Data used by SLC to offer as reference data in the application process for Advanced Learning Loans.

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Learning Aim Reference Application (LARA) was the learning aims search for 2011 to 2014 for learning aims recognised by the Education Funding Agency and the Skills Funding Agency, both funded and non-funded. This is no longer being updated and was replaced with LARS-Lite. And LARS-Lite has now been replaced by Learning Aim Reference Service (LARS).

Education and training national achievement rate tables.Academic year: 2021/22 and 2022/23Indicators: Achievement rate, Pass rate, Retention rate, Leavers, Completers, AchieversFilters: Age group, Sector subject area, Qualification Type, Learning Aim Title with Learning Aim Reference, Level, Provider Type

Learning Aim Reference Service (LARS) offers a web based search facility. It allows users to search by most commonly used fields for Qualifications, Units, Apprenticeship Frameworks and Apprenticeship Standards and their associated validity and funding details.

LARS downloads are available to allow colleges, training organisations and employers (providers) to interrogate the same LARS data set that is used in the Funding Information System (FIS).

Other statistics published alongside the statistical first release. These are not National Statistics, but complement the information in the main release. FE trends FE trends provides an overview of adult (19+) government-funded further education and all age apprenticeships in England. It looks to provide trends between 2008/09 and 2013/14 and to give an overview of FE provision, characteristics of learners and outcomes over time. International Comparisons Supplementary Tables The Organisation for Economic Co-operation and Development (OECD) produces an annual publication, Education at a Glance, providing a variety of comparisons between OECD countries. The table provided here contains a summary of the relative ranking in education attainment of the 25-64 year old population in OECD countries in 2012. The OECD statistics use the International Standard Classification of Education. Within this, “at least upper secondary education” is equivalent to holding qualifications at Level 2 or above in the UK, and “tertiary education” is equivalent to holding qualifications at Level 4 or above in the UK. STEM This research is the result of a Department for Business, Innovation and Skills (BIS) funded, sector led project to gather and analyse data to inform the contribution that further education makes to STEM in England. This project was led by The Royal Academy of Engineering, and governance of the project was specifically designed to ensure that those with an interest in STEM were actively engaged and involved in directing and prioritising outputs. The November 2012 report builds on the FE and Skills STEM Data report published in July 2011 (below). It provides further analysis and interpretation of the existing data in a highly graphical format. It uses the same classified list of S,T, E and M qualifications as the 2011 report compiled through an analysis of the Register of Regulated Qualifications and the Learning Aim Database, updated with the most recent completions and achievements data taken from the Individualised Learner Record and the National Pupil Database.

A list of all non regulated learning aims which the Skills Funding Agency has approved for public funding in Community Learning for 2013/14.

One applies to new learners.

A separate list applies to continuing learners from 2012/13.

Education and training national achievement rate tables. Note qualification level does not reflect the level of attainment and is only provided to allow users to more easily filter for different types of provision.

From 1 August 2019, the Secretary of State for Education delegated responsibility for the commissioning, delivery and management of London’s Adult Education Budget (AEB) to the Mayor of London. The AEB helps Londoners to get the skills they need to progress both in life and work. The overarching aim of London’s AEB is to make adult education in London even more accessible, impactful and locally relevant. Through the London Learner Survey (LLS), the GLA are measuring how learners’ lives change after they complete their learning, whether that is, for instance, improvements in their health and wellbeing, progression in work or learning or entering new employment. The LLS surveys learners who have participated in GLA- funded AEB learning aims during the academic year. The survey focuses on seven outcomes, which can be divided into economic and social outcomes, as approved by the Mayor of London in the Skills Roadmap. The economic outcomes are progression into employment, progression within work and progression into further learning. Meanwhile, the social outcomes are improved health and wellbeing, improved social integration, improved self-efficacy and participation in volunteering. The LLS consists of two linked surveys of people participating in GLA-funded Adult Education Budget (AEB) learning: a baseline survey administered when the learner starts their course, and a follow-up survey approximately five to seven months after the end of learning. All eligible learners are asked to complete the survey .

This dataset documents the learning behaviors, learning outcomes, and technology proficiency of 123 liberal arts scholars.

Gateway courses are foundational prerequisite courses that undergraduate students must complete prior to enrolling in major courses (e.g., first-year mathematics, chemistry, psychology, statistics). Gateway courses often have high enrolment, and provide less support, structure, and feedback compared to previous experiences (e.g., secondary school). Declines in students' motivation and performance are common. This PhD project investigated two sources of engagement and motivation: self-efficacy and interest across two mathematics gateway courses. In particular, factors related to how self-efficacy and interest changed during the courses were examined across the studies. Four studies were conducted across five offerings of these two courses from 2020-2022. Participants were students enrolled in these courses. Study 1 (n=175; Sept-Dec 2020; Course 1) was conducted in an online (pandemic) setting. The interplay between students' (amounts of) self-efficacy, interest, and performances (i.e., quizzes) across the course was investigated. Study 2 (n=349; Sept-Dec 2021; Course 1) was conducted the next year, and examined how overall self-efficacy changes, and how those changes were associated with performances across a course, and interest at the end of the course. Study 3 (n=313; Sept-Dec 2021; Course 2) investigated short-term changes in interest, and how they were related to performance, and self-efficacy. Lastly, Study 4 contained two studies (n=299; n=407; Studies 4a, 4b; Courses 1 & 2) that investigated the interplay between perceived difficulty on performance tasks (i.e., quizzes), short-term changes in self-efficacy, performances, and interest (in the second study).The data files are the datasets used to conduct the analyses across the four studies. These included students' responses on formative quizzes, and self-reported data on self-efficacy, interest, perceived difficulty, and gender. These data were used for quantitative analysis using MPlus and other software. Each folder contains the relevant files each study (presented in the respective chapter of the thesis).1) Chapter 3 - Study 1 contains the dataset used for the first study. This study is already published.2) Chapter 4 - Study 2 contains the datasets used for the second study, including for the full model, invariance and reliability testing, and dataset for IRT.3) Chapter 5 - Study 3 contains the datasets used for the third study, including for the full model and dataset for IRT.4) Chapter 6 - Study 4 (Studies 4a and 4b) contains the datasets used for the last study, including those used for the full model, dataset for IRT, and perceived difficulty.

This report reviews the collection, availability and quality of system-level data and metadata on education from countries participating in the PISA for Development project: Cambodia, Ecuador, Guatemala, Paraguay, Senegal and Zambia. PISA for Development aims to increase low income countries’ use of PISA assessments for monitoring progress towards national goals for improving education and for analysing the factors associated with student learning outcomes, particularly among poor and marginalised populations. The project also helps track progress towards the international education targets defined in the Education 2030 Framework for Action, which the international community adopted in 2015 as the strategy for achieving the Education Sustainable Development Goal (SDG). The report suggests technically sound and viable options for improving data quality, completeness and international comparability in the six countries that are reviewed. It also provides insights into overcoming some of the challenges common to countries that participate in PISA for Development and to other middle income and low income countries.

This dataset contains the Country Learning Outcomes (CLO) of harmonized learning assessments, which includes PISA, TIMSS, PIRLS, LLECE, PASEC, SEA-PLM, AMPL-b, and SACMEQ. The country level estimates are also disaggregated by sex, urban/rural, and wealth quintile.Eligible assessments are also used to generate the Learning Deprivation component of the latest Learning Poverty estimates. The June 2022 release of Learning Poverty estimates involves several changes to the data underlying the country-level Learning Poverty figures. Some country-level estimates have changed or become available for the first time due to new learning data from recent assessments: AMPL-b 2021, TIMSS 2019, LLECE 2019, PASEC 2019, and SEA-PLM 2019. In the June 2022 release, country-level estimates of Learning Poverty are available for 122 countries. A new global aggregate was also created, and the accompanying Global Learning Poverty Database includes the measures of GAP and SEVERITY for both Learning Deprivation and Learning Poverty, as introduced by Azevedo (2020).

This database presents the results of nine different scales aimed at directly evaluating learning outcomes as generic attributes in engineering programs, as defined by the Washington Accord and the International Alliance of Engineering. Data was collected at a higher education institution focused on engineering and technology as part of quality assurance processes. Each scale features a distinct number of indicators. The data correspond to the following scales: AC (Lifelong Learning), AF (Project Management and Finance), AP (Problem Analysis), DI (Design/Development of Solutions), EE (Ethics), HC (Communication), HI (Tool Usage), IN (Investigation), and TE (Individual and Collaborative Teamwork).

The two datasets shared here are: (1) participants' self-reported ratings for their four-level learning outcomes, and (2) transcriptions of seven small-group discussion sessions.

Analysis of ‘Harmonized Learning Outcomes (HLO) Database’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/search/dataset/0038001/ on 21 November 2021.

--- Dataset description provided by original source is as follows ---

Learning metrics that are comparable for countries globally are necessary to understand and track the formation of human capital. The increasing use of international achievement tests is an important step in this direction. However, such tests are administered primarily in high-income countries, limiting our ability to analyze learning patterns in low- and middle-income countries that may have the most to gain from the formation of human capital. The Harmonized Learning Outcomes (HLO) database bridges this gap by constructing a globally comparable database of 164 countries from 2000 to 2017. The data represent 98% of the global population and developing economies comprise two-thirds of the included countries. The data is publicly available and will be updated regularly.

--- Original source retains full ownership of the source dataset ---

🎓 Coursera Courses Metadata Dataset

🏷️ Overview

This dataset provides comprehensive, structured information about online courses offered on Coursera.

Each entry corresponds to a single course and captures essential metadata such as its title, category, URL, language, instructors, key learning outcomes, and associated skills.

Designed for education researchers, data scientists, and developers, this dataset enables analysis and innovation in the domains of:

📚 Learning Analytics & Education Data Mining

🤖 Recommender Systems for Online Learning

🧠 Skill Extraction and Curriculum Mapping

🌍 MOOC Landscape and Accessibility Studies

📂 Dataset Structure

💡 Potential Use Cases

📈 Analyze course trends by category, skill, or language

🧭 Build recommendation systems for online education platforms

🧩 Map relationships between skills and academic disciplines

🌐 Study linguistic diversity and accessibility in global MOOCs

🏫 Support academic research in learning design and curriculum analytics

⚖️ License

License: CC BY-NC-SA 4.0

✅ Free to use for educational and research purposes

🚫 Commercial use is not permitted

📢 Please attribute Coursera as the original data source

All course data and trademarks belong to Coursera and the respective content creators.

🧾 Citation

If you use this dataset, please cite as:

“Coursera Courses Metadata for Data and Learning Analytics (2025). Collected by Phi Long Nguyen. Available on Kaggle.”

🔗 Disclaimer

This dataset was created from publicly available Coursera course metadata. It does not include any copyrighted materials, paid content, or private data. It is intended solely for non-commercial academic and research use.