The community junior college and the second reconstruction in higher education

CAUGHT IN THE WEB OF WORDS: JAMES MURRAY AND THE OXFORD ENGLISH DICTIONARY by K. M. Elisabeth Murray. New Haven: Yale University Press, 1977. xiii + 386 pp. $15.00.

THE WORLD OF LEARNING 1978–79. 29th Edition. Published by Europa Publications Ltd., 1978. Available in America from Gale Research Company, Detroit, Michigan. 2,038 pages in two volumes. $84.00/set.     

Keep DRAGging ON: Is solving more problems in DragonBox 12+ associated with higher mathematical performance during the COVID-19 pandemic?

Prior research has shown that game-based learning tools, such as DragonBox 12+, support algebraic understanding and that students' in-game progress positively predicts their later performance. Using data from 253 seventh-graders (12–13 years old) who played DragonBox as a part of technology intervention, we examined (a) the relations between students' progress within DragonBox and their algebraic knowledge and general mathematics achievement, (b) the moderating effects of students' prior performance on these relations and (c) the potential factors associated with students' in-game progress. Among students with higher prior algebraic knowledge, higher in-game progress was related to higher algebraic knowledge after the intervention. Higher in-game progress was also associated with higher end-of-year mathematics achievement, and this association was stronger among students with lower prior mathematics achievement. Students' demographic characteristics, prior knowledge and prior achievement did not significantly predict in-game progress beyond the number of intervention sessions students completed. These findings advance research on how, for whom and in what contexts game-based interventions, such as DragonBox, support mathematical learning and have implications for practice using game-based technologies to supplement instruction.

Practitioner notes

What is already known about this topic

• DragonBox 12+ may support students' understanding of algebra but the findings are mixed.
• Students who solve more problems within math games tend to show higher performance after gameplay.
• Students' engagement with mathematics is often related to their prior math performance.

What this paper adds

• For students with higher prior algebraic knowledge, solving more problems in DragonBox 12+ is related to higher algebraic performance after gameplay.
• Students who make more in-game progress also have higher mathematics achievement, especially for students with lower prior achievement.
• Students who spend more time playing DragonBox 12+ make more in-game progress; their demographic, prior knowledge and prior achievement are not related to in-game progress.

Implications for practice and/or policy

• DragonBox 12+ can be beneficial as a supplement to algebra instruction for students with some understanding of algebra.
• DragonBox 12+ can engage students with mathematics across achievement levels.
• Dedicating time and encouraging students to play DragonBox 12+ may help them make more in-game progress, and in turn, support math learning.

     

Effects of implementation support on children's reading outcomes following an online early reading programme: A cluster-randomised controlled trial

Well-designed computer or app-based instruction has a number of potential benefits (eg increasing accessibility and feasibility of high-quality instruction, reducing time and resources required for training expert delivery, saving instructional time). However, variation in implementation can still affect outcomes when using educational technology. Research generally suggests that without follow-up support after training, implementation of educational interventions is often poor and outcomes reduced. However, the extent to which this is the case when the core element of an intervention is computer or app-delivered is not yet clear. This study investigated the effects of providing ongoing implementation support for Headsprout Early Reading (HER, an early reading programme accessible via a computer or an app), to determine whether such support leads to better outcomes. Twenty-two primary schools (269 learners) participated in a cluster-randomised controlled trial. Eleven schools received initial training followed by ongoing support across the school year, whereas the other 11 schools received initial training and technical support only. Pre- and post-measures of reading skills were conducted using the York Assessment of Reading for Comprehension. We found no effect of implementation support on outcomes, and no effect of implementation support on delivery of the core element of HER. However, there were some effects of implementation support on the implementation of other HER elements relating to the responsiveness of educators to learners' learning within HER. These findings have implications for providing access to high quality online instruction in early reading skills at scale, with minimal training. More broadly, the current study suggests that well-designed computer or app-based instruction can yield positive outcomes with minimal implementation support and training. However, further research is required to ensure the interplay between learners' app-based learning and teacher intervention functions as intended to provide additional support for those who need it.

Practitioner notes

What is already known about this topic

• Well-designed computer or app-based instruction has a number of potential benefits (eg increasing accessibility and feasibility of high-quality instruction, reducing time and resources required for training expert delivery, saving instructional time).
• Implementation can still affect outcomes when using educational technology, and without follow-up support after training, implementation of educational interventions is often poor and outcomes reduced.
• The extent to which this is the case when the core element of an intervention is computer or app-delivered is not yet clear.

What this paper adds

• We found that providing implementation support for teachers and teaching assistants delivering Headsprout Early Reading (HER; an early reading programme accessible via a computer or an app) did not affect the reading outcomes of learners.
• We also found the implementation support did not affect delivery of the core, app-delivered element of the programme.
• However, there were notable differences in implementation of other aspects of the programme, particularly in relation to the role of the teacher or educational practitioner in managing the interplay between the app-based learning and teacher intervention for learners who require further support.

Implications for practice and policy

• These findings have implications for providing access to high quality instruction in early reading skills at scale, with minimal training.
• More broadly, the current study suggests that well-designed computer or app-based instruction can yield positive outcomes with minimal implementation support and training.
• However, the findings of this study identify some potential risk of an over-reliance on technology to facilitate the learning of all learners accessing the programme.
• Further research is required to ensure the interplay between learners' app-based learning and teacher intervention functions as intended to provide additional support for those who need it.

     

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.