Student Engagement

Predicting Engagement and Exam Performance

By [source]

About this dataset

This dataset contains information on student engagement with Tableau, including quizzes, exams, and lessons. The data includes the course title, the rating of the course, the date the course was rated, the exam category, the exam duration, whether the answer was correct or not, the number of quizzes completed, the number of exams completed, the number of lessons completed, the date engaged, the exam result, and more

How to use the dataset

The 'Student Engagement with Tableau' dataset offers insights into student engagement with the Tableau software. The data includes information on courses, exams, quizzes, and student learning.

This dataset can be used to examine how students use Tableau, what kind of engagement leads to better learning outcomes, and whether certain course or exam characteristics are associated with student engagement

Research Ideas

• Creating a heat map of student engagement by course and location
• Determining which courses are most popular among students from different countries
• Identifying patterns in students' exam results

Acknowledgements

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 365_course_info.csv | Column name | Description | |:-----------------|:----------------------------------| | course_title | The title of the course. (String) |

File: 365_course_ratings.csv | Column name | Description | |:------------------|:---------------------------------------------------------| | course_rating | The rating given to the course by the student. (Numeric) | | date_rated | The date on which the course was rated. (Date) |

File: 365_exam_info.csv | Column name | Description | |:------------------|:-------------------------------------------------| | exam_category | The category of the exam. (Categorical) | | exam_duration | The duration of the exam in minutes. (Numerical) |

File: 365_quiz_info.csv | Column name | Description | |:-------------------|:----------------------------------------------------------------------| | answer_correct | Whether or not the student answered the question correctly. (Boolean) |

File: 365_student_engagement.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | engagement_quizzes | The number of times a student has engaged with quizzes. (Numeric) | | engagement_exams | The number of times a student has engaged with exams. (Numeric) | | engagement_lessons | The number of times a student has engaged with lessons. (Numeric) | | date_engaged | The date of the student's engagement. (Date) |

File: 365_student_exams.csv | Column name | Description | |:-------------------------|:---------------------------------------------------| | exam_result | The result of the exam. (Categorical) | | exam_completion_time | The time it took to complete the exam. (Numerical) | | date_exam_completed | The date the exam was completed. (Date) |

File: 365_student_hub_questions.csv | Column name | Description | |:------------------------|:----------------------------------------| | date_question_asked | The date the question was asked. (Date) |

File: 365_student_info.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | student_country | The country of the student. (Categorical) | | date_registered | The date the student registered for the course. (Date) |

File: 365_student_learning.csv | Column name | Description | |:--------------------|:------------------------------...

This dataset comes from the Biennial City of Tempe Employee Survey questions related to employee engagement. Survey respondents are asked to rate their level of agreement on a scale of 5 to 1, where 5 means "Strongly Agree" and 1 means "Strongly Disagree".This dataset includes responses to the following statements:I have received fair consideration for advancement & promotion, when available, within City of TempeI have been mentored at workThe City's programs related to professional development & career mobility, such as educational partnerships, Tempe Professional Development Network, etc., are useful to meThe following adequately support my work-related needs: City Manager's OfficeThe following adequately support my work-related needs: Strategic Management & Diversity OfficeI believe my opinions seem to countConflict in my work area is resolved effectivelyI believe exceptional job performance is recognized appropriately by managers/supervisors in my work unitThe amount that I pay for health care benefits is reasonableI think the amount I am paid is adequate for the work I doCommunication between my work unit/pision & work units/pisions OUTSIDE my department is goodEmployees in my department take personal accountability for their actions and work performance (starting in 2018 survey)Participation in the survey is voluntary and confidential.This page provides data for the Employee Engagement performance measure. The performance measure dashboard is available at 2.13 Employee Engagement.Additional InformationSource: paper and digital survey submissionsContact: Aaron PetersonContact E-Mail: Aaron_Peterson@tempe.govData Source Type: ExcelPreparation Method: NAPublish Frequency: biennialPublish Method: ManualData Dictionary

Discipline in schools is typically disproportionate, reactive and punitive. Evidence-based strategies that have been recently developed focus on shifting schools to a more proactive and positive approach by detecting warning signs and intervening early. This project evaluates the implementation of an evidence-based intervention to improve students' mindsets and feelings of school belonging. This grant-funded project was designed to enhance school capacity to implement a Tier 2 intervention, Student Engagement and Empowerment (SEE), to improve student attendance, behavior, and achievement, while simultaneously evaluating the effects of this intervention. The intervention and research project were individualized to fit existing school operations in the school district. A grant-funded coach supported delivery of SEE at each school for the duration of the 3-year grant. SEE was delivered by trained teachers in the classroom over the course of a seven-session curriculum. The overarching project goal was to scale up and simultaneously evaluate a Tier 2 intervention that could be sustained after completion of the grant. The originally proposed research procedures consisted of an evaluation of the effects of the SEE program on the outcomes of students at elevated risk for disciplinary action and school dropout. Outcome data was collected for at-risk students in classrooms delivering the SEE program, and a comparison sample of at-risk students in classrooms not delivering the SEE program. Researchers initially hypothesized that students receiving the program would evidence a greater sense of belonging to school, endorse greater growth mindset, have better attendance and fewer suspensions/expulsions and course failure, and have better behavioral outcomes than students in the comparison group.

This machine-generated dataset simulates social media engagement data across various metrics, including likes, shares, comments, impressions, sentiment scores, toxicity, and engagement growth. It is designed for analysis and visualization of trends, buzz frequency, public sentiment, and user behavior on digital platforms.

The dataset can be used to:

Identify spikes or drops in engagement

Analyze changes in sentiment over time

Build dashboards for digital trend tracking

Test algorithms for sentiment analysis or trend prediction

LearnPlatform is a unique technology platform in the K-12 market providing the only broadly interoperable platform to the breadth of edtech solutions in the US K12 field. A key component of edtech effectiveness is integrated reporting on tool usage and, where applicable, evidence of efficacy. With COVID closures, LearnPlatform has emerged as an important and singular resource to measure whether students are accessing digital resources within distance learning constraints. This platform provides a unique and needed source of data to understand if students are accessing digital resources, and where resources have disparate usage and impact.In this dataset we are sharing educational technology usage across the 8,000+ tools used in the education field in 2020. We make this dataset available to public so that educators, district leaders, researchers, institutions, policy-makers or anyone interested to learn about digital learning in 2020, can use this dataset to understand student engagement with core learning activities during the COVID-19 pandemic. Some example research questions that this dataset can help stakeholders answer: What is the picture of digital connectivity and engagement in 2020?What is the effect of the COVID-19 pandemic on online and distance learning, and how might this evolve in the future?How does student engagement with different types of education technology change over the course of the pandemic?How does student engagement with online learning platforms relate to different geography? Demographic context (e.g., race/ethnicity, ESL, learning disability)? Learning context? Socioeconomic status?Do certain state interventions, practices or policies (e.g., stimulus, reopening, eviction moratorium) correlate with increases or decreases in online engagement?

Data DescriptionThe DIPSER dataset is designed to assess student attention and emotion in in-person classroom settings, consisting of RGB camera data, smartwatch sensor data, and labeled attention and emotion metrics. It includes multiple camera angles per student to capture posture and facial expressions, complemented by smartwatch data for inertial and biometric metrics. Attention and emotion labels are derived from self-reports and expert evaluations. The dataset includes diverse demographic groups, with data collected in real-world classroom environments, facilitating the training of machine learning models for predicting attention and correlating it with emotional states.Data Collection and Generation ProceduresThe dataset was collected in a natural classroom environment at the University of Alicante, Spain. The recording setup consisted of six general cameras positioned to capture the overall classroom context and individual cameras placed at each student’s desk. Additionally, smartwatches were used to collect biometric data, such as heart rate, accelerometer, and gyroscope readings.Experimental SessionsNine distinct educational activities were designed to ensure a comprehensive range of engagement scenarios:News Reading – Students read projected or device-displayed news.Brainstorming Session – Idea generation for problem-solving.Lecture – Passive listening to an instructor-led session.Information Organization – Synthesizing information from different sources.Lecture Test – Assessment of lecture content via mobile devices.Individual Presentations – Students present their projects.Knowledge Test – Conducted using Kahoot.Robotics Experimentation – Hands-on session with robotics.MTINY Activity Design – Development of educational activities with computational thinking.Technical SpecificationsRGB Cameras: Individual cameras recorded at 640×480 pixels, while context cameras captured at 1280×720 pixels.Frame Rate: 9-10 FPS depending on the setup.Smartwatch Sensors: Collected heart rate, accelerometer, gyroscope, rotation vector, and light sensor data at a frequency of 1–100 Hz.Data Organization and FormatsThe dataset follows a structured directory format:/groupX/experimentY/subjectZ.zip Each subject-specific folder contains:images/ (individual facial images)watch_sensors/ (sensor readings in JSON format)labels/ (engagement & emotion annotations)metadata/ (subject demographics & session details)Annotations and LabelingEach data entry includes engagement levels (1-5) and emotional states (9 categories) based on both self-reported labels and evaluations by four independent experts. A custom annotation tool was developed to ensure consistency across evaluations.Missing Data and Data QualitySynchronization: A centralized server ensured time alignment across devices. Brightness changes were used to verify synchronization.Completeness: No major missing data, except for occasional random frame drops due to embedded device performance.Data Consistency: Uniform collection methodology across sessions, ensuring high reliability.Data Processing MethodsTo enhance usability, the dataset includes preprocessed bounding boxes for face, body, and hands, along with gaze estimation and head pose annotations. These were generated using YOLO, MediaPipe, and DeepFace.File Formats and AccessibilityImages: Stored in standard JPEG format.Sensor Data: Provided as structured JSON files.Labels: Available as CSV files with timestamps.The dataset is publicly available under the CC-BY license and can be accessed along with the necessary processing scripts via the DIPSER GitHub repository.Potential Errors and LimitationsDue to camera angles, some student movements may be out of frame in collaborative sessions.Lighting conditions vary slightly across experiments.Sensor latency variations are minimal but exist due to embedded device constraints.CitationIf you find this project helpful for your research, please cite our work using the following bibtex entry:@misc{marquezcarpintero2025dipserdatasetinpersonstudent1, title={DIPSER: A Dataset for In-Person Student Engagement Recognition in the Wild}, author={Luis Marquez-Carpintero and Sergio Suescun-Ferrandiz and Carolina Lorenzo Álvarez and Jorge Fernandez-Herrero and Diego Viejo and Rosabel Roig-Vila and Miguel Cazorla}, year={2025}, eprint={2502.20209}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2502.20209}, } Usage and ReproducibilityResearchers can utilize standard tools like OpenCV, TensorFlow, and PyTorch for analysis. The dataset supports research in machine learning, affective computing, and education analytics, offering a unique resource for engagement and attention studies in real-world classroom environments.

Customer Engagement Data from Social Media Users

SPSS Data set for an original study carried out in 2019-2020 in which academic engagement was measured using a modified work engagement scales, and looked at three domains: dedication, absorption, vigor. A number of individual, psychological and institutional level variables were explored with regard to academic engagement.

About Dataset This dataset captures the pulse of viral social media trends across Facebook, Instagram and Twitter. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

Trend analysis 🔍 Sentiment modeling 💭 Understanding influencer marketing 📈 Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

This is a small longitudinal data set relating to teachers' perceptions of parental engagement before and during the COVID-19 pandemic. The data collected using online surveys of teachers at one large English primary school.

Profile growth - the growth on our social platforms to see where and when we're gaining followers. Engagement rate - a ratio of how many people interacted with ours posts based on when users are usually online. Reach - the number of feeds our posts appeared in (doesn't mean people interacted with the post).

Collaboratory is a software product developed and maintained by HandsOn Connect Cloud Solutions. It is intended to help higher education institutions accurately and comprehensively track their relationships with the community through engagement and service activities. Institutions that use Collaboratory are given the option to opt-in to a data sharing initiative at the time of onboarding, which grants us permission to de-identify their data and make it publicly available for research purposes. HandsOn Connect is committed to making Collaboratory data accessible to scholars for research, toward the goal of advancing the field of community engagement and social impact.Collaboratory is not a survey, but is instead a dynamic software tool designed to facilitate comprehensive, longitudinal data collection on community engagement and public service activities conducted by faculty, staff, and students in higher education. We provide a standard questionnaire that was developed by Collaboratory’s co-founders (Janke, Medlin, and Holland) in the Institute for Community and Economic Engagement at UNC Greensboro, which continues to be closely monitored and adapted by staff at HandsOn Connect and academic colleagues. It includes descriptive characteristics (what, where, when, with whom, to what end) of activities and invites participants to periodically update their information in accordance with activity progress over time. Examples of individual questions include the focus areas addressed, populations served, on- and off-campus collaborators, connections to teaching and research, and location information, among others.The Collaboratory dataset contains data from 45 institutions beginning in March 2016 and continues to grow as more institutions adopt Collaboratory and continue to expand its use. The data represent over 6,200 published activities (and additional associated content) across our user base.Please cite this data as:Medlin, Kristin and Singh, Manmeet. Dataset on Higher Education Community Engagement and Public Service Activities, 2016-2023. Collaboratory [producer], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-07-07. https://doi.org/10.3886/E136322V1When you cite this data, please also include: Janke, E., Medlin, K., & Holland, B. (2021, November 9). To What End? Ten Years of Collaboratory. https://doi.org/10.31219/osf.io/a27nb

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Data quality tools market in APAC overview

The need to improve customer engagement is the primary factor driving the growth of data quality tools market in APAC. The reputation of a company gets hampered if there is a delay in product delivery or response to payment-related queries. To avoid such issues organizations are integrating their data with software such as CRM for effective communication with customers. To capitalize on market opportunities, organizations are adopting data quality strategies to perform accurate customer profiling and improve customer satisfaction.

Also, by using data quality tools, companies can ensure that targeted communications reach the right customers which will enable companies to take real-time action as per the requirements of the customer. Organizations use data quality tool to validate e-mails at the point of capture and clean their database of junk e-mail addresses. Thus, the need to improve customer engagement is driving the data quality tools market growth in APAC at a CAGR of close to 23% during the forecast period.

Top data quality tools companies in APAC covered in this report

The data quality tools market in APAC is highly concentrated. To help clients improve their revenue shares in the market, this research report provides an analysis of the market’s competitive landscape and offers information on the products offered by various leading companies. Additionally, this data quality tools market in APAC analysis report suggests strategies companies can follow and recommends key areas they should focus on, to make the most of upcoming growth opportunities.

The report offers a detailed analysis of several leading companies, including:

IBM
Informatica
Oracle
SAS Institute
Talend

Data quality tools market in APAC segmentation based on end-user

Banking, financial services, and insurance (BFSI)
Telecommunication
Retail
Healthcare
Others

BFSI was the largest end-user segment of the data quality tools market in APAC in 2018. The market share of this segment will continue to dominate the market throughout the next five years.

Data quality tools market in APAC segmentation based on region

China
Japan
Australia
Rest of Asia

China accounted for the largest data quality tools market share in APAC in 2018. This region will witness an increase in its market share and remain the market leader for the next five years.

Key highlights of the data quality tools market in APAC for the forecast years 2019-2023:

CAGR of the market during the forecast period 2019-2023
Detailed information on factors that will accelerate the growth of the data quality tools market in APAC during the next five years
Precise estimation of the data quality tools market size in APAC and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the data quality tools market in APAC across China, Japan, Australia, and Rest of Asia
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive details on factors that will challenge the growth of data quality tools companies in APAC

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The report examines the ways online Canadian adults are engaging politically on social media. This is the third and final report based on a census-balanced survey of 1,500 Canadians using quota sampling by age, gender, and geographical region. The other two reports in this series are: "The State of Social Media in Canada 2017" and "Social Media Privacy in Canada". The series is published by the Social Media Lab, an interdisciplinary research lab at Ted Rogers School of Management, Ryerson University. The lab studies how social media is changing the ways in which people communicate, share information, conduct business and how these changes are impacting our society.

Employee engagement data from an employee survey conducted by Pierce County and completed voluntarily by employees. Numeric responses correspond with the following answers: 0=N/A, 1=Strongly Disagree, 2=Disagree, 3=Agree, 4=Strongly Agree.

The table Engagement Data is part of the dataset Local Government Renewables Action Tracker, available at https://redivis.com/datasets/j0zr-17a6d7sk8. It contains 62 rows across 17 variables.

Civic engagement activities agencies completed between September 15 and April 30 for the 2020-2022 reports. The latest data is available at https://data.cityofnewyork.us/d/9hhx-gjf8

Interview and workshop transcripts from EPSRC Digital Transformations Communities and Cultures Network + (http://www.communitiesandculture.org/) project Digital Data Analytics, Public Engagement and the Social Life of Methods (http://www.communitiesandculture.org/projects/digital-data-analysis/). Methodology described in papers available at the above link.

Employee Engagement Enterprise Performance Indicator Data

At Factori, we gather various data sets from leading publishers, data platforms, online services, and data aggregators globally, linked to consumers, places, and businesses. We combine this data with public and private sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms clean, enrich, unify, and aggregate these data sets for use in our products. We categorize our audience data into consumable categories such as interest, demographics, behavior, geography, etc.