The College Experience Study represents the most extensive longitudinal mobile sensing study to date, leveraging passive and automatic sensing data from the smartphones of over 200 Dartmouth students across five years (2017 - 2022). This groundbreaking research aimed to assess their mental health (e.g., depression, stress), the impact of COVID-19, and general behavioral trends.

The study's importance has been magnified during the global pandemic, necessitating a better understanding of mental health dynamics among undergraduate students throughout their college years. By tracking two cohorts of first-year students both on and off campus, we have accumulated a rich dataset offering insights into changing behaviors, resilience, and mental health in college life. We hope that this dataset will serve as a cornerstone for researchers, educators, and policymakers alike, seeking to enhance their understanding and interventions related to student mental health and behavior.

This dataset is unique for several reasons. It encompasses deep phone sensing data and self-reports spanning four continuous years for 200 undergraduate students at Dartmouth College, both during term time and breaks. Additionally, it incorporates periodic brain-imaging data for this cohort of students, along with surveys. The College Experience dataset enables researchers to explore numerous issues in behavioral sensing and brain imaging to advance our understanding of college students' mental health.

:rocket: Updates

• Apr 15th 2025: Raw app usage (i.e., list of running apps) is now available.
• Dec 5th 2024: Raw call logs, sms logs, and unlocks are now available.
• Oct 27th 2024: Raw sensing data will be released in batches over the next few weeks!

Content

College Experience Study makes use of the StudentLife app, developed for Android and iOS, autonomously capturing a variety of human behaviors 24/7, including:

• Bed time, wake up time, and sleep duration
• The number of conversations and the duration of each conversation per day (Android only)
• Physical activity (walking, sitting, running, standing)
• Locations visited and duration of stay (e.g., dorm, class, party, gym)
• Stress levels over weeks and throughout college
• App usage (Android only)
• COVID concern
• and more

In addition to passive sensing data, our study also involved gathering responses from detailed surveys and conducting brain scans throughout the research period. These diverse data sources can be used together to uncover insightful correlations and draw meaningful conclusions. An illustrative example of this potential is explored in the study "Predicting Brain Functional Connectivity Using Mobile Sensing", which demonstrates how mobile sensing data can predict brain functional connectivity, offering new avenues for understanding mental health conditions.

Data Availability

Note: Some features are exclusive to Android phones. Each folder includes a data definition file detailing the features and their availability across Android and iOS. Also, note that some features like conversation tracking initially covered both user groups but were later restricted due to iOS policy changes so they might be available for iOS users only during the beginning of the study.

For more details, refer to the College Experience Study paper and the original StudentLife website.

Term Definitions and Academic Calendars

For additional context and understanding of the timeline relevant to the dataset, below are the archived links to Dartmouth College's calendars. These archives provide an overview and detailed breakdown of significant dates for each academic year covered by the study:

Context

StudentLife is the first study that uses passive and automatic sensing data from the phones of a class of 48 Dartmouth students over a 10 week term to assess their mental health (e.g., depression, loneliness, stress), academic performance (grades across all their classes, term GPA and cumulative GPA) and behavioral trends (e.g., how stress, sleep, visits to the gym, etc. change in response to college workload -- i.e., assignments, midterms, finals -- as the term progresses).

Much of the stress and strain of student life remains hidden. In reality faculty, student deans, clinicians know little about their students outside of the classroom. Students might know about their own circumstances and patterns but know little about classmates. To shine a light on student life we develop the first of a kind StudentLife smartphone app and sensing system to automatically infer human behavior. Why do some students do better than others? Under similar conditions, why do some individuals excel while others fail? Why do students burnout, drop classes, even drop out of college? What is the impact of stress, mood, workload, sociability, sleep and mental health on academic performance (i.e., GPA)? The study used an android app we developed for smartphones carried by 48 students over a 10 week term to find answers to some of these pressing questions.

Content

The StudentLife app that ran on students' phones automatically measured the following human behaviors 24/7 without any user interaction: - bed time, wake up time and sleep duration - the number of conversations and duration of each conversation per day - physical activity (walking, sitting, running, standing) - where they were located and who long they stayed there (i.e., dorm, class, party, gym) - the number of people around a student through the day - outdoor and indoor (in campus buildings) mobility - stress level through the day, across the week and term - positive affect (how good they felt about themselves) - eating habits (where and when they ate) - app usage - in-situ comments on campus and national events: dimension protest, cancelled classes; Boston bombing.

Other Links

For more detail, please check the original StudentLife Study Website.

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell. "StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones." In Proceedings of the ACM Conference on Ubiquitous Computing. 2014. Nominated for the best paper award (top 5% of all papers) pdf

Data Contact

Andrew Campbell, Dartmouth College Weichen Wang (weichen.wang.gr@dartmouth.edu)

StudentLife Team Dror Ben-Zeev, Andrew Campbell, Fanglin Chen, Zhenyu Chen, Tianxing Li, Rui Wang and Xia Zhou (Dartmouth College), Gabriella Harari (University of Texas at Austin), Stefanie Tignor (Northeastern University)

We would like to thank the following people for their input and guidance is getting the study going. Ethan Berke (DHMC), Tanzeem Choudhury (Cornell), Randy Colvin (Northeasten), Sam Gosling (UT Austin) and Catherine Norris (Swarthmore)

Percentage of smartphone users by selected smartphone use habits in a typical day.

Description:

This dataset explores the relationship between students' health and their academic performance. It contains multiple rows of data, each representing a student, and multiple columns, including variables such as:

About Columns:

• Names : Student names

• Age : Student ages (in years)

• Gender : Male/Female

• Mobile phone : Do students own a mobile phone? (Yes/No)

• Mobile Operating System : Type of mobile operating system used (e.g. Android, iOS, Other)

• Mobile phone use for education : Do students use their mobile phone for educational purposes?(Sometime/ Frequently /Rarely )

• Mobile phone activities : List of mobile phone activities used for educational purposes (e.g. online research, educational apps, email, online learning platforms)

• Helpful for studying : Do students find mobile phone use helpful for studying? (Yes/No)

• Educational Apps : List of educational apps used.

• Daily usages : Average daily time spent using mobile phone for educational purposes (in hours)

• Performance impact : How does mobile phone use impact academic performance? (Agree/ Neutral/ Strongly agree)

• Usage distraction : Does mobile phone use distract from studying? (During Exams / Not Distracting / During Class Lectures / While Studying )

• Attention span : Has mobile phone use affected attention span? (Yes / No)

• Useful features : What features of mobile phones are useful for learning? (e.g. Internet Access, Camera, Calculator , Notes Taking App )

• Health Risks : Are students aware of potential health risks associated with excessive mobile phone use? (Yes / No / Only Partially)

• Beneficial subject : Which subjects benefit most from mobile phone use? (e.g. Accounting, Browsing Material, Research)

• Usage symptoms : Are students experiencing any physical or mental symptoms related to mobile phone use? (e.g. Sleep disturbance, headaches, Anxiety or Stress, All of these)

• Symptom frequency : How often are symptoms experienced? (Sometimes / Never / Rarely / Frequently)

• Health precautions : Are students taking precautions to mitigate potential health risks? (Taking Break during prolonged use / Using Blue light filter / Limiting Screen Time / None of Above)

• Health rating : How would students rate their overall physical and mental health? (Excellent / Good / Fair / Poor)

This dataset can be used to analyze the impact of health on academic success, identify potential predictors of academic performance, and inform interventions to support students' overall well-being and academic achievement.

The survey charted late teens' use of mobile phones in Finland. The archived data consist of interviews conducted with late teens (aged around 16-19) between 2000 and 2002. Topics covered the use of mobile phones with friends, in everyday life and in school. The dataset comprises 38 interviews. The dataset is only available in Finnish.

[Research on Dataset 1]1. Participants 700 paper questionnaires were distributed to junior high school students in a middle school in central China. After excluding invalid questionnaires such as regular responses, 651 valid questionnaires were finally collected, with a response rate of 93%. Among them, there were 344 male students and 307 female students, with an average age of 12.70 years (SD=0.69). 2. Research tools The natural contact questionnaire uses the items used by Xu and Jiang (2022). The questionnaire consists of 3 questions and is scored on a 5-point scale, with higher scores indicating higher levels of natural contact. In this study, the Cronbach's alpha coefficient of the questionnaire was 0.76. The Adolescent Mobile Phone Dependence Questionnaire was developed by Tao Shuman et al. (2013). The scale consists of 13 items and is composed of three dimensions: withdrawal, physical and mental impact, and craving. It uses a 5-point scoring system, with higher scores indicating higher levels of dependence on mobile phones. In this study, the overall Cronbach's alpha coefficient of the questionnaire was 0.90. The Chinese version of the stress perception scale was revised by Yang Tingzhong (2003) based on Cohen's stress perception scale. The scale consists of 14 items, which can be divided into two dimensions: tension and loss of control. It is scored on a 5-point scale, with higher scores indicating higher levels of stress experienced by individuals. In this study, the Cronbach's alpha coefficient of the scale was 0.85. [Study 2 Dataset] 1 participant On the premise of respecting students' wishes, 20 participants were selected from the top 27% of participants in Study 1 regarding their level of mobile phone dependence. Among them, there were 10 participants in the intervention group (3 males and 7 females), with an age distribution of 12-14 years old (12.60 ± 0.70); The control group consists of 10 individuals (5 males and 5 females), with an age distribution of 12-13 years old (12.70 ± 0.48). There was no significant difference in pre-test scores between the control group and the intervention group in terms of natural contact, stress perception, and mobile phone dependence. 2 Experimental Design Adopting a two factor mixed design of 2 (intervention group, control group) × 3 (time points: pre-test, post test, and follow-up test), the intra group variable is the group, the inter group variable is the measurement time point, and the dependent variables are natural contact, stress perception, and mobile phone dependence level. 3 Programs The intervention group received natural contact group counseling for 45 minutes once a week for 8 consecutive weeks. The control group did not intervene in any way. Both groups of students underwent pre-test, post test, and follow-up tests using the Natural Exposure Scale, Chinese version of the Stress Perception Scale, and Mobile Phone Dependence Scale (same study one) before, after, and one month after the intervention. The design of the natural contact group assistance program refers to horticultural therapy and fully utilizes the natural stimulation on campus. The content of horticultural therapy includes five categories: indoor planting, outdoor planting, hiking, group activities, and handicrafts (Guo Yuren, 2005). Under the premise of school safety management regulations, the plan (see dataset) should include the above content as much as possible.

This is for the LLM - Detect AI Generated Text (DAIGT) competition.

Versions

1. Very light processing out of LLM. 1k from llama-70b-chat, 1k from falcon-180b-chat across all persuade prompts and some extras from gpt-4

2. Added llama70b_v2.csv by cleaning up Llama 70b output as seen in this notebook. Same data, just with some text removed from samples.

3. 500 generated samples from llama 70b and falcon 180b for each prompt in RDizzl3_seven. (3,500 total for llama70b; 3,500 total for falcon180b). These had sources in the the prompt, unlike earlier versions.

I will be updating it more in the future as I improve the prompts. If you notice anything odd or if you have any questions, please don't hesitate to ask!

Prompts

The prompts were a combination of the PERSUADE corpus and some from GPT-4. Essays for the same prompt are generated with different temperatures, top_k values, and slightly different prompts.

All together there were 15 prompts from PERSUADE and 20 from GPT-4. All prompts are below:

persuade_prompts = ['Today the majority of humans own and operate cell phones on a daily basis. In essay form, explain if drivers should or should not be able to use cell phones in any capacity while operating a vehicle.',
 'Write an explanatory essay to inform fellow citizens about the advantages of limiting car usage. Your essay must be based on ideas and information that can be found in the passage set. Manage your time carefully so that you can read the passages; plan your response; write your response; and revise and edit your response. Be sure to use evidence from multiple sources; and avoid overly relying on one source. Your response should be in the form of a multiparagraph essay. Write your essay in the space provided.',
 'Some schools require students to complete summer projects to assure they continue learning during their break. Should these summer projects be teacher-designed or student-designed? Take a position on this question. Support your response with reasons and specific examples.',
 "You have just read the article, 'A Cowboy Who Rode the Waves.' Luke's participation in the Seagoing Cowboys program allowed him to experience adventures and visit many unique places. Using information from the article, write an argument from Luke's point of view convincing others to participate in the Seagoing Cowboys program. Be sure to include: reasons to join the program; details from the article to support Luke's claims; an introduction, a body, and a conclusion to your essay.",
 'Your principal has decided that all students must participate in at least one extracurricular activity. For example, students could participate in sports, work on the yearbook, or serve on the student council. Do you agree or disagree with this decision? Use specific details and examples to convince others to support your position. ',
 'In "The Challenge of Exploring Venus," the author suggests studying Venus is a worthy pursuit despite the dangers it presents. Using details from the article, write an essay evaluating how well the author supports this idea. Be sure to include: a claim that evaluates how well the author supports the idea that studying Venus is a worthy pursuit despite the dangers; an explanation of the evidence from the article that supports your claim; an introduction, a body, and a conclusion to your essay.',
 'In the article "Making Mona Lisa Smile," the author describes how a new technology called the Facial Action Coding System enables computers to identify human emotions. Using details from the article, write an essay arguing whether the use of this technology to read the emotional expressions of students in a classroom is valuable.',
 "You have read the article 'Unmasking the Face on Mars.' Imagine you are a scientist at NASA discussing the Face with someone who thinks it was created by aliens. Using information in the article, write an argumentative essay to convince someone that the Face is just a natural landform.Be sure to include: claims to support your argument that the Face is a natural landform; evidence from the article to support your claims; an introduction, a body, and a conclusion to your argumentative essay.",
 'Some of your friends perform community service. For example, some tutor elementary school children and others clean up litter. They think helping the community is very important. But other friends of yours think community service takes too much time away from what they need or want to do. 
Your principal is deciding whether to require all students to perform community service. 
Write a letter to your principal in which you take a position on whether students should be required to perform community service. Support your position with examples.',
 "Your principal is considering changing school po...

Dataset Description This dataset contains information about 8,500+ mobile service customers, including demographic details, device usage, billing patterns, and call behavior. The primary goal of this dataset is to enable analysis and modeling to predict customer churn — i.e., customers who decide to drop their mobile service provider.

The data includes 33 features and one binary target column (customer_dropped). This dataset is ideal for exploring churn prediction models, customer segmentation, lifetime value analysis, and marketing strategy development.

Features - customer_id: Unique identifier for each customer - age: Age of the customer - job: Occupation or profession of the customer - urban_rural: Indicates whether the customer resides in an urban or rural area - marital_status: Marital status of the customer - kids: Number of children the customer has - disposable_income: Disposable income of the customer - mobiles_changed: Number of times the customer has changed their mobile device - mobile_age: Age of the current mobile device - own_smartphone: Indicates whether the customer owns a smartphone - current_mobile_price: Price of the customer's current mobile device - credit_card_type: Type of credit card held - own_house: Indicates whether the customer owns a house - own_cr_card: Indicates whether the customer owns a credit card - monthly_bill: Monthly bill for mobile service - call_mins: Total call minutes used - basic_plan_amount: Basic mobile plan amount - extra_mins: Extra minutes used beyond the plan - roam_call_mins: Roaming call minutes - call_mins_delta: Change in call minutes compared to the previous billing period - bill_amount_delta: Change in bill amount compared to the previous billing period - incoming_call_mins: Total incoming call minutes - outgoing_calls: Number of outgoing calls - incoming_calls: Number of incoming calls - day_night_call_ratio: Ratio of call minutes during the day versus night - day_night_call_delta: Change in day vs night call minutes compared to the previous period - calls_dropped: Number of calls dropped - loyalty_months: Customer tenure in months - complaint_calls: Number of complaint calls made - promo_calls_made: Number of promotional calls made - promo_offers_accepted: Number of promotional offers accepted - new_numbers_called: Number of new contacts called - customer_dropped: Target column indicating churn (1 = churned, 0 = retained)

Use Cases - Develop machine learning models for churn prediction - Perform customer segmentation and behavioral profiling - Analyze call usage trends and billing sensitivity - Identify key drivers of customer loyalty or attrition - Design data-driven retention strategies

Alexithymia and mobile phone addiction are common phenomena in daily life. Many studies have explored the internal relationship between them based on different theoretical perspectives, but the extent of the exact correlation is still controversial. To address this controversy and clarify the reasons for the divergence, a meta-analysis of 26 articles comprising 23,387 Chinese students was conducted. The results show that alexithymia was highly positively correlated with mobile phone addiction (r = 0.41, 95% CI = [0.37, 0.45]). Furthermore, the relationship was moderated by mobile phone addiction measurement tool and year of publication, with studies using the Mobile Phone Addiction Tendency Scale (MPATS) having higher correlation coefficients than those using the Mobile Phone Addiction Index (MPAI) or other measurement tools. Studies published in 2020–2021 yielded higher correlations than those published in 2014–2016 and 2017–2019. However, the relationship was not moderated by gender, region, or measures of alexithymia. Therefore, our meta-analysis of available published data indicated that alexithymia and mobile phone addiction in Chinese students are not only highly positively correlated but also affected by mobile phone addiction measurement tools and publication year. Longitudinal studies or experimental studies should be strengthened in the future to further establish the direction(s) of causality for the relation between alexithymia and mobile phone addiction.

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED

Context

Recently me and my friends were doing a research study on Dry eye Disease(which is completely curable), We were curious to know how is usage of smartphones.Tablets or even watching Television has changed in this lockdown.

Content

The Study mainly focuses on finding the symptoms such as painful or sore eyes, gritty eyes etc. Based on Ocular Surface Diseast Index (OSDI).

Acknowledgements

The Data was collected raw(as you see) of our fellow medical students. Data Size is of respectable amount.

Inspiration

Wanted to check how this lockdown due to COVID-19 is affecting our usage of mobile devices.

Parameter names and associated values for the ML models.

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED

This dataset tracks annual white student percentage from 1993 to 2023 for Cl Scarborough Model Middle School vs. Alabama and Mobile County School District

This dataset tracks annual black student percentage from 1993 to 2023 for Pillans Middle School vs. Alabama and Mobile County School District

This dataset tracks annual two or more races student percentage from 2014 to 2023 for Pillans Middle School vs. Alabama and Mobile County School District

This dataset tracks annual science proficiency from 2021 to 2022 for Cl Scarborough Model Middle School vs. Alabama and Mobile County School District

This dataset tracks annual black student percentage from 1993 to 2023 for Cl Scarborough Model Middle School vs. Alabama and Mobile County School District

This dataset tracks annual two or more races student percentage from 2019 to 2023 for Cl Scarborough Model Middle School vs. Alabama and Mobile County School District