Data was collected from 876 students who used a learning management system (LMS) in their educational activities at Vietnam's higher education institutions. Research constructs are measured using 14 items that have been inherited from prior studies and altered to meet the Vietnam context.

Abstract

The primary purpose of the Harmonized Host and Refugee Labor Market Survey (HHR-LMS) in Uganda is to provide information relevant for studying the impact of forced displacement on labor market outcomes for host communities, both among Ugandan nationals and refugees. The survey aims to obtain detailed information that helps explore labor market outcomes for host and refugee communities living side by side and engaging in shared labor market settings.

Geographic coverage

The survey covers three locations in Uganda: Kampala, Isingiro district, and the Nakivale refugee settlement.

Analysis unit

Household or respondent, depending on survey module.

Universe

The survey covered all de jure households excluding prison, hospitals, military barracks and school dormitories. It includes both national and refugee households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design included 265 initial enumeration areas selected using probability proportionate to size with the number of households used as a measure of size. The enumeration areas were select-ed based on the sample frame constructed during the 2014 population census of Uganda. The Ugan-dan Bureau of Statistics (UBOS) conducted the selection of the EAs and provided their list along with detailed maps of the areas.

Using maps of the selected enumeration areas provided by UBOS, the study team conducted the list-ing of all households in the selected EAs with door-to-door visits. The listing exercise was carried out during November 2021-January 2022 by a team of local field workers recruited and trained for this purpose.

In Kampala city, in addition to a traditional PPS sample, we employed adaptive cluster sampling (ACS) (Thomson 1997; Thomson and Seber 1996) to capture a sufficient number of refugee house-holds. Using the listing of households in the initial 150 clusters in Kampala, the survey team identified those EAs that have 10 percent or more refugee households and conducted the listing of all of their neighbors. This resulted in listing additional 49 clusters that are identified as neighbors to these initial clusters. The exercise served as a basis for selection of both refugees and national households in Kampala.

In general, the sample design is a two-stage sample, with EAs first selected randomly for listing, followed by random selection of households from the listing. There is an extra third stage of choosing individuals randomly selected in households (RSI). Within each household, one person is selected at random (RSI) from the list of eligible members: persons aged between 18 and 65 years old in a national household; or refugees aged between 18 and 65 years old in a non-national household.

Sampling deviation

Description provided in "Harmonized Host and Refugee Labor Market Survey in Uganda, Sampling Description" documentation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two main questionnaires were used to two sampling units: the household head and a randomly selected individual from within the household among the members of the household who are in the age range of 18 to 65 years old.

Response rate

Description provided in "Harmonized Host and Refugee Labor Market Survey in Uganda, Sampling Description" documentation.

Results of a survey in the higher education area. Participants are professors, lecturers, and tutors.

The final definitions for the elements are:

• Brief Overview (BO): Short summary or recap without details of the actual learning material
• Quiz (QU): Quiz questions related to the content taught
• Learning Goal (LG): Description of the competences, skills or abilities that the learners should acquire in relation to a specific learning content
• Manuscript (MS): Complete or brief elaboration of a speech, a lecture, a course, or similar
• Exercise (EX): Opportunity to apply and deepen the learned. Varied tasks are possible beside the classic exercise sheet
• Summary (SU): Elementalization (reduction to the essentials) of the actual content with details
• Auditory additional material (AAM): Material with the aim of applying and deepening the learned with audio files
• Textual additional material (TAM): Material with the aim of applying and deepening the learned with textual further information (also named additional literature)
• Visual additional material (VAM): Material with the aim of applying and deepening the learned with videos or similar
• Collaboration Tool (CT): Cooperative and interactive communication medium with the aim of knowledge sharing between learners and learners and/or lecturers, and is used for collaborative work

The corresponding scientific paper can be found via ORCID as of December 2023.

This statistic shows the use of learning management systems by learning and development (L&D) departments in North America from 2016 to 2020. During the 2020 survey, ** percent of the respondents stated that they used a learning management system.

The data was collected to gauge Deakin University students' perceptions of the university's centralised learning management system, especially if it enhanced their learning experience. It identified the features that were most used and valued by the students and those they felt could be improved. The dataset consists of a set of survey questions and the survey results in an electronic database. It comprises of 2908 responses from 2004 and 2526 responses from 2005; including approximately 1000 open-ended comments providing rich qualitative data. The data includes the following categories of information: • demographic and background information (including gender, mode of study); • perception of importance of and satisfaction with a range of LMS functions; • a number of overall LMS satisfaction measures; and • open-ended written comments about the LMS.

This statistic shows the top complaints reported about their learning management system (LMS) by worldwide learning and development (L&D) departments from 2016 to 2018. During the 2018 survey, ** percent of respondents stated that their LMS offered a poor end-user experience.

The data set includes survey answers collected in research about Learning Management Systems' success. The data was collected from students of an engineering college.

Abstract

The main purpose of the Harmonized Host and Refugee Labor Market Survey (HHR-LMS) in Ethiopia is to provide information relevant for studying the impact of forced displacement on labor market out-comes in host communities, both among Ethiopian nationals and refugees. The survey aims at obtaining detailed information that help explore labor market outcomes for host and refugees communities living side by side and engaging in a shared labor market settings.

Geographic coverage

The survey covers select localities two regions in Ethiopia: Addis Ababa and Somali region of Ethiopia. In the Somali region, the localities of Jigjiga city; Kebribeyah town and Kebribeyah refugee camp were covered. Within these localities, the survey is representative of the national and refugee population.

Analysis unit

Individual and household.

Universe

The survey covered all de jure households excluding prison, hospitals, military barracks and school dormitories. It includes both national and refugee households.

Kind of data

Sample survey data [ssd]

Sampling procedure

[Further described in "Harmonized Host and Refugee Labor Market Survey in Ethiopia, Sampling Description" documentation.]

The sample design included 150 initial enumeration areas in Addis Ababa and 79 EAs in Somali region. These EAs were selected using probability proportionate to size where size is measured by the number of households. The enumeration areas were selected based on the sample frame prepared for the population census of Ethiopia planned for 2020 but not implemented due to the COVID pandemic and overall security challenges in the country. The Ethiopian Central Statistical Service (CSS) conducted the selection of the EAs and provided their list along with detailed maps of the areas.

Using maps of the selected enumeration areas provided by CSS, the study team conducted the listing of all households in the selected EAs with door-to-door visits. The listing exercise was carried out during February-March 2022 in Addis Ababa and during May-June 2022 in Somali region by a team of local field workers recruited and trained for this purpose.

In Addis Ababa, we employed adaptive cluster sampling (ACS) to capture enough refugee households. Using the listing of households in the initial 150 clusters in Ababa, we identified those EAs that have 10 percent or more refugee households and conducted the listing of all their neighbors. This resulted in listing additional 71 EAs clusters that are identified as neighbors to these initial clusters. The exer-cise served as a basis for selection of both refugees and national households in Addis Ababa.

In general, the sample design is a two-stage sample, with an extra third stage for individuals random-ly selected in households (RSI). Within each household, one person is selected at random (RSI) from the list of the eligible members: persons aged between 18 and 65 years old in a national household; or refugees aged between 18 and 65 years old in a non-national household.

Sampling deviation

Description provided in "Harmonized Host and Refugee Labor Market Survey in Ethiopia, Sampling Description" documentation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two main questionnaires were used to two sampling units: the household head and a randomly selected individual from within the household among the members of the household who are in the age range of 18 to 65 years old.

Response rate

Description provided in "Harmonized Host and Refugee Labor Market Survey in Ethiopia, Sampling Description" documentation.

As part of the E-LEARNING IN TRANSLATOR TRAINING project, the present survey aimed to explore the experiences, motivation and workload of translator trainers participating in the online training programs at the Department of Translation and Interpreting of Eötvös Loránd University (ELTE). The survey also focused on the difficulties and challenges they faced.Published files (English and Hungarian):E-learning in translatior training - Trainer questionnaire (Pdf)Accumulated data of the Trainer Questionnaire (Pdf)Detailed quantitative & qualitative data of the Trainer Questionnaire (Excel)Az E-TANULÁS A FORDÍTÓKÉPZÉSBEN című projekt keretében készült jelen felmérés célja az volt, hogy feltérképezze az Eötvös Loránd Tudományegyetem (ELTE) Fordító- és Tolmácsképző Tanszékének online képzési programjaiban részt vevő oktatók tapasztalatait, motivációját és munkaterhelését. A kutatás emellett kitért az általuk tapasztalt nehézségekre és kihívásokra is.Közzétett fájlok (angol és magyar nyelven):E-learning a fordítóképzésben - Oktatói kérdőív (Pdf)Az oktatói kérdőív összesített adatai (Pdf)Az oktatói kérdőív részletes kvantitatív és kvalitatív adatai (Excel)

Results of a survey in the higher education area. Participants are german students.

The final definitions for the elements are:

• Brief Overview (BO): Short summary or recap without details of the actual learning material
• Quiz (QU): Quiz questions related to the content taught
• Learning Goal (LG): Description of the competences, skills or abilities that the learners should acquire in relation to a specific learning content
• Manuscript (MS): Complete or brief elaboration of a speech, a lecture, a course, or similar
• Exercise (EX): Opportunity to apply and deepen the learned. Varied tasks are possible beside the classic exercise sheet
• Summary (SU): Elementalization (reduction to the essentials) of the actual content with details
• Auditory additional material (AAM): Material with the aim of applying and deepening the learned with audio files
• Textual additional material (TAM): Material with the aim of applying and deepening the learned with textual further information (also named additional literature)
• Visual additional material (VAM): Material with the aim of applying and deepening the learned with videos or similar
• Collaboration Tool (CT): Cooperative and interactive communication medium with the aim of knowledge sharing between learners and learners and/or lecturers, and is used for collaborative work

The corresponding scientific paper can be found via ORCID as of July 2024.

"Results_Evaluation_LE_Students.xlsx" contains the answers of the students.

The data was collected to guage Deakin University staff perceptions of the university's centralised learning management system, especially if it enhanced their teaching. It identified the features that were most used and valued by the teaching staff and those they felt could be improved.

The dataset is comprised of a set of survey questions and the survey results in an electronic database. It consists of 156 responses from 2004 and 120 responses from 2005; including approximately 100 open-ended comments providing rich qualitative data.

The data includes the following categories of information:
• demographic and background information (including gender, mode of study);
• perception of importance of and satisfaction with a range of LMS functions;
• a number of overall LMS satisfaction measures; and
• open-ended written comments about the LMS.

Main labour market statistics time series data (large dataset).

Abstract

In 1991, the Egyptian government initiated a major Economic Reform and Structural Adjustment Program (ERSAP). This reform included a stabilization component to eliminate external and external imbalances, a reform agenda for the trade and financial sectors and the exchange rate regime, and an ambitious privatization program. Until recently, however, little was known about the impact of this program on employment and earnings in the Egyptian labor market. Therefore ERF conducted The Egypt Labor Market survey with a nationally-representative household survey covering 5,000 households which aimed to assess the major changes in labor market conditions that occurred during the period from 1988 to 1998, a period of significant economic reform and structural adjustment.

This project investigated changes in the supply and demand for labor, including the extent to which the private sector has contributed to employment creation, and the groups that have benefited from employment growth. Trends in labor earnings and wages, in women’s and youth employment, and in child labor and schooling are analyzed and the role of the informal sector in employment creation is explored, as well as the extent to which the labor market itself has become more informal over the period.

Geographic coverage

The sample was designed to provide estimates of the indicators at the national level, for urban and rural areas,and for all regions.

Analysis unit

individuals, households

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The process of sample extraction was primarily executed by CAPMAS staff in close coordination with the ERF team. The 5,000 household, which constitute the survey sample, were selected from a CAPMAS master sample prepared in 1995. The master sample consists of 750,000 households in 500 primary sampling units (PSUs) each consisting of 1500 households. Since the master sample is the basis for the survey sample, we find it necessary to start by explaining how the master sample was extracted in the first place.

Sampling deviation

There was adeviation from sample design.

Regional variations show that the greatest incidence of closed households has been in Urban Upper Egypt, especially in Minia (16 cases) and Sohag (13 cases). It is difficult to account for the closure of these units. One explanation can be the escalating violence in this region in the past couple of year. The second region with high incidence of closed units has been the Alexandria and Suez Canal region. CAPMAS staff note that since many of the dwelling units, especially in Alexandria city, are used by residents of other governorates as summer resorts, these dwellings were closed at the time of the survey, which took place in October.

The sample had a very small rate of rejection cases. Only 23 cases, constituting 0.48% of the final sample size, consisted of total rejections to respond to the questionnaire.There were additional cases were the respondent refused to answer some parts of the questionnaire. As with most surveys, rejection cases are primarily in urban metropolitan areas, especially Cairo and Alexandria governorates.

While the majority of non-response cases are in urban areas, the majority of added households come from rural areas. This unintentional sampling bias towards rural areas can be corrected with the appropriate sampling weights.

Mode of data collection

Face-to-face [f2f]

Research instrument

Includes three questionnaires: 1) the household questionnaire; 2) the individual questionnaire; 3) the family enterprise questionnaire. Each household should have at least one household questionnaire and one individual questionnaire. If any of the members of the household was self-employed or an employer, there has to be a family enterprise questionnaire for this household. 1) Data for the household questionnaire is collected from the head of the household. It includes the roster of members of the household, each individual's relationship to the head of the household, demographic characteristics of the household, access to public services, availability of durable goods and sources of income other than work for the household. This questionnaire serves as a springboard for interviewers to determine the individuals who should carry on with the subsequent questions in the individual questionnaire - those who are six years and older. Also, in this questionnaire each individual is assigned a person code (pn) that is used in the subsequent questionnaires as an identification code. The roaster for the household questionnaire allows space for 20 members of the household. In case the household had more than 20 members, as it happened in some rural areas, another copy of the questionnaire is used. 2) The individual questionnaire applies to individuals six years old and above. It includes modules on parents, education, detection of work during the reference week, unemployment, characteristics of employment during the reference three months, mobility and career history, and earnings. The latter applies to wage workers only. Data for this questionnaire are collected from the individual him/herself. Unless the research team fails to meet the individual personally after three visits, with prior appointment before each subsequent visit, data can be collected from another member of the household. For individuals less than 15 years old, data is collected from their parents or any adult household member in order to save these youngsters the interviewing process. 3) The family enterprise questionnaire applies to all individuals who are self-employed or employers (those who chose answers 2, 3 or 4 in questions number q1316 or 2122 at the individual questionnaire). Data for this questionnaire is collected from the individual responsible for the enterprise, unless interviewers fail to meet her/him after three trials as in the case of the individual questionnaire.

Cleaning operations

Raw Data

The data collection phase was then followed by the data processing stage accomplished through the following procedures: 1- Data coding This stage involved turning the text describing occupation, economic activity, educational attainment and geographic localities into numeric codes. Since one of the major objectives of this project was to compare data with the results of the 1988 labor survey, the research team decided to use the 1986 coding manuals for occupations and economic activities, despite the fact that CAPMAS has issued more recent coding manuals. However, for the coding of localities (administrative units) and educational attainment, the 1996 coding manuals were used, while making sure that the equivalent codes for 1986 be obtained. 2-Office checking Office checkers had many tasks to do. First, they had to review the consistency of replies throughout the different sections of the questionnaires for each household. Second, they had to translate the options chosen under "other" according to the lists generated by the coding team. Third, they had to prepare the questionnaires for the data entry stage. This included adding -9 and ?? in places of missing data4, deleting replies that were not applicable and making sure that the person number is written on all pages of the individual questionnaires as well as project numbers in the family enterprise questionnaire. The last task for the office checking team was to provide a list of the total production of each field interviewer and reviewer by counting household questionnaires, number of individuals interviewed (six year old and above) and number of family enterprises for each reviewer and interviewer. 3-Data Entry Data entry started before the end of the office checking stage. It lasted from February 16 till April 8, 1999 and took place at CAPMAS premises within the Statistics Department using the PCs and the LAN provided by ERF. This is not a regular arrangement since CAPMAS has a department for computer data processing. However, the arrangement proved to be significantly more efficient, specifically in comparison to the 1988 experience where the data processing stage took more than a year (Fergany, 1990:9). 4-Data Validation The data validation process works as follows: First, the program produces lists of likely or mandatory errors in each questionnaire, identifying the question number and the individual person number (pn). The four supervisors, with consultation with the two reviewers, read the program message carefully and consult the questionnaire for data validation. One of two measures takes place: either change the data upon reviewing the questionnaire, or hand-write a note on the list that although there could be an inconsistency in the data provided, the case at hand is a unique case and hence data should remain as is. The reviewer and supervisor both sign their names on the program printout beside the message and the decision they reached. If changes need to be done, data entry clerks are given directions to input them. During the data validation stage, the program pointed to discrepancies in the way occupations and economic activities were coded. As noted earlier, the ERF team decided to use the 1988 coding system to ensure comparability of data. However, the program pinpointed some inconsistent codes in relation to data in other parts of the questionnaire. The discrepant codes were mistakenly done according to the 1996 coding manual. As a result, two of CAPMAS specialists in coding were stationed at the data entry room to

Sequential optimisation process for the expressive one word picture vocabulary test using sampling weights (n = 1,027).

Description

Student's Academic Performance Dataset (xAPI-Edu-Data)

Data Set Characteristics: Multivariate

Number of Instances: 480

Area: E-learning, Education, Predictive models, Educational Data Mining

Attribute Characteristics: Integer/Categorical

Number of Attributes: 16

Date: 2016-11-8

Associated Tasks: Classification

Missing Values? No

File formats: xAPI-Edu-Data.csv

Source:

Elaf Abu Amrieh, Thair Hamtini, and Ibrahim Aljarah, The University of Jordan, Amman, Jordan, http://www.Ibrahimaljarah.com www.ju.edu.jo

Dataset Information:

This is an educational data set which is collected from learning management system (LMS) called Kalboard 360. Kalboard 360 is a multi-agent LMS, which has been designed to facilitate learning through the use of leading-edge technology. Such system provides users with a synchronous access to educational resources from any device with Internet connection.

The data is collected using a learner activity tracker tool, which called experience API (xAPI). The xAPI is a component of the training and learning architecture (TLA) that enables to monitor learning progress and learner’s actions like reading an article or watching a training video. The experience API helps the learning activity providers to determine the learner, activity and objects that describe a learning experience. The dataset consists of 480 student records and 16 features. The features are classified into three major categories: (1) Demographic features such as gender and nationality. (2) Academic background features such as educational stage, grade Level and section. (3) Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction.

The dataset consists of 305 males and 175 females. The students come from different origins such as 179 students are from Kuwait, 172 students are from Jordan, 28 students from Palestine, 22 students are from Iraq, 17 students from Lebanon, 12 students from Tunis, 11 students from Saudi Arabia, 9 students from Egypt, 7 students from Syria, 6 students from USA, Iran and Libya, 4 students from Morocco and one student from Venezuela.

The dataset is collected through two educational semesters: 245 student records are collected during the first semester and 235 student records are collected during the second semester.

The data set includes also the school attendance feature such as the students are classified into two categories based on their absence days: 191 students exceed 7 absence days and 289 students their absence days under 7.

This dataset includes also a new category of features; this feature is parent parturition in the educational process. Parent participation feature have two sub features: Parent Answering Survey and Parent School Satisfaction. There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not.

(See the related papers for more details).

Attributes

1 Gender - student's gender (nominal: 'Male' or 'Female’)

2 Nationality- student's nationality (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

3 Place of birth- student's Place of birth (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

4 Educational Stages- educational level student belongs (nominal: ‘lowerlevel’,’MiddleSchool’,’HighSchool’)

5 Grade Levels- grade student belongs (nominal: ‘G-01’, ‘G-02’, ‘G-03’, ‘G-04’, ‘G-05’, ‘G-06’, ‘G-07’, ‘G-08’, ‘G-09’, ‘G-10’, ‘G-11’, ‘G-12 ‘)

6 Section ID- classroom student belongs (nominal:’A’,’B’,’C’)

7 Topic- course topic (nominal:’ English’,’ Spanish’, ‘French’,’ Arabic’,’ IT’,’ Math’,’ Chemistry’, ‘Biology’, ‘Science’,’ History’,’ Quran’,’ Geology’)

8 Semester- school year semester (nominal:’ First’,’ Second’)

9 Parent responsible for student (nominal:’mom’,’father’)

10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100)

11- Visited resources- how many times the student visits a course content(numeric:0-100)

12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100)

13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100)

14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:’Yes’,’No’)

15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:’Yes’,’No’)

16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7)

The students are classified into three numerical intervals based on their total grade/mark:

Low-Level: interval includes values from 0 to 69,

Middle-Level: interval includes values from 70 to 89,

High-Level: interval includes values from 90-100.

Relevant Papers:

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

Citation Request:

Please include these citations if you plan to use this dataset:

• Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

The WHO standards are available on the website of WHO (http://www.who.int/childgrowth/en/). You find there the software Anthro2005 for calculation of individual data (Anthro2005). For calculating the z-scores of the new standards the LMS procedure is used. The principle is to get from a table for a certain age and sex 3 values (LMS) which are put into a formula For example WAZ = ((weight/M)^L-1)/(L*S)) e.g.: Female, 9kg, 365 days => L = -0.2022, M = 8.9462, S = 0.12267 WAZ = ((9/M)^L-1)/(L*S) = ((9/8.9462)^-0.2022-1)/(-0.2022*0.12267) = 0.04885 Z-scores On the WHO website you find only the LMS values for each month. Wt L M S 9 -0.2022 8.9462 0.12267 0.048847178 To calculate the actual weight from the LSM statistics at any cut off point use the following formula: wt = M * ((Z * S * L) +1)^(1/L)Where Z = Zscore you are trying to obtainThis is the reverse of the formula to calculate WAZ etc.And will give the standard to more decimal places for interpolation [purposesI have taken 10 boys and 10 girls at 0.5 cm intervals from 60 to 110cm in height with weight for height mean at -1Z with SD or 1.0. These are in the sheet WFH_Population.

For the simulation I have now added a random variable to the weight or the height in the POP_weight and the POP_height sheets.

Aerial survey photos of the Czech Republic. Photos from 1936 to 2002 were acquired by Ministry of Defence and Armed Forces of the Czech Republic, more precisely by its predecessors. Colour aerial survey photos, acquired from 2003 until 2010 in cooperation with CUZK and Ministry of Defence, are digitized images from analogue sources. Digital colour photos, which were acquired from 2010 in cooperation with CUZK and Ministry of Defence, are provided together with an image in NIR band. Historical photos (approx. 770 thousand photos from 1936 - 2002) are made available gradually, depending on the progress of the digitalisation. In addition, each year photos of a half of the Czech Republic are taken. Aerial survey photos are acquired by central projection, therefore they are not orthophotos and it is not possible to use them directly for measuring spatial relationships among geographical objects. Aerial survey photos can be used for measuring spatial relationship with the use of special photogrammetric methods.

Homeworking data from Labour Market Survey (LMS), split by age, sex, region, ethnicity and occupation, UK.

ABSTRACT Information Technology (IT) can be an important component for innovation as enables e-learning and it can provide conditions for an organization to be able to work with new businesses and improved processes. In this regard, Learning Management Systems (LMS) allow communication and interaction between teachers and students in virtual spaces. However, the literature indicates that there are gaps in research, especially concerning the use of IT for the management of e-learning. The purpose of this paper is to analyze the available literature about the application of LMS for the e-learning management, seeking to present possibilities for research in the field. An integrative literature review was performed considering the Web of Science, Scopus, Ebsco and Scielo databases, where 78 references were found, of which 25 were full papers. By eliminating duplication, 14 papers remained, which came to constitute the portfolio of the study. The analysis of the papers allowed to conclude that: 1) the most frequent research strategy was the quantitative; 2) survey was the most used research design; 3) the most frequent categories in the studied educational platforms belong to Instructional Resources and the less frequently ones belong to Interface and, 4) most of the studies are related to administrative function control; 5) LMS in e-learning management is still incipiently discussed in the literature. This analysis derives interesting characteristics from scientific studies, highlighting gaps and guidelines for future research, including learning analytics. The main contribution of this paper is related to the management of e-learning using LMS.

Abstract

This dataset defines the 2019 LiDAR survey extent of the Cooper GBA region floodplain. The total area of survey is 31,780 km². The extent is shown in a map figure which accompanies this dataset. The survey was conducted to develop a Digital Elevation Model and to determine bathymetry which is required for a two-dimensional hydrodynamic flood inundation model of the Cooper Creek floodplain. GBA shall have full Intellectual Property rights and ownership of all contract generated material produced as part of this survey project.

Attribution

Geological and Bioregional Assessment Program

History

The shapefile extent was developed to define the total area of LiDAR survey. The acquisition phases occurred over two mobilisations. The first mobilisation was flown from 20 March - 4 April, 2019 with an aircraft equipped with a Riegl LMS Q780 LiDAR system. The second mobilisation was flown from 6 - 17 October, 2019 with an aircraft equipped with a Riegl LMS Q1560 LiDAR system.