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OpenWHO is the World Health Organization's online learning platform that was launched in 2017. The COVID-19 pandemic led to massive growth in the number of courses, enrolments and reach of the platform. The platform is built on a stable and scalable basis that can host a large volume of learners. The authors aim to identify key factors that led to this growth.

In this research paper, the authors examined OpenWHO metadata, end-of-course surveys and internal processes using a quantitative approach.

OpenWHO metadata showed that the platform has hosted over 190 health courses in 65 languages and over seven million course enrolments. Since the onset of the pandemic, there have been more women, older people and people from middle income countries accessing courses than before. Following data analysis of the platform metadata and course production process, it was found that several key factors contributed to the growth of the platform. First, OpenWHO has a standardised course production pathway that ensures efficiency, consistency and quality. Further, providing courses in different languages increased its reach to a variety of populations throughout the world. For this, multi-language translation is achieved through a network of translators and an automated system to ensure the efficient translation of learning products. Lastly, it was found that access was promoted for learners with disabilities by optimising accessibility in course production. Data analysis of learner feedback surveys for selected courses showed that the courses were well received in that learners found it useful to complete courses that were self-paced and flexible. In addition, results indicated that preferred learning methods included videos, downloadable documents, slides, quizzes and learning exercises.

Lessons learnt from the WHO's learning response will help prepare researchers for the next health emergency to ensure timely, equitable access to quality health knowledge for everyone. Findings of this study will provide valuable insights for educators, policymakers and researchers in the field who intend to use online learning to optimise knowledge acquisition and performance.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.