Contrasting Social and Cognitive Accounts on Overimitation: The Role of Causal Transparency and Prior Experiences

Three experiments (N = 100) examine the influence of causal information on overimitation. In Experiment 1, a transparent reward location reveals that the reward is unaffected by nonfunctional actions. When 5‐year‐olds observe an inefficient and subsequently an efficient strategy to retrieve a reward, they show overimitation in both phases—even though the reward is visible. In Experiment 2, children observe first the efficient then the inefficient strategy. The latter is always demonstrated communicatively, whereas the efficient strategy is presented communicatively (2a) or noncommunicatively (2b). Regardless of whether the efficient strategy is emphasized through communication or not, most children do not switch from the efficient to the inefficient strategy. Depending on the situation, children base their behavior on social motivations or causal information.     

How elementary school students' motivation is connected to self-regulation

Empirical research reveals that students face difficulties engaging in learning and achieving their goals in a variety of learning contexts. To study effectively, students need to regulate their learning process. In spite of increased understanding of cognitive aspects of self-regulation, motivational aspects of regulation have not yet been thoroughly probed. This study investigates how motivation is connected to self-regulated learning when elementary school students (N = 32) study science in real classroom contexts using gStudy software. This was done by: (1) identifying students' situated motivation during the learning process, (2) analyzing how the students with different motivational approaches activated cognitive self-regulation in authentic learning situations across multiple learning episodes, and (3) complementing the understanding with the students' individual accounts of their motivation regulation during the learning process. The results show that there are qualitative differences in the self-regulation tactics used by the high- and low-motivation students as they study. Motivation is linked closely to active self-regulation.     

The Role of Family Characteristics for Students’ Academic Outcomes: A Person‐Centered Approach

Using data from 1,571 ninth‐grade students (M_age = 14.62) from 82 academic track schools in Germany and their predominantly Caucasian middle‐class parents, configurations of different family characteristics reported by parents were investigated. Latent profile analyses considering academic involvement, family interest, parents’ self‐concept, child's need for support, and parents’ time and energy identified average, indifferent, motivated and engaged, motivated and disengaged, and involved families. Cross‐sectional and longitudinal associations with students’ motivational (self‐concept, effort, and interest) and achievement outcomes (achievement test and grades) in math were analyzed. Students from families classified as motivated and disengaged showed higher initial levels motivation and achievement. Over 5 months, these students also showed an increase in self‐concept and higher achievement than students from other family types.     

Scientific Perspectivism in Secondary-School Chemistry Education

Science & Education - The importance of learning chemical ways of thinking is widely recognized. Various frameworks have been developed to address the essence of chemistry and chemical...     

话语标记研究综述

话语标记语是近年来国内外学术界研究的热点问题。文章简要回顾了话语标记研究的形成与发展的历程,并就话语标记的研究现状作了介绍与归纳,同时着重介绍了目前学术界研究的一些热点问题。此外,文章还介绍了话语标记研究这一交叉领域的最近动向。     

Teachers’ collaborative learning – development of teaching in group discussions

Teacher learning and the development of in-service education for teachers are both an active area of research and a broad challenge. In working as teachers, theory and practice are often seen as divided, or problems in teaching and the requirements of development are solved on the basis of experience. This study focuses on collaborative teacher learning as part of an in-service education course that supports teachers in connecting learning theories with practical knowledge and that aims to create new knowledge and practices for teaching. The starting points for the study are the changes of the curriculum and theories of learning. The aim is to analyse developing of teaching in teachers’ collaborative group discussions and how the theoretical knowledge of learning theories is connected with developing teaching practices. The research method of the study was data driven, systematic and qualitative analysis of the content of the discussion. The research data consist of video recorded group discussions of two teacher teams from the in-service education course meeting. The analysis focused on developing talk and further, on finding interconnections between the developing talk and the theories of learning. The analysis shows three different ways how the learning theories emerged in method developing talk. The findings indicate that theoretical knowledge can be used or left unused and imply that creating and changing teaching practices is challenging. However, the findings show that theoretical, research-based knowledge on learning theories can be applied to developing teaching practices in the teachers’ collaborative knowledge creation process. Long-term in-service education based on collaborative knowledge creation and supporting the interconnection of theoretical knowledge of learning and experience knowledge of teaching is a promising way to arrange and develop in-service education.     

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.

Practitioner notes

What is already known about this topic

• Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
• It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
• Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.

What this paper adds

• Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
• Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
• Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
• Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.

Implications for practice and/or policy

• Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
• This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.