Background: The expectancy-value and achievement goal theories are arguably the two most dominant theories of achievement motivation in the contemporary literature. However, very few studies have examined how the constructs derived from both theories are related to deep learning. Moreover, although there is evidence demonstrating the links between achievement goals and deep learning, little research has examined the mediating processes involved. Aims: The aims of this research were to: (a) investigate the role of task- and self-related beliefs (task value and self-efficacy) as well as achievement goals in predicting deep learning in mathematics and (b) examine how classroom attentiveness and group participation mediated the relations between achievement goals and deep learning. Sample: The sample comprised 1,476 Grade-9 students from 39 schools in Singapore. Methods: Students' self-efficacy, task value, achievement goals, classroom attentiveness, group participation, and deep learning in mathematics were assessed by a self-reported questionnaire administered on-line. Structural equation modelling was performed to test the hypothesized model linking these variables. Results and conclusions: Task value was predictive of task-related achievement goals whereas self-efficacy was predictive of task-approach, performance-approach, and performance-avoidance goals. Achievement goals were found to fully mediate the relations between task value and self-efficacy on the one hand, and classroom attentiveness, group participation, and deep learning on the other. Classroom attentiveness and group participation partially mediated the relations between achievement goal adoption and deep learning. The findings suggest that (a) task- and self-related pathways are two possible routes through which students could be motivated to learn and (b) like task-approach goals, performance-approach goals could lead to adaptive processes and outcomes.