The student reflections showed similar outcomes for each action research intervention across the four action research cycles of the experiential learning business process management projects. The first intervention – reflection-on-action – identified four motivators for learning which were confirmed in the other cycles. In cycle two, students were required to reflect-in- action by maintaining reflective journals. This cycle had the lowest response but the richest reflections. Cycle three – reflection-for-action – had similar outcomes to the first two cycles.
The data from cycle three was not as rich as for cycle two. Cycle four encouraged the use of all three forms of reflection. Even though the lecturing staff changed, similar findings to the first three cycles were observed. A comparison of the influencers of learning across the cycles is presented in Table 5.19.
The four learning influencers answered research question one [RQ1], which sought to identify potential generative mechanisms influencing learning. The similarity in the influencer findings is observed in each influencer's percentage across the cycles. The net effect is a negligible difference in findings for the four learning influencers across the four cycles, irrespective of the action research or lecturing staff changes. This resonates with education research findings of no significant difference (Russell, 1999).
The qualitative analysis of the student reflections findings showed that applied effort was the most common influencer of learning. This finding was confirmed in the quantitative analysis, where applying effort was the most significant influencer. The other influencers of learning
188 were shown to support applying effort. Applying effort transcends an absolute answer provided by traditional education methods while epistemologically supporting an absolute answer or outcome, but this is unknown to the student learners at the time of learning.
Students need to operate in an environment where information is readily obtainable but not supplied directly to ensure that effort is applied. These findings answered the second [RQ2]
and third [RQ3] research questions which sought to identify how the identified learning mechanisms influence learning and what effect learning influencers have on learning outcomes.
Table 5.19. Learning influencers comparison across cycles.
Learning Influencer Mechanism
Levels of Learning (Helsing et
al., 2004)
ARC1 % ARC2 % ARC3 % ARC4 % Total %
Undertaking Tasks
Level 1
Absolutist 42 13% 143 22% 31 19% 33 14% 249 18%
Applying Effort Level 2
Transitionist 143 45% 240 38% 94 57% 122 51% 599 44%
Increasing Understanding
Level 3
Relativist 48 15% 66 10% 13 8% 33 14% 160 12%
Pursuing Quality
Level 4
Contextist 85 27% 188 30% 26 16% 50 21% 349 26%
The question then is what action is required to provide scenarios where business process learners are forced to apply effort. According to the reflective literature (Miettinen, 2000;
Schatzki, 2016; Turnbull, 2008), this requires a disruptive influence that forces the students to move beyond an absolutist frame of mind. The next chapter describes an analysis of the four action research cycles, which was explicitly undertaken to identify disruptors that may lead to applied effort. The identified disruptors are labelled drivers of effort.
189
6 Findings: Application and Assessment of Generative Mechanisms (Drivers of Effort)
This chapter continues the findings reported in Chapter 5, which showed evidence of four learning influencers with effort as the most significant learning influencer. In this chapter, methods for activating the effort learning influencer and assessing the learning outcomes are described. This is done in response to the fourth [RQ4] and fifth [RQ5] research questions, respectively. The answers require the identification of mechanisms to activate the effort influencer and assess learning outcomes. Whereas levels of learning were identified, learning should be considered a “process of interaction between individuals and their environment that influences many dimensions of an individual’s life” (Helsing et al., 2004, p. 159). In experience- based learning, breadth and depth are essential (Coker et al., 2017). Depth of learning is a product of effort applied over time which resonates with the findings in Chapter 5, which associated time with the applied effort generative learning mechanism. While the breadth of knowledge has been associated with improving working relationships (Coker et al., 2017), depth of knowledge has been linked to Bloom’s higher-order thinking skills (synthesis and application) and overall educational experience. Coker et al. (2017, p. 19) propose that “in experiential learning depth should be taken more seriously across higher education.” Thus, while breadth of knowledge is represented by content, learning should be assessed based on the depth of knowledge. Webb (2002) provides a method for assessing depth of knowledge using four levels of learning incorporated into the outcome of this study.
This chapter begins by reviewing data associated with applying effort to determine what aspects of applied effort may provide insight into improved learning. The review showed that each learning mechanism was embedded in applying effort, which led to reifying the learning mechanisms into four forms of effort. Epistemologically, Helsing et al. (2004) provide insight into how to assess the learning mechanisms in the classroom while the depth of knowledge model (Webb, 2002) provided support for assessing effort. Combining Helsing et al. (2004) and Webb (2002) provided a method by which the generative mechanism of effort can be planned prospectively and assessed retrospectively. The retrospective-prospective view combined with the effort drivers of learning from this study support a student-dominant
190 approach (Díaz-Méndez et al., 2019; Dziewanowska, 2017; Guilbault, 2018) based on learner- centric reflection (Boud et al., 2005).