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. 2023 Apr 26:1–19. Online ahead of print. doi: 10.1007/s10984-023-09467-9

Studying in an innovative teaching–learning environment: design-based education at a university of applied sciences

Gerry Geitz 1,✉, Anouk Donker 2, Anna Parpala 3
PMCID: PMC10130808  PMID: 37360383

Abstract

In higher education, a need is felt to redesign curricula to better prepare students for the evolving ‘world of work’. The current exploratory study investigated first-year (N = 414) students’ approaches to learning, well-being and perceptions of their learning environment in the context of an innovative educational concept: design-based education. Also, the relations between these concepts were explored. Regarding the teaching–learning environment, it was found that students experienced peer-support to a large extent whereas alignment in their programs scored lowest. Based on our analysis, it seems that alignment did not influence students’ deep approach to learning however, as this approach was predicted by their experienced relevance of the program and feedback from the teachers. Student well-being was predicted by the same elements that also predicted their deep approach of learning, and also alignment appeared to be a significant predictor of well-being. This study provides first insights in students’ experiences of an innovative learning environment in higher education and raises important questions for further, longitudinal, research. As the current study already shows that certain aspects of the teaching–learning environment can be used to impact students’ learning and well-being, answers can help in (re)designing new learning environments.

Keywords: Approaches to learning, Design-based education, Higher education, Innovative learning environment, Student well-being

Introduction

In higher education, a variation of educational concepts such as problem-based learning (PBL) and competence-based education are widely implemented (Hallinger, 2020). However, today, it can be questioned whether these concepts still meet the requirements of society and educational institutions, as PBL, for example, has had varying results in terms of its effectiveness (Kirschner et al., 2006). A distinguishing feature of PBL is its strong focus on knowledge construction, although it has been acknowledged, especially in universities of applied sciences, that students need to have both practical and theoretical knowledge and skills. As a consequence, it seems important to integrate the working field in the learning environment (Geitz & De Geus, 2019). Therefore, there is a need to further develop the educational concept and redesign learning environments in order to educate students in line with societal requirements. However, the redesign of learning environments always raises the questions of how students experience these new learning environments and whether they support quality learning.

Student experiences of learning environments have been shown to be related to student learning processes, such as their approaches to learning (Diseth et al., 2010; Lizzio et al., 2002; Richardson & Price, 2003). Approaches to learning have also been found to be related to study success (Cano, 2005; Diseth, 2003; Parpala et al., 2021; Watters & Watters, 2007) and student well-being (Asikainen et al., 2020, 2022), thus indicating a relation between approaches to and quality of student learning. Moreover, we know that approaches to learning are contextual and dynamic; in other words, they can be developed (Baeten et al., 2010; Coertjens et al., 2016).

Design-based education (DBE) is a further development (i.e. redesign) of the existing concepts of problem-based learning and competence-based education (Geitz & de Geus, 2019). Innovative elements are added to these two concepts to design a sustainable educational concept: a learning environment in which an effective and efficient learning process is stimulated and sustainable goals can be achieved by offering an ambitious learning climate that challenges students and offers room for talent development and profiling. In this DBE learning environment, formal and informal learning are increasingly intertwined (Geitz & de Geus, 2019). The DBE teaching and learning approach adds value to the learning of students, the professional field and lecturers in terms of gaining multidisciplinary knowledge, developing metacognitive skills and creating social value (Geitz & de Geus, 2019). Important elements derived from the theory of constructive alignment (Biggs, 1996) are at the core of the concept: in developing new curricula, a clear focus is on aligning the learning environment with the learning outcomes, on the one hand, and assessments, on the other. The DBE learning environment is aligned with each study programme’s professional field.

Studies focusing on DBE and students’ perceptions of it, as well as how students approach their studies, are not yet available. Thus, the presents study explored the way students experience this new learning environment in terms of students approaches to learning and well-being. It is likely that learning environments in higher education will develop in a similar (DBE) way in order to address societal issues and increase authenticity in higher education. The aim of this study was to provide a description of the state of the student learning in a DBE context as this new knowledge will be used for developing the learning environment further and may also be used for future comparative studies.

Student experiences of the teaching–learning environment

Learning environments can be described as “social, psychological and pedagogical contexts in which learning occurs and which affects student achievement and attitudes” (Fraser, 1998, p. 3). Put simply, students learn better and have a higher sense of efficacy when they perceive their learning environments positively. Although this refers to students’ perceptions of learning environments, theory and research in higher education also suggest that there are dimensions of quality teaching that are rather general and support student learning across different contexts (Entwistle et al., 2003). For example, there is a common understanding that alignment in learning environments should be addressed to ultimately reach the intended goals of students (Biggs, 2014). There are four major steps to consider while (re)designing education so that it is constructively aligned: defining the intended learning outcomes, choosing teaching/learning activities, assessing students’ actual learning outcomes in relating to the intended learning goals, and arriving at a final grade and in-depth learning (Biggs, 1996). In constructive alignment, student collaboration in the active processes of information has been highlighted (Biggs, 1996), and there is evidence that student experience of peer support enhances deeper learning processes and successful studying in higher education contexts (Rytkönen et al., 2012). Moreover, assessment should not only be aligned with teaching methods and learning outcomes, but it should also consist of feedback that is constructive in nature, providing information that students will be able to use to develop their understanding (Higgings et al., 2002). Finally, students’ interest in and experience of the relevance of the tasks in a course are strong predictors of students’ engagement with their studies (Fryer et al., 2021).

However, it can be questioned whether students perceive learning environments in the very same way that their teachers and educators intend. Several studies showed that students’ perceptions of elements of the learning environment influenced the way they approached and carried out learning (Adcroft, 2011; Evans, 2013; Poulos & Mahony, 2008). For example, Nijhuis et al. (2005) found a decrease in deep learning after implementing a PBL course and attributed the disappointing results to aspects such as the alignment of the assessment and tutor behaviour.

Student approaches to learning

Students differ in the way they approach and engage in learning tasks (Entwistle, 2009). Previous research has shown that students’ approaches to learning are related to several student characteristics, such as their well-being, study success and employability (Heikkilä et al., 2012; Tuononen et al., 2017), all of which are important elements of quality learning. In addition, the way students perceive their learning environment influences their approaches to learning (Kreber, 2003; Parpala et al., 2010; Richardson & Price, 2003).

The research tradition of approaches to learning started in the 1970s, with Marton and Säljö (1979) distinguishing between two approaches: deep and surface. These approaches consist of students’ intentions in studying and their study processes (Entwistle, 2003; Marton & Säljö, 1979). Students applying the deep approach relate ideas and search for evidence to understand the fundamental idea of the content to be learned, whereas students applying the surface approach concentrate on bits and pieces of information and try to remember word by word (Entwistle, 2009). For the latter approach (surface), a new term has been introduced, namely, an unreflective approach, as this highlights an essential element of the surface approach among students studying in the current higher education context (Linblom-Ylänne et al., 2018). The core of this unreflective approach in today’s studying is students’ inability to relate ideas; thus, the outcome of learning is fragmented knowledge (Lindblom-Ylänne et al., 2018).

A third approach to learning, organised studying, emphasises time management, organised studying and effort in higher education (Entwistle & McCune, 2004). It has been found to be related to study success and progression in study at university (Asikainen et al., 2014; Hailikari & Parpala, 2014; Hailikari et al., 2018; Rytkönen et al., 2012).

Constructs of well-being

Learning environments can not only benefit academic achievement but also affect student-related efficacy and sense of well-being (Postareff et al., 2017; Wasson, et al., 2016). Furthermore, it is known that feelings of self-efficacy influence academic performance (Van Dinther et al., 2011). Self-efficacy is a person’s belief in his/her capabilities to execute behaviour that is required to achieve prospective outcomes (Bandura, 1977). Feelings of student self-efficacy are related to students’ belief that they themselves can influence outcomes through their own behaviours (Bandura, 1989). In education, this is important because students with high self-efficacy achieve high performance outcomes and display deep learning behaviours. In learning environments such as DBE, students have many opportunities to influence their learning process and have more responsibility for their own learning. Here, self-efficacy beliefs might become even more important for their study-related well-being. It is known that students with higher self-efficacy beliefs show intrinsic motivation and persistence in the face of failure, have strong outcome expectations, and attribute success or failure to effort (Bandura, 2012; Obilo & Alford, 2015; Usher & Pajares, 2008; Van Dinther et al., 2011; Zimmerman, 2000). In addition, self-efficacy beliefs have been found to be in relation to study-related burnout (Bresó et al., 2011).

Burnout was originally used in the context of professional work, and it includes elements of emotional exhaustion, cynicism and reduced professional efficacy (Maslach et al., 2001). Research has demonstrated that school pupils and university students can also experience burnout related to their studies (Kiuru et al., 2008; Salmela-Aro et al., 2009; Schaufeli et al., 2002). This study-related burnout consists of elements of cynicism (e.g. questioning the value of one’s study), emotional exhaustion (e.g. feeling overwhelmed) and a sense of inadequacy or low self-efficacy (Salmela-Aro et al., 2009). In addition to personal characteristics, learning environments can contribute to student burnout (Meriläinen, 2014). Moreover, study-related burnout has been shown to be related to students’ unreflective approaches to learning (Asikainen et al., 2020).

Research questions

The rationale for designing a new teaching–learning environment (design-based education, DBE) is the need for alignment with the changing world and working field. The overall research question is how students perceive the quality of DBE. The following subsidiary research questions can be distinguished:

  1. How do students approach their learning, experience their teaching–learning environment and evaluate their well-being during the first year of their studies in a DBE context?

  2. How are approaches to learning, the teaching–learning environment and well-being related to each other among students studying in a DBE context?

  3. How do students’ experiences of the DBE teaching–learning environment predict their approaches to learning and their well-being (i.e. self-efficacy and study-related burnout)?

Context: design-based education

The context of the current study is a University of Applied Sciences (UAS) in the Netherlands, which only recently came into existence because of a merger of two quite different UASs. Prior to the merger, both UASs had their own characteristic educational concepts: one focusing on problem-based learning and the other on competence-based education. At the time of the merger, a new educational concept was introduced for the new UAS: design-based education (DBE). This concept is based on the reasoning of design thinking. Actual and complex issues are faced via iterative processes to bridge the gap between a current situation and an intended situation. Characteristics of the non-linear, iterative DBE processes include empathising, defining, ideating, applying, testing, evaluating and improving in order to bridge this gap. The methodological trialogical interaction between students, the professional field and lecturers is domain specific. For example, in specific modules that are the heart of each study programme (the so-called ateliers), professionals, teachers and students work closely together. Feedback is considered a crucial element, as teachers’ roles shift to a more coaching-oriented approach, and it is assumed that this new learning environment is beneficial for students’ well-being. Because of this new educational concept, study programmes were required to change their teaching–learning environment in order to align with the core concepts of DBE (i.e. authentic learning tasks, closer involvement between the curricula and the professional field, and a focus on learning outcomes and individual routes towards these outcomes, providing more flexibility in study trajectories).

To paint a more concrete picture, a concise description is provided of one of the most typical learning environments of DBE: an ‘atelier.’ In an atelier, students from different study programs work together to contribute to an authentic, real-life issue (e.g. loneliness amongst young adults). An actual organisation or municipality participates in the process by taking on the role of the client for whom the students have to deliver a solution. This solution can take many forms depending on students’ own learning goals combined with the demands from the curriculum of their study programme (their formal learning objectives). In the case in which students are confronted with the topic of loneliness among young adults, students from programmes such as Social Work but also Public Administration work together to create initiatives to decrease loneliness. Social work students might focus on accessibility of youth workers or creation of communities where young adults can come together. The Public Administration students might write a report for the local government to adjust its policy regarding the topic of youth care. The teacher in this DBE-learning environment is not necessarily expert on the topics students work on, but he/she mainly facilitates the processes going on in the atelier; the learning processes of individual students and the group dynamics because of collaboration between students from different backgrounds. In this example, important elements of the DBE concept are represented: an authentic learning environment with a complex, real-life issue provided by an actual organisation or institution; multidisciplinary collaboration; the role of the teacher; and the openness of the learning process and outcomes with the accompanying responsibility of students.

Method

Sample

Participants were first-year students in higher education, studying at the UAS (N = 414; 34% males, 65% females; Mage = 20.44 years; SD = 3.09 years [97% of the students were aged 17–24 years, with almost 60% between 18 and 20 years]). Students were enrolled in different study programmes, ranging from economics and ICT programmes to social studies and teacher education. In total, 18 individual study programmes, covering nine clusters of study programmes offered at the UAS, were represented.

Instruments

The instrument used in the present study was the HowULearn questionnaire (Parpala & Lindblom-Ylänne, 2012). The three sections used in the present study were developed especially for the HowULearn questionnaire (Parpala & Lindblom-Ylänne, 2012). The first measures students’ approaches to learning (deep, unreflective [previously surface] and organised approaches), and the second measures students’ perceptions of the teaching–learning environment (interest and relevance, peer support, alignment, and constructive feedback). These parts have been shown to be robust in many different contexts (Herrmann et al., 2017; Postareff et al., 2018). The third part measures students’ self-efficacy beliefs and, in the present study, was used to measure student well-being. This has also been tested in different contexts (Parpala et al., 2021). In addition, students’ well-being was measured in terms of their study-related burnout, using the Student Burnout Inventory (SBI; Salmela-Aro et al., 2009). Items in the questionnaire were formulated as statements with answers on a five-point scale, where 1 stands for “completely disagree” and 5 for “totally agree”. Table 1 presents the scales, the internal reliability of the scales and exemplary items in the present study.

Table 1.

Overview of HowULearn instrument

Scale Cronbach’s α Exemplary item
Deep 0.56 I try to relate new material to my previous knowledge
Unreflective 0.58 Much of what I’ve learned seems no more than unrelated bits and pieces
Organised 0.73 I carefully prioritise my time to make sure I can fit everything in
Self-efficacy 0.77 I believe I will do well in my studies
Study-related burnout 0.81 I feel overwhelmed by the work related to my studies
Interest 0.67 I can see the relevance of what we are taught
Peer support 0.64 Students support each other and try to give help when it is needed
Alignment 0.81 What we are taught seems to match what we are supposed to learn
Feedback 0.73 The feedback given on my work helps me to improve my ways of learning and studying
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Procedure

The questionnaire was distributed among the students via an online link. Students were contacted by their own study program. The study programmes were voluntarily sent the invitation. In the invitation letter, it was briefly explained that the UAS had introduced a new educational concept and conducts research on student experiences for developing purposes. Participation of the students was voluntary.

Analyses

Multiple exploratory analyses were conducted to answer all the research questions. First, we calculated student scores on learning approaches, experiences of the teaching–learning environment, and well-being. Second, we focused on relations between the concepts in a more general way. We calculated the correlations between the approaches to learning, perceptions of the teaching–learning environment, and well-being constructs. As we calculated many correlations at once, we used the Bonferroni correction, to avoid making the familywise error. This means that we divided our original significance level of 0.01 by the number of correlations we calculated (i.e. 36) to work with the adjusted significance level of 0.001.

Next, using regression analysis, we explored whether students’ approaches to learning, or their well-being, could be predicted by their experienced teaching–learning environment. Before testing the regression model, we tested if our data met the necessary assumptions of the absence of multicollinearity and the independence and normal distribution of residuals. For each model we plotted the residuals in a Normal P–P plot and found these to be independent of each other and normally distributed. Furthermore, we used the variance inflation factor (VIF) and tolerance statistics to check whether our data were multicollinear. As this was not the case, we could continue with the regression model. Based on our previous findings, first, we included only the relevant variables that correlated significantly with the dependent variable in the model. For completeness, we checked the models for all variables as well.

Results

Our first research question concerned the experiences of students regarding their approaches to learning (i.e. deep, unreflective and organised), their perceptions of the teaching–learning environment (i.e. alignment, relevance, peer support, and feedback), their well-being (i.e. study-related burnout and self-efficacy) and their general working life competences. Table 2 shows mean scores and standard deviations for all outcomes.

Table 2.

Means and standard deviations for all scales

Scale Mean SD
Deep 3.39 0.45
Unreflective 2.57 0.62
Organised 3.49 0.70
Self-efficacy 3.93 0.50
Burnout 2.44 0.64
Relevance 3.80 0.61
Peer support 4.00 0.60
Alignment 3.44 0.71
Feedback 3.67 0.60
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The highest mean was found for peer support and the lowest for study-related burnout. The standard deviations suggest that, for practically all scales, there were quite some differences between individual students.

Our second research question focused on the relations between the approaches to learning, perceptions of the teaching–learning environment, and well-being constructs. Pearson correlations were calculated between all scales of the HowULearn (shown in Table 3), with the majority being statistically significant.

Table 3.

Correlations

Variable N M SD 1 2 3 4 5 6 7 8 9
1. Organised approach 414 3.39 0.45  − 
2. Deep approach 414 3.39 0.45 .11*  − 
3. Unreflective approach 414 2.57 0.62 .03  − .32***  − 
4. Self-efficacy 414 3.93 0.50 .18** .31***  − .36***  − 
5. Study-related burnout 414 2.44 0.64  − .09  − .18*** .49***  − .38***  − 
6. Interest/relevance 414 3.80 0.61 .15** .38***  − .30*** .30***  − .37***  − 
7. Peer support 414 4.00 0.60 .00 .23***  − .08 .25***  − .20*** .33***  − 
8. Alignment 414 3.44 0.71 .07 .31***  − .40*** .30***  − .41*** .55*** .21***  − 
9. Feedback 414 3.67 0.60 .11* .36***  − .28*** .28***  − .38*** .48*** .35*** .54***  − 
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*p < .05; **p < .01; ***p < .001

Both deep and unreflective approaches were correlated with students’ self-efficacy but in opposite directions, with the deep approach to learning correlating statistically significantly and positively and the unreflective approach correlating statistically significantly and negatively with students’ self-efficacy. In terms of burnout, a strong relation was found between the unreflective approach and study-related exhaustion. Considering the teaching–learning environment, statistically significant relations were found for interest/relevance, alignment and feedback. All three were correlated with both the deep and unreflective approaches to learning, again in opposite directions, as the unreflective approach had negative relations to experiences of the teaching–learning environment, whereas the deep approach had positive relations.

Relations between the teaching–learning environment and student well-being were also examined: a positive, statistically significant correlation between self-efficacy, deep approach, alignment, relevance and feedback was found. Study-related burnout correlated negatively and statistically significantly with alignment, relevance and feedback.

To answer our third research question on how experiences of the teaching–learning environment predicted student approaches to learning, multiple regression was used to test if students’ approaches to learning could be predicted by their experiences of the teaching–learning environment. The scales measuring the experiences of the teaching–learning environment were selected based on their statistically significant (p < 0.01) correlations to the approaches to learning scales. Because of the substantial number of significant correlations, in practice this meant that, in three out of four models, all variables of the Teaching–Learning Environment were included.

The fitted regression model was as follows: deep approach = 2.31 + 0.06*(alignment) + 0.18*(relevance) + 0.06*(peer support) + 0.09*(feedback). The overall regression was significant (R2 = 0.18, F [4, 409] = 22.21, p < 0.001). It was found that both relevance (β = 0.18, p < 0.00) and feedback (β = 0.09, p = 0.04) statistically significantly and positively predicted the deep approach to learning, whereas alignment (β = 0.05, p = 0.08) and peer support (β = 0.06, p = 0.11) did not predict the deep approach.

In the same line of reasoning, multiple regression analysis was used to test if alignment, relevance and feedback significantly predicted the unreflective approach to learning. The fitted regression model was as follows: unreflective approach = 4.14 – 0.27*(alignment)—0.10*(relevance) – 0.07*(feedback). The overall regression was significant (R2 = 0.17, F [3, 410] = 28.28, p < 0.001). It was found that only alignment (β = -0.27, p < 0.00) statistically significantly and negatively predicted the unreflective approach to learning, whereas relevance (β = -0.10, p = 0.07) and feedback (β = 0.07, p = 0.25) did not significantly predict the unreflective approach. For the sake of completeness, we also checked the model including the non-significant variables. This meant that for the unreflective approach we added peer support to the model. This resulted in the final model of unreflective approach = 4.04 – 0.27*(alignment)—0.11*(relevance) – 0.08*(feedback) – 0.04*(peer support). The overall regression remained significant with alignment being the only significant predictor.

To examine if and how experiences of the teaching–learning environment predicted students’ well-being, we also used multiple regression. First, we fitted the following regression model: self-efficacy = 2.43 + 0.10*(alignment) + 0.11*(relevance) + 0.12*(peer support) + 0.08*(feedback). The overall regression was significant (R2 = 0.14, F [4, 409] = 16.95, p < 0.000). It was found that alignment (β = 0.10, p = 0.01), relevance (β = 0.11, p = 0.03) and peer support (β = 0.12, p = 0.01) statistically significantly and positively predicted self-efficacy, whereas feedback (β = 0.08, p = 0.11) did not statistically significantly predict self-efficacy.

We used the same model to predict students’ study-related burnout: 4.63 – 0.20*(alignment) – 0.17*(relevance) – 0.05*(peer support) – 0.19*(feedback). The overall regression was significant (R2 = 0.22, F [4, 409] = 29.41, p < 0.000). It was found that alignment (β = -0.20, p < 0.00), relevance (β = −0.17, p = 0.01) and feedback (β = −0.19, p < 0.00) all statistically significantly and negatively predicted study-related burnout, whereas peer support (β = −0.05, p = 0.36) did not statistically significantly predict study-related burnout.

Conclusion and discussion

In higher education, a need is felt to redesign curricula to align with the rapidly changing society. Educational concepts with long histories and known for their effectiveness, such as PBL, are being further developed to relate more to developments in the labour market. One of the aims in these transitions is to make learning environments more interesting and relevant for students pursuing a certain career. Little knowledge is yet available on the effects of these innovative learning environments on student learning and well-being or on their perceptions. Do students consider these learning environments as contributing to their preparation for their future professions in a positive way?

The current exploratory study investigated students’ approaches to learning, well-being, and perceptions of their learning environment in the context of an innovative educational concept: DBE. Over 400 first-year students from different study programmes within one UAS participated, providing insights into their approaches to learning, well-being, and perceived learning environment, as well as the relations between these concepts.

The overall question underlying this study was as follows: What kind of learning approaches do students apply, how is students’ well-being in the DBE learning environment, and how do students perceive the DBE teaching–learning environment? First, we discuss our findings on a general level; subsequently, we discuss the findings in the context of a specific UAS.

Main findings

Student experiences of the teaching–learning environment and their approaches to learning have not been extensively studied in the UAS context. The present study assumed that the same elements emerging from the science university context (Parpala & Lindblom-Ylänne, 2012) can also be found in the UAS context. However, the reliability of some of the scales of the HowULearn questionnaire (the instrument used to measure these elements) was not as ideal as hoped, although the instruments are robust (Parpala & Lindblom-Ylänne, 2012). This suggests a need to examine items of the questionnaire in more detail to consider if there is something that does not fit into the context of a UAS. In addition, the relations found in the present study (e.g. between the teaching–learning environment and approaches to learning) are similar to previous findings (Asikainen et al., 2020).

The relation between experiences of the teaching–learning environment and well-being has not been studied much before. The present study suggests that there is a relation and that students with a lower risk of burnout also experience the teaching–learning environment more positively than other students, whereas higher self-efficacy is related to positive experiences of the teaching–learning environment.

Findings related to the context of DBE at a UAS

We now discuss the notable results of the present study in the specific context of DBE at a UAS. Peer support had the highest scores in this specific context. This can possibly be explained given that there is a lot of group work in DBE. Students collaborate intensively on authentic assignments derived from the working field. It is remarkable that, despite alignment receiving a lot of attention when designing the DBE environment, students did not experience this as much as expected. It can be questioned whether the aims and learning tasks are clear to students. Ruge et al. (2019), for example, emphasised that implementing constructive alignment needs a shift from an institutional perspective on what has been implemented to the more contextual and complex question of how the implementation process is actioned: in other words, effective alignment (e.g. selection of learning activities and forms of assessment) in a specific educational context. It might be helpful to address this issue explicitly when designing a DBE learning environment. While developing the study context, it is important to be aware of different perceptions of the teaching–learning environment, such as teachers’ perspectives versus students’ perceptions and (following Van den Akker et al., 2003) the implemented (teacher) perspective versus the attained (student) perspective. In the current study, students were questioned about elements of the learning environment (i.e. alignment, relevance, peer support, and feedback). Of course, their perceptions influence their learning, regardless of whether their perceptions match the intentions with which the environment was designed. We know from previous research how difficult it is to design a learning environment in which the ideal curriculum is reflected in the learned curriculum (Van den Akker et al., 2003).

Interestingly, alignment was not related to the deep approach to learning. As we did not examine learning profiles (i.e. combinations of approaches to learning), but focused on individual approaches, this might be why a relation between the deep approach and alignment was not found. Students do not have either a deep, an unreflective or an organised approach to learning; rather, these approaches are combined within a student in a myriad of possible forms (Parpala et al., 2021). For example, a student who approaches learning almost always in a deep way and rarely uses an unreflective approach is different from a student who uses a deep approach as often as an unreflective approach to learning, depending on the context. Moreover, whether this student can organise his/her studying influences learning for both deep and unreflective approaches to learning. So, the approaches to learning, although studied and measured individually, are intertwined within an individual student. This is why, in some research, these approaches are considered in relation by defining clusters of students based on their combined approaches to learning. For example, Parpala et al. (2010), in a study involving over 2,500 higher education students in different disciplines, found that these students could be clustered in four different profiles based on their approaches to learning (e.g. unorganised students applying a deep approach). These clusters were found to be related to students’ fields of study, although it is also known that individual factors influence students’ approaches to learning (Lindblom-Ylänne, 2003). It would be interesting for future research to investigate whether comparable profiles can be found in different learning (e.g. DBE) environments, whether or not within the same study fields.

Alignment was, however, found to be the strongest predictor of lower scores for the unreflective approach to learning. This suggests that students who experienced their teaching–learning environment as aligned refrained from studying superficially. In other words, students who struggle with relating ideas do not experience that teaching consists of clear goals and methods supporting those goals. Longitudinal studies would help us understand if alignment in teaching diminishes students’ unreflective approaches.

Regarding student well-being in a DBE learning environment, relevance, alignment and feedback predicted student self-efficacy positively and study-related burnout negatively. It was found that alignment, relevance and teachers’ constructive feedback were all significant predictors of students’ well-being. The influence of these variables on well-being was even larger than on students’ approaches to learning. These finding are in line with Postareff et al.’s (2017) study. They found that whether students’ deep learning changed during an educational course depended mainly on individual factors, such as the degree of self-regulated learning and goal setting. In the current study, it can be questioned whether the predictive value was low because the predictors of approaches to learning were mainly in the students themselves or because the environment was not yet sufficiently well developed to be able to influence their approaches to learning. The perceived alignment (mean score 3.44) indicates that there is room for improvement in this regard.

Our finding that relevance, alignment and feedback seem to be important in supporting student well-being in this specific learning context also raises a concern that students with lower self-efficacy and higher risk of burnout experience the relevance of teaching, alignment, and feedback lower than other students. During an internal evaluation of students studying in the DBE university, students described their experiences and reported feelings of being overwhelmed and a bit lost at times (Donker, 2022). This might explain why reliability of the deep approach to learning was rather low in the present study. Maybe students who are lost, overwhelmed and searching to find their way in their studying find it rather difficult to use a deep approach to learning. Also, students’ self-efficacy beliefs are being challenged. Organized studying is difficult at first and alignment of the learning environment, which the students themselves co-create, isn’t always clear to them. Furthermore students state that, while working on relevant and authentic content, they also further develop relevant competencies such as collaborating, communicating and a sensitive attitude toward others; these are competencies they need in their working life as well (Donker, 2022).

In general, the present exploratory study suggests that in the DBE-learning environment, there is a relation between experiences of the teaching–learning environment and student well-being. Furthermore, relevance, alignment and feedback seem to be important in supporting students’ well-being. Results of this first study can be considered a starting point for future reference. Research on student perceptions guides how learning environments can be further developed. A longitudinal study could be supportive in the (re)design of an innovative teaching–learning environment.

Limitations and directions for future research

First, as this is a first exploratory study, more research and especially longitudinal research with a larger number of students is needed on this educational concept of DBE and students’ experiences. Also, the role of individual student characteristics in addition to their learning processes is worth investigating. Future research should also focus on which elements in the learning environment determine success and whether this is general or if it differs between study domains (e.g. under the influence of the working field for which students are educated).

Second, although the questionnaire used had been validated and used in multiple other research studies, internal reliability of the scales varied between good and almost acceptable in our study. The results should therefore be considered carefully especially regarding the deep approach to learning. It might however be the case that the specific context of this study addresses the constructs in other perspectives compared with the context of the universities in which this instrument was used previously. Or perhaps the new learning environment is challenging students’ learning (Donker, 2022) and it is difficult for them to evaluate their own strategies and intentions in the light of deep approach to learning.

As data were collected during a time in which higher education had not yet been influenced by the worldwide COVID-19 situation, the teaching–learning environment was fully offline. Due to the shift to online education, it is important to take into account the perceptions of online or hybrid learning environments and the relations between such environments and student characteristics. Another potential limitation of this study is that the focus was on the educational concept of design-based education in general and we did not differentiate between programmes but considered students to be affected by the concept in a comparable way. However, a next step in this research would be to take the field of study into account because, in previous research, it was found that students from different fields of study perceived their learning environments in different ways (Parpala et al., 2010).

Practical importance

To educate students in line with societal requirements, there is a need to further develop educational concepts and redesign learning environments. However, the redesign of learning environments always raises questions of how students experience these new learning environments and whether they support quality learning. The question underlying this study was to provide a first descriptive exploration of how students perceive the quality of the DBE-environment. Based on the initial results, educators can further align the DBE learning environment with its intended goals, which are reflected in approaches to learning and student well-being, and monitor students’ perceptions in the future.

Working with a questionnaire to measure student perceptions requires students to be able to reflect on themselves. The items of the HowULearn questionnaire should be interpreted by the students in order to be able to understand their own learning behavior (e.g. approach to learning). Student counseling plays an important role in the development of this reflective skill. Lindblom-Ylänne (2003) found that working with a questionnaire to measure students’ approaches to learning makes students more conscious of their learning behavior. Based on this awareness, students were able to develop more effective approaches to learning required for their specific learning environment (Lindblom-Ylänne, 2004). In other words, regardless of which learning environment students enter in higher education, it seems worthwhile to explicitly make students aware of their learning behavior and the requirements of the learning environment. Student counselling might be helpful to support the reflective process of the students.

Furthermore, support of programs to develop and implement more aligned curricula seems to be important as well. The results of our exploratory study suggest that students perceiving the curriculum to be more aligned show a lower score on unreflective approach to learning. This may also increase student well-being as an unreflective approach to learning has been found to be related to the risk of burnout (Asikainen, et al., 2022). Achieving constructive alignment in programs is a complex process that is related to defining the intended learning outcomes, choosing teaching/learning activities, assessing students’ actual learning outcomes in relation to the intended learning goals, and arriving at a final grade and in-depth learning (Biggs, 1996). It also relates to aspects such as relevance and feedback (Fryer et al., 2021; Higgins et al., 2002). The educational support of programs needed to work on alignment of the curriculum, and effects on student learning and well-being, should not be underestimated.

Appendix: HowULearn Questionnaire

Approaches to learning

Organised studying:

I put a lot of effort into my studying.

On the whole, I’ve been systematic and organised in my studying.

I organise my study time carefully to make the best use of it.

I carefully prioritise my time to make sure I can fit everything in.

Deep approach:

Ideas and perspectives I’ve come across while I’m studying make me contemplate them from all sides.

I look at evidence carefully to reach my own conclusion about what I’m studying.

I try to relate new material to my previous knowledge.

I try to relate what I have learned in one course to what I learn in other courses.

Unreflective approach:

I often have trouble making sense of the things I have to learn.

Much of what I’ve learned seems no more than unrelated bits and pieces.

I am unable to understand the topics I need to learn because they are so complicated.

Often I have to repeat things in order to learn them.

Well-being

Self-efficacy beliefs:

I believe I will do well in my studies.

I’m certain I can understand the most difficult material in my studies.

I’m confident I can understand the basic concepts of my own study field.

I expect to do well in my studies.

I’m certain I can learn well the skills required in my study field.

Study related exhaustion:

I feel overwhelmed by the work related to my studies.

I feel a lack of study motivation and often think of giving up.

I often have feelings of inadequacy in my studies.

I often sleep badly because of matters related to my studies.

I feel that I am losing interest in my studies.

I’m continually wondering whether my studies have any meaning.

I brood over matters related to my studies during my free time.

I used to have higher expectations of my studies than I do now.

The pressure of my studies causes me problems in my close relationships with others.

Teaching–learning environment

Interest and relevance:

I can see the relevance of what we are taught.

I find most of what I learned in courses really interesting.

I enjoy participating in courses.

Peer Support:

Students support each other and try to give help when it is needed.

Talking with other students helps me to develop my understanding.

I can generally work comfortably with other students.

Alignment:

It is clear to me what I am expected to learn in the courses.

What we are taught seems to match what we are supposed to learn.

It is clear to me what is expected in the assessed work (i.e. final exam, exercises).

I can see how the set work fit in with what we are supposed to learn.

Constructive feedback:

The feedback given on my work helps me to improve my ways of learning and studying.

The set work helps me to make connections to my existing knowledge.

The feedback given on my set work helps to clarify things I hadn’t fully understood.

I receive enough feedback about my learning.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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