Microsoft office standard 2019 russian olp a gov free. Download Microsoft Office 2019 Volume License Pack from Official Microsoft Download Center

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Can\’t recall your password? Click here to set up a new one. In this article we will explain what is the difference between Volume and OEM licensing and standzrd businesses sstandard choose Volume products instead of OEM. Volume products provide quick and easy licensing for a large number of devices or users. The final combined standarr, e. This means 209 after the first micdosoft the software can be sold further without the conditions mentioned in the previous paragraphs: OEM software can be sold without hardware and Volume software ryssian licensing agreements.

Beware of sellers microsoft office standard 2019 russian olp a gov free offer Volume product keys without any documentation — legal software sellers provide a software copy key included with all of its accompanying documentation! OEM software products can present a lot of obstacles for companies due to their specifics as explained previouslyand within the EU free market they are often at a similar price with Volume products.

Take a look at the following example comparison between Office Professional Plus and Office Home and Business to see how much more beneficial it is to get a Volume product over an OEM product:. As seen above, it is highly recommended for businesses to always go for the Volume products when licensing their Microsoft software, especially those with a greater number of devices and users — it is the most economic, efficient and worry-free solution.

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MS Windows. Windows Windows 8. Windows 7. MS Office. Office Suites. Office Apps. Microsoft miceosoft MS Cloud Services. Server Software. MS Windows Server. MS Exchange Server. MS SharePoint Server. MS Project Server. MS Skype for Business Server. Server CALs. Developer Tools. Microsoft Visual Studio.

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Autodesk Gob Design Suites. Autodesk Plant Design Suites. Rusisan 10 Windows 8. Purpose of Volume and OEM licensing Volume products provide quick and easy licensing for a large number of devices or users. Why choose Volume over OEM?

201 is an end-user right to revert software to an older version, offered by Microsoft for products purchased via their Volume distribution channel. For example, if you have Office Professional Plus, you can downgrade it to the previous version and make use of the Office Professional Plus instead but not at the same time! OEM-based products have some downgrade rights but they are quite limited.

Volume products can be quickly and easily activated online. Transfer of the software from one device to another requires just uninstalling it from the old one, installing it on the new one and entering the activation key — no additional activation procedures needed, thanks to volume keys Russiann or dedicated services KMS.

OEM products cannot be moved as easily — the OEM key must first be deactivated from the current device through a series of commands fere Command Prompt. Then the software must be uninstalled from the old device and installed on the new one, and afterwards activated via a somewhat lengthy phone activation procedure. It can require microsoft office standard 2019 russian olp a gov free IT skills to have it properly installed and activated.

Volume products can be deployed and activated in bulk, quickly and easily — an essential feature for medium and big businesses with many devices. OEM products have to be ocfice manually, one device after the other, which slows down deployment tremendously. Volume products are also the kffice efficient option for companies with an extensive IT infrastructure with many departments and plenty of users and devices.

Volume software can be easily managed through the VAMT tool — a free management tool from Microsoft that allows tracking, activation and deactivation of Volume products. OEM software is harder to track, as it rarely comes with extensive documentation of its origins.

Back to the top. Back to shop. To shopping cart. Full downgrade rights Downgrading is an end-user right to revert software to an older version, offered microsoft office standard 2019 russian olp a gov free Microsoft for products purchased via their Volume distribution channel.

Limited downgrade rights OEM-based products have some downgrade rights but they are quite limited. Quick installation, online activation, easy transfer Volume products can be quickly and microsoft office standard 2019 russian olp a gov free activated online. Standard installation, phone activation, slow transfer OEM products cannot be moved as easily — the Microsoft office standard 2019 russian olp a gov free key must first be deactivated from the current device through a series of commands in Command Prompt.

Перейти activation Volume products can be deployed нажмите для продолжения activated in bulk, quickly and easily — an essential feature for medium and big businesses with many devices. No batch activation OEM products have to be activated manually, one device after the other, which slows down deployment tremendously.

Centralized management Volume products are also the most standzrd option for companies with an extensive IT infrastructure with many departments and plenty of users and devices. Basic documentation OEM software standarv harder to track, as it rarely comes with extensive documentation of its origins. Click on each rissian in each column for more details.

Office Professional По ссылке Volume. Suitable for. RDS support. MS account. End of support.

 
 

 

Microsoft office standard 2019 russian olp a gov free

 

If you are installing multiple Microsoft products, make sure you install the same version for all products. Your file name must either be displaying as \”configuration. In this step, you will be using command functions to tell the computer to download the installation files onto your system. It is used to give your computer instructions to perform a task. In this case, we will be using Windows Command Prompt to do two things:. This command can take around 10 minutes to finish running. It will seem as though nothing is happening, but files are being downloaded to your ODT folder.

We\’re sorry, we had a problem installing your Office program s [ You will know the command has finished running when the Command Prompt window looks like this:. Once the previous command has finished running you must copy and paste, the command below:. Once you agree, a display window will appear, showing the progress of your installation. When the installation is complete, the display will disappear. You can then close the command prompt.

The image below shows the right and wrong keys. Your most recent key is the one with the biggest licence ID number. If your license does not include Software Assurance, you will need to get your product key from your Microsoft Admin Center.

Click on that product and get your MAK product key. Do not use the key called \”Setup Key\”. Once you have copied the correct key, open one of your new Microsoft applications.

We opened Microsoft Excel. Activate your software. To make them easy to access, click on your Start Menu, and go down your list of programs to locate the Microsoft Office Apps. When you come across the ones you wish to pin, right click on the app, and then click Pin to Start.

Office Software Productivity. These applications include: Office Standard or Office Professional Plus or Access or Project Standard or Project Professional or Visio Standard or Visio Professional or Before you begin Here are the things you need before you get started: An internet connection.

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Recent changes:- Fixes for bug appearing in some combinations of languages, custom themes, and skins in Windows 10 versions and older- Fixes for menu default icons. As such, the topics address the theme of classroom teaching and learning associated with a range of delivery modes, including ICT-based and non-ICT-based. At the national level Topics at the national level focused on the nature of resources provided to schools and students before, and during the period of disruption, as well as any associated policy expectations or requirements relating to the use of resources.

At the school level At the school level, topics focused on the provision of digital infrastructure resources and support for staff and students before, and during the period of disruption, changes in time allocations for teachers to complete aspects of their work, and additional support for students with special needs, and their teachers.

The practical aspects of classroom teaching included the mode of teaching e. Assessment of student learning and provision of feedback to students This theme is most closely related to the research question addressing the impacts of the COVID pandemic on teaching and learning, and how these were mitigated by measures at the school level, although the use of assessment information to support planning is also relevant to the two research questions addressing the impact of the pandemic on staff and students and the support for students to return to regular schooling.

Under research theme 4, assessment information is assumed to be relevant for a broad range of purposes within and across national contexts. For example, assessment information may be used by teachers to inform their teaching, provided to students to support their learning, or used by teachers, schools and systems to better understand and monitor student learning outcomes.

The establishment of assessment of student learning and provision of feedback to students as a research theme includes all these possible uses of assessment information. At the national level Topics at the national level focused on the policies and practices relating to mandated assessments across learning areas, and any changes in these policies and practices associated with the disruption.

At the school level Of interest in REDS was how the role of assessment was maintained and perceived during the period of disruption. In addition, there was interest in whether schools changed the nature or emphasis of assessment during the period of disruption and what expectations there were of teachers to provide feedback to students with reference to a variety of methods, including those necessitated by remote teaching and learning.

At the teacher level At the teacher level, topics addressed assessment and providing feedback to students, both during the disruption, and as a comparison, before the disruption. Topics associated with the provision of feedback to students included the method of providing feedback, the breadth of feedback, the amount of feedback, and the frequency with which feedback is provided.

Teacher professional support The change of teaching and learning across schools brought about by the COVID disruption necessitated rapid changes in teaching practices by many teachers across countries. As a consequence, a research theme in REDS was associated with the nature of professional support needed by and made available to teachers to help them adapt to the new ways of working.

This research theme is most closely related to the two research questions relating to the impact of the pandemic on teaching and learning and on staff and students, however, it also is relevant to the research question associated with persisting challenges and implications for the future. At the national level At the national level, topics focused on system-level direction or guidance about teaching and learning practices during the COVID disruption provided to schools and teachers, and whether specific policies or plans were developed or already existed regarding professional development associated with teachers use of ICT in their teaching.

At the teacher level Teachers were the focus of the theme relating to teacher professional support. This research theme related in particular to the two research questions associated with the impact of the disruption on teaching and learning and on staff and students.

At the national level At the national level, topics focused on the provision of any support or resources that could be used by students and their families at home to assist students working remotely i.

Well-being At the forefront of discussions on the impact of the COVID disruption on schools was, and continues to be, the impact of the changed conditions in schools on the physical, social, and emotional well-being of school staff, students, and their families. There are aspects of the changed conditions associated with well-being that are common across members of school communities, but also some that are specific to the different levels of respondent in REDS.

Data collected under the well-being research theme is intended to capture an overarching picture of the factors associated with individual well-being, but also what was being done within schools and school systems to support the well-being of school staff, students, and their families.

This theme relates directly to the research question addressing the impacts of the COVID pandemic on school staff and students, and how these were mitigated by measures within countries. At the national level Topics of focus at the national level related to the existence of centralized policy and resource support measures associated with well-being. At the teacher level Topics of interest at the teacher level focused on the impact of changed working conditions for teachers on their well-being.

Under this theme, the potential impact of the experience of the disruption on future schooling are considered from two perspectives: i Changes that happened during the disruption that respondents perceived to be positive and may contribute to improvements in regular schooling in the future; and ii Changes that may result in school systems and school communities being better prepared should similar disruptions occur in the future.

At the national level At the time REDS was developed, the focus of questions at the national level was on the immediate centralized response and support provided during the period of disruption. The emphasis of the research theme associated with persisting changes was on the actions taking place within schools to support the transition to regular schooling, and the perceptions of respondents within schools to what was being done.

As a result, the theme of persisting changes following the disruption was not addressed at the national level in REDS. Waxmann Verlag GmbH. Springer International Publishing. RAND Corporation. Assessment: Getting to the essence. Follow-up costs of not learning: What we can learn from research on coronavirus- related school closures.

Participating countries administered questionnaires to national research coordinators, school principals, teachers, and students between December to July with some countries opting out of the teacher or student questionnaire option, see Section 3. All school samples were selected centrally at IEA. Implementing the sampling plan was the responsibility of the national research coordinator NRC in each participating country see Section 3. To ensure standardization, IEA provided comprehensive guidelines and trainings in English and French on survey operations procedures.

It was imperative that the procedures were both feasible, given the constraints, yet also able to fulfill IEA quality requirements. A major deviation from the regular practice of implementing large-scale assessments, was that no field trial and no translation verification were conducted. Constraints on comparability were carefully considered and discussed with stakeholders, experts, and participating countries.

The constraints and limitations are highlighted throughout Chapter 4 of this report. Usually, it takes several years to develop and implement a study of such scale. However, to accommodate the urgency to provide reliable data on the educational disruption, the period between the initiation of REDS and the writing of this report was set to one year.

Implementing REDS in such a compressed timeframe was possible only by extensively streamlining measures and procedures and accepting a few shortcuts regarding the survey design, which are detailed later in this chapter.

Similarly, some countries struggled to implement the survey according to the IEA standards, partly because of timing and partly because their education systems were under high stress due to the pandemic. In this chapter, we describe the methods and procedures implemented on the collection of the REDS data while taking into consideration the extraordinary circumstances of the survey. The potential constraints on validity and comparability are highlighted in their appropriate context.

This process was facilitated through virtual meetings and rapid parallel feedback rounds on instrument drafts. The survey instruments include the concept of a reference period see Chapter 2 for a detailed definition of this period.

This is a common anchor across all questionnaires. Respondents were asked, for many questions,2 to provide responses about their experience within the reference period and then to compare this experience to regular schooling. This approach was established as a way of asking questions about the time of disruption that is entirely inclusive of all the different forms of educational disruptions across countries.

Because teachers may have been teaching multiple subjects, classes, and grades during the COVID disruption, each teacher was asked to focus their answers on a target class.

Target classes were defined as the subject that they taught most in the target grade during the COVID disruption. Not all countries covered all three populations: India and Uruguay did not survey students, and Rwanda focused exclusively on schools. Hence, grade 8 students reflected on a situation they experienced in their seventh grade, whenever questions referred to the reference period.

Schools The school target population comprised those schools where students of the above-described target population could be found. School principals responded to a questionnaire focusing on school-level responses on the educational disruption caused by the COVID pandemic. In most countries, the selection probability of schools was proportional to the number of target grade students, aiming for self-weighted samples of students Meinck, India and the Russian Federation required additional sampling stages regional units 5.

While REDS aimed for full coverage of the target populations, countries could decide to exclude specific types of schools or students from the survey see Table 3. Stratification was used to improve the efficiency of the samples and to facilitate analyses by certain groups of schools. Commonly used stratification variables were urbanization, type of funding, and region.

The variables used for stratification are shown in Appendix A1, Table A1. The minimum school sample size was set to schools per country. Using the WinW3S software certified and provided by IEA, within each participating school, 20 students and 20 teachers were randomly sampled from eligible individuals.

In cases where there were fewer eligible students or teachers, all were selected. Denmark and Slovenia used a different within-school sampling approach for their students: they randomly selected a grade 8 class and within the selected class all students were asked to participate. Student data were collected in eight countries, teacher data in ten countries and school data in all eleven countries Table 3. Therefore, students in grade 7 during survey administration had already been in grade 7 during the reference period.

Data meeting the expectations6 were weighted to account for unequal selection probabilities caused by the sampling design. Non-response adjustments were computed to make up for non-participating units. Any analyses presented in this report referring to the data that met expectations used total weights to achieve unbiased estimates of the population features.

Data not meeting the expectations remained unweighted, inferences to populations are not recommended. Further details about the sampling design, the weighting procedure, and participation rates can be found in Appendix A1. Remarks concerning validity related to sampling yield and procedures will be presented in the last section of this chapter. The IEA developed a set of procedures to assist NRCs with implementing the survey, with the goal to aid NRCs in the uniformity of their questionnaire administration activities.

IEA designed these procedures to be flexible enough to simultaneously meet the needs of individual participants and adhere to IEA survey standards. All national centres received guidelines on the survey operations procedures for each stage of the survey. The guidelines included advice on contacting schools, listing and sampling students or classes, preparing materials for data collection, administering the survey, and creating data files.

Samples needed to be achieved by approved sampling procedures, samples achieved by unapproved sampling procedures were deemed unacceptable. The NRC acted as the main contact person for all those involved in REDS within the country and was the country representative at the international level. NRCs oversaw the overall implementation of the survey at the national level.

They also, where necessary, implemented and adapted the internationally agreed-upon procedures to their national context under the guidance of the international project staff and national experts. To facilitate successful administration of REDS, the international team required the establishment of school coordinators within countries.

Their work focused on preparing for and administering the data collection. The role of the school coordinators National centres identified and trained school coordinators for all participating schools. The school coordinator could be a teacher or other staff member in the school. In some cases, national centres appointed external individuals as school coordinators. Manuals and documentation The international study team released guidelines for the survey operations procedures to the NRCs in seven units.

The material was organized and distributed chronologically according to the stages of the study. The seven units and their accompanying software packages were: 1. The General Guidelines, which provided general information on the survey and described the roles and responsibilities of NRCs and the national staff. The School Coordinator Manual subject to translation , which described the role and responsibilities of the school coordinator.

The IEA Within-School Sampling Manual, which guided national centre staff through the activities within the national centre when working with the within-school sampling and tracking software WinW3S.

The Guidelines for Working with Schools, which contained information about how to work with schools to plan for successful administration of the REDS questionnaires.

The Guidelines for Instrument Preparation, which described the processes involved in preparing the REDS questionnaires for production and use in the countries. The Guidelines for Data Capture Procedures, which contained the description of post-data collection activities. National centres further used WinW3S to track school, teacher, and student information; prepare the survey tracking forms; and assign questionnaires to students and teachers.

The IEA DME also allowed national adaptations to be made to the questionnaires and provided a set of data quality control checks. In addition to preparing the software and manuals, IEA conducted data-management trainings designed to train national centre staff in required software programmes and procedures, i.

Working with schools In REDS, the within-school sampling process required close cooperation between the national centre and representatives from the schools. Figure 3.

NRCs were responsible for contacting the schools and encouraging them to take part in the survey, a process that often involved obtaining support from national or regional educational authorities or other stakeholders, depending on the national context. The electronic versions of the REDS school, teacher, and student questionnaires could only be completed via the internet.

Accordingly, the design ensured that online respondents needed only an internet connection and a standard internet browser. No additional software or particular operating system was required. During the administration period, respondents could log in and out as many times as they needed and could resume answering the questionnaire at the question they had last responded to in their previous session. Answers were automatically saved whenever respondents moved to another question, and respondents could change any answer at any time before completing the questionnaire.

During the administration, the national centre was available for support; the centre, in turn, could contact IEA if unable to solve a problem locally. Responses to the online questionnaires were not made mandatory, evaluated, or enforced in detail e.

Instead, some questions used soft validation, such as respondents being asked to give numerical responses to questions that had a minimum and maximum value—for example, the total number of students enrolled in a school. Because the national centres were able to monitor the responses to the online questionnaires in real-time, they could send reminders to those schools which had respondents that had not responded in the expected period.

Typically, in these cases, the national centres asked the school coordinators to follow up with those individuals who had not responded. Although countries using the online mode in REDS faced parallel workload and complexity before and during the data collection, they had the benefit of a reduction in workload afterwards.

Because answers to online questionnaires were already in an electronic format and stored on servers maintained by IEA, there was no need for separate data entry. The most frequently mentioned reason related to reduced internet accessibility. In these cases, schools were provided with paper questionnaires that were either administered by the school coordinator, or by data collectors hired by the national centre.

The completed questionnaires were shipped back to the national centre where they were digitized, i. The software also includes a data verification and statistics module. Preparing the REDS international database and ensuring its integrity was a complex endeavor, requiring extensive collaboration between IEA and the national centres.

Depending on the delivery mode, once each country had either created their data files and submitted them to IEA in the case of paper-administered questionnaires or confirmed that their online data collection window had closed in the case of online-administered questionnaires, in which case the IEA downloaded them from the central international server , data cleaning began.

Data cleaning is an extensive process of checking data for inconsistencies and formatting the data to create a standardized output. Confirming the integrity of the national databases The steps taken to ensure the integrity of the national databases varied according to the delivery mode and questionnaires administered.

In each country that administered online questionnaires, the national centre sent confirmation to IEA that their data collection window had closed and that the data were ready to be downloaded from the central international server. IEA then downloaded raw data from the server. In each country that administered paper questionnaires, the completed instruments were entered into the DME and then exported for submission to IEA. IEA then subjected these data to a comprehensive process of checking and editing, conducting the standardized cleaning procedures upon data and documentation submission.

IEA first imported and checked the data files provided by each country, and then applied a set of cleaning rules to verify the validity and consistency of the data, documenting any deviations from the international file structure. Having completed these steps, IEA staff sent cleaning queries to the national centres. After all modifications had been applied, IEA rechecked all datasets. This process of editing the data, checking the reports, and implementing corrections was repeated as many times as necessary to help ensure that data were consistent within and comparable across countries.

IEA then used this information, together with data captured by the software designed to standardize operations and tasks, to calculate sampling weights, population coverage, and school, teacher, and student participation rates. Data cleaning quality control Because REDS was a complex survey with high standards for data quality, maintaining these standards required an extensive set of interrelated data checking and data cleaning procedures.

IEA compared national adaptations recorded in the documentation for the national datasets against the structure of the submitted national data files. Whenever possible, IEA recoded national deviations to ensure consistency with the international data structure. However, if international comparability could not be guaranteed, IEA removed the corresponding data from the international database. Prior to reporting the results, IEA reviewed key diagnostic statistics for each questionnaire variable to evaluate its plausibility across the participating countries.

This variable-by-variable, country- by-country review used to detect unusual item properties or anomalous patterns played a crucial role in the quality assurance of the REDS data. Finding a faulty variable this late in the process is rare, but an unusual distribution could indicate a potential problem with either translation or printing. Following the reviewing of variable statistics, the international REDS team met with external experts in August to conduct a formal adjudication of the data in preparation of the table production and report writing.

During that meeting, decisions were made about any modifications needed to the data or if further analyses were required. Country reports about translation errors, printing issues, or other technical concerns were referenced. As a result of this process, the data were stabilized, and reporting and annotation schemes were agreed upon that would make readers aware of potential issues with the data. Total weights have been computed to account for this effect of the design and were used for any analysis presented in this report, allowing for obtaining unbiased estimates of population features Lohr, Moreover, it is not appropriate to apply formulae pertaining to simple random samples for obtaining standard errors for population estimates if data originates from complex samples.

Replication re-sampling techniques provide tools to estimate the sampling variance of population estimates more appropriately for these samples Gonzalez and Foy To prepare datasets for this technique, primary sampling units were paired into variance zones following the approach outlined in ICILS Schulz, Schools were the primary sampling units in all countries except the Russian Federation and India, where regional units comprised the first sampling stage.

Standard statistical software does not always include procedures for estimating population features and their sampling variance based on data from complex samples. This software takes the complex data structure automatically into account by using sampling weights for accurate estimation of population features, and by applying the JRR method for accurate estimation of standard errors.

For the analysis presented in Chapter 4, Section 4. This situation led to constraints on the comparability and representativeness of the REDS data and are detailed in the following section. Instrument development Normally, the production of the international version of the survey instruments is an endeavor that can take up to a year, a time span not available to the REDS international consortium. Instead, the first version of the international questionnaires was compiled in the months of September and October This was done while the recruitment of additional participants was ongoing.

The questionnaires required small adjustments to increase relevance for countries in which remote online teaching was not possible.

It was also not possible to verify the layout of the national questionnaires by the international consortium within the given timeframe. Nevertheless, this did not mean that countries were left without advice during the preparatory phase. During each step of the process, countries were offered help whenever needed. In countries with little or no experience in conducting large-scale surveys, the consortium offered regular catch-up calls, which were used extensively.

Data collection The urgency of data collection made it necessary to accept some compromises with regard to the usual procedures followed in IEA surveys, as specified in Wagemaker, In other IEA studies, procedures are trialed, staff are trained in a dedicated field trial phase, and items and response categories are tested and revised based on data collected from a small but robust sample of schools and individuals.

The truncated REDS timeline prevented a full field trial data collection phase. Furthermore, while the data collection period for the entire study stretched over eight months from December to July , data were collected within three months for all countries except Denmark. The exact time spans of the reference period and the data collection period is displayed in Chapter 4, Section 4. Non-conformity of survey administration and reference period In IEA surveys, respondents are usually asked about their experiences at present or in a very recent past.

This was not necessarily true for REDS, because, at the time the survey was administered, the challenges caused by the pandemic during the reference period i. Respondents however were asked about what they had experienced during that initial time of disruptions. We cannot disentangle from the data whether, and if so, to what amount, responses have been blurred by these later experiences. Further, the length and position of the reference and data collection periods within the school year differs between countries.

Detailed information on the reference period, the data collection period, and on the school year, can be found for all countries in Chapter 4, Section 4. Within-school sampling The IEA usually requests that all study participants strictly follow all operations procedures, as stipulated by several survey operations procedures units.

For example, countries must not use any other software packages than the ones provided by the IEA for key activities of the survey. However, to accommodate the specific national circumstances, the consortium allowed three countries—Burkina Faso, Ethiopia, and Kenya—to deviate from the defined within-school sampling procedures. Proper usage of the software, however, required that national centres get in touch with schools more than once see Figure 3.

They therefore opted for within-school sampling procedures outside the software that allowed them to contact schools only once. The employed procedures included a lottery on the day of survey implementation to select the within-school sample, leaving out absent students. Sampling teachers within schools was not necessary in the concerned countries, since all eligible teachers were surveyed. Only those teachers present at the day of the survey were considered.

National centres could not provide information on the number of absent students and teachers, preventing accurate computation of selection probabilities, sampling weights, and participation rates. Hence, results based on student and teacher data in these countries represent only the experiences and opinions of the respondents and should not be used to infer on the target populations.

This constraint is marked in all chapters presenting REDS results. Data remained unweighted and is reported without standard errors. Exclusion rates REDS aimed to fully cover the target populations in all countries. However, due to specific circumstances in the participating countries, it was not feasible to access all eligible students, teachers, and schools.

Therefore, the national survey population had to be restricted in many countries. Affected schools, students, and teachers were removed from sampling frames prior to sample selection, i. Hence, any outcome of REDS can only be representative for schools and individuals that were not excluded. Types of excluded schools per country are listed in Appendix A1, Table A1. The exclusion rates reached significant levels in some of the countries.

Differences between the surveyed population and the internationally defined target population are more likely in countries with high exclusion rates. The pandemic caused specific challenges on this aspect of REDS.

The period between the end of year until the middle of year was marked by new outbreaks of COVID in the participating countries, resulting in schools closing repeatedly, at least for some of the time in some surveyed regions, making it difficult to reach sampled schools and individuals. Some countries suffered from low participation rates, especially at the school level and with teachers within schools. Detailed participation rates for all countries are given in Appendix A1, Tables A1.

Low participation rates can result in non-response bias under specific conditions. This is when relatively high levels of non-participation rates are combined with a relatively large difference between respondents and non-respondents in the variables of interest.

If these conditions apply, there is a lack of representativeness of respondents for the underlying populations for the variable of interest. This risk may be larger for REDS than for other surveys, at least with respect to specific variables. Non-response might be directly related to the effects of the pandemic, for example, students might have been frightened to go to school because of the risk of infection and could therefore not be contacted to participate in the survey.

Others may have not been reached because of a lack of electronic devices, a problem that may also have been applied to teachers or even school principals.

These individuals may have likely responded systematically differently to parts of the REDS survey questionnaires, for example regarding their access to online learning. Weighting, especially non-response adjustments, tries to minimize the risk of non-response bias, but cannot be as efficient as sufficient participation rates.

Further, Denmark experienced particularly low participation rates for schools, students, and teachers, and Uruguay experienced particularly low participation rates for teachers. This data were therefore considered to carry high risks of bias and remained unweighted. Respondents represent only themselves, their data are accordingly interpreted in this report, and it is not recommended to infer from these samples on the respective target populations.

Standard error All estimates of population features presented in this report are provided together with their standard errors. Higher standard errors indicate a higher level of impreciseness, or uncertainty, of the estimate. Burkina Faso 3.

Readers of the report need to be aware that notable differences between estimates might not be significant if standard errors are high; in this case, differences might solely be caused by the random selection of participants. References Gonzalez, E. Estimation of sampling variance. Martin, K.

Stemler Eds. Technical standards for IEA studies: an annotated bibliography. Duxbury Press.

 
 

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It gives you the ability to download multiple files at one time and download large files quickly and reliably. It also allows you to suspend active downloads and resume downloads that have failed. Microsoft Download Manager is free and available for download now. Warning: This site requires the use of scripts, which your browser does not currently allow. See how to enable scripts. Microsoft Office Volume License Pack. Select Language:. Choose the download you want. Download Summary:. Total Size: 0.

Back Next. Microsoft recommends you install a download manager. Microsoft Download Manager. Manage all your internet downloads with this easy-to-use manager. It features a simple interface with many customizable options:. School principals responded to a questionnaire focusing on school-level responses on the educational disruption caused by the COVID pandemic. In most countries, the selection probability of schools was proportional to the number of target grade students, aiming for self-weighted samples of students Meinck, India and the Russian Federation required additional sampling stages regional units 5.

While REDS aimed for full coverage of the target populations, countries could decide to exclude specific types of schools or students from the survey see Table 3. Stratification was used to improve the efficiency of the samples and to facilitate analyses by certain groups of schools. Commonly used stratification variables were urbanization, type of funding, and region.

The variables used for stratification are shown in Appendix A1, Table A1. The minimum school sample size was set to schools per country. Using the WinW3S software certified and provided by IEA, within each participating school, 20 students and 20 teachers were randomly sampled from eligible individuals.

In cases where there were fewer eligible students or teachers, all were selected. Denmark and Slovenia used a different within-school sampling approach for their students: they randomly selected a grade 8 class and within the selected class all students were asked to participate. Student data were collected in eight countries, teacher data in ten countries and school data in all eleven countries Table 3.

Therefore, students in grade 7 during survey administration had already been in grade 7 during the reference period. Data meeting the expectations6 were weighted to account for unequal selection probabilities caused by the sampling design.

Non-response adjustments were computed to make up for non-participating units. Any analyses presented in this report referring to the data that met expectations used total weights to achieve unbiased estimates of the population features. Data not meeting the expectations remained unweighted, inferences to populations are not recommended. Further details about the sampling design, the weighting procedure, and participation rates can be found in Appendix A1. Remarks concerning validity related to sampling yield and procedures will be presented in the last section of this chapter.

The IEA developed a set of procedures to assist NRCs with implementing the survey, with the goal to aid NRCs in the uniformity of their questionnaire administration activities.

IEA designed these procedures to be flexible enough to simultaneously meet the needs of individual participants and adhere to IEA survey standards. All national centres received guidelines on the survey operations procedures for each stage of the survey. The guidelines included advice on contacting schools, listing and sampling students or classes, preparing materials for data collection, administering the survey, and creating data files.

Samples needed to be achieved by approved sampling procedures, samples achieved by unapproved sampling procedures were deemed unacceptable. The NRC acted as the main contact person for all those involved in REDS within the country and was the country representative at the international level. NRCs oversaw the overall implementation of the survey at the national level. They also, where necessary, implemented and adapted the internationally agreed-upon procedures to their national context under the guidance of the international project staff and national experts.

To facilitate successful administration of REDS, the international team required the establishment of school coordinators within countries. Their work focused on preparing for and administering the data collection. The role of the school coordinators National centres identified and trained school coordinators for all participating schools. The school coordinator could be a teacher or other staff member in the school.

In some cases, national centres appointed external individuals as school coordinators. Manuals and documentation The international study team released guidelines for the survey operations procedures to the NRCs in seven units. The material was organized and distributed chronologically according to the stages of the study. The seven units and their accompanying software packages were: 1.

The General Guidelines, which provided general information on the survey and described the roles and responsibilities of NRCs and the national staff. The School Coordinator Manual subject to translation , which described the role and responsibilities of the school coordinator.

The IEA Within-School Sampling Manual, which guided national centre staff through the activities within the national centre when working with the within-school sampling and tracking software WinW3S. The Guidelines for Working with Schools, which contained information about how to work with schools to plan for successful administration of the REDS questionnaires. The Guidelines for Instrument Preparation, which described the processes involved in preparing the REDS questionnaires for production and use in the countries.

The Guidelines for Data Capture Procedures, which contained the description of post-data collection activities. National centres further used WinW3S to track school, teacher, and student information; prepare the survey tracking forms; and assign questionnaires to students and teachers. The IEA DME also allowed national adaptations to be made to the questionnaires and provided a set of data quality control checks. In addition to preparing the software and manuals, IEA conducted data-management trainings designed to train national centre staff in required software programmes and procedures, i.

Working with schools In REDS, the within-school sampling process required close cooperation between the national centre and representatives from the schools. Figure 3. NRCs were responsible for contacting the schools and encouraging them to take part in the survey, a process that often involved obtaining support from national or regional educational authorities or other stakeholders, depending on the national context. The electronic versions of the REDS school, teacher, and student questionnaires could only be completed via the internet.

Accordingly, the design ensured that online respondents needed only an internet connection and a standard internet browser. No additional software or particular operating system was required.

During the administration period, respondents could log in and out as many times as they needed and could resume answering the questionnaire at the question they had last responded to in their previous session. Answers were automatically saved whenever respondents moved to another question, and respondents could change any answer at any time before completing the questionnaire.

During the administration, the national centre was available for support; the centre, in turn, could contact IEA if unable to solve a problem locally. Responses to the online questionnaires were not made mandatory, evaluated, or enforced in detail e. Instead, some questions used soft validation, such as respondents being asked to give numerical responses to questions that had a minimum and maximum value—for example, the total number of students enrolled in a school.

Because the national centres were able to monitor the responses to the online questionnaires in real-time, they could send reminders to those schools which had respondents that had not responded in the expected period. Typically, in these cases, the national centres asked the school coordinators to follow up with those individuals who had not responded.

Although countries using the online mode in REDS faced parallel workload and complexity before and during the data collection, they had the benefit of a reduction in workload afterwards.

Because answers to online questionnaires were already in an electronic format and stored on servers maintained by IEA, there was no need for separate data entry. The most frequently mentioned reason related to reduced internet accessibility. In these cases, schools were provided with paper questionnaires that were either administered by the school coordinator, or by data collectors hired by the national centre. The completed questionnaires were shipped back to the national centre where they were digitized, i.

The software also includes a data verification and statistics module. Preparing the REDS international database and ensuring its integrity was a complex endeavor, requiring extensive collaboration between IEA and the national centres. Depending on the delivery mode, once each country had either created their data files and submitted them to IEA in the case of paper-administered questionnaires or confirmed that their online data collection window had closed in the case of online-administered questionnaires, in which case the IEA downloaded them from the central international server , data cleaning began.

Data cleaning is an extensive process of checking data for inconsistencies and formatting the data to create a standardized output. Confirming the integrity of the national databases The steps taken to ensure the integrity of the national databases varied according to the delivery mode and questionnaires administered. In each country that administered online questionnaires, the national centre sent confirmation to IEA that their data collection window had closed and that the data were ready to be downloaded from the central international server.

IEA then downloaded raw data from the server. In each country that administered paper questionnaires, the completed instruments were entered into the DME and then exported for submission to IEA. IEA then subjected these data to a comprehensive process of checking and editing, conducting the standardized cleaning procedures upon data and documentation submission. IEA first imported and checked the data files provided by each country, and then applied a set of cleaning rules to verify the validity and consistency of the data, documenting any deviations from the international file structure.

Having completed these steps, IEA staff sent cleaning queries to the national centres. After all modifications had been applied, IEA rechecked all datasets. This process of editing the data, checking the reports, and implementing corrections was repeated as many times as necessary to help ensure that data were consistent within and comparable across countries.

IEA then used this information, together with data captured by the software designed to standardize operations and tasks, to calculate sampling weights, population coverage, and school, teacher, and student participation rates.

Data cleaning quality control Because REDS was a complex survey with high standards for data quality, maintaining these standards required an extensive set of interrelated data checking and data cleaning procedures. IEA compared national adaptations recorded in the documentation for the national datasets against the structure of the submitted national data files.

Whenever possible, IEA recoded national deviations to ensure consistency with the international data structure. However, if international comparability could not be guaranteed, IEA removed the corresponding data from the international database. Prior to reporting the results, IEA reviewed key diagnostic statistics for each questionnaire variable to evaluate its plausibility across the participating countries. This variable-by-variable, country- by-country review used to detect unusual item properties or anomalous patterns played a crucial role in the quality assurance of the REDS data.

Finding a faulty variable this late in the process is rare, but an unusual distribution could indicate a potential problem with either translation or printing. Following the reviewing of variable statistics, the international REDS team met with external experts in August to conduct a formal adjudication of the data in preparation of the table production and report writing.

During that meeting, decisions were made about any modifications needed to the data or if further analyses were required. Country reports about translation errors, printing issues, or other technical concerns were referenced.

As a result of this process, the data were stabilized, and reporting and annotation schemes were agreed upon that would make readers aware of potential issues with the data.

Total weights have been computed to account for this effect of the design and were used for any analysis presented in this report, allowing for obtaining unbiased estimates of population features Lohr, Moreover, it is not appropriate to apply formulae pertaining to simple random samples for obtaining standard errors for population estimates if data originates from complex samples. Replication re-sampling techniques provide tools to estimate the sampling variance of population estimates more appropriately for these samples Gonzalez and Foy To prepare datasets for this technique, primary sampling units were paired into variance zones following the approach outlined in ICILS Schulz, Schools were the primary sampling units in all countries except the Russian Federation and India, where regional units comprised the first sampling stage.

Standard statistical software does not always include procedures for estimating population features and their sampling variance based on data from complex samples. This software takes the complex data structure automatically into account by using sampling weights for accurate estimation of population features, and by applying the JRR method for accurate estimation of standard errors.

For the analysis presented in Chapter 4, Section 4. This situation led to constraints on the comparability and representativeness of the REDS data and are detailed in the following section. Instrument development Normally, the production of the international version of the survey instruments is an endeavor that can take up to a year, a time span not available to the REDS international consortium.

Instead, the first version of the international questionnaires was compiled in the months of September and October This was done while the recruitment of additional participants was ongoing. The questionnaires required small adjustments to increase relevance for countries in which remote online teaching was not possible.

It was also not possible to verify the layout of the national questionnaires by the international consortium within the given timeframe. Nevertheless, this did not mean that countries were left without advice during the preparatory phase. During each step of the process, countries were offered help whenever needed. In countries with little or no experience in conducting large-scale surveys, the consortium offered regular catch-up calls, which were used extensively.

Data collection The urgency of data collection made it necessary to accept some compromises with regard to the usual procedures followed in IEA surveys, as specified in Wagemaker, In other IEA studies, procedures are trialed, staff are trained in a dedicated field trial phase, and items and response categories are tested and revised based on data collected from a small but robust sample of schools and individuals.

The truncated REDS timeline prevented a full field trial data collection phase. Furthermore, while the data collection period for the entire study stretched over eight months from December to July , data were collected within three months for all countries except Denmark. The exact time spans of the reference period and the data collection period is displayed in Chapter 4, Section 4.

Non-conformity of survey administration and reference period In IEA surveys, respondents are usually asked about their experiences at present or in a very recent past.

This was not necessarily true for REDS, because, at the time the survey was administered, the challenges caused by the pandemic during the reference period i. Respondents however were asked about what they had experienced during that initial time of disruptions. We cannot disentangle from the data whether, and if so, to what amount, responses have been blurred by these later experiences.

Further, the length and position of the reference and data collection periods within the school year differs between countries. Detailed information on the reference period, the data collection period, and on the school year, can be found for all countries in Chapter 4, Section 4. Within-school sampling The IEA usually requests that all study participants strictly follow all operations procedures, as stipulated by several survey operations procedures units.

For example, countries must not use any other software packages than the ones provided by the IEA for key activities of the survey. However, to accommodate the specific national circumstances, the consortium allowed three countries—Burkina Faso, Ethiopia, and Kenya—to deviate from the defined within-school sampling procedures.

Proper usage of the software, however, required that national centres get in touch with schools more than once see Figure 3. They therefore opted for within-school sampling procedures outside the software that allowed them to contact schools only once. The employed procedures included a lottery on the day of survey implementation to select the within-school sample, leaving out absent students.

Sampling teachers within schools was not necessary in the concerned countries, since all eligible teachers were surveyed. Only those teachers present at the day of the survey were considered.

National centres could not provide information on the number of absent students and teachers, preventing accurate computation of selection probabilities, sampling weights, and participation rates. Hence, results based on student and teacher data in these countries represent only the experiences and opinions of the respondents and should not be used to infer on the target populations. This constraint is marked in all chapters presenting REDS results. Data remained unweighted and is reported without standard errors.

Exclusion rates REDS aimed to fully cover the target populations in all countries. However, due to specific circumstances in the participating countries, it was not feasible to access all eligible students, teachers, and schools. Therefore, the national survey population had to be restricted in many countries. Affected schools, students, and teachers were removed from sampling frames prior to sample selection, i.

Hence, any outcome of REDS can only be representative for schools and individuals that were not excluded. Types of excluded schools per country are listed in Appendix A1, Table A1. The exclusion rates reached significant levels in some of the countries. Differences between the surveyed population and the internationally defined target population are more likely in countries with high exclusion rates.

The pandemic caused specific challenges on this aspect of REDS. The period between the end of year until the middle of year was marked by new outbreaks of COVID in the participating countries, resulting in schools closing repeatedly, at least for some of the time in some surveyed regions, making it difficult to reach sampled schools and individuals. Some countries suffered from low participation rates, especially at the school level and with teachers within schools.

Detailed participation rates for all countries are given in Appendix A1, Tables A1. Low participation rates can result in non-response bias under specific conditions. This is when relatively high levels of non-participation rates are combined with a relatively large difference between respondents and non-respondents in the variables of interest.

If these conditions apply, there is a lack of representativeness of respondents for the underlying populations for the variable of interest. This risk may be larger for REDS than for other surveys, at least with respect to specific variables. Non-response might be directly related to the effects of the pandemic, for example, students might have been frightened to go to school because of the risk of infection and could therefore not be contacted to participate in the survey.

Others may have not been reached because of a lack of electronic devices, a problem that may also have been applied to teachers or even school principals. These individuals may have likely responded systematically differently to parts of the REDS survey questionnaires, for example regarding their access to online learning.

Weighting, especially non-response adjustments, tries to minimize the risk of non-response bias, but cannot be as efficient as sufficient participation rates.

Further, Denmark experienced particularly low participation rates for schools, students, and teachers, and Uruguay experienced particularly low participation rates for teachers.

This data were therefore considered to carry high risks of bias and remained unweighted. Respondents represent only themselves, their data are accordingly interpreted in this report, and it is not recommended to infer from these samples on the respective target populations. Standard error All estimates of population features presented in this report are provided together with their standard errors. Higher standard errors indicate a higher level of impreciseness, or uncertainty, of the estimate.

Burkina Faso 3. Readers of the report need to be aware that notable differences between estimates might not be significant if standard errors are high; in this case, differences might solely be caused by the random selection of participants.

References Gonzalez, E. Estimation of sampling variance. Martin, K. Stemler Eds. Technical standards for IEA studies: an annotated bibliography. Duxbury Press. Martin, M. Technical standards for IEA studies. Evaluating the risk of nonresponse bias in educational large-scale assessments with school nonresponse questionnaires: a theoretical study.

Sampling, weighting, and variance estimation. Wagemaker Ed. In Fraillon, J. Reliability and validity of international large-scale assessment. Introduction to variance estimation. Kennedy 4. Within countries, schools have different levels of responsibility and freedom for decision-making, depending on the level of centralization of the relevant education system or systems.

It draws on the information provided by the NRCs collected via the national questionnaire as well as during an additional review round and supplemented by data obtained from the World Health Organization WHO. Section highlights The section provides insights into system-level measures taken in REDS countries to guide and support principals and teachers during the school disruptions caused by the COVID pandemic.

The length of the reference period varied across and within countries. However, in some countries, greater autonomy was granted to schools to adapt measures to their specific context e.

Several resources were made available to schools to support learning during school closures. Countries\’ overviews reported in this section describe the national policy advice and expectations associated with practical and organizational changes in schooling resulting from the disruption.

It pertains, for example, to the implementation of school closures in countries. A major topic is the policy guidance on approaches to teaching such as remote teaching as well as with respect to changes to teacher contact hours. The data presented were reported by the national centres. Importantly, this section provides detailed information on the country specific reference period as defined in Chapter 2 of this report.

The reference period was used to establish the time-period within each country that respondents were to consider when answering the questions. It was broadly defined as the first period experienced within each country when, in response to the COVID pandemic, most schools were closed to the majority of students.

In some education systems, school holidays took place during the reference period see Appendix A2, Table A2. A detailed discussion of the definition of the reference period is included in Chapter 2.

The information on the reference period is supplemented by the number of positive tested cases recorded in each of the countries between January and July As the numbers obtained from the WHO home page WHO, are not adjusted by the number of tested persons or the number of false positive and negative cases, they should not be interpreted as infection rates of the real COVID cases, but rather provide insights on the empirical basis that governments had access to for their decision-making process.

It can be assumed that, in many cases, school closures were not solely related to the number of positive cases, but, presumably, rather to other political and global events. The concept of centralization is often researched in combination with the concept of accountability. International studies imply that higher degrees of school autonomy combined with higher degrees of accountability improve educational outcomes such as performance Parveva et al.

In some education systems, the COVID pandemic affected the autonomy and accountability mechanisms countries usually have in place. Consequently, those differences are made explicit in this section by providing a comparative view on the autonomy and accountability mechanisms prior and during the COVID pandemic as reported by the national research coordinators. Starting in mid-March, the government banned the physical attendance of students in all schools.

Schools remained closed for most students until the end of June the end of the academic school year for The number of people testing positive remained stable at a low level during the entire school disruption period. Students were allowed to return to schools at the start of the new school year October , Figure 4. The re-opening of schools in October coincided with a substantial increase in the number of people testing positive in December and January.

The number of people testing positive decreased substantially after January The reference period in Burkina Faso consisted of 7. School closure rules were taken at the national level, meaning that they applied to all schools in the country. The regular summer holidays start at the beginning of July and last until October, however, the school year was prolonged by a month, while the school year started a few weeks earlier mid-September , meaning the summer holiday period was reduced by about two months in total.

More precisely, the Ministry of Education provides instructions to the different regional governments, which are then passed on to the individual provinces within that region who are responsible for overseeing the schools. Only private schools gained slightly more autonomy to decide on teaching and learning practices during the pandemic.

During the school disruption, the final examinations were deferred by almost one month from mid-July to the end of July. Figure 4. The main resources schools were provided with to facilitate remote learning were radio transmissions, television broadcasts accessible via the website of the Ministry of Education , and paper-based materials.

The first two of these were already available to schools before the COVID pandemic, whereas the paper-based resources were mainly introduced and provided to schools during the pandemic. The Response Plan prepared by the Ministry of Education explicitly addressed the need to provide schools and teachers with digital resources and support measures that could enable them to develop remote learning strategies.

These included the provision of computer equipment and other ICT resources, internet connectivity, video conferencing software, and support for teachers on how to use the resources and develop digital learning materials. The provision of formal support for the development of digital resources for education was a direct response to the COVID disruption. Furthermore, teachers were strongly encouraged to collaborate with each other during the pandemic.

Notes: Details on the interpretation are provided in the introduction of this section. Denmark reacted quickly to stop the spread of the virus, enacting several lockdown measures, including the closure of schools affecting all grades starting on March On April 15, this rule was adjusted, allowing students from grades to attend schools physically. Approximately a month later, all students were allowed to go back to schools on May On December 16, , schools were again closed for physical attendance due to the rising number of people testing positive, hitting its highest point in December, with 1.

During this time, teachers were asked to conduct their courses remotely, as they were doing during the first closure period. Students from grades were allowed to return to school on February 8, , while remote learning continued for students in higher grades until March 19, The reference period in Denmark is defined as the first lockdown, lasting slightly more than 2 months see Figure 4.

School closure rules were taken at the national level and applied to all schools.