<p>  </p> <p>Some of the variability found in measures of mental workload (see e.g. Singleton et al. 1973; Wierwille and Connor 1983; Steelman-Allen et al. 2011; Casner and Gore 2010) may be due to the effort applied to the task by participants, rather than by the independent variable of interest. If true, capturing and removing the variation due to ‘applied effort’ could improve the ability of studies to detect effects of interest. </p> <p>While introducing participants to two sub-tasks derived from Multi-Attribute Task Battery II (Santiago-Espada et al. 2011), the study investigated the influence of applied effort on MATB-II performance measures of mental workload while holding other effects constant. Two groups of participants each completed easy and hard trials of MATB-II-derived sub-tasks. Treatment group of participants was offered an additional reward if they achieved a sufficiently high performance.</p> <p>The treatment group performed better by just under 4% in both easy and hard trials which provides a suggestion about the size of the effect of applied effort in this study. </p> <p>Additionally pilot error analysis was performed using Tracking task results. Error probability distributions did not fit known distributions and did not show consistent difference between treatment and control groups. Novel method of distribution “tails” comparison showed significant difference in extreme error durations, extents and delays between treatment and control groups.</p> <p>Measuring or controlling for applied effort can improve the ability of researchers to determine the effects of interventions on workload measures by reducing the amount of variability that is captured as error. Also, “tails” method seems to be a viable tool in comparing probability  </p>

This thesis describes the research carried out to identify mechanisms as potential causes of action that can lead to improved learning outcomes. Identifying mechanisms requires an exploratory approach because mechanisms are not always directly perceivable. The potential mechanisms in this study were identified by analysing the reflections of students who participated in experiential learning projects during business process management education. The research was necessary due to a persistent need to minimise the gap between learning and practice. The research is unique because it takes a student-dominant approach to review students' reflective practices using a novel pragmatic-critical realist paradigm. Although reflection is central to experiential learning, there is limited understanding of how students reflect and what they consider to influence their learning outcomes. This limitation was explored using action research with mixed methods analysis which combined thematic analysis and partial least squares based structural equation modelling. During four action research cycles, student reflections on business process projects as part of higher education enterprise systems and business process management courses in a South African higher education institution were observed and analysed. Each action research cycle changed one aspect of the students' projects. The first intervention required students to reflect on action, the second required students to reflect in action, and the third required reflection for action. In the fourth cycle, the teaching staff changed. The findings showed that reflection is complex and must be linked to action to improve learning. Reflections were observed to positively influence learning outcomes when students apply effort to assigned tasks. On the basis of the observations, two models were proposed. The first model is a learning influence model embedded in the second disruptive learning model. The models show that reflective practices can improve learning outcomes by recognising that students learn at different levels but are predominantly absolutist. Students need to be inspired to apply effort in completing tasks and overcoming satisficing.