The Role of Relational and Instructional Classroom Supports in the Language Development of At-Risk Preschoolers

The present study examined the extent to which preschool classroom supports—relational support (RS) and instructional support (IS)—are associated with children's language development and whether these associations vary as a function of children's language ability. The language skills of 360 children within 95 classrooms were assessed using an expressive narrative task in the fall and spring of the preschool year, teachers rated RS in the fall, and observations of IS were collected across the year. Research Findings: Hierarchical linear models revealed main effects of IS, but not RS, on preschoolers’ development of expressive language skills. In addition, the associations between RS and IS on children's expressive language development were moderated by children's fall language ability. Specifically, the association between IS and language development was stronger for children with stronger expressive language skills, and the association between RS and language development was stronger for children with weaker expressive language skills. Practice or Policy: These findings suggest that professional development for preschool teachers might focus on aligning classroom supports with the needs of children with weaker language skills who are at risk for difficulty acquiring literacy.     

Helping Students Make Sense of Graphs: An Experimental Trial of SmartGraphs Software

Graphs are commonly used in science, mathematics, and social sciences to convey important concepts; yet students at all ages demonstrate difficulties interpreting graphs. This paper reports on an experimental study of free, Web-based software called SmartGraphs that is specifically designed to help students overcome their misconceptions regarding graphs. SmartGraphs allows students to interact with graphs and provides hints and scaffolding to help students, if they need help. SmartGraphs activities can be authored to be useful in teaching and learning a variety of topics that use graphs (such as slope, velocity, half-life, and global warming). A 2-year experimental study in physical science classrooms was conducted with dozens of teachers and thousands of students. In the first year, teachers were randomly assigned to experimental or control conditions. Data show that students of teachers who use SmartGraphs as a supplement to normal instruction make greater gains understanding graphs than control students studying the same content using the same textbooks, but without SmartGraphs. Additionally, teachers believe that the SmartGraphs activities help students meet learning goals in the physical science course, and a great majority reported they would use the activities with students again. In the second year of the study, several specific variations of SmartGraphs were researched to help determine what makes SmartGraphs effective.     

The Learning Transfer Big Picture

Do not forget learning transfer while crafting your learning and development initiatives. A research‐driven model allows your organization to adapt quickly and make real‐time adjustments to your learning strategy.     

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.