Effective collaboration in a team is a crucial skill. When people interact together to perform physical tasks, they rely on gestures to convey instructions. This thesis explores gestures as means to assess physical collaborative task understanding. This research proposes a framework to represent, compare, and assess gestures’ morphology, semantics, and pragmatics, as opposed to traditional approaches that rely mostly on the gestures’ physical appearance. By leveraging this framework, functionally equivalent gestures can be identified and compared. In addition, a metric to assess the quality of assimilation of physical instructions is computed from gesture matchings, which acts as a proxy metric for task understanding based on gestural analysis. The correlations between this proposed metric and three other task understanding proxy metrics were obtained. Our framework was evaluated through three user studies in which participants completed shared tasks remotely: block assembly, origami, and ultrasound training. The results indicate that the proposed metric acts as a good estimator for task understanding. Moreover, this metric provides task understanding insights in scenarios where other proxy metrics show inconsistencies. Thereby, the approach presented in this research acts as a first step towards assessing task understanding in physical collaborative scenarios through the analysis of gestures.

This study used a correlational design to examine the contribution of university students’ task understanding and self-efficacy to performance on a grade-bearing course assignment. Participants were 38 undergraduate students enrolled in a first-year elective course. Task understanding for explicit, implicit, and contextual task features was measured using a forced-choice task analyzer quiz and an adapted version of the Epistemological Beliefs Questionnaire (Schommer, 1990). Self-efficacy for explicit, implicit, and contextual task features was assessed on a self-efficacy for performance scale. Final grade on a major course assignment was used as a measure of task performance. Results of hierarchical regression analysis indicated that task understanding significantly predicted task performance and task understanding moderated the influence of self-efficacy on task performance. Findings may help to bridge these disparate lines of research and provide support for Winne & Hadwin’s (1998) model of self-regulated learning. Practical implications for facilitating university students’ success in their academic tasks are discussed.

Understanding what to do and how to complete academic tasks is an essential yet complicated academic activity. However, this area has been under-examined. The purpose of this study is to investigate students’ understanding of academic tasks with qualitative and quantitative approaches. Ninety-eight students participated in this study. First, the study explored the kinds of tasks students identified as challenging, the disciplines in which these tasks were situated, the types of structures these tasks had, and challenges found in students’ task analysis activity. Second, the study examined the relationships between students’ task understanding and academic performance. The findings indicated that although students struggled with various tasks, they struggled even more when tasks became less pre-scribed. The results also showed that task understanding was statistically significantly co-related to academic performance and task understanding, particularly, implicit aspect of task understanding, predicted students’ academic performance. The findings supported Hadwin’s (2006) model of task understanding.