Frontline Learning Research (E-Journal - EARLI, European Association for Research on Learning)
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Volition completes the puzzle: Development and evaluation of an integrative trait model of self-regulated learning
Full text linkMost self-regulated learning theories are imbedded within a social-cognitive framework and comprise cognitive, metacognitive and motivational components. Nevertheless, these theories partly neglect volition, which is necessary for implementing learning intentions. Therefore, the present study is frontline as it aimed to integrate volition within a comprehensive trait model of self-regulated learning (SRL) while proposing a new conception of trait volition for learning. A sample of n = 377 college students (70.1% female, MAge = 23.36, SDAge = 4.12) filled out questionnaires concerning volitional, cognitive, metacognitive, and motivational belief aspects of SRL. The results of confirmatory factor analysis speak in favour of integrating the highly interrelated constructs of procrastination, future time perspective, and academic delay of gratification in order to depict volition for SRL. Moreover, the structural equation modelling results favour a twofold motivational component for SRL that comprises both motivational beliefs and volition instead of including volition as a separate component aside from cognitive, metacognitive and motivational belief components. Additionally, the comprehensive trait model of SRL is related to GPA, which is a first indication of its validity. Therefore, the study empirically investigates a new conception of trait volition for learning environments as well as its integration within a comprehensive SRL framework. Future research should consider the importance of volitional components for SRL and could investigate individual differences concerning the modelled components
Exploring the antecedents of learning-related emotions and their relations with achievement outcomes
Full text linkRecent work suggests that learning-related emotions (LREs) play a crucial role in performance especially in the first year of university, a period of transition for most students; however, additional research is needed to show how these emotions emerge. We developed a framework which links a course-contextualized antecedent - academic control in Pekrun’s (2006) Control Value Theory of Achievement Emotions - with generic antecedents - adaptive and maladaptive cognitions and behaviors from Martin’s (2007) Motivation and Engagement Wheel framework - to explain a classical problem: the emergence of LREs in a transition period. Using a large sample (N = 3451) of first year university students, our study explores these two antecedents to better understand how four LREs (enjoyment, anxiety, boredom and hopelessness) emerge in a mathematics and statistics course. Through the use of path-modelling, we found that academic control has a strong effect on all four LREs – with the strongest impact observed for learning hopelessness and secondary, for learning anxiety. Academic control, on its turn, builds on contributions from adaptive and mal-adaptive cognitions. Furthermore, adaptive cognitions have an impact on learning enjoyment (positive) and on boredom (negative). Surprisingly though, the maladaptive behaviors impact positively learning enjoyment and negatively learning anxiety. Following this, we predicted performance outcomes in the course and found again academic control as the main predictor, followed by learning hopelessness. Overall, this study brings evidence that adaptive and maladaptive cognitions and behaviours act as important antecedents of academic control, the main predictor of LREs and course performance outcomes
Turning points during the life of student project teams: A qualitative study
Full text linkIn this qualitative study a more flexible alternative of conceptualising changes over time in teams is tested within student project teams. The conceptualisation uses turning points during the lifespan of a team to outline team development, based on work by Erbert, Mearns, & Dena (2005). Turning points are moments that made a significant difference during the course of the collaboration as a team. In this study, they are tracked by means of team interviews and reflection papers of team members. A method of coding was created to collect all information about the turning points, their causes and consequences. By means of a thorough analysis of these coded data an overview of their nature and their effects on the rest of the team process as perceived by the team members themselves is provided. Results show that the development paths of the three teams were differentiated in terms of turning points that occurred and, especially, in the order in which the turning points occurred. However four types of turning points (two at the task level en two at the interpersonal level) were remarkable due to their occurrence in all three project teams
MENTORING: A REVIEW OF EARLY CAREER RESEARCHER STUDIES
Full text linkThis paper reviews 23 journal articles on ‘mentoring’ in the context of Early Career Researchers, defined as those in academia with less than 10 years of experience from the start of their PhD. Achieving a better understanding of mentoring is important since within the higher education context new dynamics have created expectations towards more supportive mechamisms for ECRs.In order to better understand the benefits of mentoring for ECRs careers and psychosocial well-being, it is important to understand (1) the core definitions of mentoring used in research, (2) the research methodologies that are applied to research mentoring, (3) the empirical evidence showing the value of mentoring and (4) the remaining gaps for which future research will be needed. Results of the review lead to the following conclusions: there is much research to do, first, to better inform our conceptualization of ECR mentoring and, second, to better understand the value of ECR mentoring support. A research agenda is outlined
Dynamics of Team Reflexivity after Feedback
Full text linkA great deal of work has been generated on feedback in teams and has shown that giving performance feedback to teams is not sufficient to improve performance. To achieve the potential of feedback, it its stated that teams need to proactively process this feedback and thus collectively evaluate their performance and strategies, look for alternatives, and make clear decisions about ways to tackle their task. This concept of team reflexivity has been commonly described as a sequence of behaviours, which relative importance has not been demonstrated. Further, empirical research investigating the dynamic aspects of reflexivity has been scarce. This study sought to explore how reflexivity evolves over time and at which moments of the team interaction it is related to team performance. Thirty-two student dyads participated to a cognitively complex task (flight simulation) over four performance episodes comprising action phases followed by transition (feedback) phases. High interdependence between participants (pilots and co-pilots) was ensured through the distribution of complementary knowledge in the dyads. The results showed that teams seldom engaged in full cycles of reflective behaviours. When looking into individual behaviours, teams exhibited more reflective behaviours during action over time, while their reflective behaviours during feedback did not change, demonstrating a suboptimal feedback processing as time goes by. Additionally, it was demonstrated that teams were capable to learn from their past and act upon feedback to better subsequent team performance but also that initial performance acts as a trigger to future reflective behaviours
Effects of Hierarchical Levels on Social Network Structures within Communities of Learning
Full text linkFacilitating an interpersonal knowledge transfer among employees constitutes a key building block in setting up organizational training initiatives. With practitioners and researchers looking for innovative training methods, online Communities of Learning (CoL) have been promoted as a promising methodology to foster this kind of transfer. However, past research has only provided limited data from actual organizations and largely neglected characteristics that constitute a major obstacle to such collaborative processes, namely participants’ hierarchical levels. The current study addresses these shortcomings by providing empirical evidence from 25 CoL of an online training program, provided for 249 staff members of a global organization. Using social network analysis, we are able to show significant differences in participants’ network behaviour and position based on their hierarchical rank. This translates into higher in- and out-degree network ties, as well as centrality scores among participants from higher up the hierarchical ladder. Finally, based on a longitudinal analysis of all indicated network measures, our results indicate that the main trend develops predominately during the first half of the training program. By incorporating these insights into the implementation of future CoL, it is not only possible to anticipate participants’ behaviour. Our findings also allow to draw conclusions about how collaborative activities within CoL should be designed and facilitated, in order to provide participants with a valuable learning experience
Advances in the Use of Neuroscience Methods in Research on Learning and Instruction
Full text linkCognitive neuroscience offers a series of tools and methodologies that allow researchers in the field of learning and instruction to complement and extend the knowledge they have accumulated through decades of behavioral research. The appropriateness of these methods depends on the research question at hand. Cognitive neuroscience methods allow researchers to investigate specific cognitive processes in a very detailed way, a goal in some but not all fields of the learning sciences. This value added will be illustrated in three ways, with examples in field of mathematics learning. Firstly, cognitive neuroscience methods allow one to understand learning at the biological level. Secondly, these methods can help to measure processes that are difficult to access by means of behavioral techniques. Finally, and more indirectly, neuroimaging data can be used as an input for research on learning and instruction. I will end this contribution by highlighting the challenges of applying neuroscience methods to research on learning and instruction
Perspectives on Learning: Methodologies for Exploring Learning Processes and Outcomes
Full text linkThe papers in this Special Issue were initially prepared for an EARLI 2013 Symposium that was designed to examine methodologies in use by researchers from two sister communities, Learning and Instruction and Learning Sciences. The four papers reflect a common ground in advances in conceptions of learning since the early days of the “cognitive revolution” in the 1960s. This commentary shows the interdependence between advances in theory and advances in methodologies. Four shifts in conceptions of learning are described. That these shifts are evident in the work of both communities suggests a blurring of the boundaries between the two
Advances in temporal analysis in learning and instruction
Full text linkThis paper focuses on a trend to analyse temporal characteristics of constructs important to learning and instruction. Different researchers have indicated that we should pay more attention to time in our research to enhance explanatory power en increase validity. Constructs formerly viewed as personal traits, such as self-regulated learning and motivation, are now conceptualized as a series of events that unfold over time. This raises new questions with regard to the temporal characteristics of these constructs and their dynamic interplay with learner and context characteristics. Even though the value of analyzing temporal characteristics slowly becomes evident a number of challenges need to be tackled in order to make progress in de field of learning and instruction. First, we need to be aware of the paradigm shift that temporal analysis entails. Second, a common understanding of different dimensions of time and the position of temporal characteristics therein can facilitate our time related research dialogue. Third, a better understanding how to answer time related questions with appropriate methodological approaches needs to emerge. Fourth, researching temporal characteristics entails segmenting time which procedures are needed for. Fifth, temporal data are mostly collected at the micro level, whereas most theory is defined at a macro level; consequently we need to bridge these differences in the granularity used between collecting, coding and theorizing to enhance meaning making. Finally, so far most examples of time related research are explorative or comparative studies, the next step is to move toward confirmative studies which constitutes the “Holy Grail” of temporal analysis
A Systemic view of the learning and differentiation of scientific concepts:The case of electric current and voltage revisited
Full text linkIn learning conceptual knowledge in physics, a common problem is the incompleteness of a learning process, where students’ personal, often undifferentiated concepts take on more scientific and differentiated form. With regard to such concept learning and differentiation, this study proposes a systemic view in which concepts are considered as complex, dynamically evolving structures. The dynamics of the concept learning and differentiation is driven by the competition of model utility in explaining the evidence. Based on the systemic view, we introduce computational model, which represents the essential features of the conceptual system in the form of directed graph (DGM), where concepts are nodes connected to other conceptual elements (nodes) in the graph. The results of a DGM are then compared to the empirical findings to identify differentiation between concepts of electric current and voltage based on a re-analysis of previously published empirical findings on upped secondary school students’ learning paths in the context of DC circuits. The comparison shows that the model predicts and explains many relevant, empirically observed features of the learning paths of concept learning and differentiation, such as: 1) Context-dependent dynamics, 2) the persistence of ontological shift and concept differentiation, and 3) the effects of communication on individual learning paths. The systemic view and the DGM model based on it make these generic features of interest in concept learning and differentiation understandable and show that these features are associated with the guidance of theoretical knowledge. Finally, we discuss briefly the implications of the results on teaching and instruction.