Task dimension: various tasks and varied paths of the process
In general, investigating how the process unfolds is essential for a proper understanding of context and its influence on behavior (R. Mehl and Conne
2012). In this section, we report how the DBL process unfolds (RQ1) by illustrating the various tasks and varied paths of the process that students followed (see Fig.
2), which was derived from the data collected with the observation (left side of Fig.
2) and EmoForm (the colourful square block of Fig.
2).
On the top legend of this figure, each colour represents one type of tasks documented in EmoForm. Each square block in the table of Fig.
2 represents 1 day. The colourful stripes in the square block represent the tasks a student has completed within a day. For instance, the data described in Fig.
1 from a student, can be compressed into a single square block filled with five differentiating coloured strips (resembling the block of student H4 on day-2 in Fig.
2). The whole colourful square table describes all participated students involved activities in this DBL project. Nine teams were denoted in this figure alphabetically ranging from “A to I,” and each group (containing three to four individual students) was denoted by a number, e.g., A1, A2 A3. The left side of this figure illustrates the observation data and primarily lists types of activities that were identified by observations rather than predetermined by EmoForm.
Due to the open-end nature of DBL, students have autonomy in planning and implementing tasks. As a consequence, how they went through a sequence of DBL activities varied across teams. For example, only teams B and E experienced all predetermined task-focused activities foreseen in the design of the EmoForm (refer to the top legend in Fig.
2). It is because some teams missed reporting some activities. Indeed, we observed that some teams (e.g., team A) participated in design documentation, and others (e.g., C, D, and F) participated in the planning despite that participants did not report on this. There is also quite some variation in how teams experienced the DBL process: e.g., two teams (F and G) did not report social interactions (getting support from a teacher or giving feedback to others). Moreover, the degree of iteration in the process also varied substantially between teams. For example, team H went through activities iteratively most times, whereas teams F or G the least times.
Students seemed to follow a relatively consistent activity pattern over time. As one might expect, the activity of empathizing and understanding users (EDU) was reported mostly at the initial steps of their design process (almost all students report this on the first day and slightly fewer on the second). Continuing on the general pattern, we see that the activity of defining a design problem (DDP) very often happens together with, or right after the event of empathizing with the design user (EDU). There were more variations with regards to ideating design solutions (IDS), which took place early on or even only in the last few days of the project (Fig. 2).
Despite that all teams participated in defining the designing problem (DDP), it was only a small number of individual students who did so: on the ninth day of this project, eighteen students in total. The activity of making prototype (MP) and testing prototypes (TP) was left for the later phases of this design project. On the 13th day, all teams were involved in making and testing. The project ended uniformly for all groups and individuals as they were all engaged in preparing and presenting (PP) their work. Getting support from the teacher (GST) and getting feedback from others (GFO) happened at various times during the project with no apparent pattern, most often in parallel with other activities. Too few instances relating to design documentation (DD) and planning (PL) were recorded to allow discerning a common pattern.
Observation data shows some patterns in the learning process. Some off-task cases were indicated in classroom observations, such as dealing with a computer problem, searching for lost documents, looking at the phone while waiting for teacher’s support, and goofing around. Apart from these non-task related activities, the most commonly reported events were documenting their designs and planning. Besides, the field observations in this study also discerned another three frequent activities relating to students’ task-related social interaction amidst DBL. (a) Asking or offering help (AOH). It can be expressed by answering peer’s questions and asking for instructions on the task, or students sometimes would help a team member with a task when they were doing some other tasks. Besides, we found offering help was often concerning trying out another team’s prototype. (b) Reviewing other’s work (ROW). It often occurred through testing prototypes and when following the presentation of another group. (c) Chatting with peers (CWP). It often happened in idle moments while waiting for other tasks, such as presentation, getting the teacher’s support, and sometimes during an off-topic conversation.
In general, characterizing and categorizing DBL activities is a fundamental step to identify the most likely elements of DBL and support learning and teaching in practice. Based on the results derived from this study, we outlined a list of project-related tasks in Table
4, which comprises the three most likely categories: Design Thinking Process (DTP), Project Management (PM), and Task-related Social Interaction (TSI). The mixed steps and tasks in parallel (see the last four rows of Table
4) will be furtherly reported and discussed in the next sub-section in terms of the multitasking strategy.
Design thinking is a general theory of design (Buchanan
1992) that has been used to characterize what individual designers know and how they approach and make sense of their work (Kimbell
2011). The design thinking process category we proposed in this table is consistent with the widely adopted design process recommended by the Stanford d. school (
2013), which consists of five main steps: Empathize, Define, Ideate, Make and Test.
Furthermore, this table identifies the category of project management to refer collectively to some auxiliary activities relating to the DBL context, e.g., presentation, design documentation, planning. Notably, an earlier study (Nieswandt and Mceneaney
2012) has regarded design documentation as one of the additional essential design skills in the high school classroom. In another study (Doppelt et al.
2008), presentation in DBL is the situation where teacher assessment and peer-assessment takes place. Besides, planning is regarded as a critical skill or practice for learning science (Kolodner
2002).
The final category pertains to social interaction, which includes getting support from the teacher or getting feedback from others. The importance of social context has already been emphasized in earlier works, e.g., the teacher’s supportive coach role on students’ tasks and processes in DBL (Bekker et al.
2015; Gómez Puente et al.
2013a,
b). While earlier studies have argued that it is more enjoyable to do tasks with peers, e.g., see (Carroll et al.
2010), little empirical evidence of such collaboration can be found in DBL literature. Table
4 explicitly emphasizes project-related social interaction and de-emphasizes off-topic social interactions, e.g., goofing around, making jokes. Besides, task-related social interactions are further classified as asking or offering help, reviewing other’s work, and chatting with peers (e.g., casually discussing with peers).
Task strategy dimension (multi-tasking versus single-tasking strategy)
During the DBL process, students sometimes engaged in one singular task after the other, and at other times carried out multiple tasks in parallel.
Single-
tasking refers to a single activity (e.g., one specific stage of the design thinking process, or separate action of receiving help from the teacher, etc.) on which students dedicated a sustained period before interleaving and switching to other tasks. For instance, the EmoForm in Fig.
1 illustrates a single task from the timeframe of 50 min until 75 min. On the contrary,
multi-
tasking refers to two to three project-related tasks carried out in parallel within the same timeslot. Note that the term multi-tasking here does not apply to non-project-related tasks (such as working while listening to music, or goofing around, etc.).
From the data collected from the first section of EmoForm, students reported working on a single task more often rather than in multi-tasking. For single tasks, students tend to spend the most time on empathizing design users (
M = 365 mins,
SD = 275 mins), making a prototype (
M = 328 mins,
SD = 213 mins), and ideating design solutions (
M = 318 mins,
SD = 205 mins). Overall, students worked on at most two to three tasks within any single time interval (25 mins), and six types of task co-occurrences can be discerned that are detailed below. Notably, the first and third types of task co-occurrences stand more robust than the rest types based on the frequency of involved students.
(1)
Combining different design process steps;
(2)
Combing task-related social interaction;
(3)
Mixing design process steps and task-related social interaction;
(4)
Combining design thinking process steps and project management;
(5)
Mixing project management and task-related social interaction; and
(6)
All categorized tasks in parallel.
When interviewed about their multi-tasking behavior, most students did not mention any motivation or purpose for starting to multi-task. During the interviews, students reported multi-tasking activities when they were involved in different successive tasks (or, in other words, sequential tasking) in a single time interval (e.g., 25 mins). Furthermore, analysis of the interview data (which were also consistent with the results obtained from the observations and the EmoForm) helped identify the following two particular situations which appear to be related to multi-tasking:
(a)
When helping out a team member working on a different task than them; this was confirmed by our observation in the classroom where we could notice, e.g., one child making prototype while helping other with painting their work.
(b)
When tasks are inherently interdependent, in which case they discover new insights for one task while working on a different task [reflecting the opportunistic nature of the design process (Guindon and Raymonde
1990)]. For example, students explained that the task of making the prototype in parallel with ideating for the cases in which they would come up with new ideas (little changes or additional features) when they were building their prototype. Moreover, tasks (e.g., empathizing and ideating) were done in parallel in some cases, in which the team was searching for design inspiration. The combined task of defining the design problem and ideating design solutions happened when students discovered a new aspect of design problems during the time they were designing their solutions, which also in line with the observation findings.
Figure
3 visualizes all the co-occurrence relationships between different DBL tasks, which are extracted from EmoForm data. This figure shows how DBL activities were all be combined to some extent, except for planning. Nodes in the graph represent different types of activities, while lines in the figure represent by their thickness how often the specific activities are combined in multi-tasking. How often here means how many students have reported such a co-occurrence relationship between DBL tasks.
Overall, there is a stronger co-occurrence between empathizing design user (EDU), ideating design solution (IDS), getting support from the teacher (GST), and getting feedback from others (GFO). Additionally, the task of empathizing with the design user (EDU) was carried out throughout the entire design process and often in parallel with other minds-on activities (e.g., DDP, IDS) and also in parallel with other’s input such as teacher’s support (GST) and other’s feedback (GFO). Interestingly, identifying the design problem (DDP) and testing prototype (TP) were not combined. Defining the design problem (DDP) is often combined with empathizing with users (EDU), which is a good example of the first type of task co-occurrence (i.e., connecting different design process steps) as described in the bullet lists above. This combination is understandable, as it provides a springboard for an in-depth understanding of the design challenge. Interestingly, as an example of the fourth type of task co-occurrence (i.e., combining design thinking process steps and project management), one child reported combining the activity of defining the design problem (DDP) with design documentation (DD).
The open-ended nature of DBL, on the one hand, gives students freedom in task implementation to encourage diversity in design approaches (Gómez Puente et al.
2013a,
b). On the other hand, the opportunistic nature of the inquiry process inherited from the design thinking notion encourages students to move among tasks (Razzouk and Shute
2012). Such an opportunistic approach is well known and may include, for example, immediate recognition of a partial solution in another part of the problem, immediate handling of inferred or added requirements, drifting through partial solutions, and interleaving problem specification with solution development (Guindon and Raymonde
1990). This may be the reason why students combine different DBL tasks to varying extents. To theoretically describe this manner of executing tasks in DBL, we introduce the task strategy dimension includes a dichotomy of strategies—Single-tasking (ST) and Multi-tasking (MT). The distinction of multi-tasking from single-tasking in this paper pertains to how and whether tasks are inherently interdependent or may reflect an opportunistic approach to solution development. Specifically, we refine this task strategy dimension of DBL activity as follow:
Single-
tasking refers to students spending a continuous time interval on a single task before interleaving and/or switching to others. For example, as seen in Fig.
1, the student applied the single-tasking on ideation between minute 50 and minute 75.
Multi-
tasking is defined by Pashler (
1994) as a mode of doing multiple activities simultaneously in an interleaved manner. Defined by Salvucci et al. (
2009), multi-tasking is represented along a continuum in terms of the time spent on one task before switching to another. However, the emphasis of multi-tasking representing such phenomenon was largely lacking and underrepresented in prior DBL works. Multi-tasking in this paper refers to performing two to three project-related tasks contrary to how multi-tasking is often defined in earlier works, i.e., including off-topic tasks done either concurrently or sequentially in a particular time interval. For example, in the case of Fig.
1, the student applies multi-tasking on empathizing with the design user and getting support from the teacher during the first 25 mins.
Collaboration strategy dimension (collaborative-tasking versus individual-tasking)
Students sometimes worked in small groups or individually during this project. Initially, students seemed to spend similar amounts of time on collaborative versus individual tasks. Different students exhibited different patterns in using collaborative or individual tasking strategy. For instance, the data reported in the EmoForm indicates that student H4 did almost all of the tasks collaboratively, except for a short moment of individual tasking on the fourth day. On the contrary, student A1 spent most time single-tasking except for the first 2 days of collaborative tasking and a few moments of collaborative-tasking on the nineth day. Overall, we found that all students followed both of these strategies, and most of them worked increasingly in collaboration as the project progressed.
Students seemed to spend more time on collaborative tasks than individual tasks based on data collected from the first section of EmoForm. Table
6 shows how students relatively spent relatively more time on collaborative tasks (see activities with boldface numbers) such as empathizing design user (EDU), ideating design solution (IDS), making prototype (MP), and design documentation (DD). Besides, students often engaged with multi-tasking activities collaboratively. Likewise, for individual tasks, as shown in Table
7, students tend to spend more time on empathizing design users (EDU), ideating design solutions (IDS), and making prototype (MP). Comparing the time spent on individual versus collaborative tasks (Table
6 versus Table
7), it seems that some tasks favoured individual works, such as defining the design problem (DDP), presentation (PP), and planning (PL). In some rare instances, students were involved in interleaved collaborative and individual work within a single 25-min timeslot. For example, a student spent half of the time collaboratively getting feedback from others collaboratively and individually for the remaining time.
These findings above are related to the social environment of DBL, which are driven by the peer learning process within and across teams when they share resources, engaging in debate, and exercise freedom in task implementation (Gómez Puente et al.
2013a,
b). In this paper, we address the collaboration strategy dimension of DBL activity distinguishing between three strategies; Collaborative task (CT), Individual task (IT), and intertwined IT and CT (intT) in this paper.
Specifically, we refine that the
collaborative task refers to small groups of students working together to achieve the same task goal or working together to finish assigned tasks. In contrast,
individual tasks are situations when students work on tasks alone. In
intertwined individual and collaborative tasks, students interleave individual and collaborative tasks frequently. As shown in the example of Fig.
1, the student is intertwining individual and collaborative tasks regarding empathizing design user and ideating design solution between the minute 125 and minute 150.
A conceptual framework: the activity-and-affect model of DBL
The Activity-and-Affect Model of DBL (as shown in Fig.
4) was synthesized from the results in this study to capture how students experience emotions during DBL activities (to answer RQ1 and furtherly understand RQ2 synthetically). It is intended as a conceptual model, and it expands upon earlier descriptions of the DBL process such as the DBL framework (Gómez Puente et al.
2013a,
b), the Reflective DBL framework (Bekker et al.
2015), and the Learning-by-Design framework (Kolodner et al.
2003). Additionally, the Activity-and-Affect Model of DBL is proposed to address the following two intentions that are underrepresented in the existing literature:
(1)
Describing the DBL activities from a multi-dimensional perspective. More specifically, all these vital elements of DBL are mapped along the task dimension, task strategy dimension, and collaboration strategy dimension, respectively.
(2)
Having a nuanced view of how a specific activity could be associated with an emotional experience. This model establishes the nuanced channels between DBL activities and students’ emotional experiences.
As shown in Fig.
4, this model introduces three dimensions of DBL activities using three identified colours. The blue rectangle block represents the task dimension, which includes three categorized tasks (see Table
4); the yellow circle and red circle represent the task strategy dimension and collaboration strategy dimension, respectively. These circles and rectangles presented with identified abbreviations and colors function as the foundations for constructing DBL activities from three dimensions. Further, every loop (connecting three nodes from each of the three dimensions, respectively) in this model represents a possible type of activity in DBL. For example, the loop on edge stringing with the nodes of “IT-MT-DTP” stands for the activity that individually involved multiple tasks in parallel to a design thinking process. In general, the arc connecting nodes represent one indicator of the emotional experience (e.g., enjoyment, frustration, etc.; see Tables
1 and
2 for the indicators of students’ emotional experience). For example, the feeling of enjoyment when mixing multiple (task strategy dimension: MT) design stages (task dimension: DTP) in teamwork (collaboration strategy dimension: CT) is a part of a student’s emotional experience of DBL. Likewise, the feeling of boredom when mixing multiple (task strategy dimension: MT) design stages (task dimension: DTP) alone (task strategy dimension: IT) is also a possible part of a student’s emotional experience of DBL.
The discussion above introduced the Activity-and-Affect model of DBL to describe students’ emotional experiences of DBL activities. Potentially this model is intended to explain students’ emotional experience in DBL. For this purpose, we fitted a linear regression model to the data collected by EmoForms from a sample of 30 students on a repeated basis adhering to the taxonomy of our proposed Activity-and-Affect model of DBL. Specifically, we performed a linear regression using a hierarchical data structure (i.e., a linear regression-based analysis that takes the hierarchical structure of the data into account) to explain individual student’s emotional experiences from DBL activities. Therefore, we sorted the data set collected by EmoForms into a three-level nested structure:
(1)
Level 1 (activity level) measurement occasions, i.e., DBL activities (where is coded as the dimension of task, task strategy, and collaboration strategy according to the structure of Activity-and-Affect model of DBL) nested within-day within-person.
(2)
Level 2 (day level) repeated measurement nested within-day (which is measured as fifteen different lessons/days) within-person.
(3)
Level 3 (student level) repeated measurements nested within-person (which is measured as 30 students).
The three-level multilevel multiple linear regression was calculated to explain six dependent variables of emotional experience (including enjoyment, relaxation, frustration, boredom, concentration, and learning better respectively) based DBL activities within days within-person. It is important to note that only one-time measurement rather than repeated measurement is designed within an occasion for this set of outcome-focused emotions (as seen in the last section of EmoForm in Fig.
1). Therefore, the two-level regression was calculated to explain four dependent variables of emotional experience (including contentment, pride, anxiety, and hopelessness). Overall, the results indicate that a regression model using a multilevel nested structure was a significant predictor of individual student’s emotional experience within a day (as seen in Table
8, all outcome variables of emotional experience having the value of
R2 > .5).
For example, the results (as seen in Table 8) indicate that a three-level structured regression model can significantly explain 84.8% of enjoyment variance, F (571, 1114) = 10.904. The level of enjoyment is significantly dependent on individual student (level 3), \(\eta_{p}^{2}\) = .695, and student’s enjoyment is dependent on the day (level 2) on which the activity took place, \(\eta_{p}^{2}\)= .723. Specifically, the level of student’s enjoyment is dependent on both of the task (level 1) and collaboration strategy (level 1) of activity within a day, \(\eta_{p}^{2}\) = .047, and .129, respectively.
This three-level structured regression model can significantly explain 87.6% of relaxation variance, F (566, 1099) = 13.714. The level of relaxation was found to be dependent on individual students (level 3), \(\eta_{p}^{2}\) = .736, and student’s relaxation is dependent on the day (level 2) the activity took place, \(\eta_{p}^{2}\) = .746. More specifically, student’s relaxation within a day was greatly influenced by the three elements of an activity: task (level 1), task strategy (level 1), and collaboration strategy (level 1), \(\eta_{p}^{2}\) = .033, .045 and .085 respectively.
Likewise, the three-level structured regression model can significantly explain 85.2% of boredom variance, F (563, 1100) = 11.239. The level of boredom is significantly dependent on individual students (level 3), \(\eta_{p}^{2}\) = .629, and student’s boredom is dependent on the day (level 2) the activity participated in, \(\eta_{p}^{2}\) = .754. Moreover, all three aspects of the activity, including task (level 1), task strategy (level 1), and collaboration strategy (level 1), greatly influenced student’s boredom within a day, \(\eta_{p}^{2}\) = .033, .044, and .109 respectively.
With regard to frustration, results indicate that the three-level structured regression model can significantly explain 84.6% of frustration variance, F (562, 1101) = 10.767. The level of frustration is significantly dependent on individual students (level 3), \(\eta_{p}^{2}\) = .616, and student’s frustration is also dependent on which day the activity occurred, \(\eta_{p}^{2}\) = .749. More specifically, the task (level 1) and collaboration strategy significantly account for student’s frustration within a day, \(\eta_{p}^{2}\) = .044, and .082, respectively.
Our three-level structured regression model significantly explains 79.9% of concentration variance, F (572, 1117) = 7.750. The level of student’s self-perception of concentration was found to be significantly dependent on individual students (level 3), \(\eta_{p}^{2}\) = .555, and student’s concentration is dependent on the day on which activity took place, \(\eta_{p}^{2}\) = .672. Additionally, both the task (level 1) and collaboration strategy (level 1) of activity significantly account for student’s self-perception of concentration within a day, \(\eta_{p}^{2}\) = .029, and .086, respectively.
Similarly, the three-level structured model significantly explains 83.6% of learning better variance, F (570, 1100) = 9.893. The level of perception of learning better is significantly dependent on individual students (level 3), \(\eta_{p}^{2}\) = .595, and student’s perception of learning better is significantly dependent on the day that an activity took place, \(\eta_{p}^{2}\) = .718. Specifically, student’s perception of learning better within a day was greatly influenced by the three elements of an activity: task (level 1), task strategy (level 1), and collaboration strategy (level 1), \(\eta_{p}^{2}\) = .069, .063 and .093 respectively.
In terms of four outcome-related achievement emotions (e.g., contentment, pride, anxiety and hopelessness), the two-level structured regression model significantly explains 60.9% of contentment variance, F (161,248) = 2.403; 64.0% of pride variance (F (161,248) = 2.740); 52.9% of anxiety variance (F (161,248) = 1.728); and 61.2% of hopelessness variance (F (161,247) = 2.419) respectively. Furthermore, the level of contentment, pride, anxiety and hopelessness are significantly dependent on individual student (level 3), \(\eta_{p}^{2}\) = .301, .342, .196 and .289 respectively.
Relationship between students’ emotional experience and DBL activities (RQ2)
In the previous section, our results suggest emotional experience (including a total of ten dependent variables as summarized in Tables
1 and
2) in DBL is significantly dependent on individual students (level 3). Student’s enjoyment, relaxation, frustration, boredom, concentration, and self-perception of learning better are dependent on the day when students took part in an activity (level 2). At the activity level (level 1), the multilevel regression results only indicate the general type of activity (from the dimensions of the
task,
task strategy, and
collaboration strategy, respectively) in DBL.
To answer how specific DBL activities are related to the emotional experience (RQ2), we conducted multiple linear regression using a stepwise method to furtherly measure which DBL activities significantly contribute to students’ emotional experience. Specifically, this analysis investigates the fine-grained types of DBL activities from the dimensions of the
task,
task strategy, and
collaboration strategy, respectively. For instance, the
task dimension is coded as the different sub-tasks (as summarized in Table
4). The
task strategy dimension is coded as single-tasking and multi-tasking. Besides, the
collaboration strategy dimension is coded as individual tasking, collaborative tasking, and intertwined individual and collaborative tasking. All the multiple regression analyses were calculated on a group level, rather than distinguish individual differences. The detailed results regarding multiple linear regressions are displayed in “
Appendix 3” (see Table
9).
Overall, the descriptive results of each variable showed that students had a positive experience in DBL according to the low scored negative emotions and high scored positive emotions. For instance, the mean scores for anxiety (M = 1.16, SD = .56; N.B. score “1” as “not at all” and “5” as “very much”), hopelessness (M = 1.47, SD = .92) and frustration (M = 1.61, SD = .99) are low. The positive indicators of emotional experience, e.g., enjoyment (M = 3.24, SD = 1.16), contentment (M = 3.56, SD = 1.13), pride (M = 3.18, SD = 1.28) and self-perception of concentration (M = 3.50, SD = 1.11) had a relatively high scores. The remaining three indicators of emotional experience are to different extents near to the middle point of the scale, including boredom (M = 2.47, SD = 1.31), relaxation (M = 2.82, SD = 1.21) and self-perception of learning better (M = 2.32, SD = 1.11).
Getting feedback from others (GST) has a positive effect on student’s relaxation (β = .101). Additionally, students who were busy with making prototype (MP) report a higher level of enjoyment (β = .129), relaxation (β = .086), pride (β = .133) and self-perception of concentration (β = .201) but also a lower level of boredom (β = − .198) and hopelessness (β = − .130). A mixed design thinking steps and project management (%DTP-PM) is a specific type of task where applying a multi-tasking strategy was found to have a positive effect on student’s self-perception of learning better (β = .050) but a negative impact on student’s boredom (β = − .054). Likewise, students using the collaborative-tasking strategy (CT) report a higher level of enjoyment (β = .112).
Some other tasks during the design thinking process seem to have a negative relationship with students’ emotional experience. For example, students who were involved in the task of empathizing design user (EDU) report a lower level of relaxation (β = − .062), contentment (β = − .197), and in the meanwhile report a higher level of frustration (β = .157) and boredom (β = .148). Students working on the task of defining design problems (DDP) indicate a lower level of student’s enjoyment (β = − .122), and a higher level of boredom (β = .075) and hopelessness (β = .109). Students, when busy with testing a prototype (TP), report a lower level of contentment (β = − .132) and a higher level of frustration (β = .070). Similarly, presentation (PP) and design documentation (DD) are the two project management tasks that both seem to be positively related to frustration (β = .082; β = .049, respectively). Besides, when working on design documentation (DD), students report a lower level of relaxation (β = − .064). In comparison, when busy with presentation (PP), students indicate a lower level of enjoyment (β = − .049) and a higher level of anxiety (β = .148).
The combined design thinking process and task-related social interaction (%DTP-TSI) is another specific type of task in which particularly applying a multi-tasking strategy was found to make students feel a higher level of anxiety (β = .170). Moreover, the single-tasking strategy (ST) seems to have a negative effect on student’s enjoyment (β = − .138), pride (β = − .339), self-perception of concentration (β = − .163), and self-perception of learning better (β = − .158). Students using the individual-tasking strategy (IT) report a lower level of self-perception of concentration (β = − .074).
Interestingly, students involved in the task of planning (PL) report a lower level of boredom (β = − .061) and a lower level of self-perception of learning better (β = − .058). The combined design thinking steps (%DTP) seemed to be negatively related to both boredom (β = − .071) and pride (β = − .127). Similarly, students, when getting support from the teacher (GST) and involving a particular task of mixed design thinking steps and task-related social interaction (%DTP-TSI), report a higher level of relaxation (β = .075, β = .090 respectively) and also higher level of frustration (β = .049, β = .231 respectively). Furthermore, getting support from the teacher (GST) seemed also to be positively related to boredom (β = .059).