*3.2. Regression Analyses*

Higher inequality was significantly associated with lower zero-dose prevalence and higher DTP3 coverage in unadjusted and adjusted fractional logistic regression analyses (Table 3). In subnational regions with higher gender inequality, zero-dose prevalence odds were 1.7 times higher (AOR = 1.74, 95% CI: 1.38–2.19) compared to subnational regions with lower inequality. Consistently, the odds of DTP3 coverage were 39% lower (AOR = 0.61, 95% CI: 0.51–0.75) in regions with higher gender inequality relative to regions with lower inequality.

**Table 3.** Odds ratios for zero-dose DTP prevalence and DTP3 immunization coverage by SGDI category (702 subnational regions across 57 countries, 2010–2019).

\*\*\* *p* < 0.001.

We also estimated the average marginal effects of SGDI to indicate the average percentage point change in the outcome variable (zero-dose DTP or DTP3 coverage) by higher versus lower gender inequality (See Figure 4). A subnational region with higher inequality (favoring men) is expected to have 5.8 percentage points higher prevalence of zero-dose DTP relative to a region with lower inequality, increasing from 10.6% (95% CI 9.5–11.7%) for regions with lower inequality to 16.4% (95% CI 14.5–18.4%) for regions with higher inequality. A subnational region with higher gender inequality is expected to have 8.2 percentage points lower coverage of DTP3 immunization than a region with lower gender inequality, dropping from 79.2% (95% CI 77.7–80.7%) for regions with lower inequality to 71.0% (95% CI 68.3–73.7%) for regions with higher inequality.

**Figure 4.** Adjusted \* expected proportions of zero-dose DTP and DTP3 immunization coverage by SGDI category, 702 subnational regions across 57 countries, 2010–2019. Estimated proportions are adjusted for annual population growth and age structure (measured as the percentage of the population under 15 years of age), percentage of urban population, and the three individual dimensional indices of the SHDI (health index, education index, and income index).


**1. Introduction**

The Global Higher Education community is nowadays facing new educational challenges due to the Coronavirus pandemic. There is an opportunity for this community to implement a new strategy at the university level [1]. Migrating from traditional or blended learning to a fully virtual and online deliverable strategy is, therefore, crucial to ensure quality education. This transition occurs gradually. Linked to this issue are several questions related to the lack of "home office" infrastructure; skillsets needed for professional design, and online/virtual education options.

Since World Health Organization declared a Coronavirus (COVID-19) pandemic in January 2020, new challenges appeared in the Higher Education ecosystem.

Top ten most affected countries, reported on March 2020, were: China, Italy, the United States, Spain, Germany, Iran, France, South Korea, Switzerland, and

the United Kingdom [2]. That context raised the opportunity in on-line and virtual education, to meliorate e-learning, and infrastructures.

The knowledge and skills required can empower workers for future challenges of new jobs that are appearing along with technological advances. Nowadays, it is important to create solutions, simultaneously at operational, services, and technological levels at Higher Education Institutions (HEIs) that can help students develop those competencies.

To date, in e-learning, there is not sufficient research that investigates intelligent technological artifacts designed to enhance student attention through the monitoring process.

Additionally, there is a lack of research in technological integration frameworks that propose design strategies for those artifacts that includes sensitive aspects in education, such as emotions or attention.

E-Learning allows academic institutions to deliver the learning content electronically, both in mobile and online environments. This content might commonly be delivered through Learning Management Systems (LMS). One might say that the tendency of LMS is to become complex when compared with the earlier versions. Nowadays, LMS deals with complex representations of the relationship between resources, teachers, and students, and these systems have become much more customized. These LMS platforms can be implemented by a Service-Oriented Architectures (SOA), a conceptualization that supports the development of web applications. Specifically, in SOA the service provider manages services designed and its implementation. Services are published in the registry, and then will be available for service requests, find the service specifications and the correspondent service provider.

A branch of approaches to e-learning systems focuses on the ability to sense a situation, interface, as well as interact and communicate effectively with the environment. These smart-systems can incorporate sensors and actuators, interacting with other systems, and be incorporated into platforms. It is important to notice that those technologies might be intelligently and methodologically and introduced them into the learning context effectively.

In digital environments, it is important to monitor and manage student emotions and attention. In this work, the term "attention" might be seen as an integration of different aspects or perspectives of attention, aggregating cleverly these aspects. Thus, focused attention sustained attention, selective attention, alternating attention, and divided attention are considered different types of attention and can be monitored depending on the task to be performed. Attention, thus, might be managed in the educational virtual settings, which in this study is done with the support of NeuroIS, a relatively recent branch of information system which allows one to establish a close and fast correspondence between the variables of a problem specification and those of the solution space [3]. Behind that correspondence are the devices that allow one to monitor student attention which is well framed and delineated in the NeuroIS approach. These devices can be used in more complex systems.

Attention-aware systems manage attention using sensory mechanisms, both detecting student focus and making predictions, which allows one to offer customized learning. Several ways have been used for attention detection in the e-learning field, for instance, eye tracking, video, electroencephalogram webcam, electrocardiogram. These mechanisms, for instance, webcam or electroencephalogram, can have an accuracy rate of up to 90%.

The hereby-presented research work encloses the following research question: How to enhance student's attention in an e-learning environment?

Concerning the proposed research question, aforementioned, the authors argue by the hypothesis that if it is possible to sense student's attention based on bio-signals, the e-learning environment can be adapted for each student profile. *Improvement of Student Attention Monitoring Supported by Precision Sensing in Learning… DOI: http://dx.doi.org/10.5772/intechopen.98764*

The definition of an attention-aware system under the paradigm of IoT could be an available solution.