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This article’s purpose is to develop The Model for Smart, Self-learning and Adaptive Resilience Building (SARB).

Products and patents of methods and systems analysis was carried out in the fields of BIM application, Smart, Self-learning and Adaptive Resilience Building. Based on other researchers’ findings, The SARB Model was proposed.

Analysis of the literature showed that traditional decisions on the informational modelling do not satisfy all the needs of smart building technologies owing to their static nature. The SARB Model was developed to take care of its efficiency from the brief stage to the end of its service life.

The SARB Model was developed to take care of its efficiency from the brief stage to the end of its service life. The SARB Model does have some limitations: (1) the processes followed require the collection of much unstructured and semi-structured data from many sources, along with their analyses to support stakeholders in decision-making; (2) stakeholders need to be aware of the broader context of decision-making and (3) the proposal is process-oriented, which can be a disadvantage during the model’s implementation.

Two directions can be identified for the practical implications of the SARB Model. The initial expectation is the widespread installation of SARB Model within real estate and construction organisations. Furthermore, development of the SARB Model will be used to implement the ERASMUS+ project, “Advancing Skill Creation to ENhance Transformation—ASCENT” Project No. 561712-EPP-1-2015-UK-EPPKA2-CBHE-JP.

The practical implications of this paper are valuable.

This paper seeks to ascertain the effectiveness of a two-week-long faculty development programme (FDP), organized by Indira Gandhi National Open University (IGNOU), for teachers of the open and distance learning (ODL) system, with the aim of upgrading their knowledge and skills for developing print self-learning materials (SLMs). The specific aim of this study is to ascertain whether the objectives of the FDP have been achieved, to examine the effectiveness of the training programme and to suggest measures for improvement in future FDPs regarding the design and development of print SLMs.

The study was conducted using a descriptive survey research method, through semi-structured questionnaires and adopted purposive sampling. The first survey was conducted immediately after the completion of the programme, in order to collect feedback from the trainees, while the second survey was conducted after six months of the FDP, using the follow-up approach, so that more reliable and authentic results could be obtained.

The outcomes of the study revealed that the training had been effective in imparting appropriate knowledge and skills to the trainees, with respect to designing print SLMs. However, certain gaps were also identified and have been reported in this paper. Key suggestions have been made to address the shortcomings and improve forthcoming FDPs.

The present research focused on a specific training programme regarding the design and development of SLMs. Therefore, only the teachers and academics who participated in this specialized training activity were considered for the collection of feedback.

The recommendations of this work may be useful for the trainers, FDP coordinators, training institutions and ODL policymakers for planning and designing effective staff development activities for teachers and academics working in ODL institutions. These would be especially useful in the design of FDPs, aimed at orienting teachers who are involved in curriculum planning, design and development of learning resources.

This study is an original research based on the empirical primary data obtained by the researchers. As the largest open university in the world, IGNOU has been playing a key role in staff development for and in ODL in developing countries. This evaluative study of a specialized FDP in the area of SLM design and development is a significant work that may be valuable for planning the staff development strategies and building a training evaluation mechanism.

With the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.

Based on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.

Analyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.

Scholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.

The framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.

This article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.

In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

The paper reports the feedback collected from students of the Master of Social Work (MSW) Programme of the School of Social Work (SOSW), Indira Gandhi National Open University (IGNOU), through open and distance learning (ODL), in India. The paper discusses findings related to learner profile, student support services, assignments, academic counselling, fieldwork, audio/video/teleconferencing facilities, Internet access and challenges faced by the learners. The findings will be useful for researchers and practitioners, will help in improving the overall quality of the programme, in designing the delivery mechanism as per the needs of MSW learners and in preparing them to be trained professionals to work in social development sector in India.

For data collection, a questionnaire was prepared and sent to all the students of the MSW programme along with assignment, across India. Responses from 290 students were voluntarily received.

The research findings are that MSW (ODL) students are older, mostly married with the average male learners age being 35 years and that of female learners being 30 years, there are more female learners than male learners, majority of the learners are Hindu from general category, tend to be employed, mostly full-time and some part-time, with work experience. They are from urban, semi-urban, rural and tribal areas with Internet access. Most of the students preferred to read printed self-learning materials than digitally available on eGyanKosh or IGNOUmobile app especially in rural areas though with increasing access to Internet, students are gradually opting for online materials while filling up the admission form. Majority of students found the quality and standard of study materials to be very good. Though maximum respondents gave positive feedback about the student support services and their learning experiences, some of the learners faced challenges like unco-operative staff members, administrative delays, non-allotment of academic counsellor/fieldwork supervisor, irregularity, late reception of study materials, lack of staff members at study centre, far distance of regional centre/study centre from residence, etc.

The findings will help in designing and delivering the MSW programme in a more effective way. Based on the feedback received, the next revision of the programme will take into consideration the concerns of the learner. The limitation of the study is that not all learners responded to all the questions. Not all potential MSW learners filled the questionnaire and submitted it at the school. And those who responded had left some questions unanswered. Those who did not submit response may differ in their responses from what is received.

It is an original work and will be valuable in understanding the distance learner of MSW programme in India, programme delivery and challenges.

In the undergraduate physics laboratory, a student is expected to make precise measurements, hone investigative skills and discover the interplay between experimentation and fundamental principles underlying physical phenomena. But in India, the mainstream conventional UG physics laboratory instruction has all along been cookbook in nature. In such a scenario, incorporating innovative and meaningful laboratory experience in Open and Distance Education programmes becomes a formidable challenge. Recently we evaluated the physics laboratory courses, which are integral to the B.Sc (Physics) programme of IGNOU offered at a distance, for their quality, relevance and effectiveness. Our findings reveal that these courses are being received well particularly by those students who are pursuing the B.Sc (Major) in Physics. The success rates improve as learners evolve in the system and their satisfaction levels are high. However, factor analysis of learners' perceptions brings forth six factors guided approach, student-centered learning and assessment, emphasis on self-learning, use of multimedia and innovative non-conventional teaching strategies, increased student participation and emphasis on problem solving as key determinants for further improving the quality of learning in physics laboratory.

This research aims to understand how satisfied students were with their online learning experience and how actively engaged they were in their studies.

To study the breadth and depth of students' experiences and fully address the research aims, the researchers utilized a mixed method. Through a survey questionnaire with both closed-ended and open-ended questions, student responses were gathered. On a five-point Likert scale, the closed-ended questions were co-constructed. The research participants included students attending Pakistan's private university in Karachi. Participants from all programs were the authors' focus (i.e. undergraduate and graduate). A total of 552 students completed the survey questionnaire.

The study reveals students' level of satisfaction with their online encounters. Contextual restrictions, such as power outages, bad internet, a lack of a private place and administrative problems, make it difficult to access and connect during learning sessions. Additionally, the student's perception of online learning as being more secure and safe in terms of their physical safety was an intriguing finding. They also see the integration of online and in-person learning as advantageous because it might reduce travel expenses and time while also giving them access to independent study resources. Given its many benefits, this research supports the use of online learning in higher education. Online education promotes a healthy mix of teacher- and student-centered instruction.

Moreover, the findings indicate that effective non-verbal communication occurs when students interact with a teacher and colleague face-to-face. In a face-to-face teaching situation, good body language may inspire, engage and motivate students. Better learning outcomes result from being able to interpret people's body language, whether it be eye contact or posture, and alter the topic and approach. Keeping in view the recent nature of the coronavirus disease 2019 (COVID-19) pandemic, no research has been carried out on this topic to date or on such a wide-scale transition to online learning, specifically in the context of higher education in Pakistan (Dincer, 2018). This research is unique in its kind as it focuses on the impact of online learning on the affective domain as well.

Given the contextual concerns, teachers must find alternative educational insights that will enable students to reduce listening demands, improve self-learning and promote engagement.

Therefore, in the authors' context, it is a unique finding that students felt socially isolated. While numerous studies have examined anxiety, still there is still a dearth of literature regarding stress factors (Dincer, 2018). The current study provided substantial information on the impact of online learning on students' stress levels, and the consequence is that they were strained out because they felt socially isolated. Additionally, these findings are in alignment with the qualitative data showing a problem of student isolation and a lack of engagement. Since the face-to-face mode had provided them with the time and space, they were able to engage in educational socialization outside the boundaries of the classroom, such as casual conversations during breaks, gatherings at coffee shops and working on group projects at the library.

This research was conducted in the context of Pakistan.

Existing literature on algorithmic management practices – defined as autonomous data-driven decision making in people's management by adoption of self-learning algorithms and artificial intelligence – suggests complex relationships with employees' well-being in the workplace. While the use of algorithms can have positive impacts on people-related decisions, they may also adversely influence job autonomy, perceived justice and – as a result – workplace well-being. Literature review revealed a significant gap in empirical research on the nature and direction of these relationships. Therefore the purpose of this paper is to analyse how algorithmic management practices directly influence workplace well-being, as well as investigating its relationships with job autonomy and total rewards practices.

Conceptual model of relationships between algorithmic management practices, job autonomy, total rewards and workplace well-being has been formulated on the basis of literature review. Proposed model has been empirically verified through confirmatory analysis by means of structural equation modelling (SEM CFA) on a sample of 21,869 European organisations, using data collected by Eurofound and Cedefop in 2019, with the focus of investigating the direct and indirect influence of algorithmic management practices on workplace well-being.

This research confirmed a moderate, direct impact of application of algorithmic management practices on workplace well-being. More importantly the authors found out that this approach has an indirect influence, through negative impact on job autonomy and total rewards practices. The authors observed significant variation in the level of influence depending on the size of the organisation, with the decreasing impacts of algorithmic management on well-being and job autonomy for larger entities.

While the influence of algorithmic management on various workplace practices and effects is now widely discussed, the empirical evidence – especially for traditional work contexts, not only gig economy – is highly limited. The study fills this gap and suggests that algorithmic management – understood as an automated decision-making vehicle – might not always lead to better, well-being focused, people management in organisations. Academic studies and practical applications need to account for possible negative consequences of algorithmic management for the workplace well-being, by better reflecting complex nature of relationships between these variables.

The open and distance learning system implemented in Universitas Terbuka (UT) requires students to do self-learning, individually or in groups. Therefore, the students of UT must be able to motivate themselves to self-study and use their time effectively. The use of integrated learning media as one of the learning resources can support them in their learning activities. The use of internet, as a part of e-Learningbased Learning Preparation, is one way for students to perform learning activities. In the study group, students can use the internet services provided in mobile telecenters and local government offices. The implementation result of this model in Kepulauan Seribu, a group of islets located in the Jakarta Bay, in which the sample were taken from students living in Pramuka Islet, Harapan Islet, Tidung Islet, Panggang Islet, Untung Jawa Islet, and Pari Islet, showed that e-Learning-based Learning Preparation Model can motivate distance students to actively study in their groups. Due to geographical constraints of those islets, it is difficult to meet their peers in groups frequently. Using the regularly scheduled study groups and the computers connected to the internet, students can easily access the information and minimize the frequency of transportation for face-to-face meetings. About 70% of the students have accessed the learning resources through internet and get experiences and knowledge during eight months period of the study in 2006.