpaci, 2021; Weinstein et al., 2000; Zimmerman, 2008). Regarding gender, findings show that girls’ motivational and cognitive outcomes appeared to profit somewhat more from visiting a museum than boys (cf. Schürmann & Quaiser-Pohl, 2022). While most of the mentioned research was conducted in formal educational settings like (high) schools or universities, the empirical basis for museums is much weaker and research is needed.
Challenges of learning in museums
From an educational point of view the learning characteristics of museums described above represent an attractive educational offer, particularly in the field of STEM learning. However, the learners must make adequate use of this opportunity to fully benefit from it. As described in the opportunity-use model of the effects of teaching (Helmke, 2012), the effective use of an educational offer cannot be taken for granted either in the classroom or in museums. In this context, extensive self-regulation opportunities, as found in museums, may support learning but also risk cognitively overwhelming learners. Not all students possess the metacognitive and motivational skills needed for effective self-guided exploration. Studies have shown that students are willing to engage in self-regulated learning only to the extent that they feel competent, requiring varying degrees of instructional support to effectively engage with the information presented (Kalyuga, 2007). Applied to the museum context, this means that if learners have too much freedom and choice in terms of content and learning activities, they may not know what to focus on (e.g., Bamberger & Tal, 2007; Sung et al., 2008). In addition, these learning challenges may leave learners with insufficient cognitive resources for effective information processing (e.g., Bamberger & Tal, 2007; Sung et al., 2008).
These challenges can be further intensified by the unfamiliarity with and novelty of the learning environment. Research indicates that spatial orientation is crucial for effective learning in museums (Falk & Dierking, 2018; Lee et al., 2020; Rennie, 2007). When students visit a museum for the first time, they may have difficulties orienting themselves and navigating the space (Orion & Hofstein, 1994). As a result, learners can lose track and miss important exhibits or use up too much mental capacity to find their way, leaving them unable to effectively process information that would be relevant to their learning (cf. Pecore et al., 2017; Sung et al., 2008). Students may encounter a wealth of information but struggle to process it meaningfully and link it to their prior knowledge, which can significantly hinder learning (cf. Rosenshine, 2009).
Motivational challenges also play a significant role in the success of learning processes in museums. Several factors can negatively impact students' motivation and engagement. They range from thematic disinterest and boredom over museum fatigue (Kim et al., 2020) to overstructured and controlled museum visits that allow no freedom for individual engagement with the content presented. If the exhibits and information do not connect to the students' everyday lives and prior experiences, their willingness to engage with the material often decreases, and deeper processing will not take place either (e.g., Brophy, 2008; Martin et al., 2016).
To reduce these challenges, cooperation between schools and out-of-school learning environments is essential.
Interplay between school and out-of-school learning environments
For the benefits of out-of-school learning environments to be realized and the challenges mentioned above to be successfully met, cooperative interaction between schools and out-of-school learning environments like museums seems to be beneficial. This interplay can be realized on different levels. It can occur, for example, either through appropriate integration of the visit in the classroom or through the school's influence on important learning prerequisites for out-of-school learning.
Integration of the museum visit in the classroom
A straightforward and effective way to integrate a museum visit into classroom learning is through preparatory and follow-up activities at school. Multiple studies have highlighted the importance of integrating museum visits into classroom teaching and ensuring a direct connection between the visit content and the school curriculum to maximize cognitive and motivational benefits (Behrendt & Franklin, 2014; DeWitt et al., 2008; Lee et al., 2020; Morentin & Guisasola, 2015; Rennie, 2007). Rennie (2007) emphasizes that the effectiveness of the visit depends largely on the degree to which it complements the school curriculum (see also Lee et al., 2020).
Before the visit, it seems beneficial to prepare the students by introducing them to the venue and its layout (Lee et al., 2020), the focus or purpose, and the tasks and expectations of the visit (Behrendt & Franklin, 2014). This information serves as a kind of “advanced organizer” to clarify the learning objectives and tasks of the visit. So that the students can learn during the visit in a self-regulated manner regarding the requirements of the subsequent follow-up in school lessons (e.g., Rennie, 2007). It forms the conceptual foundation for students to build their experiences. In this way, the students’ cognitive overload and spatial orientation difficulties can be prevented. Furthermore, when the content of the visit is connected to the curriculum (Behrendt & Franklin, 2014; Rennie, 2007), students can clearly recognize the relevance of the visit content in general and for learning at school. This helps to develop and maintain their motivation to learn during the museum visit (Rennie, 2007). However, not all studies indicate a positive effect of preparatory activities on learning outcomes (Lavie Alon & Tal, 2015). Studies have shown that too much information should be avoided in order not to diminish students’ curiosity about the museum (e.g., Pecore et al., 2017).
After the visit, students should be given sufficient time to reflect on their experiences in the museum and put them into a larger context, e.g. by discussing their observations and thoughts during the visit, clarifying open questions, and making connections to the curriculum (Behrendt & Franklin, 2014). Students need to consolidate their new ideas and observations that are not yet linked. This is also shown by the fact that learning effects are not immediately apparent after a museum visit but only after a time delay (Falk & Dierking, 1998; Mujtaba et al., 2018). Reflection will help to make these connections and reinforce the links already successfully made on the field trip (Kisiel, 2006; Rennie, 2007). By integrating field trips into classroom teaching, “Field trips offer an opportunity to motivate and connect students to appreciate and understand classroom concepts, which increase a student’s knowledge foundation, promoting further learning and higher-level thinking strategies.” (Behrendt & Franklin, 2014, p. 242). Accordingly, schools play a key role in facilitating museum visits to ensure they proceed efficiently and effectively.
Despite the above considerations, underscoring the importance of integrating excursions into classroom teaching, research on the actual use of integration activities remains limited. Existing research suggests that such integration is not yet a firmly established component of school practice (Lee et al., 2020).
It is therefore appropriate to investigate whether and how museum visits are integrated into classroom teaching. In this context, it is important to distinguish between preparation and follow-up activities, as each may have different impacts on the learning process and outcomes.
Schools' impact on learner prerequisites
From a broader perspective, it might also be revealing to consider another, more indirect connection between the two learning sites: the application of general skills acquired at school and in out-of-school learning environments. This is another important connection between the two learning venues that has so far only been considered to a limited extent. Our knowledge about ‘in what ways school-based learning substantively transfers to non-school life’ (Bransford et al., 2006, p. 216) is quite limited. This also holds true for out-of-school learning at the museum and applies to the application of learning strategies learned at school in an out-of-school learning environment. In view of the above-mentioned situational challenges associated with museums as out-of-school environments for self-regulated learning, it is insightful to consider learners’ competencies at a more general level—specifically, their knowledge of learning strategies. Since this knowledge is primarily acquired within the school context, it is instructive to examine its relevance for the learning process and the learning success at the out-of-school learning site.
In addition, subject-specific knowledge can be considered in the form of prior knowledge if the visit relates to a clearly definable subject area. This has already been examined in several studies (e.g., Kellberg et al., 2024; Pecore et al., 2017). Regarding the role of individual interest in out-of-school learning, there are heterogeneous findings (DeWitt & Storcksdieck, 2008; Kellberg et al., 2024).
Based on these theoretical considerations and research findings, the presented study will contribute to answering the following research questions:
School preparation and follow-up of museum visit in class
1) To what extent are there any preparatory or follow-up activities related to the museum visit integrated into classroom instruction?
Learning process during the museum visit
2 (a) To what extent do the characteristics of learners influenced by school and school preparation of the visit predict the learning process characteristics during the museum visit?
2 (b) To what extent is this influence affected by learners' prior knowledge (both self-perceived and objectively assessed) and prior interest, as well as gender?
Learning effects after the museum visit
3 (a) To what extent do the characteristics of learners influenced by school and school preparation and follow-up activities of the visit predict the medium-term learning effects of the museum visit?
3 (b) To what extent is this influence affected by learners' prior knowledge (both self-perceived and objectively assessed) and prior interest, as well as gender?
Methods
Context
To empirically investigate the research questions, a tablet-based visit program titled "Mobility Transition—What Moves Us in the Future? Traffic and Mobility Challenges of the 21st Century" was developed within the ProMus project by the Technical University of Munich, in collaboration with the exhibition on mobility and traffic at the Deutsches Museum München in Germany.
By taking part in the program, the students had the opportunity to explore current mobility challenges and possible solutions at different exhibits through self-regulated exploration in small groups of two or three students. Additional information and discussion prompts on twelve pre-selected exhibits covering the four topics of (1) individual mobility behavior, (2) traffic and environment, (3) technology and technological history, and (4) future visions of mobility were provided by an app on a tablet developed for the visit program. The app includes an introductory text, an exhibition map with the twelve pre-selected exhibits, additional information, images, discussion prompts, and assistance for each exhibit.
Pre-information for teachers
Teachers were invited via email to participate in the study by visiting the exhibition with their class. For this purpose, we used a large email distribution list of high schools in and around Munich, which repeatedly cooperated with the Technical University of Munich on various projects and educational offers. The email informed the teachers about the organization, the content, and the objectives of the museum visit by using a flyer and a 3-page information letter. Furthermore, the connections between the visit topics and the 10th-grade curriculum at the gymnasium were clarified, along with an explanation of the details of the accompanying scientific study. Based on this information, teachers registered to participate in the field trip and the study.
Implementation
At the start of the museum visit, the students were briefed about the visit schedule, content, and the app on the tablet designed to guide their exploration of the exhibition on mobility and traffic. Subsequently, the students of each school class were randomly assigned to small groups consisting of two or three students.
Each group started at a different exhibit to ensure an initial random distribution of the school class throughout the entire exhibition. The subsequent 90-min exploration of the exhibition, however, was self-guided. This offers the learners enough time to engage intensively with the twelve exhibits, as according to Shaby and colleagues (2017), five minutes for intensive engagement leading to "breakthrough" behavior is sufficient, leaving time for engaging with other exhibits.
During the visit, the students gathered general information, concepts, and terms on the respective topics, examined the past, present, and future of mobility, reflected on mobility patterns, evaluated and developed characteristics and strategies for sustainable mobility, and drew conclusions for their own (future) actions.
Design
Within the ProMus project the students were anonymously surveyed through an online questionnaire one week before their museum visit in class (pre: T1), immediately after their visit (post: T2), and again through an online questionnaire approximately three months after their visit in class (follow-up: T3). The research methodology for this project was approved by the Educational Ministry of Bavaria.
The school museum visit and the data collection tools were tested in a pilot study (N = 37) and revised based on student feedback as well as statistical factor and item analysis results.
Sample
In total, 409 students of 21 classes in the 10th grade of twelve high schools (Gymnasium) participated in the pre- and post-questionnaire (T1 and T2, MAge = 15.71; SD = 3.40; 51.8% female, 44.5% male, 3.7% diverse). 212 students of this sample also completed the follow-up-questionnaire (T3, MAge = 15.41; SD = 3.40; 56.6% female, 42% male, 1.4% diverse). Parents provided written consent for child participation.
For analyzing the learning processes, we included all students who completed the pre- and post-questionnaire (sample 1: N = 409), and for analyzing the learning effects, we included the students who completed all three questionnaires (pre-post-follow-up; sample 2: n = 212). Both samples show no difference in age and gender distribution. They also show (almost) identical mean values regarding self-perceived and objective prior content knowledge, individual interest, and knowledge of learning strategies (see Table 2, result section).
Measures
All scales (except for preparation and follow-up of the visit in class) have a five-point response format (from 1 = not at all to 5 = very or 0 = 0 points to 4 = 2 points for the objective knowledge items). Through confirmatory factor analyses, the theoretically postulated (sub-)scales were clearly proven for all scales and showed satisfactory to very good reliabilities (s. below).
School preparation and follow-up activities of the visit in class
The preparation of the museum visit in class was assessed with one item (self-developed) at T1. "Have mobility and the mobility transition been addressed in class previously?" with two response options "0 = no" and "1 = yes, the topics have already been addressed in class".
Follow-up activities after the museum visit in class were assessed with one item (self-developed) at T3 "Were mobility and mobility transition or certain aspects of these topics discussed after the museum visit in class?" with four response options (1) "no", (2) "yes, we talked generally about the museum visit again", (3) "yes, we discussed specific topics more intensively" (here students could also specify which topics were discussed more intensively) and (4) "yes, other activities" (here students could also specify other activities). For later regression model calculation, the response options (2) to (4) were combined and recoded into a 0 (no) and 1 (yes) coding.
Characteristics of learners influenced by school
Knowledge about learning strategies was measured using two subscales at T1. Ten items cover metacognitive learning strategy knowledge. This subscale was developed in reference to scales by Wild and Schiefele (LIST; 1994), Rakoczy and colleagues (2005), Daumiller and Dresel (2018), and Boerner and colleagues (2005), focusing on activities related to planning, monitoring, and regulating student learning [α = .87, M = 3.47, SD = 0.71; Example item: When something seems confusing and unclear to me while studying, I go through it slowly again.]. The four items of the subscale on knowledge about motivational self-regulation during learning (cf. Benick et al., 2018; Kuhl & Fuhrmann, 2004) capture aspects such as activating and maintaining motivation for learning, especially in connection with encountered learning difficulties, boredom, or lack of interest [α = .78, M = 2.67, SD = 0.84; Example item: When I don't feel like studying, I promise myself a reward.].
Learning process
The following indicators of the learning process were assessed directly after the museum visit (T2).
Students’ situational interest was measured using a scale with a total of twelve items adapted for this study from Knogler and colleagues (2015) and Lewalter (2020). The items refer to aspects such as attention arousal and subject curiosity as well as capturing positive emotions such as the level of enjoyment during the students’ content engagement. Furthermore, the perceived value of the topics and the epistemic orientation to further explore the content are included [α = .90, M = 3.26, SD = 0.66; Example item: I came across topics during the museum visit that I would like to learn more about.].
The experience of basic needs related to autonomy, competence, and relatedness was measured using a scale developed by Neubauer (2015) and Neubauer and colleagues (2014). The overall scale consists of 16 items [α = .90, M = 3.93, SD = 0.59; Example item: During today's engagement with the exhibits, I felt I could elaborate/work out new topics and content independently.].
The students’ perceived content relevance during the visit was measured using a scale adapted for this studys’ content in line with scales proposed by Rakoczy and colleagues (2005) and Neubauer (2015). The scale includes seven items assessing the importance and usefulness of mobility and mobility transition topics for students’ own everyday life and future as well as for society and the environment [α = .87, M = 3.70, SD = 0.69; Item stem: During today's engagement with the exhibits …; Example item: … it became clear what role the topics and content play for society and the environment.].
As a further (learning) process indicator, the self-reported engagement of the students in behavioral-cognitive, and motivational-affective aspects of their interaction with the exhibits and information on-site was retrospectively assessed using the Activity-Engagement-Survey (Ben-Eliyahu et al., 2018). A confirmatory factor analysis confirmed a 2-factor structure, which was also found in the study by Ben-Eliyahu et al. (2018). The eight items of the behavioral-cognitive engagement subscale capture aspects such as concentration, effort, reflection, and discussion while engaging with the exhibits and new information, testing understanding, trying out one’s own ideas, and experiencing and learning new things [α = .77, M = 3.38, SD = 0.62; Item stem: During today's engagement with the exhibits …; Example item: …I tried out my ideas to see what happens.]. The affective engagement includes four items and refers to the emotions the students experienced during their interaction with the exhibits, such as boredom, frustration, joy, and curiosity [α = .68, M = 3.64, SD = 0.74; Example item: …I was bored.].
Learning effects
As indicators of cognitive and motivational learning effects, the content knowledge (self-perceived and objective) and individual interest were assessed directly after the visit (T2) and three months later (T3).
Exploratory factor analyses have confirmed a 1-factor structure for both self-perceived and objective knowledge and show good reliability.
Self-perceived knowledge: With eight items, the self-assessed knowledge of the students on the general topics of mobility and mobility transition as well as on the four thematic areas of the museum visit was measured [T1: α = .84, M = 2.63, SD = 0.66; T3: α = .87, M = 2.93, SD = 0.69; Item stem: How familiar are you with the following areas of mobility and mobility transition?; Example item: Traffic & environment – solutions (e.g., sustainable mobility); The items could be answered using a 5-point Likert scale: not at all, hardly, somewhat, quite, very.].
Objective knowledge: Open-ended and multiple-choice questions were used to assess both factual knowledge and understanding of the four topics covered during the visit. Based on the results of the pilot study and item and factor analyses, 19 questions (ten open-ended and nine multiple-choice) from all four thematic areas and associated exhibits were included [T1: α = .75, M = 1.60, SD = 0.59; T3: α = .81, M = 1.77, SD = 0.58; Example open question item: Name two actions that can increase mobility while reducing traffic.]. Each question was scored from 0 = 0 points to 4 = two points. The objective knowledge scale was derived from the average mean over the 19 questions.
Individual interest in the topics of mobility and mobility transition: In line with Krapp (2002), a scale with four items was presented to capture individual interest in the topics of mobility and the mobility transition, personal importance, enjoyment during engagement with the topics, as well as the pleasure of acquiring new knowledge on the topics [T1: α = .89, M = 2.83, SD = 0.93; T3: α = .88, M = 2.78, SD = 0.85; Example item: I am interested in the topics of mobility and mobility transition.].
Control variables
As control variables, the prior knowledge (self-perceived and objective) and prior individual interest of the students (s. below) were considered, as it can be assumed that they may play an important role in relation to learning in the museum, although it cannot be clearly stated where exactly they were acquired (in or out-of-school) at T1. Furthermore, gender, which was captured through one item with response options "male", "female", and "diverse", was used as a control variable and assessed at T1.
Analyses
Analyses were conducted and reported in the following order corresponding to the three research questions in respectively the two question areas:
School preparation and follow-up of visit in class (RQ 1)
To assess the extent to which preparation and follow-up activities regarding the visit/topic in class occurred, the frequency distributions of the two variables, preparation and follow-up in class, were analyzed. Additionally, descriptive values for all independent and dependent variables used, as well as control variables, were also calculated.
Learning process during and learning effects after the visit (RQ 2a, 2b and 3a, 3b)
Two hierarchical regression models were calculated to test whether school-influenced learner characteristics, preparation, and follow-up of the museum visit, predicted the learning process during the visit and the learning effects after the visit.
Inter-correlations among the predictors and effect measures were carried out in advance to check whether the regression requirements (e.g., collinearity problems) were met.
As first step, the subject-independent knowledge of learning strategies influenced by school was introduced, which is assumed to play a central role in learning at out-of-school learning sites. Additionally, other important school-related factors include the preparation in class (affecting learning processes as dependent variables) and both the preparation and follow-up activities in class (affecting learning effects as dependent variables). In the second step, the control variables were included, namely, self-perceived and objective prior knowledge, prior individual interest and gender. Based on our theoretical considerations, we assume that the control variables of the second block play a role in learning at out-of-school learning sites, even though we cannot specify whether they were acquired in school and/or outside of school during the students' leisure time.
Results
Within the following chapter, the calculations and respective results will be presented to address each of the research questions.
RQ 1: Preparation and follow-up activities of visit in class
Regarding the first research question, Table 1 shows that most students (n = 310; 75%) reported no preparation in class before visiting the exhibition. Additionally, most students (n = 128; 60,4%) received no follow-up activities after their museum visit in class. Only 21 students (9,9%) reported both preparation as well as follow-up activities in class.
Table 1. Indication of received preparation before the museum visit and follow-up activities in class
Yes | No | ||
|---|---|---|---|
n (%) | n (%) | N | |
Preparation of visit | 98 (24%) | 310 (75%) | 409 (1 missing; T1) |
Follow-up activities | 78 (36.8%) | 128 (60.4%) | 212 (6 missing; T3) |
Preparation and follow-up | 21 (9.9%) | 184 (86.8%) | 212 (7 missing; T1 + T3) |
Of the students who had follow-up activities in class, 71 stated that they generally talked about the museum visit. Additionally, seven students noted that they delved into specific topics in greater detail, such as climate change, future mobility, and environmental protection.
Learning processes and learning effects
The findings shown in Table 2 are based on two samples: Sample 1 (N = 409) includes all students who participated in T1 and T2. Sample 2 (n = 212) includes only those students who filled in all questionnaires at T1, T2, and T3 (see also method section). The descriptive results reveal that both samples do not differ in their baseline characteristics at time point T1 (see also the method section on sample description). Furthermore, the standard deviation of each of the variables that will serve as predictors is sufficiently large, so that each measure has sufficient variability to serve as an important predictor. Also, there is a significant positive change in self-perceived and objective knowledge between T1 and T3 (self-perceived knowledge: t(211) = -6.99; p < .001; objective knowledge: t(211) = -4.15; p < .001), but there is no significant difference in individual interest between T1 and T3 (t(211) = .49; p = .311; for descriptive values see Table 2).
Table 2. Characteristics of the learning process and learning effects
Baseline characteristics, process and effect variables | T1a Sample 1/ sample 2 | T2a Sample 1 | T3a Sample 2 | Changeb T3-T1 |
|---|---|---|---|---|
M (SD) | M (SD) | M (SD) | M (SD) | |
Knowledge of learning strategies | ||||
Metacognitive | 3.47 (.71) / 3.47 (.70) | |||
Motivational | 2.67 (.84) / 2.66 (.85) | |||
Self-perceived (prior) knowledge | 2.63 (.66) / 2.60 (.67) | 2.93 (.69) | .33 (.69)** | |
Objective (prior) knowledge | 1.60 (.59) / 1.60 (.61) | 1.77 (.58) | .17 (.59)** | |
(prior) Individual interest | 2.83 (.93) / 2.80 (.97) | 2.78 (.85) | − .03 (.83) | |
Situational interest | 3.26 (.66) | |||
Basic needs | 3.93 (.59) | |||
Perceived relevance of content | 3.70 (.69) | |||
Engagement | ||||
Behavioral-cognitive | 3.38 (.62) | |||
Affective | 3.64 (.74) | |||
**p < .01. All others are non-significant
aT1 = Time point 1 (one week before the visit); T2 = Time point 2 (immediately after the visit); T3 = Time point 3 (follow-up approximately 3 months after the visit)
bThe change values represent differences in the mean scores (higher scores in self-perceived knowledge after the visit represent a higher level of self-perceived knowledge and higher scores in objective knowledge represent more correct answers)
RQ 2a and 2b: Predictors of the characteristics of the learning process during the museum visit
Before conducting the regression analyses to answer research questions 2a and 2b, correlations were calculated to identify linear relationships between the learner characteristics influenced by the school, preparation of the museum visit in class, and the learning process variables (see Table 3).
Table 3. Correlations between learner characteristics, preparation of the museum visit in class, and the learning process variables
Situational interest | Basic needs | Content relevance | Engagement behavioral-cognitive | Engagement affective | |
|---|---|---|---|---|---|
Knowledge of learning strategiesa | |||||
Metacognitive | .130* | .246** | .243** | .211** | .059 |
Motivational | .195** | .141* | .144* | .192** | .124* |
Preparation of visitb | .026 | .003 | .033 | .050 | .090+ |
Self-perceived prior Knowledgea | .214** | .047 | .101* | .181** | .079 |
Objective prior Knowledgea | .164** | .127* | .012 | .184** | .062 |
Prior individual Interesta | .391** | .205** | .300** | .323** | .216** |
Genderb | |||||
Female | .032 | .165** | .145** | .040 | .037 |
Diverse | .072 | .010 | .041 | .090+ | .089+ |
**p < .01. *p < .05. +p < .10. All others are non-significant
aPearson correlation coefficient
bEta correlation ratio
The data reveal significant positive relationships with only small to moderate effect sizes; therefore, multicollinearity is unlikely. Prior individual interest and knowledge of motivational learning strategies correlate with all process variables significantly, while preparation of the visit in class correlates only with affective engagement. Students who reported preparation of the visit tend to report higher affective engagement (p = .069). Compared to males, the female students reveal significantly higher values for basic needs and content relevance, while the diverse students tend to show higher values regarding both forms of engagement.
Hierarchical regression was then calculated to identify possible predictors of the learning process variables. Each set of entered variables in Table 4 represents one model and shows the added value of each set of variables. For the categorical variables, the unstandardized beta coefficients are reported.
Table 4. Regression with learning process variables as dependent variables
Baseline characteristics | Estimate | ||||
|---|---|---|---|---|---|
Situational interest | Basic needs | Content relevance | Engagement behavioral-cognitive | Engagement affective | |
β | β | β | β | β | |
Model 1 | |||||
KLS metacognitivea | .042 | .236** | .226** | .148** | .011 |
KLS motivationala | .175** | .024 | .032 | .119* | .119* |
Preparation of visit | .033 | − .032 | .014 | .049 | − .158 |
R2 | .040 | .061 | .060 | .056 | .024 |
Max. VIF | 1.346 | 1.346 | 1.346 | 1.346 | 1.346 |
Model 2 | |||||
KLS metacognitivea | − .001 | .161** | .168** | .104 | − .008 |
KLS motivationala | .085 | .011 | − .035 | .050 | .060 |
Preparation of visit | − .041 | − .079 | − .041 | − .018 | − .188* |
Self-per. prior knowledgeb | .008 | − .037 | .028 | .015 | − .036 |
Obj. prior knowledgeb | .041 | .101 | − .095 | .085 | .009 |
Prior individual interest | .351** | .176** | .304** | .251** | .223** |
Gender | |||||
Female | .004 | .196** | .191** | .059 | − .046 |
Diverse | − .140 | .198 | − .014 | − .174 | − .296 |
R2 | .165 | .116 | .148 | .137 | .070 |
Max. VIF | 1.677 | 1.677 | 1.677 | 1.677 | 1.677 |
Obj. Objective
**p < .01. *p < .05. All others are non-significant. N = 409
aKLS = Knowledge of learning strategies
bSelf-per. = Self-perceived.
With the first model (Tab. 4), we include variables related to learners’ knowledge of learning strategies and the preparation of the visit as school-related variables to predict learning process variables. The results of the regression indicate that the three predictors explain 4% of the variance in situational interest (R2 = .040, F(3, 404) = 5.63, p = .001), 6.1% of the variance in basic needs (R2 = .061, F(3, 404) = 8.79, p = .001), 6% of the variance in perceived content relevance (R2 = .060, F(3, 404) = 8.56, p = .001), 5.6% of the variance in behavioral-cognitive engagement (R2 = .056, F(3, 404) = 7.95, p = .001), and 2.4% of the variance in affective engagement (R2 = .024, F(3, 404) = 3.26, p = .022). Max VIF values show a good model fit. The data reveal that knowledge of metacognitive learning strategies positively predicts basic needs, perception of content relevance, and behavioral-cognitive engagement. Knowledge of motivational learning strategies positively predicts situational interest, behavioral-cognitive, and affective engagement. Preparation of the visit in class is not a significant predictor of the learning process variables investigated.
In Model 2, we included additional learning characteristics only partly influenced by school (i.e. self-perceived and objective prior knowledge as well as prior individual interest) to see if they explain additional variance, and also gender as an additional variable to consider gender differences. It can be seen that a substantial amount of additional variance is explained for all dependent variables: all predictors now explain 16.5% of the variance in situational interest (R2 = .165, F(6, 399) = 9.82, p = .001), 11.6% of the variance in basic needs (R2 = .116, F(6, 399) = 6.54, p = .001), 14.8% of the variance in perceived relevance (R2 = .14.8, F(6, 399) = 8.65, p = .001), 13.7% of the variance in behavioral-cognitive engagement (R2 = .137, F(6, 399) = 7.91, p = .001), and 7% of the variance in affective engagement (R2 = .070, F(6, 399) = 3.78, p = .001). Max VIF values show a good model fit. Girls show higher scores for basic needs and a greater perception of content relevance when compared to boys; however, since these are unstandardized Beta values, the absolute value should not be considered. Prior individual interest is a significant predictor of all dependent variables. Knowledge of metacognitive learning strategies and preparation of visit in class show lower significant values when compared to Model 1.
Overall, the regressions show prior individual interest as the main positive predictor. Regarding variables mainly based on school education, knowledge of metacognitive learning strategies emerges as the main predictor. The whole set of variables can explain a notable portion of the total variance. Gender effects are only partially evident.
RQ 3a and b: Predictors of the characteristics of the learning effects three months after the museum visit
Again, before conducting the regression analyses to answer research questions 3a and 3b, correlations were calculated to identify linear relationships between the learner characteristics influenced by school, the preparation of the visit in class, and the learning effect variables (see Table 5). Further, now the follow-up activities in class were also included.
Table 5. Correlations between learner characteristics, preparation of the visit and follow-up activities in class, and learning effect variables
Individual interest follow-up | Self-perceived knowledge follow-up | Objective knowledge follow-up | |
|---|---|---|---|
Knowledge of learning Strategiesa | |||
Metacognitive | .180* | .276** | .133* |
Motivational | .300** | .349** | .104 |
Preparation of visitb | .053 | .053 | .028 |
Follow-up activitiesb | .141* | .168* | .222** |
Self-perceived prior Knowledgea | .369** | .452** | .239** |
Objective prior knowledgea | .190* | .243** | .515** |
Prior individual interesta | .599** | .479** | .278** |
Genderb | |||
Female | 032 | .000 | .090 |
Diverse | .039 | .024 | .083 |
**p < .01. *p < .05. All others are non-significant
aPearson correlation coefficient
bEta correlation ratio
The data in Table 5 reveal significant positive relationships with small to moderate effect sizes; therefore, multicollinearity is unlikely. Except for the preparation of the visit in class and gender, all learner characteristics show significant positive relationships with the learning effect variables. Knowledge of motivational learning strategies does not correlate with objective knowledge in the follow-up test.
Again, hierarchical regression was then used to identify possible predictors of the learning effect variables. Each set of entered variables in Table 6 represents one model and shows the added value of each set of variables. For the categorical variables, the unstandardized beta coefficients are reported.
Table 6. Regression with learning effect variables as dependent variables
Individual interest follow-up | Self-perceived knowledge follow-up | Objective knowledge follow-up | |
|---|---|---|---|
β | β | β | |
Model 1 | |||
KLS metacognitivea | .061 | .166* | .142 |
KLS motivationala | .287** | .290** | .059 |
Preparation of visit | .037 | .000 | − .023 |
Follow-up activities | .304* | .309** | .294** |
R2 | .122 | .185 | .079 |
Max. VIF | 1.370 | 1.370 | 1.370 |
Model 2 | |||
KLS metacognitivea | .012 | .149* | .049 |
KLS motivationala | .118 | .149* | .011 |
Preparation of visit | − .058 | − .115 | − .103 |
Follow-up activities | .134 | .181* | .184* |
Self-per. prior knowledgeb | .063 | .264** | .017 |
Obj. prior knowledgeb | − .003 | .044 | .473** |
Prior individual interest | .519** | .239** | .104 |
Gender | |||
Female | − .071 | .021 | .149 |
Diverse | .003 | .222 | .033 |
R2 | .382 | .355 | .329 |
Max. VIF | 1.672 | 1.672 | 1.672 |
Obj. Objective
**p < .01. *p < .05. All others are non-significant. N = 212
aKLS = Knowledge of learning strategies
bSelf-per. = Self-perceived.
With the first model (Tab. 6), we include variables related to learners’ knowledge of learning strategies and the preparation of the visit as well as the follow-up activities in class as school-related variables to predict learning effect variables. The results of the regression indicate that the three predictors explain 12.2% of the variance in individual interest (R2 = .122, F(4, 200) = 6.96, p = .001), 18.5% of the variance in self-perceived knowledge (R2 = .185, F(4, 200) = 11.33, p = .001), and 7.9% of the variance in objective knowledge (R2 = .079, F(4, 200) = 4.27, p = .002). Max VIF values show a good model fit. The data reveal that knowledge of metacognitive learning strategies positively predicts self-perceived knowledge. Knowledge of motivational learning strategies positively predicts individual interest and self-perceived knowledge. Preparation of the visit shows no relationship, while follow-up activities show positive relationships with all dependent variables, meaning that those students with follow-up activities after the visit report higher individual interest and self-perceived and objective knowledge.
In Model 2, we enter additional learning characteristics only partly influenced by school (i.e. self-perceived and objective prior knowledge as well as prior individual interest) to see if they explain additional variance. Further, we included gender as a basic additional variable. R2 values reveal that for all dependent variables, substantial additional variance can be explained. The predictors now explain 38.2% of the variance in individual interest (R2 = .382, F(9, 195) = 13.38, p = .001), 35.5% of the variance in self-perceived knowledge (R2 = .355, F(9, 195) = 11.94, p = .001), and 32.9% of the variance in objective knowledge (R2 = .329, F(9, 195) = 10.60, p = .001). Max VIF values show a good model fit. Gender does not significantly predict learning effect variables. Prior individual interest is the most relevant predictor of the follow-up individual interest and is also relevant for self-perceived knowledge. Prior self-perceived knowledge, knowledge of learning strategies, and follow-up activities after the visit predict self-perceived knowledge. Knowledge of learning strategies and follow-up activities now show lower and less significant values when compared to Model 1. Objective follow-up knowledge is predicted by objective prior knowledge and follow-up activities.
Taken together, the regressions show that the previously measured variables-prior individual interest, as well as self-perceived and objective prior knowledge-are significant predictors of the respective follow-up test scores. Regarding variables mainly based on school education, knowledge of learning strategies positively predicts self-perceived knowledge. Additionally, prior individual interest predicts self-perceived knowledge. Further, the follow-up activities in class have a positive effect on self-perceived and objective knowledge. These variables explain a remarkable portion of the total variance of the investigated learning effects. Gender effects are not evident.
Discussion
This paper contributes to existing research on the collaboration between in-school and out-of-school STEM education by exploring how direct and indirect school-related factors predict STEM learning in an out-of-school learning environment, specifically a museum exhibition on mobility and traffic. Its added value lies in the combined investigation of both school-related learner characteristics and the preparatory as well as the follow-up activities conducted in class. Furthermore, these factors are linked to both the learning process and the learning effects, allowing for a more detailed examination of how school-based (STEM) learning influences out-of-school STEM learning.
Regarding RQ 1, which examines the integration of the STEM museum visit into the classroom, separately examining both the visit-related preparation and follow-up activities in class provides valuable insights. The findings indicate that most of the students were neither prepared for the museum visit nor did they discuss the visit afterward in class. Follow-up activities take place slightly more frequently than preparatory activities. Although the number of school classes in this study is relatively small, the information provided by the students indicates that the integration of the museum visit, even when it offers explicit references to the school curriculum, is an exception. The significance of these results lies in the fact that the preparation and follow-up activities at school were not influenced by the research team. As a result, the data provides an authentic picture of how museum visits are actually integrated into classroom teaching. The study thus reveals that, although the content of museum visits offers numerous connections to the curriculum, relatively few teachers make use of these opportunities through targeted preparatory and follow-up activities.
Thus, within the scope of this study, integration of the visit into classroom instruction cannot be considered an intrinsic part of an out-of-school field trip. Only one-tenth of the students received complete integration of the museum visit via preparatory and follow-up activities, which would be beneficial for the effectiveness of the visit, as studies have shown (Behrendt & Franklin, 2014; DeWitt et al., 2008; Itzek-Greulich et al., 2016; Lee et al., 2020; Morentin & Guisasola, 2015; Rennie, 2007). Other studies also reflect this finding (Glowinski, 2007; Guderian & Priemer, 2008; Morentin & Guisasola, 2015).
With respect to RQ 2a,b, which investigate how general learner characteristics influenced by school predict the learning process in the museum, the results emphasize the crucial role of students’ knowledge of learning strategies, especially metacognitive strategies. This knowledge mainly explains up to six percent of the variance in basic needs, relevance perception, and behavioral-cognitive engagement. Regarding the motivational characteristics of the learning process, knowledge of motivational learning strategies proves to be a significant predictor. Overall, greater knowledge of learning strategies supports learners in managing the demands of self-regulated learning in out-of-school settings and allows them to experience the defining features of the learning process more fully. In contrast, preparatory activities in class were not identified as a significant predictor. This contradicts findings that indicate positive effects on the learning process by reducing the novelty of the learning environment through preparation (Orion & Hofstein, 1994; Rennie, 2007). One possible explanation is that the topic of the mobility transition is widely known, and therefore, no basic content preparation is required. The app on the tablet also acts as accompanying instructional support. It gives the students a spatial orientation and some content structure for exploring the mobility and traffic exhibition. It can be assumed that this prevents them from becoming spatially overwhelmed (cf. Falk & Dierking, 2018; Pecore et al., 2017; Sung et al., 2008). Under these conditions, the students do not seem to require additional preparation regarding the learning content of the self-regulated learning process investigated.
When considering learner characteristics that are only partly acquired in school, such as prior knowledge and prior individual interest in the topic, the predictive value of motivational learning strategies diminishes. One possible explanation could lie in the high predictive power of prior individual interest, which is consistent with other research findings (Cahill et al., 2011; King & Datu, 2017; Patall et al., 2016; Renninger & Bachrach, 2015). As prior individual interest is a strong (the strongest) predictor for all learning process variables considered, the finding underlines the assumption that museums are out-of-school learning sites for interest-based self-regulated (free-choice) learning (Falk & Dierking, 1998, 2018). Learners who are highly interested in the subject matter do not require motivational learning strategies to motivate themselves or enjoy learning (Harackiewicz et al., 2016). Metacognitive strategy knowledge remains a strong predictor only in relation to basic needs, as the experience of competence and autonomy presumably depends heavily on whether learners possess the learning strategies necessary to engage in self-regulated learning outside of school (Orsini et al., 2018; Renninger & Hidi, 2016; Ryan & Deci, 2017). The same effect holds true for the perception of relevance. The lack of predictive power of self-perceived and objective prior knowledge contradicts other findings from the museum context (Kellberg et al., 2024). One possible explanation could be that the learning strategy knowledge and the instructional support provided by the app can undermine the prior knowledge effect for this topic. The finding that preparation of the visit in class, considering prior individual interest, negatively predicts affective engagement can be interpreted in light of the findings of Pecore and colleagues (2017). The authors note that some forms of preparation in class can have a demotivating effect, as learners may feel they have already learned everything beforehand, leading them to perceive the on-site experience as less novel and resulting in lower engagement. Gender only proves to be a significant predictor for basic needs and the perception of content relevance, which reduces the corresponding predictive power of metacognitive learning strategies and dissolves it for behavioral-cognitive engagement. This finding is consistent with studies that indicate that girls benefit motivationally and cognitively more from out-of-school learning venues than boys (Schürmann & Quaiser-Pohl, 2022).
Regarding RQ 3a,b on school-related factors influencing the learning effect of museum visits three months after the visit, the findings reveal the visit-related follow-up activities in class as the strongest predictor for all three learning effect variables in the first model. In contrast, preparatory activities in class have no predictive power for any of the learning effect variables. Therefore, these results align with previous findings for follow-up activities, but not for in-class preparation, as only post-visit activities consolidate the knowledge—both self-perceived and objectively measured—gained during the visit (Falk & Dierking, 1998; Mujtaba et al., 2018; Rennie, 2007). Follow-up activities in the classroom can help learners reflect on the knowledge they have acquired during the museum visit and to link it to what they have learned at school. Students can get to know other perspectives and insights and exchange them with their classmates, which should contribute to a greater awareness of their knowledge (Falk & Dierking, 2018; Mujtaba et al., 2018).
In addition, the results indicate a high predictive power of knowledge of motivational learning strategies on individual interest and also of meta-cognitive learning strategies on self-perceived knowledge three months after the museum visit. If the learners know how to deal with the information in the museum and how to motivate themselves to learn, they can deal with the information more intensively and effectively and, as a result, rate their self-perceived knowledge higher. However, this relationship does not appear for objective knowledge. One explanation, which cannot be proved by the present study and could be addressed in further studies, could be that the learners did not engage with the topics assessed in the objective knowledge test due to free choice learning conditions. Therefore, the learning strategies may not be applied to the content relevant to the objective knowledge test and in consequence are not identified as significant predictors.
Considering learner characteristics that are only partly influenced by school, such as prior knowledge and individual interest, the predictive power of the four school-related variables (learning strategy knowledge and integration of museum visits into the classroom) for individual interest disappears. Only prior individual interest is a very strong predictor. This finding is only partly surprising, as individual interest is a rather stable form of motivation (e.g., Harackiewicz et al., 2016; Krapp et al., 2014), which can hardly be changed by preparatory and follow-up activities for a one-time museum visit or the use of strategic knowledge. On the contrary, it is solely predicted by the individual interest prior to the visit.
In contrast, the prediction of self-perceived knowledge three months after the visit includes all variables except preparation and objective prior knowledge. Here, prior interest also plays an important role, in addition to self-perceived prior knowledge. It can be assumed that the individual interest and prior self-perceived knowledge of the learners provide a framework for what they engage with and how intensively they engage with exhibits (cf. Corredor, 2006). This, in turn, influences how well the learners cope and how competent they feel. The higher these levels are, the greater their self-perceived knowledge. It seems that prior individual interest guides the visit, and this is reflected in the self-perceived knowledge.
In comparison, the objective knowledge three months after the visit is only predicted by the objective prior knowledge and the follow-up activities. This can be explained by the corresponding contents of the visit and the follow-up activities. During the follow-up activities, the topics of the visit and the learners' experiences can be consolidated (Falk & Dierking, 2018; Mujtaba et al., 2018). However, it is striking that prior individual interest does not play a predictive role here. The students did gain objective knowledge independently from their prior individual interests. Regarding methodology, the findings thus point to the importance of considering both self-perceived and objective measures of knowledge to obtain a complete picture of the learning effects of museum visits, which is consistent with the notion of free-choice learning in museums (Falk & Dierking, 1998).
Finally, the findings show no gender effect for any of the three effect variables. This could possibly be due to the subject matter which is close to everyday life for all the students. In addition, the content of the mobility transition is presented in its entire spectrum and thus from very different perspectives in the sense of the socio-scientific issues approach, which offers different ways of approaching the topic (individual, social, technical, historical, scientific; Kellberg et al., 2023; Kollmann et al., 2013; Pedretti & Iannini, 2018; Yun et al., 2020).
Limitations, implications, and scope for future studies
On the one hand, the findings highlight the infrequent integration of museum visits in school-based learning. On the other hand, they offer only limited insights into relevant characteristics of the integration activities. To address this gap, future studies would benefit from incorporating the teachers' perspective. This could help clarify which factors hinder or facilitate the preparation and follow-up of STEM museum visits in the classroom, and how teachers design these activities in detail.
While the presented analyses are limited to a separate insight into the learning process and learning outcome, in the next step of the analysis, it is now indicated to examine the relationships between the considered process and effect characteristics in more detail. Furthermore, the study only takes a quite limited set of variables into account. There is a wide range of other learner, process, and effect characteristics that should be considered in future studies. Regarding learner characteristics, factors such as the learners' self-efficacy or willingness to act could be of significant interest as potential effects of the out-of-school learning processes. Finally, it should also be noted that the museum visit relates to a STEM topic close to everyday life. Further studies should, therefore, examine the extent to which relationships can also be found in other topics that are less familiar to learners. In summary, the findings of the presented study point to the close relationship between STEM education in and out of school, which takes place on several levels. Although the study shows no significant effect of an explicit preparation of the visit, the findings demonstrate a the predictive effect of learning strategy knowledge on all learning process variables and on self-perceived knowledge. Thus, it emphasizes that the school should prepare learners on a general level for out-of-school learning by teaching them effective learning strategies, as this can enhance both the learning processes and effects of STEM museum visits. In addition, the school can also contribute to the development of essential content-related learner characteristics, such as prior knowledge and interest, which are relevant for out-of-school STEM learning. On the other hand, it is of great importance that the learning content and experiences in the out-of-school learning environment are followed up in the classroom. As the findings of this study show, these two levels are interlinked, and therefore, further research is needed to gain more precise insights into their mutual relationships.
Conclusion
In conclusion, the study highlights the strong interconnection between in-school and out-of-school STEM learning. Schools play a key role in preparing students for informal learning experiences by fostering effective learning strategies and supporting the development of relevant learner characteristics such as prior knowledge and interest. At the same time, integrating and reflecting on out-of-school experiences within the classroom is essential. These reciprocal links call for further research to better understand and leverage the synergies between learning environments.
Acknowledgements
We would like to acknowledge the reviewers for their constructive comments.
Author contribution
KN and DL designed the study, KN conducted the study, DL developed the outline of the manuscript, DL, KN, and SM contributed to methodology and data analysis, KN conducted the data analysis. DL, KN and SM were major contributors in writing the manuscript. All authors read and approved the final manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. The project was funded by the German Research Foundation (DFG)—Deutsche Forschungsgemeinschaft, LE1303/13-1.
Data availability
The datasets generated and analyzed during the current study are not publicly available due to data protection reasons but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Human ethics were approved by the Ministry of Education of Bavaria. Parents provided written consent for child participation. Participation in the survey was voluntarily.
Competing interests
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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