Research concerning students’ emotions as well as their way of coping with their emotions in different situations has been scarce. The purpose of this study is to explore the relationship between self-regulated learning, academic emotions, psychological flexibility, study success and study pace during university-level studies. A total of 274 arts students participated in this study. Academic emotions were first explored with factor analyses, parallel analysis and confirmatory factor analyses. The relationship between self-regulated learning, psychological flexibility, academic emotions and study success and study pace was analysed using Pearson’s correlations and path analysis using Amos. The results revealed that there is a strong connection between self-regulated learning and academic emotions and that students’ self-regulations appear to affect students’ emotions with regard to their academic study. Furthermore, psychological flexibility appeared to be an important mediator between academic emotions and study pace. The study suggests that students’ emotions and their way of coping with these emotions should be taken into account when considering learning and teaching.

In the description of the complex relationship between individual students and their education context, as well as understanding of questions related to progression, retention or dropouts in higher education, student engagement is considered the primary construct. In particular, the significance of the first year of higher education in terms of engagement is decisive. We aim at developing a multidimensional conceptualization of engagement and utilized network analysis. Data were collected as part of the annual Student Barometer survey in Finland during the 2012–2013 academic year, and we gathered a nationally representative sample (n = 2422) of first-year students in different disciplines at 13 Finnish universities. Network analysis confirmed the multidimensional process model of engagement and its six dimensions. The central dimensions of engagement are identity and sense of belonging, which develop in the interplay between individual and collective dimensions as a long-term process. Additional network analyses with covariates identified positive and negative factors that affect engagement. The study adds new perspectives to existing knowledge of engagement. It is important to understand the process-like nature of engagement and make visible factors affecting the process. Based on these findings, we provide novel practical recommendations for interventions for university students who struggle with engagement during their first year.

This study examines the profiles of self-regulation of learning, peer learning and peer support among students. The study investigates whether the profiles differ in terms of reported study-related exhaustion. Students completed a questionnaire regarding their use of self-regulation of learning and peer learning and perceived peer support and study-related exhaustion. Four different student profiles were found. The profiles differed in terms of self-reported study-related exhaustion. Self-regulated students with a low level of peer learning and low perceived value of peer support reported the lowest levels of study-related exhaustion, whereas students with self-regulation problems, a high level of peer learning and high perceived value of peer support reported the highest levels of study-related exhaustion. The results showed that problems in self-regulation were positively related to self-reported study-related exhaustion. Identifying different student profiles helps to recognise students who may need more support in studying.

This paper examines disciplinary differences in the combinations of approaches to learning (i.e., learning profiles) among students, and how those combinations are related to academic achievement. In addition, the study focuses on how different learning profiles are related to students’ self-efficacy beliefs in different disciplines. Data consist of HowULearn survey responses from 4,294 full-time students from six different disciplines. We used a person-oriented approach; that is, the latent profile analysis (LPA) with various functions. The results showed that it is possible to detect different learning profiles of students in different disciplines. The study highlights that students who struggle in almost every discipline have a dissonant learning profile or the deep unorganised profile. Therefore, special attention should be paid to identifying the students with dissonant learning profiles and to support them in recognising their own learning processes. Moreover, students’ time and effort management skills should be fostered during university studies.