Search results

In recent years, it has become evident that self-regulation plays a central role in human functioning, including learning and achievement in school. Although there are different definitions of self-regulation, there is general consensus that it refers to a multi-component, iterative, self-steering process that targets one's own cognitions, feelings, and actions, as well as features of the environment for modulation in the service of one's own goals (Boekaerts, Maes, & Karoly, 2005). Educational psychologists agree that learning in the classroom involves cognitive and affective processing and is heavily influenced by social processes. This implies that students should be able and willing to regulate their cognitions, motivation, and emotions, as well as to adapt to the social context in order to facilitate their learning. Yet, there is at present neither a uniformly accepted definition of self-regulation nor that of self-regulated learning. Most theorists agree that self-regulation in the classroom is neither an all-or-none process nor a property of the learning system. Rather, it consists of multiple processes and components that interact in complex ways. Definitions have focused either on the structure of self-regulation, describing the different components of the self-regulation process, or on the processes that are involved.

The continuous outpouring of studies from motivation researchers begs for a periodic taking of stock, as well as prognostications about future directions in the field. The dawning of a new decade presents a worthwhile opportunity for such a reflective examination. This volume, the 16th in the Advances in Motivation and Achievement series, published in 2010, offers this reflection on where we stand and where we are going as a scientific discipline in the decade ahead.

This paper aims to understand what drives people – their motivations, autonomous learning attitudes and learning interests – to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.

A three-phase method was used to build volunteer mentor profiles; elicit topics of interest and establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitude toward lifelong learning and self-regulated learning strategies. The topics of interest were elicited through content analysis of answers to reflection questions. Statistical methods were applied to analyze the relationship between the interests and the mentor profiles.

Bottom-up clustering led to the identification of three mentor groups (G1 “low”; G2 “high” and G3 “medium”) based on pre-survey data. While content analysis led to identifying topics of interest: communication skills; learning AI; mentoring; prototype development; problem-solving skills; working with families. Analyzing relationships between mentor profile and the topics of interest, the group G3 “medium,” with strong intrinsic motivation, showed significantly more interest in working with families. The group with the overall highest scores (G2 “high”) evidenced also substantial interest in learning about AI, but with high variability between members of the group.

The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes toward lifelong learning. Such knowledge can help organizations shape the volunteering experience to provide more value to volunteers. Furthermore, the reflection questions can be used by volunteers as an instrument for reflection and by organizations to elicit the learning interests of volunteers.

The aim of this paper is to develop an instrument to assess the developmental space that teams create; examine whether creating more developmental space leads to greater satisfaction with team results; and decide which of three models best predicts perceived results.

The paper presents a quantitative study of individuals (N = 257). An instrument was designed to assess developmental space and was validated with a factor analysis. Multiple regression analyses were used to examine whether creating developmental space led to greater satisfaction with team results.

This study confirms the four-factor structure of developmental space suggested by earlier research. Creating more developmental space is positively related to perceived team results.

This research highlights the importance of creating developmental space and provides teams with an instrument to assess their developmental space as a starting point for improvement.

The interactions teams use are crucial in explaining the effects of teamwork, but seem underexposed in team research. Creating developmental space is a relatively new concept, hitherto only researched qualitatively. This empirical study extends and endorses previous research on developmental space by providing a quantitative assessment.