The datasets provided by UK based online learning university "Open University". More about the dataset: https://analyse.kmi.open.ac.uk/open_dataset

Context

MOOC dataset to study behavior of students for online courses.

Content

It contains data about courses, students and their interactions with Virtual Learning Environment (VLE) for seven selected courses (called modules). Presentations of courses start in February and October - they are marked by “B” and “J” respectively. The dataset consists of tables connected using unique identifiers. All tables are stored in the csv format.

Acknowledgements

Kuzilek J., Hlosta M., Zdrahal Z. Open University Learning Analytics dataset Sci. Data 4:170171 doi: 10.1038/sdata.2017.171 (2017).

The Open University (OU) dataset is an open database containing student demographic and click-stream interaction with the virtual learning platform. The available data are structured in different CSV files. You can find more information about the original dataset at the following link: https://analyse.kmi.open.ac.uk/open_dataset.

We extracted a subset of the original dataset that focuses on student information. 25,819 records were collected referring to a specific student, course and semester. Each record is described by the following 20 attributes: code_module, code_presentation, gender, highest_education, imd_band, age_band, num_of_prev_attempts, studies_credits, disability, resource, homepage, forum, glossary, outcontent, subpage, url, outcollaborate, quiz, AvgScore, count.

Two target classes were considered, namely Fail and Pass, combining the original four classes (Fail and Withdrawn and Pass and Distinction, respectively). The final_result attribute contains the target values.

All features have been converted to numbers for automatic processing.

Below is the mapping used to convert categorical values to numeric:

code_module: 'AAA'=0, 'BBB'=1, 'CCC'=2, 'DDD'=3, 'EEE'=4, 'FFF'=5, 'GGG'=6

code_presentation: '2013B'=0, '2013J'=1, '2014B'=2, '2014J'=3

gender: 'F'=0, 'M'=1

highest_education: 'No_Formal_quals'=0, 'Post_Graduate_Qualification'=1, 'HE_Qualification'=2, 'Lower_Than_A_Level'=3, 'A_level_or_Equivalent'=4

IMBD_band: 'unknown'=0, 'between_0_and_10_percent'=1, 'between_10_and_20_percent'=2, 'between_20_and_30_percent'=3, 'between_30_and_40_percent'=4, 'between_40_and_50_percent'=5, 'between_50_and_60_percent'=6, 'between_60_and_70_percent'=7, 'between_70_and_80_percent'=8, 'between_80_and_90_percent'=9, 'between_90_and_100_percent'=10

age_band: 'between_0_and_35'=0, 'between_35_and_55'=1, 'higher_than_55'=2

disability: 'N'=0, 'Y'=1

student's outcome: 'Fail'=0, 'Pass'=1

For more detailed information, please refer to:

Casalino G., Castellano G., Vessio G. (2021) Exploiting Time in Adaptive Learning from Educational Data. In: Agrati L.S. et al. (eds) Bridges and Mediation in Higher Distance Education. HELMeTO 2020. Communications in Computer and Information Science, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-67435-9_1

The Open University Learning Analytics Dataset (OULAD) will be used for the performance evaluation of the suggested method utilizing f1-score, accuracy, precision, and recall metrics.

GENERAL INFORMATIONThe dataset represents supporting data for the research findings of the paper accepted for AIED'21 conference: http://oro.open.ac.uk/76042/ SHARING/ACCESS INFORMATIONLinks to publications that cite or use the data: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.Was data derived from another source? Yes - the data was derived from the internal OU data Recommended citation for this dataset: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.DATA & FILE OVERVIEWThe dataset contains coefficients of a logistic and linear regression that was used to model 3 student outcomes in 3 STEM courses - 1) completion, 2) passing and 3) overall score. The results are split into four tabs1. Regression BetasBets coefficients and the Standard Error for each variable student outcome , i.e. - completion: comp_B comp_SE - passing: pass_B pass_SE - overall score: score_B score_SE 2. LogReg Marginal Effectsthe marginal effect coefficients for the two dichotomous outcomes from the previous tab (completion and passing) More information about the marginal effects: https://www.statisticshowto.com/marginal-effects/3. Reg_BAME - These are the regression coefficients reported in the in the first tab, for the same outcomes (i.e. completion/passing/overall score), but disaggregated by whether the student is identified as BAME or not. Note that the analysis does not contain the 'BAME' coefficients, because it would be constant4. Red_IMDSimilarly as for BAME (point 3), these are regression coefficients disaggregated by IMD quintiles. IMD_Missing is a special category capturing the students without any IMD, i.e. international students.Regression coefficient variablesThe variables entering the regressions can be split into three categories and the intercept(1) Student level - age - banded into age_60, age_MISSING (reference category: age_[21-24]) - gender - gender_F (reference category Gender_M) - an indicator of linked qualification - linked_qual (reference category: linked_qual =False) - declared disability - disability (reference category: disability=False) - caring responsibility carer_NO, carer_YES (reference category: carer_MISSING) - flag whether the student is new at the OU - is_new (reference category: is_new=False) - highest previous education - ed_NoFormal, ed_HE_Qual, ed_PostGrad (reference category: ed_A Level/Equivalent) - average previous score - discretised into prev_score_LOW, prev_score_MOD, prev_score_VERY_HIGH (avg.prev.score=MISSING, i.e. the student did not study any previous course) these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have values of the average score. - number of other credits studied - banded as credits_other_[1-60], credits_other_>=61 (reference category: credits_other=0) - number of previous attempts of the course - prev_attempt_=1, prev_attempt >1 (reference category: prev_attempt_0) - IMD (Index of Multiple Deprivation) - banded into quintiles, i.e. imd_=81 imd_MISSING (reference category: imd_[41-60]) - whether the student is identified as BAME - BAME_YES (reference category: BAME_NO) - Membership in the intervention group - group_INT (reference category: group_INT=0) (2) Teacher level - no. of students the teacher is responsible for - stud_in_group - avg. student pass rate in the previous years they were teaching - tut_pr_pass_LOW, tut_pr_pass_HIGH, tut_pr_pass_VERY_HIGH, tut_pr_pass_MISSING (reference category: tut_pr_pass_MOD) - these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have different pass rates (3) Course level - dummy variable encoded as - course_1, course_2 (reference category: course_3)(4) intercept

Context

The early identification of students facing learning difficulties is one of the most critical challenges in modern education. Intervening effectively requires leveraging data to understand the complex interplay between student demographics, engagement patterns, and academic performance.

This dataset was created to serve as a high-quality, pre-processed resource for building machine learning models to tackle this very problem. It is a unique hybrid dataset, meticulously crafted by unifying three distinct sources:

The Open University Learning Analytics Dataset (OULAD): A rich dataset detailing student interactions with a Virtual Learning Environment (VLE). We have aggregated the raw, granular data (over 10 million interaction logs) into powerful features, such as total clicks, average assessment scores, and distinct days of activity for each student registration.

The UCI Student Performance Dataset: A classic educational dataset containing demographic information and final grades in Portuguese and Math subjects from two Portuguese schools.

A Synthetic Data Component: A synthetically generated portion of the data, created to balance the dataset or represent specific student profiles.

Data Unification and Pre-processing

A direct merge of these sources was not possible as the student identifiers were not shared. Instead, a strategy of intelligent concatenation was employed. The final dataset has undergone a rigorous pre-processing pipeline to make it immediately usable for machine learning tasks:

• Advanced Imputation: Missing values were handled using a sophisticated iterative imputation method powered by Gaussian Mixture Models (GMM), ensuring the dataset's integrity.

• One-Hot Encoding: All categorical features have been converted to a numerical format.

• Feature Scaling: All numerical features have been standardized (using StandardScaler) to have a mean of 0 and a standard deviation of 1, preventing model bias from features with different scales.

The result is a clean, comprehensive dataset ready for modeling.

File Information

Instance

Each row represents a student profile, and the columns are the features and the target.

Feature

Features include aggregated online engagement metrics (e.g., clicks, distinct activities), academic performance (grades, scores), and student demographics (e.g., gender, age band). A key feature indicates the original data source (OULAD, UCI, Synthetic).

Sensitive Information

The dataset contains no Personally Identifiable Information (PII). Demographic information is presented in broad, anonymized categories.

Key Columns:

Target Variable:

  had_difficulty: The primary target for classification. This binary variable has been engineered from the original final_result column of the OULAD dataset.

    1: The student either failed (Fail) or withdrew (Withdrawn) from the course.

    0: The student passed (Pass or Distinction).

Feature Groups:

  OULAD Aggregated Features (e.g., oulad_total_cliques, oulad_media_notas): Quantitative metrics summarizing a student's engagement and performance within the VLE.

  Academic Performance Features (e.g., nota_matematica_harmonizada): Harmonized grades from different data sources.

  Demographic Features (e.g., gender_*, age_band_*): One-hot encoded columns representing student demographics.

  Origin Features (e.g., origem_dado_OULAD, origem_dado_UCI): One-hot encoded columns indicating the original source of the data for each row. This allows for source-specific analysis.

(Note: All numerical feature names are post-scaling and may not directly reflect their original names. Please refer to the complete column list for details.)

Acknowledgements

This dataset would not be possible without the original data providers. Please acknowledge them in any work that uses this data:

OULAD Dataset: Kuzilek, J., Hlosta, M., and Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4. https://analyse.kmi.open.ac.uk/open_dataset

UCI Student Performance Dataset: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS. https://archive.ics.uci.edu/ml/datasets/student+performance

Inspiration

This dataset is perfect for a variety of predictive modeling tasks. Here are a few ideas to get you started:

Can you build a classification model to predict had_difficulty with high recall? (Minimizing the number of at-risk students we fail to identify).

• Which features are the most powerful predictors of student failure or withdrawal? (Feature Importance Analysis).

• Can you build separate models for each data origin (origem_dado_*) and compare ...

Learning Analytics data collected in the EU H2020-funded ARETE project four comprehensive pilot studies, in total involving more than 2,900 students and teachers: learning of English literacy, Maths & Geography, Positive Behaviour, and authoring of AR learning content.The data set is described in depth in: Ana Domínguez, Guillermo Pacho, Lisa Bowers, Fridolin Wild, Sarah Alcock, Giuseppe Chiazzese, Mariella Farella, Marco Arrigo, David Ross, Rita Treacy, Darya Yegorina, Eleni Mangina, Stefano Masneri (2023): Dataset of user interactions across four large pilots on the use of augmented reality in learning experiences. Nature Scientific Data 10:823. https://doi.org/10.1038/s41597-023-02743-6

Snapshot img

Distance Learning Market Size 2024-2028

The distance learning market size is forecast to increase by USD 149.23 billion at a CAGR of 9.65% between 2023 and 2028.

The growing demand for distance learning, fueled by the continuous development of technology, is a key driver of the distance learning market. As technology improves, online education becomes more accessible, engaging, and effective, allowing students to learn remotely with ease. The integration of advanced tools such as video conferencing, AI-driven assessments, and interactive content is further enhancing the appeal of distance learning.
In North America, the market is experiencing significant growth due to the integration of advanced technologies and shifting educational preferences. With a growing emphasis on flexible, personalized learning experiences, including self-paced e-learning, institutions are increasingly offering distance learning programs that cater to diverse student needs. This trend is expected to continue, contributing to the market's expansion in the region.

What will be the Size of the Distance Learning Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of remote learning solutions among K-12 students and higher education students. Online assessments, video conferencing sessions, and virtual schools are becoming popular flexible education options for students who require flexibility in their learning schedules. Website-based mediums and application-based mediums, such as e-learning platforms, are increasingly being used to deliver educational programs. Internet access is essential for distance learning, making online learning platforms an indispensable tool for universities and colleges.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Type

  Traditional
  Online


Method

  Synchronous distance learning
  Asynchronous distance learning


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Type Insights

The traditional segment is estimated to witness significant growth during the forecast period. The market encompasses various methods and technologies, including gamification, personalized learning pathways, educational environments, and remote learning techniques. Traditional distance learning, characterized by asynchronous online courses, pre-recorded lecture books, and minimal instructor interaction, remains a significant revenue contributor. This approach caters to a broad audience, particularly those with limited access to digital devices or high-internet connectivity. Academic institutions and the government sector continue to offer traditional distance learning programs, such as those provided by the Open University in the UK via mail. However, corporate blended learning, online education solutions, and personalized learning solutions are gaining popularity due to their interactive and technologically advanced nature.

These methods include learning management systems, virtual classrooms, mobile e-learning platforms, and cloud-based e-Learning platforms. Moreover, the use of intranet connection, computers, tutorials, podcasts, recorded lectures, e-books, and machine learning technology enhances the learning experience. The market also serves academic users and corporate users through service providers and content providers. The increasing literacy rate, internet penetration, and the need for continuous skill upgrading further fuel the market's growth.

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The traditional segment accounted for USD 152.29 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 34% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in North America is experiencing significant growth due to the integration of advanced technologies and shifting educational preferences. With the rise of gamification, personalized learning pathways, and educational environments, online education solutions have become increasingly popular. Academic institutions and the government sector are expanding their digital services, offering distance learning programs through Learning Management Systems and cloud-based e-Learning platforms. Remote learning methods, such as pre-recorded lectures, tutorials, podcas

This study aims to examine the effect of the use of two Open Educational Resources (OER) (a Khan Academy online tutorial and an open textbook hosted on Wikibooks) with respect to the increase of logical-mathematical outcomes of first- and second-year higher education Chilean students. It also aims to uncover teacher and student perceptions about the use of OER in order to understand how these resources are used and valued. For these purposes, quantitative and qualitative methods were used to analyse student performance data and data produced from semi-structured interviews, focus groups and a survey.The research process was comprised of a generation of an impact analysis database (regarding student’s performance through grades in a blended-learning mode setting and grades plus attendance in a contact mode setting) followed by student focus group and teacher interviews that led to a student survey. The published dataset does not include the student survey instrument and microdata, as access to this is restricted due to ethical constraints.Findings indicate that students in a contact-study mathematics course who used a Khan Academy online mathematics tutorial obtained better examination results than students who did not use any additional resources, or those who used the open textbook. Moreover, it was also found that teachers and students have positive perceptions about the use of Khan Academy and Wikibooks materials.This study is Sub-project 9 of the Research on Open Educational Resources for Development (ROER4D) project, hosted by the Centre for Innovation in Learning and Teaching (CILT) at the University of Cape Town, South Africa, and Wawasan Open University, Malaysia.The interview data is in Spanish and the impact analysis database is in English. It is considered to be of potential interest to higher education and social science researchers (with a particular focus on Latin America and Chile), as well as policy-makers and advocates of open education policy and practice.This dataset was first published by DataFirst.

The Digital Education Monitor (Monitor Digitale Bildung) creates for the first time a comprehensive and representative empirical database on the state of digitized learning in the various education sectors in Germany - schools, vocational training, higher education, and advanced training.

Use of digital forms of learning and learning concepts at the university. Assessment of the university strategy for the digitization of teaching. Assessment of digital learning. Future visions for the digital university. Digital learning for specific target groups. Challenges. Qualification measures for the use of digital media.

Topics: 1. Technical equipment: media technology or hardware available for use in courses (tablet, PC and notebook, digital camera, interactive whiteboard, beamer, other); planned acquisition, available in small or sufficient numbers; learning applications available with licenses for university staff, for students, resp. for individual devices (office programs, access to paid content, e.g. e-books, learning management system, e.g. Moodle, software such as statistics or design programs, literature management programs, e-portfolio, other applications (open); sufficient WLAN at own university; assessment of technical equipment for digital learning at own university.

1. Students´ device equipment: assessment of the use of private devices by students in events (appears disruptive, can be used well during the event, e.g. for research, promotes opportunities for cheating, increases participation, increases distraction through emails or social media use); university´s own attitude or strategy regarding the use of private mobile devices by students in events (responsibility of lecturers, university or faculty regulations apply, no regulation to date).

2. Assessments of digital learning: assessment of the university strategy for the digitalization of teaching (no university-wide systematic use of digital learning media, university participates in the implementation of digital learning within the framework of pilot projects, good equipment with technical devices and programs, consulting and support services for digital teaching, university management increasingly invests in hardware and software, digital media also not relevant in the future at the own university).

3. Introduction of digital learning: importance of the use of digital forms of learning within the strategic orientation of the university; driving forces for the introduction of digital media at the university (students, individual university lecturers, central university institution, university management, ministry, funding institutions, nobody, other (open)).

4. Visions: evaluation of selected future visions for the digital university (meetings and face-to-face events will increasingly be replaced by virtual meetings, future supplementation of face-to-face events with online offerings, conducting aptitude and final tests increasingly online, students will increasingly cooperate and learn online, student support increasingly through social media, university will offer completely online degree programs in the future, tailored services for higher- and lower-performing students through learning analytics.

5. Open educational resources - content and applications: external learning materials and learning technologies made available centrally at the university (learning apps, learning management systems, e.g. Moodle or ILIAS, digital learning resources, e.g. e-books, learning videos, software, e.g. statistics and calculation programs, business games, literature management programs, e-assessment systems, examination systems).

6. Digital learning for specific target groups: additional resources used or not used at the university to support students with special needs (providing equipment, e.g. loan notebooks, assistive systems to compensate for physical handicaps, use of small evaluations combined with short tasks (quests), support for self-determined learning, video offerings that demonstrate complex issues and processes, texts that take into account native language competence, cost coverage for paid offerings, e.g., remedial courses, scripts, aptitude assessment and tests using digital media, other forms of support (open).

7. Assessment of digital learning in general: assessment of digital teaching and learning offerings (motivating, time-consuming, improve learning outcomes, difficult to check for success, relieve teaching staff, reduce dropout rates i...

The seventh cycle of the Ithaka S+R Faculty Survey queried a random sample of higher education faculty members in the United States to learn about their attitudes and practices related to research, teaching, and communicating. Respondents were asked about resource discovery and access; research topics and practices; research dissemination, including data management and preservation; instruction and perceptions of student research skills; the role and value of the academic library; open-educational resources; and learning analytics tools. Demographic variables include the respondent's age, gender, primary academic field, how many years the respondent has worked at his or her current college or university, how many years the respondent has worked in his or her field, and whether the respondent primarily identifies as a researcher, teacher, or somewhere in between.

The information refers to NI domiciled students gaining higher education qualifications from UK higher education institutions. The dataset is collected annually and is based on students obtaining a qualification at UK higher education institutions. The dataset is collected by the Higher Education Statistics Agency from higher education institutions throughout the UK and provided to the Department for Employment and Learning, Northern Ireland, for analysis. For 2013/14, NI Domiciled enrolments and qualifications at Open University are available. In previous years, these figures were included in NI students studying in England, as the administrative centre of the Open University is located in England.

The information refers to NI domiciled students enrolled at higher education institutions in the UK. The dataset is collected annually and is based on enrolments in higher education institutions in the UK on 1st December each year. The dataset is collected by the Higher Education Statistics Agency from higher education institutions throughout the UK and provided to the Department for Employment and Learning, Northern Ireland, for analysis. For 2013/14, NI Domiciled enrolments and qualifications at Open University are available. In previous years, these figures were included in NI students studying in England, as the administrative centre of the Open University is located in England. The specification of the HESA Standard Registration Population has changed for 2007/08 enrolments onwards. Writing up and sabbatical students are now excluded from this population where they were previously included in published enrolment data and therefore 2007/08 data onwards cannot be directly compared to previous years.

Abstract:

In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.

Research:

As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary:

Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, In 2024 Symposium on Eye Tracking Research and Applications. ETRA.

Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany

Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In ICERI2023 Proceedings (pp. 5153-5162). IATED.

Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 138-147). ECSEE.

The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly.

Experimental Setup:

In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. In order to reduce the external influence on the experiment, variables such as:

order, number, and presentation of the stimuli,

instruction to the participant prior to the experiment,

position of the participant in respect to the Eye Tracking equipment,

environment such as illuminance and ambient noise for the participant,

Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration

were attempted to keep constant and consistent throughout the experiment.

Equipment:

In this study, the Tobii Pro Fusion (https://go.tobii.com/tobii-pro-fusion-user-manual) eye tracker is utilized without a chin rest along with the Tobii IVT filter for fixation detection and Tobii Pro Lab software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.

Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design.

Procedure:

Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment.

Stimuli:

The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. illustrations or key words, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. In detail, the AOI grouping regarding:

table of contents and summary contain only a single AOI each,

illustrations, key words, theory, exercise, example and additional material contain three AOIs each,

supporting text and multiple choice question contain two AOIs each.

Research data management:

To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire.

Endowment returns for many universities skyrocketed early in the current period, largely fueled by booming private equity and hedge fund activity. In 2021, private nonprofit universities saw a staggering 684.0% jump in investment returns. In contrast, public universities, which typically hold smaller endowments invested more in US equities and fixed-income assets, experienced more modest gains. Meanwhile, inflation and rising interest rates in 2022 reversed the boom for private nonprofits, while public universities' endowments' focus on fixed-income assets stabilized their returns. Skyrocketing investment returns bolstered surpluses, but rising wage expenditures among expanding staff sizes have since brought down profit. Revenue has been sinking at a CAGR of 1.3% over the five years through 2025 to an estimated $591.1 billion despite an expected 0.7% rise in 2025 alone. Colleges and universities are contending with sluggish enrollment growth. Lackluster job placement rates and the highly publicized student debt crisis have made many potential students skeptical of a college degree's return on investment. With judicial reviews rendering the Biden administration's efforts to ease the burden of student debt unsuccessful, student loans remain a major deterrent for consumers. Many have instead opted for cheaper trade schools with reliable connections to employers. Community colleges' affordable prices are also making them a larger competitive threat to four-year universities. In response, universities are hiring capable staff and ramping up marketing campaigns to promote the value of their degree programs. Mounting automation will encourage many to enroll in a university to switch to a new field with more job security. Student loans will become more attractive as inflation stabilizes and the Federal Reserve continues to lower interest rates, encouraging traditional university enrollment. Still, the Trump administration's end to student debt forgiveness initiatives will lead to more price sensitivity among potential students, intensifying competition both between universities and with other cheaper options for postsecondary education. The new budget reconciliation bill will also impose both benefits and challenges for universities, including higher taxes on endowments, lower graduate program borrowing limits and tightened gainful employment rules. International students will remain a valuable revenue stream, especially as legislative changes in Canada promote higher education in the US with students from overseas. Revenue is set to swell at a CAGR of 0.7% to an estimated $610.8 billion through the end of 2030.

Histograms and results of k-means and Ward's clustering for Hidden Room game (Open Simulator) in University of Cadiz (Spain) by DeutschUCAThe fileset contains information from three sources:1. Histograms files:* Lexical_histogram.png (histogram of lexical error ratios)* Grammatical_histogram.png (histogram of grammatical error ratios)2. K-means clustering files:*

elbow-lex kmeans.png (clustering by lexical aspects: error curves

obtained for applying elbow method to determinate the optimal number of

clusters)* cube-lex kmeans.png (clustering by lexical aspects: a

three-dimensional representation of clusters obtained after applying

k-means method)* Lexical_clusters (table) kmeans.xls (clustering by

lexical aspects: centroids, standard deviations and number of instances

assigned to each cluster)* elbow-gram kmeans.png (clustering by

grammatical aspects: error curves obtained for applying elbow method to

determinate the optimal number of clusters)* cube-gramm kmeans.png

(clustering by grammatical aspects: a three-dimensional representation

of clusters obtained after applying k-means method)*

Grammatical_clusters (table) kmeans.xls (clustering by grammatical

aspects: centroids, standard deviations and number of instances assigned

to each cluster)* elbow-lexgram kmeans.png (clustering by lexical

and grammatical aspects: error curves obtained for applying elbow method

to determinate the optimal number of clusters)*

Lexical_Grammatical_clusters (table) kmeans.xls (clustering by lexical

and grammatical aspects: centroids, standard deviations and number of

instances assigned to each cluster)*

Grammatical_clusters_number_of_words (table) kmeans.xls

number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to grammatical error ratios.* Lexical_clusters_number_of_words (table) kmeans.xls

number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to lexical error ratios.* Lexical_Grammatical_clusters_number_of_words (table) kmeans.xls

number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying k-means clustering to lexical and grammatical error ratios.3. Ward’s Agglomerative Hierarchical Clustering files:* Lexical_Cluster_Dendrogram_ward.png (clustering by lexical aspects: dendrogram obtained after applying Ward's clustering method).* Grammatical_Cluster_Dendrogram_ward.png (clustering by grammatical aspects: dendrogram obtained after applying Ward's clustering method)* Lexical_Grammatical_Cluster_Dendrogram_ward.png (clustering by lexical and grammatical aspects: dendrogram obtained after applying Ward's clustering method)* Lexical_Grammatical_clusters (table) ward.xls:
Centroids (from column 2 to 7) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to lexical and grammatical error ratios.* Grammatical_clusters (table) ward.xls: Centroids (from column 2 to 4) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to grammatical error ratios.* Lexical_clusters (table) ward.xls: Centroids (from column 2 to 4) and cluster sizes (last column) obtained by applying Ward's agglomerative hierarchical clustering to lexical error ratios.* Lexical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to lexical error ratios.* Grammatical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to grammatical error ratios.* Lexical_Grammatical_clusters_number_of_words (table) ward.xls: number of words (from column 2 to 4) and sizes (last column) obtained per each cluster by applying Ward's agglomerative hierarchical clustering to lexical and grammatical error ratios.

The global Massive Open Online Course (MOOC) market is projected to reach a value of USD 11,170 million by 2033, expanding at a CAGR of 3.0% from 2025 to 2033. The increasing demand for flexible and accessible education, coupled with the rising adoption of online learning platforms, is driving the market growth. Key trends shaping the market include the expansion of MOOCs into new application areas, such as corporate training and professional development, and the integration of artificial intelligence (AI) and machine learning (ML) to enhance learner engagement and personalization. The market is segmented by type (cMOOCs and xMOOCs) and application (K-12 education, university education, adult and elderly education, and corporate). North America and Europe are currently the largest regional markets, with Asia Pacific expected to experience significant growth in the coming years. Major players in the industry include LinkedIn Learning, Pluralsight, Coursera, Udemy, Udacity, and Alison, among others.

Over the five years through 2024-25, the Universities industry's revenue is expected to grow at a compound annual rate of 2.1% to £56.5 billion. The increase in tuition fees to £9,250 in 2017-18 has been an important driver of revenue growth. However, with tuition fees frozen since then, inflation and particularly high inflation in the two years through 2023-24 has meant their real value has plummeted. The cap on undergraduate student numbers in the UK was lifted in 2020-21, leading to a rise in student enrolments, which has helped prop up universities’ income. Universities have struggled in the face of cuts to the level of research funding received from the government and disruption caused by the pandemic. Teaching went online in the final term of 2019-20 and remained there for most of the 2020-21 academic year due to restrictions imposed following the COVID-19 outbreak, pushing down revenue. However, despite fears of a fall in demand, student applications for 2020-21 rose and continued to climb in the following year. Universities are benefiting from the UK rejoining Horizon Europe, the EU’s flagship research programme – they’ve been able to access funding since January 2024. However, there are big concerns over ailing international student numbers since they currently prop up univeristy finances and help to subsidise domestic students' places. The introduction of the dependant ban in January 2024 on overseas students bringing family with them on their student visa for taught masters has seen applications from several countries fall. Revenue is still set to grow 1.9% in 2024-25 as funding levels rise and student numbers remain high. From April 2025, the DfE has confirmed tuition fees are due to increase in line with inflation to £9,535, which should lift revenue for universities. Over the five years through 2029-30, university revenue is forecast to climb at a compound annual rate of 1.2% to reach £60 billion. Strong demand from domestic students will further support revenue growth, with rising tuition fees boosting revenue post 2025. Commitment to the graduate visa route could be a positive signal for international student applicants, but the industry will need more funding intervention to prevent closures and budget cuts.

Data research project by the Centre for Higher Education Transformation (CHET) and the Higher Education Research and Advocacy Network in Africa (HERANA) into eight African 'flagship' universities. The dataset consists primarily of data on student enrolments, graduate outputs, academic staff and knowledge outputs at the eight universities. The findings of the study are published in the report "An Empirical Overview of Eight Flagship Universities in Africa (2001 - 2011). The report describes the collecting and analysis of cross-national higher education data for the group of eight universities in Africa. It concludes with an analysis of performance which focuses on the links between high-level academic staffing resources and high-level knowledge outputs. These eight universities are described as flagship universities because each is the most prominent public university in its country, and because all of the universities have broad flagship goals built into their vision and mission statements.

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.