Learning from Writing in College Biology

This study used both quantitative and qualitative analyses to examine the influence of written arguments on learning in a college level introductory biology class and the types of metacognition employed by students while writing. Comparison of a treatment and control group indicates that the writing assignments used had minimal impact on overall content learning as measured by conventional exams. Subsequent interviews and think-aloud protocols with a subset of students indicated that writing arguments had the potential to foster learning through forward and backward search strategies. However, few of the students took advantage of this opportunity to use metacognitive skills. This study suggests that preparing written arguments is not sufficient, by itself, to have a reliable effect on student learning and is consistent with the view that students must be explicitly taught when and how to use different metacognitive strategies.     

Growth and diversity in doctoral education: assessing the Australian experience

The major growth of doctoral education in recent decades has attracted attention from policy makers and researchers. In this article we explore the growth of doctoral education in Australia, its impact on diversity in respect of the doctoral population, shifts in disciplinary strengths, institutional concentration and award programs. We conclude that there has been both change and continuity in the provision of doctoral education with extensive variation at the level of practice in what is a reasonably stable system featuring continuing hierarchical institutional diversification. The limitations of available data and issues for further research, policy and practice are discussed.

The Effect of a State Department of Education Teacher Mentor Initiative on Science Achievement

This study investigated the effectiveness of a southern state??s department of education program to improve science achievement through embedded professional development of science teachers in the lowest performing schools. The Science Mentor Program provided content and inquiry-based coaching by teacher leaders to science teachers in their own classrooms. The study analyzed the mean scale scores for the science portion of the state??s high school graduation test for the years 2004 through 2007 to determine whether schools receiving the intervention scored significantly higher than comparison schools receiving no intervention. The results showed that all schools achieved significant improvement of scale scores between 2004 and 2007, but there were no significant performance differences between intervention and comparison schools, nor were there any significant differences between various subgroups in intervention and comparison schools. However, one subgroup, economically disadvantaged (ED) students, from high-level intervention schools closed the achievement gap with ED students from no-intervention schools across the period of the study. The study provides important information to guide future research on and design of large-scale professional development programs to foster inquiry-based science.     

3D printing as an educational technology: theoretical perspectives,learning outcomes,and recommendations for practice

Education and Information Technologies - 3D printing is an emerging educational technology that is said to prepare learners for a more technologically designed world. In this review, 3D printing...     

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.