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Teacher noticing is a critical aspect of teaching competence that has garnered significant scholarly attention. This systematic review aims to comprehensively analyze the integration of teacher noticing into lesson study, exploring how different stages of lesson study influence the development of teacher noticing.

This article systematically reviews 15 empirical studies on teacher noticing within the context of lesson study, focusing on research design, subjects, methods, theoretical frameworks and the specific impact of different stages of lesson study on teacher noticing.

The review reveals a geographical concentration of studies in Western countries, particularly in the United States, with a relative scarcity of research in East Asian contexts. Most studies focus on pre-service teachers, employ qualitative methods and are grounded in Van Es’s (2011) Learning to Notice framework. The findings indicate that different stages of lesson study significantly influence teacher noticing, particularly in the planning, teaching and reflection stages, where shifts in focus and depth of noticing are evident.

This paper explores how various lesson study stages impact teacher noticing development. It offers future research directions and calls for more cross-cultural studies. Certain activities within classroom research may restrict attention development, particularly when these activities fail to encourage in-depth cognitive analysis across all stages sufficiently. Future research should explore how to avoid these limitations in the classroom research process and design more effective strategies to support deep observation and analysis by teachers at each stage. In the reflection stage of classroom research, certain factors may restrict the focus on student thinking.

By synthesizing the existing research into a comprehensive narrative, we provide an essential foundation for future studies on teacher noticing within lesson study contexts. This work not only charts the historical development of the field but also encourages more profound and actionable research engagement with the nuanced processes of teacher observation and reflection during lesson studies.

This paper explores how various lesson study stages impact teacher noticing development. It offers future research directions and calls for more cross-cultural studies and a combination of quantitative and qualitative methods to fully understand the effects of lesson studies on teacher noticing.

Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.

In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.

In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.

The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.

Housing energy consumption is a significant contributor to climate change. Encouraging the adoption of energy-efficient products can be an effective way to reduce energy consumption. The impacts of social influences, such as peer effects and social norms, on energy efficiency adoptions were identified; however, these social influences are not quantified and compared with each other or with other influences. This study aims to investigate the choice of energy-efficient product adoption with different costs and how different social influences affect the choice through different processes and paths.

Two stated choice experiments were employed in Wuhan, China, to examine the impact of social influences on energy-efficient product adoption in low-cost and high-cost scenarios. Appliance packages (including fridges and washing machines) and heating and cooling systems were used for the two cost scenarios, respectively. The social influences are evaluated in three aspects: positive versus negative information, physical versus online social networks and peer effects versus social norms.

The study revealed how various factors, including social influences, impact energy-efficient product choices. Research results show that: (1) social influences have greater and wider impacts in the low-cost scenario than in the high-cost scenario; (2) negative information decreases the adoption of low-cost energy-efficient products, while positive information boosts high-cost energy-efficient product adoption and (3) people value the information provided by those they know personally and are more influenced by physical social networks.

This study contributes to a better understanding of social influence in energy-efficient product adoption with different costs. This study provides a comprehensive framework to investigate social influences comparing the impact of different processes, paths and types of information. The findings can also provide practical implications for policymakers to accelerate the energy transition in the built environment.