Probability Sample: Stratified Sample: Disproportional

Data collection in order to examine the relationships between social presence and satisfaction in online learning. In addition, the present study identified, validated, and examined the items and factors that contribute social presence and satisfaction in online learning. The present study uses cross-sectional survey design where the researcher collects the data one point in time. Quantitative data collection consists of self-report survey data from surveys administered to participants online as earlier mentioned. The participants surveyed respond to specific questions regarding to some dimensions about social presence and satisfaction dimension. For survey research process in this study, there are several phases as a guideline as follows: (1) establishing information base, (2) determining sampling frame, (3) determining the sample size and sample selection procedures, (4) designing the survey instrument, (5) pretesting the survey instrument, (6) implementing the survey, and computerizing the data, and (8) analyzing the data and preparing the final report.

Covid-19 has prompted higher education institutions around the globe to relocate offline classes to online classes. Universities in Indonesia were no exception. Indonesia has already developed distance education systems, and there were many challenges to utilizing e-learning. Due to the pandemic, all colleges across Indonesia were compelled to use online platforms to resume their studies.

This study indicates e-learning perceptions' importance in knowledge mastery, social competence, and media literacy abilities. The study assesses college students' attitudes toward e-learning during the ongoing COVID-19 and will be utilized as an evaluation tool by The Higher Education of Education and Culture of the Republic of Indonesia.

The research methods used with a quantitative model, where the sample tested represented the student of 1137 [DJ1] respondents from 43 universities in Indonesia.

The study's findings show a commonly perceived weakness of e-learning was that the majority of respondents got a lack interaction with lecturers (57,6%). The study also identified that the majority of respondents have a moderate mastery of technology 1038 respondents (77.6%), while the remainder has poor and high knowledge of technology. One of the key reason students implement e-learning is the ease with which they may obtain study resources.

According to the study, e-learning technology enables quick access to information, which results in students developing a favorable attitude toward it based on its utility, self-efficacy, the convenience of use, and student behavior related to e-learning. The study verifies the utility of e-learning by demonstrating how it enables students to study from any geographical location, which is not achievable with face-to-face instruction.

The study explores university students' perceptions of e-learning in the context of the ongoing COVID-19 epidemic. The study finds that students prefer e-learning because it enables them to connect with their lecturers and fellow students and engage with their study materials at their leisure, and with the freedom to choose their preferred location and time. One of the key reason students choose e-learning is the ease with which they may obtain study resources. The research methods used with a quantitative model, where the sample tested represented the student of 1137 respondents from 43 universities in Indonesia. The study's findings show the weakness of e-learning is that the majority of respondents responded in turn to interaction with lecturers (52,7%). The study also identified that the majority of respondents have a moderate mastery of technology, 1038 individuals (77.6%), while the remainder has poor knowledge of technology, as many as 52 people (3.9%). According to the study, e-learning technology enables quick access to information, which results in students developing a favorable attitude toward it based on its utility, self-efficacy, the convenience of use, and student behavior related to e-learning. The study verifies the utility of e-learning by demonstrating how it enables students to study from any geographical location, which is not achievable with face-to-face instruction.

Probability Sample: Stratified Sample: Disproportional

This is the dataset "European remote e-learning ecosystem survey data" of the e-DIPLOMA project.

In 2022 the evaluation of the European tertiary training ecosystem capacity for using disruptive technologies in practice based e-learning was explored. It was done in the eDiploma project WP2. The research problem was: What are the main gaps in tertiary education in the institutional capacity to perform practice based e-learning with disruptive technologies? Three survey instruments were developed for three target groups in institutions: technology specialists, educators and students. The survey was composed of four blocks of capacity elements:

infrastructural capacities,

normative and regulatory capacities (institutional level),

teaching cultures (community level),

competences, attitudes and values (personal level).

The data were collected with the anonymous web based survey approach in countries: Spain, Estonia, Hungary, Bulgaria, Italy, Cyprus.

In each HEI or VET institution the respondents were:

Technical and didactical support staff: educational technologist, IT or technical support specialists, lecturers responsible for technology training, Digital policy administrative specialist

Lecturers or researchers who have experiences with some forms of group-learning or practice based learning

Students from the institution who have experiences with some forms of group-learning or practice based learning / to be spread among each institution, so that different areas students respond, these should not be one group from one class only)

The answers were collected totally from the following number of the technology specialists-experts (N=96), the educators (N=351), and the students (N=516). The generalizability of the data is limited due to the sampling structure: it was not attempted to reach regional coverage because countries in our sample differ greatly in size. In Estonia responses were collected from 9 institutions (3 vocational schools and 6 HEIs). In Bulgaria responses were from 3 institutions (all HEIs). In Cyprus responses were from 3 institutions (all HEIs). In Hungary responses were from 6 institutions (1 vocational school and 5 HEIs). In Spain responses were from 116 institutions (28 high schools, 41 vocational schools, 47 HEIs). In Italy responses were from 9 institutions (4 HEIs and 5 social enterprises).

The learning management system (LMS) is claimed to be a crucial strategy for creating successful e-learning and teaching methods, enhancing students’ learning satisfaction and achieving academic outcomes. Although the Indonesia Open University has implemented e-learning innovation through LMS in higher education, it has not gained popularity. Hence, the present research aimed to investigate the updated DeLone and McLean information system (IS) success model on the effectiveness of LMS implementation, focusing on system quality, information quality, service quality, perceived usefulness and user satisfaction. A total of 386 respondents from undergraduate and postgraduate programs were selected through stratified random sampling. Subsequently, research data were collected through online survey questionnaires administered to students enrolled at the Indonesia Open University. Structural equation modeling with AMOS version 24 software was deployed to analyze data and test hypothesis. Findings revealed that the updated DeLone and McLean IS success model had a positive and significant influence on the effectiveness of LMS. Therefore, the top-level management of the Indonesia Open University and decision-makers in the Indonesian government could provide a better LMS practice environment infrastructure utilizing the updated DeLone and McLean IS success model.

Snapshot img

Self-Paced E-Learning Market Size 2025-2029

The self-paced e-learning market size is forecast to increase by USD 6.96 billion, at a CAGR of 2.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the availability of subject proficiency assessments and certifications. These assessments enable learners to measure their progress and gain recognition for their achievements, making self-paced e-learning an attractive option for individuals seeking to upskill or reskill. Additionally, the popularity of microlearning, which offers short, focused learning modules, has expanded the market's reach. This flexible learning format caters to learners' busy schedules and diverse learning styles, further fueling market growth. However, the increasing number of free online courses poses a challenge for market players. As more free resources become available, competition intensifies, and providers must differentiate themselves through high-quality content, user experience, and additional features to maintain market share. To capitalize on opportunities and navigate challenges effectively, companies should focus on delivering personalized learning experiences, leveraging technology to enhance engagement, and continuously improving content offerings.

What will be the Size of the Self-Paced E-Learning Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the shifting dynamics of talent development and curriculum design in various sectors. Marketing automation and data analytics play a pivotal role in reaching and engaging learners through personalized and interactive approaches. Technical training and corporate learning are embracing subscription models, enabling flexible access to software training and professional development. E-learning platforms seamlessly integrate pricing strategies, SCORM compliance, and blended learning, offering a mix of self-paced and instructor-led sessions. Digital marketing, learning analytics, and user segmentation help target the right audience with tailored content and revenue models. A/B testing and sales funnels optimize the learning experience, ensuring alignment with learning objectives. Course authoring tools and analytics dashboards facilitate the creation and tracking of progress in self-paced learning, while virtual classrooms and video tutorials provide opportunities for live sessions and interactive learning. User experience (UX) and content strategy are crucial in delivering engaging and effective educational content. Adaptive learning and social media marketing cater to the diverse needs of learners, enhancing their overall experience. Elearning authoring tools and progress tracking enable the creation and management of online courses, while customer personas guide the development of effective educational content. In this ever-changing landscape, the market continues to unfold, offering innovative solutions for talent development, curriculum design, and professional growth.

How is this Self-Paced E-Learning Industry segmented?

The self-paced e-learning industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductPackaged contentServicesEnd-userStudentsEmployeesGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Product Insights

The packaged content segment is estimated to witness significant growth during the forecast period.The market is witnessing significant growth due to the increasing adoption of technology in talent development and corporate training. Packaged e-learning content, which includes on-demand, off-the-shelf courses, is gaining popularity for its effectiveness, contextualization, and precision. This segment encompasses various elements, such as videos, gamified content, and microlearning, catering to the increasing demand for personalized learning experiences. The education sector, particularly post-secondary institutions, and corporations are major contributors to this market's growth. The need for off-the-shelf courses that can be easily integrated into existing curriculum design and training programs is driving the demand for packaged e-learning content. companies, such as City and Guilds Group, are meeting this demand by offering a wide range of courses. Marketing automation and data analytics are essential tools for e-learning platforms to optimize pricing strategies, learning objectives, and sales funnels. These platforms also offer features like A/B testing, compliance training, and progress tracking to cater to the diverse

Anonymised dataset of 150 online questionnaire (2017) and classroom survey (2018) responses to investigate the role of Emotional Attachment (EA) for online learning. The continuous and ordinal data covers learning background, DV (learning success) & various indicators (constituents) of the IV (Emotional Attachment). The data also includes some qualitative (open-ended) views about the relative importance of EA for learning. Methodology and data The research used mixed methods and sequential explanatory approach to investigate how learning dispositif (LD) influences emotional attachment (EA) and to identify the significant constituents of EA. The first research phase involved the development of the explanatory framework from the diverse strands of literature, as discussed. For the second phase, the research developed a survey instrument, geared around the learning framework and surveyed 150 international tertiary level students in Europe, Russia, Asia, the Middle East and China. Finally, for its third phase to enrich its insights into EA and learning the research conducted qualitative expert interviews.

To identify survey respondents, the research used pragmatic, convenience sampling, distributing the instrument either online or in hard copy version to accessible university cohorts. As far as we are aware, there exists no other quantitative studies on EA and learning, so it was not possible to conduct a meta-analysis to investigate effects size. The research generated three questions and associated hypotheses. The first question concerned the role of LD. The second involved the influence of EA on LS. The third queried the multi-faceted constituents of EA itself. Formally: 1. RQ1: Does LD influence LS? LD is insignificant (H-LD0) for LS vs. the alternative hypothesis that cohort LS varies (H-LD1) 2. RQ2: Does EAs significantly influence learning success? EA is insignificant for learning success (H-EA0) vs. the alternative that EA is significant (H-EA1) 3. RQ3: What are EA’s main constituents? The draft framework underpinned the questions for an online survey of learners and instructors. Likert scales assessed student and instructor perceptions or attitudes to various aspects of EA and learning but the survey also included some open-ended questions. Subsequently, expert interviews and discussions validated and enriched the refined framework. After ethical review, the research piloted interviews with a couple of experienced UK instructors to fine-tune the instrument. Although much of the data was ordinal, it was analysed using non-parametric and parametric statistics.

Snapshot img

Corporate E-Learning Market Size 2025-2029

The corporate e-learning market size is forecast to increase by USD 131.01 billion at a CAGR of 12.7% between 2024 and 2029.

The market is experiencing significant growth due to the reduction in employee training costs for employers and the increasing adoption of microlearning. Companies are recognizing the cost-effective benefits of e-learning, enabling them to train large workforces more efficiently and economically. Furthermore, the flexibility and convenience of microlearning, which allows learners to access content in short, bite-sized modules, is driving widespread adoption. However, regulatory hurdles impact adoption in certain industries, particularly those with stringent compliance requirements. Additionally, supply chain inconsistencies in delivering high-quality e-learning content can temper growth potential.
To capitalize on market opportunities, companies must focus on developing effective e-learning strategies, ensuring regulatory compliance, and addressing supply chain challenges to deliver high-quality, cost-effective training solutions. By staying abreast of market trends and addressing these challenges, organizations can effectively leverage e-learning to enhance employee skills, improve productivity, and drive business growth.

What will be the Size of the Corporate E-Learning Market during the forecast period?

Request Free Sample

The US is experiencing significant growth, driven by the increasing importance of performance support, talent management, and employee development. Learning resources are becoming more immersive, with learning journeys that incorporate case studies, personalized recommendations, and learning analytics dashboards. Learning objectives are being met through live training sessions via video conferencing and data visualization tools. Performance evaluation is enhanced through AI-powered learning, which provides learning insights and 360-degree feedback. The skills gap is being addressed through training needs analysis and learning paths that lead to knowledge repositories.
Learning communities foster peer-to-peer learning, while virtual assistants offer just-in-time support. Scenario-based learning and interactive simulations are gamified to engage learners, while knowledge bases are being enriched with the latest learning technologies, such as AI and social learning. The future of work is shaping the training budget, with assessment tools and learning paths being prioritized to ensure a skilled workforce. In this evolving landscape, companies are offering innovative solutions to meet the diverse needs of businesses. Learning resources are becoming more accessible through mobile learning and knowledge sharing, enabling employees to learn on-the-go. The use of AI in learning is revolutionizing the industry, providing a more personalized and effective learning experience.

How is this Corporate E-Learning Industry segmented?

The corporate e-learning industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Services
  Manufacturing
  Retail
  Others


Deployment

  On-premises
  Cloud-based


Learning Type

  Distance Learning
  Instructor-led Training
  Blended Learning


Technology

  Web-Based
  LMS
  Learning Content Management Systems
  Podcasts
  Virtual Classrooms
  Mobile E-Learning


Training Type

  Instructor-led & Text-based
  Outsourced


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The services segment is estimated to witness significant growth during the forecast period.

In the dynamic hospitality industry, where customer interactions are frequent and expectations are high, continuous employee development is crucial for maintaining superior service quality. E-learning solutions have gained popularity due to their cost-effective and flexible nature, enabling organizations to train employees regularly. However, the industry's high turnover rate, resulting from long working hours and demanding customers, necessitates addressing skill gaps. Consequently, organizations invest in various e-learning approaches, such as performance support, talent management, and employee development, to ensure a well-informed and skilled workforce. Advancements in learning technologies, including mobile learning, knowledge sharing, and social learning, facilitate accessible and collaborative training opportunities.

Artificial intelligence and machine learning enhance the learning experience by personalizing cont

This spring, students across the globe transitioned from in-person classes to remote learning as a result of the COVID-19 pandemic. This unprecedented change to undergraduate education saw institutions adopting multiple online teaching modalities and instructional platforms. We sought to understand students’ experiences with and perspectives on those methods of remote instruction in order to inform pedagogical decisions during the current pandemic and in future development of online courses and virtual learning experiences. Our survey gathered quantitative and qualitative data regarding students’ experiences with synchronous and asynchronous methods of remote learning and specific pedagogical techniques associated with each. A total of 4,789 undergraduate participants representing institutions across 95 countries were recruited via Instagram. We find that most students prefer synchronous online classes, and students whose primary mode of remote instruction has been synchronous report being more engaged and motivated. Our qualitative data show that students miss the social aspects of learning on campus, and it is possible that synchronous learning helps to mitigate some feelings of isolation. Students whose synchronous classes include active-learning techniques (which are inherently more social) report significantly higher levels of engagement, motivation, enjoyment, and satisfaction with instruction. Respondents’ recommendations for changes emphasize increased engagement, interaction, and student participation. We conclude that active-learning methods, which are known to increase motivation, engagement, and learning in traditional classrooms, also have a positive impact in the remote-learning environment. Integrating these elements into online courses will improve the student experience.

Probability Sample: Stratified Sample: Disproportional

The study involved data analyses using SPSS to analyse the survey data collected from undergraduate students in Multimedia University. The sampling technique applied for this study was convenient sampling technique. For data collection purpose, self-administered online surveys was applied in this study. A total of 210 diploma students responded to the online surveys. As for data analyses, the statistical technique applied in the context of this study was multiple regression analysis.

        Based on the feedbacks from students’ surveys, the findings of this study provided

understanding on the factors affecting virtual learning together with the impact on students’ performance in virtual learning environment. The factors affecting virtual learning were segregated to three categories, which are: virtual teaching techniques, technology and environment distraction. https://forms.gle/uJ8VZ6sD82KzXvQ8A

The American Institutes for Research conducted a multisite randomized study that tested an online learning model for credit recovery at 24 high schools in Los Angeles, California in 2018 and 2019. The study focused on first-year high school students who failed Algebra 1 or English 9 (their ninth-grade English course) and retook the course during the summer before their second year of high school. Within each participating school, we used a lottery to determine whether each student was placed in either the school’s typical teacher-directed class (business-as-usual control condition) or a class that used an online learning model (treatment condition). For the online learning model, an online provider supplied the main course content, and the school provided a subject-appropriate, credentialed in-class teacher who could supplement the digital content with additional instruction.The study compared outcomes of students assigned to the treatment condition to outcomes of students assigned to the control condition. Analyses focused both on proximal outcomes (ex: student course experiences, content knowledge, and credit recovery rates) and distal outcomes (ex: on-time graduation and cumulative credits earned by the end of the 4th year of high school). We estimated average treatment effects for the intent-to-treat sample using regression models that control for student characteristics and randomization blocks. We conducted separate analyses for students who failed Algebra 1 and students who failed at least one semester of their English 9 course.This ICPSR data deposit includes our final analytical dataset and three supplemental files. Data come from three sources: (1) extant district data on student information and academic outcomes, (2) end-of-course surveys of students’ and teachers’ experiences, and (3) end-of-course test of students’ content knowledge. Data fields include:Sample information: term, school (anonymized), teacher (anonymized), course, randomization block, student cohort, treatment statusDemographics: sex, race/ethnicity, National School Lunch Program status, inclusion in the Gifted/Talented program, Special Education status, and English language learner statusPre-treatment information (treatment group only): 9th grade GPA, 9th grade attendance rate, number of 9th grade courses failed, 8th grade test scoresOnline course engagement information: percentage of online course completed, average score on online activities, minutes spent in online platformStudent survey data: responses a survey administered at the end of the course for treatment and control students. Questions cover degree of student engagement with the course, perceptions of teacher support and course difficulty, and clarity of course expectations.End-of-course test data: answers and scores on an end-of-course assessment administered to treatment and control students to evaluate content knowledge (Algebra 1 or English 9). The test did not count towards the final course grade and included 17-20 multiple choice questions.Academic outcomes: grade in credit recovery course, credits attempted/earned in each year of high school, GPA in each year of high school, credits/GPA in math and ELA in each year of high school, indicator for on-time high school graduation, 10th grade PSAT scoresTeacher survey and logs: teacher-reported logs on the use of different instructional activities and responses to surveys about course pacing, content, goals, and degree of student support

Pre & post analysis of e-learning sessions for mothers.

The Chinese E-Learning market has surged dramatically over the past decade, transforming how education and training are delivered across various sectors. Currently valued at over USD 50 billion, this market has experienced a steady annual growth rate of approximately 20%, driven by increased internet penetration, th

Open Science in (Higher) Education – data of the February 2017 survey

This data set contains:

• Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.
• Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.
• Readme file (txt)

Survey structure

The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS).

Demographic questions

Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:

• Natural Sciences
• Arts and Humanities or Social Sciences
• Economics
• Law
• Medicine
• Computer Sciences, Engineering, Technics
• Other

The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:

• Professor
• Special education teacher
• Academic/scientific assistant or research fellow (research and teaching)
• Academic staff (teaching)
• Student assistant
• Other

We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.

Remark on OER question

Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER^[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them.

Data collection

The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.

The survey was online from Feb 6^th to March 3^rd 2017, e-mails were mainly sent at the beginning and around mid-term.

Data clearance

We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.

Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N^o 24 (email address).

References

Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.

First results of the survey are presented in the poster:

Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561

Contact:

Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.

[1] https://www.limesurvey.org

[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”.

The main aim of this work was to explore how online training events, outside of traditional academic courses, can support Social Science, Arts and Humanities (SHAPE) students to develop data skills.

There is international demand for data skills in the workplace, and Governments are increasingly concerned with how quantitative data skills are acquired for 21st Century jobs. If SHAPE students are trained in data skills they can enter into statistical professions and data careers. Online learning has risen significantly in the last decade, and even more so since the COVID-19 pandemic. Within the field of social research methods, online learning has grown rapidly and there is an emerging literature on the pedagogy. Data skills are vital for research and there is acknowledgement that online learning can play a role in data skills training.

The data collection contains anonymised transcripts from 8 qualitative semi-structured individual interviews with SHAPE students who attended UK Data Service online training events. The interviews explored why the student had attended the UKDS event, what they learnt from it, how it has supported the development of data skills and how this will help their career. Ten students participated in the original study – we have consent from eight of ten the students to share their anonymised data in this repository.

Probability Sample: Stratified Sample: Disproportional

In a 2019 survey, 23 percent of grade 6-12 students reported that their teacher spent all of the class using digital learning technologies to teach in science, math, and history/social studies. All of these were significantly below computer science/information technology though, with 60 percent of surveyed students stating that they were taught using digital learning tools for the entire class.

Note the time teachers spend teaching a subject with digital tools was differentiated in the survey from the time students spent learning with digital learning tools. For almost all subjects, the time spent learning with digital tools was lower than the time spent teaching.