IndexIntroductionCollective Learning and a Social Learning ToolLILAC vs LILAC, MVHR, and User ExperienceEvidence for Architectural InfluenceKey Findings and Notable CommonalitiesIntroductionI think It is important to note that my original research topic aimed at the direct link between individual social learning, sustainability and affordability within a co-housing community. Through my research, I have found that social learning can be facilitated on a much larger scale than individual learning, rather than collective and collaborative learning. Stevenson and colleagues argue that if social and physical redundancy is created within a built environment, occupants will have the potential ability to utilize that redundancy through co-production and collective learning. Narozny and colleagues believe that the framework for collective learning cannot be successful without understanding the barriers and opportunities of the learning process itself. Ergan and colleagues, through a crowdsourcing experiment, find that architectural features can have positive and negative influences on users, thus influencing the impact of a learning experience. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The purpose of this article is to develop further research into the connection between human behavior, especially collective social learning, and architectural design. The scope includes reviewing a quantitative study on the influence of architectural features, the need for physical and social redundancy, and the need for a social learning tool to be used alongside current building evaluation investigations, such as the Building Performance Evaluation (BPE ) ) and post-employment evaluation (POE). Before continuing, I would like to define some key terms regarding social learning that will help to better understand this article and define learning theories in the context of the literature reviewed. Social Learning Theory (SLT), created by Albert Bandura (1977) assumes that people learn from each other through observation, imitation, and modeling. These three influencers must have the necessary conditions to be effective. These conditions are attention, retention, reproduction and motivation. Collective learning involves the ability to share information between people efficiently and safely so that it can be transmitted over time (Oxford Reference). Collaborative learning involves two or more people learning something as a group. Typically, those involved in collaborative learning tend to benefit from each other's skills and knowledge. The following is a brief overview of the literature reviewed indicating the premise, methodology and findings. The first section that follows will be a combination and comparison of two articles, Stevenson and Narozny, as they share the same case study and both focus on the goal of collective learning. Subsequently, a review of the effects of architecture on human experience and quantitative meaning will be carried out. Assumptions and limitations will be addressed sequentially. Collective learning and social learning tool Stevenson et al (2016) used LILAC (Low Impact Low Living Affordable Community) as a primary example of how physical and social redundancy within a built environment can address the problemof perennial climate change, an unstable economy and a reduction in the UK's energy sources. The predominantly qualitative research was conducted using questionnaires, participant observation, field study, guided visits and evaluations. Themes of physical and social redundancy were prevalent in the study. On the physical side, LILAC provided emergency heating and water supply, self-contained emergency accommodation and alternative non-electric ventilation systems. This physically constructed redundancy exists due to the collaboration of its occupants' feedback.Stevenson et al (2016) found that social redundancy refers to having "collective access to accumulated experiences". This means that tenants were not only able to use their personal skills to help produce solutions, but were also able to learn from each other, resulting in a community melting pot of knowledge that will continue to benefit them in the future . Narozny et al (2014) use LILAC as an example of the potential for collective learning opportunities through the initial use of a social learning tool as an extension of BPE and POE. Using both quantitative and qualitative methods, such as user satisfaction surveys and usability surveys, explores the conceptual framework of collective learning in connection with the need for a social learning tool to improve user experience and understanding of systems and their controls within a community. Themes include the role of motivation adjacent to the learning process and decision making that is essential for the existence of collective learning at home. Narozny (2014) conducted a partial application experiment, focusing on the MVHR system and the usability of its controls based on user experience, to see whether applying an SL tool as an extension of BPE would be beneficial for its users. Using a case study methodology, Narozny (2014) found that occupants had a consistent lack of understanding of how to use the system due to the ineffectiveness of LILAC's delivery procedures, resulting in lower quality environments. The SL tool, which specifically focuses on user skills and understanding, monitoring of MVHR system-specific meter readings, decision-making procedures and home environment mapping, highlighted barriers in the LILAC system, but also enabled improvement opportunities such as making users aware of the different uses of each system in order to prevent long-term bad habits based on incorrect assumptions about how a home system is used. LILAC vs LILAC, MVHR and user experience Both authors discussed the initial Home User Guidance (HUG) and collective learning application in relation to Mechanical Ventilation and Heat Recovery Units (MVHR). The LILAC handover process involves verbal guidance on systems throughout the community, including the MVHR, and is conducted by the subcontractor on a select group of occupants. This information is then passed on to community members; ideally during the move, or at least within the first 8 months of employment. Stevenson (2016) applauded this method of collective learning. Learning through observation and passing down from generation to generation. This is the ideal definition of collective learning. He (2016) further stated that, due to “comfort issues,” the community rejected the subcontractor's advice that the MVHR should be on at all times to prevent construction-related mold growthin wood and straw of the houses. Narozny (2014), argues that the reason for shutting down the system was not due to “comfort issues,” but rather a lack of understanding of how to use the system. It was just easier to turn it off. According to his study, through his usability survey he found that those who did not feel competent in using the system found open ways to solve their problem, such as turning off the system. This goes hand in hand with his research that the current delivery method used is not working well but has the potential to succeed through the adaptation of a social learning tool. Over the course of 15 months, a research team conducted a field study that included observation of these HUG demonstrations. Through this, the main problems were identified: lack of practical experience, concentration difficulties due to the environment or the character of the person doing the demonstration and lack of experience on the part of the guide which could lead to misconceptions. Stevenson said the occupants took collaborative learning a step further when they each shared information with others about how they used their windows as ventilation systems in place of the MVHR. With this information, 17 configurations were shared and learned. He also said the “comfort issues” caused by the MVHR encouraged the community to take action. This involved recommissioning the MVHR system, removing false ceilings, opening dampers in ventilation units and instructing all occupants to use the fan above the stove when cooking. Narozny (2014) believed that if occupants had known how to use the system through the SL Tool in the first place, they would not have had to make any of these changes. Stevenson et al's (2016) study included data collected from the entire community while Narozny (2014) focused on only six homes (out of 20). This limits its ability to understand the rest of the occupant's skills in relation to the user experience. However, Stevenson's assumptions included that all users had equal or higher level skills and were able to easily understand and reproduce home demonstrations. Both authors encountered a similar limitation: lack of time. LILAC occupants must have a collective desire to learn and communicate their needs to others. LILAC provides a task management team to solve the time problem. The advantage of this is that a problem is solved quickly and the resolution is documented, thus providing the next generation with the same solution to the same problem. This is a good example of collective learning. However, the asset management team focuses on current individual cases rather than future planning, studying how occupants consistently use their home, which could lead to future improvements in both the built environment and collective social learning . Through the SL tool, we enable users to better understand home use, thus creating a user with higher quality skills. Once the quality of a group is controlled, then we can begin to achieve the level of collective and collaborative social learning that both Stevenson and Narozny want to create. Another aspect that can be controlled is the human experience within the built environment. Stevenson (2016) briefly discusses some of LILAC's architectural features, such as an open kitchen and easily accessible central pond and garden, which supported social and physical redundancy. Semiha..