Open University Learning Analytics Dataset

Student Performance and Engagement Data at The Open University

By UCI [source]

About this dataset

This dataset provides an intimate look into student performance and engagement. It grants researchers access to numerous salient metrics of academic performance which illuminate a broad spectrum of student behaviors: how students interact with online learning material; quantitative indicators reflecting their academic outcomes; as well as demographic data such as age group, gender, prior education level among others.

The main objective of this dataset is to enable analysts and educators alike with empirical insights underpinning individualized learning experiences - specifically in identifying cases when students may be 'at risk'. Given that preventive early interventions have been shown to significantly mitigate chances of course or program withdrawal among struggling students - having accurate predictive measures such as this can greatly steer pedagogical strategies towards being more success oriented.

One unique feature about this dataset is its intricate detailing. Not only does it provide overarching summaries on a per-student basis for each presented courses but it also furnishes data related to assessments (scores & submission dates) along with information on individuals' interactions within VLEs (virtual learning environments) - spanning different types like forums, content pages etc... Such comprehensive collation across multiple contextual layers helps paint an encompassing portrayal of student experience that can guide better instructional design.

Due credit must be given when utilizing this database for research purposes through citation. Specifically referencing (Kuzilek et al., 2015) OU Analyse: Analysing At-Risk Students at The Open University published in Learning Analytics Review is required due to its seminal work related groundings regarding analysis methodologies stem from there.

Immaterial aspects aside - it is important to note that protection of student privacy is paramount within this dataset's terms and conditions. Stringent anonymization techniques have been implemented across sensitive variables - while detailed, profiles can't be traced back to original respondents.

How to use the dataset

How To Use This Dataset:

• Understanding Your Objectives: Ideal objectives for using this dataset could be to identify at-risk students before they drop out of a class or program, improving course design by analyzing how assignments contribute to final grades, or simply examining relationships between different variables and student performance.

• Set up your Analytical Environment: Before starting any analysis make sure you have an analytical environment set up where you can load the CSV files included in this dataset. You can use Python notebooks (Jupyter), R Studio or Tableau based software in case you want visual representation as well.

• Explore Data Individually: There are seven separate datasets available: Assessments; Courses; Student Assessment; Student Info; Vle (Virtual Learning Environment); Student Registeration and Student Vle. Load these CSVs separately into your environment and do an initial exploration of each one: find out what kind of data they contain (numerical/categorical), if they have missing values etc.

• Merge Datasets As the core idea is to track a student’s journey through multiple courses over time, combining these datasets will provide insights from wider perspectives. One way could be merging them using common key columns such as 'code_module', 'code_presentation', & 'id_student'. But make sure that merge should depend on what question you're trying to answer.

• Identify Key Metrics Your key metrics will depend on your objectives but might include: overall grade averages per course or assessment type/student/region/gender/age group etc., number of clicks in virtual learning environment, student registration status etc.

• Run Your Analysis Now you can run queries to analyze the data relevant to your objectives. Try questions like: What factors most strongly predict whether a student will fail an assessment? or How does course difficulty or the number of allotments per week change students' scores?

• Visualization: Visualizing your data can be crucial for understanding patterns and relationships between variables. Use graphs like bar plots, heatmaps, and histograms to represent different aspects of your analyses.

• Actionable Insights: The final step is interpreting these results in ways that are meaningf...

Over the five years through 2025-26, revenue is expected to increase at a compound annual rate of 2.3% to £5 billion. The numerous benefits of online education and training (e.g. reduced learning and development costs, time savings and flexibility and promotion of continuous development) have spurred growth. Recognising its advantages, the government has implemented a series of measures to hasten the uptake of educational technology by investing in improving household internet connections across the UK. The rise in student numbers has supported demand for additional training courses for those looking to boost their grades. The COVID-19 pandemic hiked revenue during 2020-21, with the forced closure of schools and universities pushing many courses online. Many universities are now committed to ensuring lectures and course material are uploaded online, a legacy of the COVID-19 technological wave. Revenue is anticipated to grow by 2.6% over 2025-26, with growth picking up again after naturally slowing following the surge in demand during the pandemic and encouragement from many critics to return to face-to-face learning to improve the learning experience and re-connect classmates. Over the five years through 2030-31, revenue is forecast to climb at a compound annual rate of 4.6% to £6.2 billion. The COVID-19 pandemic has hastened the adoption of online education and training, as lockdown periods normalised the use of technology and individuals have become accustomed to a new learning method. Unemployment rates are low, but a high number of vacancies remain, despite falling from COVID-19 highs, that aren't being met with the right skills, which is encouraging online learning and training. The number of UK 16- to 18-year-olds participating in full-time education is high, with record university applications that will boost online learning too. The growing skills gap will sustain demand as online platforms look to adapt to the changing job market and provide employees with the skills needed to secure work. The rise of free educational content through social media platforms like YouTube and LinkedIn will constrain future growth. The average profit margin is expected to expand to 18.4% in 2030-31.

This dataset is derived from a study that seeks to understand the determinants of student dropout in the courses offered at the Open University of Brazil system at the Federal University of Santa Maria. Are part of the population of all students who enrolled in graduated and postgraduate courses offered by OUB/UFSM, from 2005 to 2018, totaling 18,025 enrollments. The research instrument was sent by e-mail, at the Data Processing Center of UFSM, to the entire population and was available online, for 15 days. After this period, 859 valid instruments were obtained, of which 364 were regularly enrolled and 495 were evaded students. The research was approved by the Research Ethics Committee (CAAE: 00982218.0.0000.5346).

Open Educational Resources (OER) have been lauded for their ability to reduce student costs and improve equity in higher education. Research examining whether OER provides learning benefits have produced mixed results, with most studies showing null effects. We argue that the common methods used to examine OER efficacy are unlikely to detect positive effects based on predictions of the access hypothesis. The access hypothesis states that OER benefits learning by providing access to critical course materials, and therefore predicts that OER should only benefit students who would not otherwise have access to the materials. Through the use of simulation analysis, we demonstrate that even if there is a learning benefit of OER, standard research methods are unlikely to detect it.

Besides research, education is the raison d’être of each university. Education can help close equity gaps and maintain social cohesion between and within countries. In this context, the digitisation era offers new opportunities, for example, in the form of distance and online learning. However, innovations can also come with challenges, such as employed and unemployed people requiring to adapt to a progressing working environment at ever shorter intervals (life-long learning). Consequently, it is increasingly important to gain free access to up-to-date educational materials about a wide range of subjects and at multiple academic levels.
In this document, we introduce the concept of Open Educational Resources (OER). We start with establishing a definition of OER, what is needed to call educational materials OER, and the differences in comparison to related concepts, such as Massive open online courses. We then address the question of who can benefit from OER. It reports on the incentives to publish OER taking into account the perspectives of the involved stakeholders, i.e., the general public, universities and lecturers, and students. Afterwards, we pay attention to the challenges that come with OER. Subsequently, we provide a list of potential business models around OER, their underlying concepts, benefits, limitations, and projects making use of them. We also consider the paradox that OER are not intended to generate revenue but that ignoring income can make OER unsustainable. The document concludes by outlining possible steps to realize OER (e.g., organizing a round table to initiate a discussion about how to realise OER at the faculty level).

In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course.

The upload consists of the following .csv files:

BackgroundMassive Open Online Courses (MOOCs) have the potential to improve access to quality education for health care workers (HCWs) globally. Although studies have reported on the use of MOOCs in low- and middle-income countries (LMICs), our understanding of the scope of their utilization or access barriers and facilitators for this cohort is limited. We conducted a scoping review to map published peer-reviewed literature on MOOCs for HCW education in LMICs. We systematically searched four academic databases (Scopus, Web of Science, PubMed, ERIC) and Google Scholar, and undertook a two-stage screening process. The analysis included studies that reported on MOOCs relevant to HCWs' education accessed by HCWs based in LMICs.ResultsThe search identified 1,317 studies with 39 studies included in the analysis, representing 40 MOOCs accessed in over 90 LMICs. We found that MOOCs covered a wide range of HCWs' including nurses, midwives, physicians, dentists, psychologists, and other workers from the broader health care sector, mainly at a post-graduate level. Dominant topics covered by the MOOCs included infectious diseases and epidemic response, treatment and prevention of non-communicable diseases, communication techniques and patient interaction, as well as research practice. Time contribution and internet connection were recognized barriers to MOOC completion, whilst deadlines, email reminders, graphical design of the MOOC, and blended learning modes facilitated uptake and completion. MOOCs were predominantly taught in English (20%), French (12.5%), Spanish (7.5%) and Portuguese (7.5%). Overall, evaluation outcomes were positive and focused on completion rate, learner gain, and student satisfaction.ConclusionWe conclude that MOOCs can be an adequate tool to support HCWs' education in LMICs and may be particularly suited for supporting knowledge and understanding. Heterogeneous reporting of MOOC characteristics and lack of cohort-specific reporting limits our ability to evaluate MOOCs at a broader scale; we make suggestions on how standardized reporting may offset this problem. Further research should focus on the impact of learning through MOOCs, as well as on the work of HCWs and the apparent lack of courses covering the key causes of diseases in LMICs. This will result in increased understanding of the extent to which MOOCs can be utilized in this context.

Explore the booming MOOC industry, projected for massive growth with a CAGR of 39.20% and a market size reaching $22.80 million by 2025. Discover key drivers, emerging trends, and regional market insights shaping the future of online learning. Key drivers for this market are: Demand for Cost Effective Education Platforms, Increasing Requirement of Global Training. Potential restraints include: Low Course Completion Rate, Poor Discussion Forum and Mentoring. Notable trends are: Technology Subject is expected to hold Major Share.

Global massive open online course market size is set to expand from USD 16.03 Billion in 2023 to USD 210.98 Billion by 2032, at a CAGR of around 33.16%.

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 6th to March 3rd 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 No 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".

This dataset contains eight transcripts from interviews conducted with educators as part of a PhD study exploring open educational practices. The research focuses on how educators are using openly accessible sources of knowledge and open-source tools in ways that impact their pedagogical designs. Using a phenomenological approach with self-identifying open education practitioners, I explore how open educational practices are being actualized in formal higher education and impacting learning design. Specifically, I examine how educators are bringing elements of openness into their everyday teaching and learning practice using educational technologies. I draw upon Giddens (1986) structuration theory, further developed for use in technology adoption research most notably by DeSanctis and Poole (1994) and Orlikowski (2000). This approach positions technologies as being continually socially constructed, interpreted, and put into practice. In an organizational context, the use of technology is intrinsically linked with institutional properties, rules and norms, as well as individual perceptions and knowledge. The findings suggest that open educational practices represents an emerging form of learning design, which draws from existing models of constructivist and networked pedagogy. Open technologies are being used to support and enable active learning experiences, presenting and sharing learners work in real-time, allowing for formative feedback, peer review, and ultimately, promoting community-engaged coursework. By designing learning in this way, faculty offer learners an opportunity to consider and practice developing themselves as public citizens and develop the knowledge and literacies for working with copyright and controlling access to their online contributions, while presenting options for extending some of those rights to others. Inviting learners to share their work widely, demonstrates to them that their work has inherent value beyond the course and can be an opportunity to engage with their community.

The global massive open online courses market size was worth over USD 25.39 billion in 2025 and is poised to witness a CAGR of around 24.8%, crossing USD 232.71 billion revenue by 2035, attributed to increasing demand for affordable education.

Subject: EducationSpecific: Online Learning and FunType: Questionnaire survey data (csv / excel)Date: February - March 2020Content: Students' views about online learning and fun Data Source: Project OLAFValue: These data provide students' beliefs about how learning occurs and correlations with fun. Participants were 206 students from the OU

Vietnam Massive Open Online Courses market valued at USD 200 million, driven by flexible learning, digital literacy, and government support, with growth in internet penetration and online education adoption.

US MOOC market may grow over 22.07% CAGR by 2030, driven by rising demand for remote education and corporate training. Get a free sample of report today.

This dataset is part of the eNaBlS deliverable D1.1 to map the position of biodiversity and Nature-Based Solutions (NBS) in Europe. it provides an overview of platforms and repositories offering Open Educational Resources (OER) and other educational materials related to NBS and biodiversity. The table lists 31 identified platforms and webpages where relevant training materials, such as videos, webinars, MOOCs, modules, and full courses, can be accessed. It distinguishes between generic repositories (e.g., OER Commons, Coursera, TED Talks, Zenodo, Scientix), institutional repositories, and more topic-specific platforms, with Oppla, NBS EduWorld, and NetworkNature highlighted as key resources. The table also indicates the availability of open-source materials and specifies whether the resources are tailored for higher education (HE), technical and vocational education and training (TVET), or both. This compilation serves as a basis for the development of new educational materials in ongoing and future projects.

Explore the booming Massive Open Online Courses (MOOCs) market, driven by upskilling demands and digital education trends. Discover market size, CAGR, drivers, restraints, and key players for a comprehensive overview.

Abstract

This study aims to determine how educators at the University of Cape Town (UCT) engage with Open Education Resources (OER) and openness as part of developing open online courses, and how this informs their practices and attitudes afterwards. Deepening understandings of these changes is important for informing strategies involving helping educators in adopting productive Open Educational Practices (OEP).

In 2014, UCT initiated a Massive Open Online Course (MOOC) project in which 12 MOOCs were developed over a three-year period. This study employs an Activity Theory conceptual framework as a heuristic tool to investigate whether and how the integration of OER in the design of four of these MOOCs impacted upon educators' OEP. The research centred on the educators and their motivations, rather than the MOOCs per se or on the MOOC participants. While there was an interest in OER as content, it is the intersection of OER and educator practices which is the focus of the research.

The overarching question which this study set out to answer is: How does MOOC-making with OER adoption influence educators' Open Educational Practices?

The study draws on semi-structured interviews conducted with lead educators of the four MOOCs at three time intervals: just before the MOOC was launched (T1), after the first run of the MOOC (T2), and ten months after the launch (T3). A total of 19 interviews were conducted with MOOC lead educators. Certain questions were modified or additional questions asked in each of the lead educator interviews due to the semi-structured nature of the interviews and the differing subject matter and timing of the MOOCs.

This dataset makes a unique contribution to establishing empirical evidence about the practices of lead educators in a MOOC development process, how these practices are mediated, contextual considerations, and the kinds of tensions which arise as practices change. It will be of use to researchers and practitioners working in the areas of MOOC production, OER, Open Education, course development, and higher education studies.

Analysis unit

Units of analysis were individuals

Universe

The survey covered educators in the single institution involved in the study.

Kind of data

Qualitative data

Mode of data collection

Face-to-face [f2f]

With the growth of open pedagogy has come the growth of library support for open pedagogy. Likewise, more and more case studies are demonstrating how librarians use open pedagogy to support student growth in information literacy, specifically the Association of College & Research Libraries’ Framework for Information Literacy in Higher Education. However, little has been done to look at the broader picture of how librarians are supporting open pedagogy and how ready they feel to do so, especially in connecting open pedagogy to information literacy. This data comes from a survey of librarians in the United States and Canada who work in information literacy instruction and/or open education about their practice in open pedagogy.

Methods The project used a survey, created in Qualtrics, to help answer the research questions. The survey was designed with four broad sections. The first focused on gathering background information about the participants and their institution, as well as their comfort level with open pedagogy, support provided at their institutions for open education, and then finally whether they have supported open pedagogy in a higher education course. Those who answered negatively to supporting open pedagogy were then directed to a second section available only to them about their interest in eventually supporting open pedagogy. Those who answered they had supported open pedagogy were directed to a third section that asked them about their experience with open pedagogy. Finally, all participants were directed to the fourth section, which asked them about barriers and needs to help support open pedagogy.

The University of Nevada, Reno's Institutional Review Board granted the research project an exempt status. The survey was open to any active academic librarian in the United States or Canada who currently works in library instruction and/or open education. The authors opted to focus on these two areas of librarianship as the most likely areas to support open pedagogy.

The survey was launched to seven listservs: ACRL Scholarly Communication, ACRL Instruction Section, ACRL Library Instruction Roundtable, ACRL Community and Junior College Libraries Section, SPARC’s LibOER, Creative Commons Open Education Platform, and the Medical Library Association’s MEDLIB-L. Reminder emails were sent on July 19 and August 7. Two hundred and eight respondents began the survey; 15 did not meet the inclusion criteria, and 48 did not complete the survey, leaving 145 respondents. No question required a response, however, meaning response totals for some questions might be less than 145. The data was cleaned and analyzed using RStudio version 2022.02.3+492.

Open-text responses were removed from this dataset to help protect participants' privacy.

This study draws on the HarvardX Person-Course Academic Year 2013 De-Identified dataset, version 3.0 to explore patterns of learner engagement across HarvardX courses offered on the edX platform during Fall 2012, Spring 2013, and Summer 2013. The dataset comprises de-identified, aggregate-level records, where each entry corresponds to an individual's participation in a single course. It includes extensive information on registration, participation, and achievement, serving as a foundational resource for research into online learning behaviors and outcomes.

The dataset was released by HarvardX and curated in the Harvard Dataverse, providing open access to data from the first year of HarvardX courses (HarvardX, 2014). For a broader context and analysis of these data, see Ho et al. (2014), who present an overview in “HarvardX and MITx: The First Year of Open Online Courses.”

Citation (Harvard style): HarvardX (2014) HarvardX Person-Course Academic Year 2013 De-Identified dataset, version 3.0. Harvard Dataverse. https://doi.org/10.7910/DVN/26147

Ho, A., Reich, J., Nesterko, S., Seaton, D., Mullaney, T., Waldo, J. and Chuang, I. (2014) HarvardX and MITx: The First Year of Open Online Courses. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2381263