Frontline Learning Research (E-Journal - EARLI, European Association for Research on Learning)
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256 research outputs found
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The Future of Learning by Searching the Web: Mobile, Social, and Multimodal
Full text linkRecent technological developments related to the World Wide Web including mobile computing, social media, and online videos are shaping the way we learn. As argued in the present commentary, the majority of educational psychological research that has examined how individuals learn by searching the Web, however, has not kept up with this pace. Therefore, the goal of this commentary is to discuss how recent technological developments might affect how learners acquire knowledge through Web search and to provide a respective research agenda. Specifically, we will focus on the use of mobile devices and digital assistants, social networking sites, and online videos, and the opportunities and challenges they present to learners. In addition, we suggest that future research should study the ongoing learning processes during Web search in greater detail. We believe that examining the research questions raised in the present commentary will uniquely contribute to the literature on Web-based searching and learning.
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Application of mathematical and machine learning techniques to analyse eye-tracking data enabling better understanding of children’s visual-cognitive behaviours
Full text linkIn this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements explored using machine learning approaches. Two different techniques of machine-learning were used (Bayesian and K-Means) to obtain separate model sequences or average scan-paths for those children who responded either correctly and incorrectly to the graph task. Application of these machine-learning approaches indicated distinct differences in the resulting scan-paths for children who completed the graph task correctly or incorrectly: children who responded correctly accessed information that was mostly categorised as critical, whereas children responding incorrectly did not. There was also evidence that the children who were correct accessed the graph information in a different, more logical order, compared to the children who were incorrect. The visual behaviours aligned with different aspects of graph comprehension, such as initial understanding and orienting to the graph, and later interpretation and use of relevant information on the graph. The findings are discussed in terms of the implications for early mathematics teaching and learning, particularly in the development of graph comprehension, as well as the application of machine learning techniques to investigations of other visual-cognitive behaviours
Editorial The journey to proficiency: Exploring new objective methodologies to capture the process of learning and professional development
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Teaching the problem-solving process in a progressive or in a simultaneous way: a question of making sense?
Full text linkOver the past two decades, the perennial low success rates of elementary students in math problem-solving and the difficulties experienced by teachers in helping their students with this type of task has become quite a hot topic. In response, several instructional interventions aiming to develop an expert and reflexive approach to problem-solving have been designed. However, these interventions are based on two contrasting learning approaches, either teaching the components of the problem-solving process at the same time or teaching them one at the time. The two approaches have never been compared. Moreover, they have mainly been assessed in terms of cognitive outcomes. Yet, recent studies stress the importance of analyzing the cognitive, motivational and emotional processes involved in problem-solving learning together in order to gain a full understanding of the process. Addressing these limitations is essential to enhance our understanding of problem-solving learning and to design more effective interventions. This paper focuses on this issue by investigating whether it is preferable as regards cognitive, motivational and emotional outcomes, to teach the problem-solving process in all its complexity or one component at a time. This issue is handled both for novice and expert solvers. Data were gathered among 267 upper elementary students. Findings showed that both learning approaches support the short- and long-term acquisition of cognitive problem-solving strategies, regardless of the student’s profile. However, beneficial emotional and motivational outcomes occur only when the problem-solving process is taught in all its complexity, i.e., makes sense for the learner. Novice solvers made less use of the help seeking strategy and persisted more
Promoting deep learning through online feedback in SPOCs
Full text linkHigher education aims for deep learning and increasingly uses a specific form of online education: Small Private Online Courses (SPOCs). To overcome challenges that instructors face in order to promote deep learning through that format, the use of feedback may have significant potential. We interviewed eleven instructors and four students and organized a focus group to formulate scalable design propositions for instructors in SPOCs to promote deep learning. Propositions have been formulated according to the CIMO-logic.
This study resulted in identification of four mechanisms by which the desired outcome (deep learning) can be achieved, which we describe here along with proposed interventions.
Results show that the “online learning interaction model” can be deepened with these mechanisms: 1) Feeling personally committed, 2) Asking and providing relevant feedback, 3) Probing back and forth, and 4) Understanding one’s own learning process. To activate these mechanisms, scalable feedback interventions are described in three categories. Results at this relatively young field of SPOCs also show that feedback as a dialogical process may contribute to solving the current challenges of instructors in SPOCs to achieve deep learning with their students
Using sensor technology to capture the structure and content of team interactions in medical emergency teams during stressful moments
Full text linkIn healthcare, action teams are carrying out complex medical procedures in intense and unpredictable situations to save lives. Previous research has shown that efficient communication, high-quality coordination, and coping with stress are particularly essential for high performance. However, precisely and objectively capturing these team interactions during stressful moments remains a challenge. In this study, we used a multimodal design to capture the structure and content of team interactions of medical teams at moments of high arousal during a simulated crisis situation. Sociometric badges were used to measure the structure of team interactions, including speaking time, overlapping speech and conversational imbalance. Video coding was used to reveal the content of the team interactions. Furthermore, the Empatica E4 was used to unobtrusively measure the team leader’s skin conductance to identify moments of high arousal. In total, 21 four-person teamsof technical medicine students in the Netherlands were monitored in a simulation environment while they diagnosed and managed a patient with cardiac arrest. Outcomes of this exploratory study revealed that more effective teams showed greater conversational imbalance than less effective teams, but during moments of high arousal the opposite was found. Also, a number of differences were found for the content of team interaction. Combining sensor technology with traditional measures can enhance our understanding of the complex interaction processes underlying effective team performance, but technological advances together with more knowledge about the simultaneous application of these methods are needed to tap into the full potential of wearable sensor technology in team research
Capturing motivation and emotion regulation during a learning process
Full text linkThis paper describes our research approach in which we have focused on situational and contextual variations in motivation and emotion regulation to better understand its role, appearance and function in collaborative learning situations. We have used research designs that employ process-oriented measures combined with subjective interpretations to capture motivation and emotion regulation. Analysing on-line process data poses several challenges such as variation in the granularity of different data sources, problems that emerge due to the complexity of contextual and situational factors in ecologically-valid learning situations or, currently, challenges in the use of multiple data channels and their analyses.
In this paper, we present three claims underlying our research, particularly the motivationand emotions and their regulation in learning. The claims are as follows: (1) motivation and emotion regulation is situation and context specific, (2) motivation and emotion regulation is influenced by multi-layered nature of motivationand(3) Motivation and emotion regulation is intertwined with other processes of learning and can be captured from their temporal manifestation. We present an example from our empirical study to discuss how these claims have led us to employ multiple process-oriented methods that include both subjective and objective data sources, including different combinations of situation-specific self-reports, video and physiological data. We then describe opportunities and challenges involved in the empirical studies
‘A double-edged sword. This is powerful but it could be used destructively’: Perspectives of early career researchers on learning analytics
No full textLearning analytics has been increasingly outlined as a powerful tool for measuring, analysing, and predicting learning experiences and behaviours. The rising use of learning analytics means that many educational researchers now require new ranges of technical analytical skills to contribute to an increasingly data-heavy field. However, it has been argued that educational data scientists are a ‘scarce breed’ (Buckingham Shum et al., 2013) and that more resources are needed to support the next generation of early career education researchers. At the same time, little is known about how early career researchers feel towards learning analytics and whether it is important to their current and future research practices. Using a thematic analysis of a participatory learning analytics workshop discussions with 25 early career researchers, we outline in this article their ambitions, challenges and anxieties towards learning analytics. In doing so, we have provided a roadmap for how the learning analytics field might progress and practical implications for supporting early career researchers’ development
Aligning with complexity: system-theoretical principles for research on differentiated instruction
Full text linkMuch scholarly research was dedicated over the last years to address the difficult task of responding adequately to student differences. Differentiated instruction is a teaching philosophy and practice that deals with this ambitious target. The aim of this paper is to reflect on how system theory methodologically challenges research on differentiated instruction. Based on these insights which are only recently applied in educational sciences it is documented how current research on differentiated instruction does not yet mirror the full complexity of the concept of differentiated instruction. Three challenges for research on differentiated instruction are presented: to focus on the interplay between micro- an meso-level interaction; to acknowledge for external influences in research design; and, to use patterns of non-linear causality. Three design principles for research on differentiated instruction are presented to cope with these challenges: organic design, interactionality and reflectivity. By using these principles we believe research on differentiated instruction would be more aligned with the theoretical foundations of the concept
Fixation-related EEG frequency band power analysis: A promising methodology for studying instructional design effects of multimedia learning material
Full text linkDuring the last decade the combined recording of eye-tracking data and electroencephalographic (EEG) data has led to the methodology of fixation-related potentials analysis (FRP). This methodology has been increasingly and successfully used to study EEG correlates in the time domain (i.e., event-related potentials, ERPs) of cognitive processing in free viewing situations like text reading or natural scene perception. Basically, fixation-onset serves as time-locking event for epoching and analysing the EEG data. Here, we propose a methodology of fixation-related frequency band power analysis (FRBP) to study cognitive load and affective variations in learners during free viewing situations of multimedia learning materials (i.e., combinations of textual and pictorial elements). The EEG alpha frequency band power at parietal electrodes may serve as a valid measure of cognitive load, whereas the frontal alpha asymmetry may serve as a measure of affective variations. We will briefly introduce and motivate the measures and the methodology, and discuss methodological challenges. The methodology is frontline for learning research, first, as to date the EEG has been seldom used to study design effects of multimedia learning materials and second, as fixation-related EEG data analysis has rarely been done focussing on the frequency domain (i.e., FRBP). Despite methodological challenges still to be solved, FRBP may provide a more in-depth picture of cognitive processing during multimedia learning compared to eye-tracking data or EEG data in isolation and thus may help clarifying effects of multimedia design decisions