Ingroup and role identity influences on the initiation and maintenance of students' voluntary attendance at peer study sessions for statistics

Background: Although class attendance is linked to academic performance, questions remain about what determines students’ decisions to attend or miss class. Aims: In addition to the constructs of a common decision-making model, the theory of planned behaviour, the present study examined the influence of student role identity and university student (in-group) identification for predicting both the initiation and maintenance of students’ attendance at voluntary peer-assisted study sessions in a statistics subject. Sample: University students enrolled in a statistics subject were invited to complete a questionnaire at two time points across the academic semester. A total of 79 university students completed questionnaires at the first data collection point, with 46 students completing the questionnaire at the second data collection point. Method: Twice during the semester, students’ attitudes, subjective norm, perceived behavioural control, student role identity, in-group identification, and intention to attend study sessions were assessed via on-line questionnaires. Objective measures of class attendance records for each half-semester (or ‘term’) were obtained. Results: Across both terms, students’ attitudes predicted their attendance intentions, with intentions predicting class attendance. Earlier in the semester, in addition to perceived behavioural control, both student role identity and in-group identification predicted students’ attendance intentions, with only role identity influencing intentions later in the semester. Conclusions: These findings highlight the possible chronology that different identity influences have in determining students’ initial and maintained attendance at voluntary sessions designed to facilitate their learning.

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.