Explore the vast range of titles available.

Although the theoretical framework of cognitive load theory has acknowledged a role for the learning environment, the specific characteristics of the physical learning environment that could affect cognitive load have never been considered, neither theoretically nor empirically. In this article, we argue that the physical learning environment, and more specifically its effects on cognitive load, can be regarded as a determinant of the effectiveness of instruction. We present an updated version of the cognitive load model of Paas and Van Merriënboer (Educational Psychology Review, 6:351-371, 1994a), in which the physical learning environment is considered a distinct causal factor that can interact with learner characteristics, learning-task characteristics, or a combination of both. Previous research into effects of the physical learning environment on cognitive performance that could inspire new cognitive load research is discussed, and a future research agenda is sketched.

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.

In order to construct a shared body of knowledge, research involving the relationship between the psychosocial learning environment (PSLE) and the physical learning environment (PLE) needs a commonly-accepted conceptual framework. By means of a thematic literature review, we collected the main aspects of the PSLE and PLE, their definitions and their relations as identified by earlier research. These findings led to a conceptual framework that structures the dimension of the PSLE into the sub-dimensions of personal development, relationships, and system maintenance and change, and the dimension of the PLE into the sub-dimensions of naturalness, individualisation, and stimulation. For each of these sub-dimensions, the framework distinguishes an intended, implemented and attained representation. A conceptual PSLE-PLE Relationship (PPR) model enables relations to be visualised. The review confirms that PSLE and PLE are interrelated in interactions between different sub-dimensions and their representations. However, evidence regarding these relationships is still weak because of the limited number of studies and their methodological limitations.

Research into the relationship between innovative physical learning environments (PLEs) and innovative psychosocial learning environments (PSLEs) indicates that it must be understood as a network of relationships between multiple psychosocial and physical aspects. Actors shape this network by attaching meanings to these aspects and their relationships in a continuous process of gaining and exchanging experiences. This study used a psychosocial-physical, relational approach for exploring teachers’ and students’ experiences with six innovative PLEs in a higher educational institute, with the application of a psychosocial-physical relationship (PPR) framework. This framework, which brings together the multitude of PLE and PSLE aspects, was used to map and analyse teachers’ and students’ experiences that were gathered in focus group interviews. The PPR framework proved useful in analysing the results and comparing them with previous research. Previously-identified relationships were confirmed, clarified, and nuanced. The results underline the importance of the attunement of system aspects to pedagogical and spatial changes, and of a psychosocial-physical relational approach in designing and implementing new learning environments, including the involvement of actors in the discourse within and between the different system levels. Interventions can be less invasive, resistance to processes could be reduced, and innovative PLEs could be used more effectively.

This article critically reviews the methodologies and methods that have been used for the evaluation of physical learning environments. To contextualise discussion about the evaluation of learning spaces, it initially charts the development of post-occupancy evaluation (POE) for non-domestic buildings. It then discusses the recent evolution of POE into the broader evaluative framework of building performance evaluation. Subsequently, a selection of approaches used to evaluate higher education and school learning environments are compared and critically analysed in view of contemporary approaches to teaching and learning. Gaps in these evaluative approaches are identified and an argument is put forward for the evaluation of physical learning environments from a more rigorous pedagogical perspective. [Author abstract, ed]

The study employed a qualitative multiple case study approach to investigate second-grade students’ perceptions of their classrooms’ physical learning environment. Data were collected through interviews, participant-generated photographs, and observations. Participants in the study were 16 students in four classrooms in three school districts. A physical learning environment tool, Assessing the Pillars of the Physical Environment for Academic Learning (APPEAL), developed by Evanshen and Faulk and published in 2019, was used to select classrooms to participate in the study. According to the scale, the top-scoring classrooms were more learner-centered (more constructivist) than the lowest-scoring (more traditional) classrooms. Generally, participants believed that classroom physical learning environments that were best for them were meaningful, offered easy access to resources and materials, and provided active learning and social engagement opportunities. Both physical and emotional comfort were important to participants. There were more similarities than differences between the participants’ perceptions in the classrooms that scored highest on the APPEAL and the classes that scored lowest. The findings suggested that young children’s perceptions of the environment can be influenced by their experiences or contexts and their differences. The results encourage teachers of young children to think about their students as actively affected by their environment and challenge them to design classroom physical learning environments that support the diverse needs of students within these spaces.

This article brings a novel perspective to the relationship between the physical dimensions of Home Learning Environments (HLE) and young people's learning motivations during COVID-19 pandemic in UK. The architectural/physical focus of this investigation helps orient the reader to the literature/expertise I draw on. Based on 28 young people (16-18 years-old), the article evidences that comparing the HLE with the School Learning Environment (SLE), participants recognised the value of peer pressure and social learning environment to enhance learning motivations. Most participants found little use in home-schooling and wanted to return to in-person teaching. Students who adjusted the physical dimensions of the HLE were more motivated, especially if they had a private, semi-dedicated, or dedicated HLE. The article ends by exploring how the home-schooling experience during the COVID-19 pandemic offers an opportunity to re-imagine HLE as a complementary learning environment to SLE, motivate young people to learn and support independent learning activities.

Most undergraduates in Mainland China receive distance education from home because of the COVID-19 outbreak. The unique physical environment of distance learning may have effects not only on student learning but also on student physical and mental health. This study reviewed related studies of the physical learning environment and its influence on learners' physical and mental health. Using factor analysis, the authors developed a questionnaire to measure students' perceptions towards their physical learning environment and their physical and mental health and verified the reliability. Regression analysis was used to explore the influence of physical environment perception on physical health and mental health perception, as well as the predictive ability of these three factors on learning satisfaction. The results of this study provide researchers and educators with insight into learners' perceptions of the physical learning environment and their physical and mental health under COVID-19 and offer suggestions for improving distance-learning satisfaction.

Purpose>This paper aims to illustrate the importance of the quality of Online Learning Physical Environment (OLPE) and Online Learning Self-efficacy (OLSE) in predicting academic performance in online learning, which was the primary mode of teaching during the outbreak of COVID-19 in Hong Kong. Policy recommendations were made based on the findings from a psychological perspective.Design/methodology/approach>Responses from 104 Hong Kong undergraduate students were collected through a questionnaire survey. Data were analysed using multiple linear regression, simple linear regression, and Pearson correlation.Findings>Despite the fact that OLSE showed no significant direct effect on academic performance in online learning, OLSE was positively correlated with and predictive of OLPE, while OLPE was positively correlated with and predictive of online learning performance. The findings indicated that undergraduate students from low-income families tended to have less superior academic performance, which was associated with poorer OLPE and OLSE.Originality/value>The findings suggested that in order to alleviate learning inequality in online learning, policy makers may allocate funding to enhance OLPE and OLSE of undergraduate students from low-income families.

The influence of learning space on users has been broadly accepted and tested. However, the literature has focused on single factor research, instead of holistic approaches. Additionally, lower educational levels have been the focus of interest, while higher education is moving towards multi-method teaching. This paper focuses on how learning spaces for different purposes (practice and lecture rooms) may influence academic performance from a holistic approach of learning physical environment perception. For this, the iPEP scale (Indoor physical environment perception) is used and validated through Cronbach Alpha and Exploratory Factorial Analysis. Then, multiple linear regression is conducted. The results indicate that iPEP measures near to 63 percent of the construct, which is structured in six factors. Moreover, linear regression analyses support previous literature concerning the influence of learning physical environment on academic performance (R2 = 0.154). The differences obtained between practice and lecture room in terms of predictor variables bring to the light the need to diagnose learning environments before designing changes in educational buildings. This research provides a self-reported way to measure indoor environments, as well as evidence concerning the modern university, which desires to combine several teaching methods.