We did data analysis on a open dataset which contained responses regarding a survey about how useful students find AI in the educational process. We cleaned the data, preprocessed and then did analysis on it. We did an EDA (Exploratory Data Analysis) on the dataset and visualized the results and our findings. Then we interpreted the findings into our digital poster.

Abstract:This dataset presents survey responses from first-year engineering students on their use of ChatGPT and other AI tools in a project-based learning environment. Collected as part of a study on AI’s role in engineering education, the data captures key insights into how students utilize ChatGPT for coding assistance, conceptual understanding, and collaborative work. The dataset includes responses on frequency of AI usage, perceived benefits and challenges, ethical concerns, and the impact of AI on learning outcomes and problem-solving skills.With AI increasingly integrated into education, this dataset provides valuable empirical evidence for researchers, educators, and policymakers interested in AI-assisted learning, STEM education, and academic integrity. It enables further analysis of student perceptions, responsible AI use, and the evolving role of generative AI in higher education.By making this dataset publicly available, we aim to support future research on AI literacy, pedagogy, and best practices for integrating AI into engineering and science curricula..................................................................................................................................................................Related PublicationThis dataset supports the findings presented in the following peer-reviewed article:ChatGPT in Engineering Education: A Breakthrough or a Challenge?Davood KhodadadPublished: 7 May 2025 | Physics Education, Volume 60, Number 4© 2025 The Author(s). Published by IOP Publishing LtdCitation: Davood Khodadad 2025 Phys. Educ. 60 045006DOI: 10.1088/1361-6552/add073If you use or reference this dataset, please consider citing the above publication......................................................................................................................................................................Description of the data and file structureTitle: ChatGPT in Engineering Education: Survey Data on AI Usage, Learning Impact, and CollaborationDescription of Data Collection:This dataset was collected through a survey distributed via the Canvas learning platform following the completion of group projects in an introductory engineering course. The survey aimed to investigate how students engaged with ChatGPT and other AI tools in a project-based learning environment, particularly in relation to coding, report writing, idea generation, and collaboration.The survey consisted of 15 questions:12 multiple-choice questions to capture quantitative insights on AI usage patterns, frequency, and perceived benefits.3 open-ended questions to collect qualitative perspectives on challenges, ethical concerns, and students' reflections on AI-assisted learning.Key areas assessed in the survey include:Students’ prior familiarity with AI tools before the course.Frequency and purpose of ChatGPT usage (e.g., coding assistance, conceptual learning, collaboration).Perceived benefits and limitations of using AI tools in an engineering learning environment.Ethical considerations, including concerns about over-reliance and academic integrity.The dataset provides valuable empirical insights into the evolving role of AI in STEM education and can support further research on AI-assisted learning, responsible AI usage, and best practices for integrating AI tools in engineering education.

The data set records the perceptions of Bangladeshi university students on the influence that AI tools, especially ChatGPT, have on their academic practices, learning experiences, and problem-solving abilities. The varying role of AI in education, which covers common usage statistics, what AI does to our creative abilities, its impact on our learning, and whether it could invade our privacy. This dataset reveals perspective on how AI tools are changing education in the country and offering valuable information for researchers, educators, policymakers, to understand trends, challenges, and opportunities in the adoption of AI in the academic contex.

Methodology Data Collection Method: Online survey using google from Participants: A total of 3,512 students from various Bangladeshi universities participated. Survey Questions:The survey included questions on demographic information, frequency of AI tool usage, perceived benefits, concerns regarding privacy, and impacts on creativity and learning.

Sampling Technique: Random sampling of university students Data Collection Period: June 2024 to December 2024

Privacy Compliance This dataset has been anonymized to remove any personally identifiable information (PII). It adheres to relevant privacy regulations to ensure the confidentiality of participants.

For further inquiries, please contact: Name: Md Jhirul Islam, Daffodil International University Email: jhirul15-4063@diu.edu.bd Phone: 01316317573

Artificial Intelligence In Education Market size was valued at USD 3.2 Billion in 2023 and is projected to reach USD 42 Billion by 2031, growing at a CAGR of 44.30% during the forecast period 2024-2031.

Global Artificial Intelligence In Education Market Drivers

The market drivers for the Artificial Intelligence In Education Market can be influenced by various factors. These may include:

Personalized Learning: AI makes it possible to design learning routes that are specifically catered to the strengths, weaknesses, and learning style of each student, increasing engagement and yielding better results.

Adaptive Learning Platforms: AI-driven adaptive learning platforms leverage data analytics to continuously evaluate student performance and modify the pace and content to help students grasp the material.

Efficiency and Automation: AI frees up instructors' time to concentrate on teaching and mentoring by automating administrative activities like scheduling, grading, and course preparation.

Improved Content Creation: AI tools can produce interactive tutorials, games, and simulations at scale, which makes it easier to create a variety of interesting and captivating learning resources.

Data-driven Insights: AI analytics give teachers useful information on learning preferences, trends in student performance, and areas for development. This information helps them make data-driven decisions and implement interventions.

Accessibility and Inclusion: AI technologies can provide students with individualized help who face linguistic challenges or disabilities by accommodating a variety of learning methods and needs.

Global Demand for Education Technology: The use of artificial intelligence (AI) in education is being fueled by the growing demand for education technology solutions worldwide, which is being driven by factors including the expanding penetration of the internet, the digitization of classrooms, and the growing significance of lifelong learning.

Government Initiatives and Corporate Investments: Government initiatives supporting digital literacy and STEM education as well as corporate investments in AI firms specializing in education technology drive market expansion.

Acceleration caused by the Pandemic: The COVID-19 pandemic has prompted the demand for AI-powered solutions that can improve the delivery of remote education and assist distant learning, hence accelerating the adoption of online and blended learning models.

Institutions aiming to stand out from the competition and draw in students are spending more in AI-powered learning technology as a means of providing cutting-edge instruction and maintaining an advantage over rivals in the market.

AI Tool Usage by Indian College Students 2025

This unique dataset, collected via a May 2025 survey, captures how 496 Indian college students use AI tools (e.g., ChatGPT, Gemini, Copilot) in academics. It includes 16 attributes like AI tool usage, trust, impact on grades, and internet access, ideal for education analytics and machine learning.

Columns

• Student_Name: Anonymized student name.
• College_Name: College attended.
• Stream: Academic discipline (e.g., Engineering, Arts).
• Year_of_Study: Year of study (1–4).
• AI_Tools_Used: Tools used (e.g., ChatGPT, Gemini).
• Daily_Usage_Hours: Hours spent daily on AI tools.
• Use_Cases: Purposes (e.g., Assignments, Exam Prep).
• Trust_in_AI_Tools: Trust level (1–5).
• Impact_on_Grades: Grade impact (-3 to +3).
• Do_Professors_Allow_Use: Professor approval (Yes/No).
• Preferred_AI_Tool: Preferred tool.
• Awareness_Level: AI awareness (1–10).
• Willing_to_Pay_for_Access: Willingness to pay (Yes/No).
• State: Indian state.
• Device_Used: Device (e.g., Laptop, Mobile).
• Internet_Access: Access quality (Poor/Medium/High).

Use Cases

• Predict academic performance using AI tool usage.
• Analyze trust in AI across streams or regions.
• Cluster students by usage patterns.
• Study digital divide via Internet_Access.

Source: Collected via Google Forms survey in May 2025, ensuring diverse representation across India. Note: First dataset of its kind on Kaggle!

During a global survey of students conducted in mid-2024, it was found that a whopping ** percent said they were using artificial intelligence tools in their schoolwork. Almost a ****** of them used it on a daily basis.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

This table contains data on the percent of population age 25 and up with a four-year college degree or higher for California, its regions, counties, county subdivisions, cities, towns, and census tracts. Greater educational attainment has been associated with health-promoting behaviors including consumption of fruits and vegetables and other aspects of healthy eating, engaging in regular physical activity, and refraining from excessive consumption of alcohol and from smoking. Completion of formal education (e.g., high school) is a key pathway to employment and access to healthier and higher paying jobs that can provide food, housing, transportation, health insurance, and other basic necessities for a healthy life. Education is linked with social and psychological factors, including sense of control, social standing and social support. These factors can improve health through reducing stress, influencing health-related behaviors and providing practical and emotional support. More information on the data table and a data dictionary can be found in the Data and Resources section. The educational attainment table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf The format of the educational attainment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

This dataset captures insights into the use of Artificial Intelligence (AI) among 535 students in Indonesian higher education, focusing on their expectations, challenges, attitudes, perceptions, and motivations regarding AI-based learning tools. Collected through a structured survey, the dataset includes demographic variables such as university type, field of study, and educational level, along with students' self-reported experiences with AI in academic settings. The dataset serves as a valuable resource for understanding AI adoption trends in higher education, identifying barriers to AI integration, and evaluating its impact on student engagement and learning outcomes. It enables comparative analysis across different academic disciplines and institutional contexts, offering opportunities for policymakers and educators to design AI-informed curricula. Additionally, this dataset is structured for reproducibility and reuse, allowing researchers to extend its findings, apply alternative analytical approaches, and conduct cross-regional or longitudinal studies on AI integration in higher education.

This project investigated teacher attitudes towards Generative Artificial Intelligence Tools (GAITs). In excess of three hundred teachers were surveyed across a broad variety of teaching levels, demographic areas, experience levels, and disciplinary areas, to better understand how they believe teaching and assessment should change as a result of GAITs such as ChatGPT.Teachers were invited to complete an online survey relating to their perceptions of the open Artificial Intelligence (AI) tool ChatGPT, and how it will influence what they teach and how they assess. The purpose of the study is to provide teachers, policymakers, and society at large with an understanding of the potential impact of tools such as ChatGPT on Education.This dataset contains public data files used for the ChatGPT survey (XLSX) and the survey containing variable selection codes (DOCX). See the second sheet of the XLSX file for variable descriptions.

The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum.

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.

Success.ai’s Education Marketing Data offers businesses and organizations direct access to verified contact details for educators, administrators, and marketing professionals in the education sector. Sourced from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles, ensuring precise and meaningful connections with decision-makers at schools, universities, training centers, and educational service providers. By using continuously updated and AI-validated data, Success.ai empowers you to engage with the right contacts and drive targeted marketing campaigns, recruitment efforts, and partnership opportunities within the education landscape.

Why Choose Success.ai’s Education Marketing Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and social profiles of school administrators, university professors, department heads, and education marketers.
   • AI-driven validation ensures 99% accuracy, enabling confident outreach and reducing wasted efforts.

2. Global Reach Across Education Segments

   • Includes contacts from K-12 schools, higher education institutions, vocational training centers, e-learning platforms, and professional certification organizations.
   • Covers regions including North America, Europe, Asia-Pacific, South America, and the Middle East, ensuring a broad spectrum of educational institutions and markets.

3. Continuously Updated Datasets

   • Real-time updates guarantee that your contact data remains current, reflecting changes in roles, institutional structures, and academic priorities.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring your outreach is both ethical and legally compliant.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes education sector leaders, influencers, and key decision-makers.
• 50M Work Emails: AI-validated for seamless communication and reduced bounce rates.
• 30M Company (Institution) Profiles: Gain insights into school types, program offerings, and organizational structures.
• 700M Global Professional Profiles: Enriched datasets to support market analysis, competitive benchmarking, and strategic planning.

Key Features of the Dataset:

1. Education Decision-Maker Profiles

   • Identify and connect with principals, superintendents, deans, admissions directors, marketing managers, and department heads shaping curriculum, enrollment, and academic initiatives.

2. Advanced Filters for Precision Targeting

   • Filter by institution type, geographic region, academic level, specialty programs, or job function to refine your outreach and campaigns.
   • Tailor messaging to align with unique educational needs, cultural contexts, and policy frameworks.

3. AI-Driven Enrichment

   • Profiles are enriched with actionable data, offering insights into institutional priorities, enrollment trends, and academic focal points, enabling more personalized and effective engagement.

Strategic Use Cases:

1. Marketing and Enrollment Campaigns

   • Target admissions and marketing professionals at universities, colleges, and language schools to promote your educational products, tutoring services, or learning management systems.
   • Craft campaigns that resonate with educators’ challenges, such as student retention, curriculum innovation, or digital learning adoption.

2. EdTech and Resource Partnerships

   • Connect with decision-makers evaluating new technologies, software platforms, and resource providers to enhance teaching and learning experiences.
   • Position your EdTech solutions to solve institutional pain points like remote learning effectiveness or data-driven student success strategies.

3. Academic Collaboration and Research

   • Identify contacts in academic research, curriculum development, or accreditation bodies to foster partnerships, co-develop programs, or share research findings.
   • Engage with administrators overseeing funding, grants, and educational policy to influence institutional decision-making.

4. Recruitment and Talent Acquisition

   • Find HR professionals and department heads seeking qualified instructors, administrative staff, or specialized educators.
   • Offer recruitment and professional development services to institutions aiming to attract top-tier academic talent.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-quality verified data at competitive prices, ensuring cost-effective growth and strategic advantage in education-focused outreach.

2. Seamless Integration

   • Integrate verified contact data into your CRM or marketing automation tools using APIs or downloadable formats for efficient data management.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to inform decisions, refine targeting, and enhance campai...

Generative Artificial Intelligence (AI) models such as OpenAI’s ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT’s ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

The dataset depicts data collected measuring participants' frequency of AI usage, Attitudes towards Technology using the Technological Readiness Index (TRI) 2.0, Attitudes towards AI using the General Attitudes towards Artificial Intelligence Scale, Personality using the Big Five Inventory, Learning Approaches using the short Approaches and Study Skills Inventory for Students scale, Need for Cognition using the 18-item Need For Cognition Scale. The analysis conducted using the dataset includes correlation, mediation, and moderation.

This dataset was collected as part of a study exploring high school mathematics teachers’ perceptions and use of artificial intelligence, with a particular focus on the perceived usefulness and perceived ease of use of artificial intelligence (AI) in teaching. ChatGPT was used as the artificial intelligence technology used in this study. The study employed a sequential explanatory mixed-methods design, guided by the Technology Acceptance Model 3 as a theoretical framework. Quantitative data were gathered through an online survey, in which structured Technology Acceptance Model 3 questionnaires were adapted and administered to examine participants' perceived usefulness and perceived ease of use of artificial intelligence, as well as the determinants. The quantitative data were analysed using the Statistical Package for the Social Sciences version 26. Descriptive statistics was used to interpret the data.Qualitative data were obtained through classroom observations and semi-structured interviews. Observations focused on how participants use artificial intelligence in their teaching, while the interviews provided deeper insights into their experiences and perspectives. All observations and interviews were recorded and subsequently transcribed for the dissertation. In order to open this data, Microsoft Excel, an MP4 video player, an audio player, and a portable document format reader will be needed.

The OECD has initiated PISA for Development (PISA-D) in response to the rising need of developing countries to collect data about their education systems and the capacity of their student bodies. This report aims to compare and contrast approaches regarding the instruments that are used to collect data on (a) component skills and cognitive instruments, (b) contextual frameworks, and (c) the implementation of the different international assessments, as well as approaches to include children who are not at school, and the ways in which data are used. It then seeks to identify assessment practices in these three areas that will be useful for developing countries. This report reviews the major international and regional large-scale educational assessments: large-scale international surveys, school-based surveys and household-based surveys. For each of the issues discussed, there is a description of the prevailing international situation, followed by a consideration of the issue for developing countries and then a description of the relevance of the issue to PISA for Development.

Problem Statement

👉 Download the case studies here

Traditional education systems often fail to address the diverse learning needs of students. A leading EdTech company faced challenges in providing tailored educational experiences, leading to decreased student engagement and inconsistent learning outcomes. The company sought an innovative solution to create adaptive learning platforms that cater to individual learning styles and pace.

Challenge

Creating a personalized education platform involved overcoming the following challenges:

Analyzing diverse datasets, including student performance, engagement metrics, and learning preferences.

Designing adaptive content delivery that adjusts to each student’s progress in real-time.

Maintaining a balance between personalized learning and curriculum standards.

Solution Provided

An adaptive learning system was developed using machine learning algorithms and AI-driven educational software. The solution was designed to:

Analyze student data to identify strengths, weaknesses, and preferred learning styles.

Provide personalized learning paths, including targeted content, quizzes, and feedback.

Continuously adapt content delivery based on real-time performance and engagement metrics.

Development Steps

Data Collection

Aggregated student data, including assessment scores, engagement patterns, and interaction histories, from existing learning management systems.

Preprocessing

Cleaned and structured data to identify trends and learning gaps, ensuring accurate input for machine learning models.

Model Training

Built recommendation algorithms to suggest tailored learning materials based on student progress. Developed predictive models to identify students at risk of falling behind and provide timely interventions.

Validation

Tested the system with diverse student groups to ensure its adaptability and effectiveness in various educational contexts.

Deployment

Integrated the adaptive learning platform with the company’s existing educational software, ensuring seamless operation across devices.

Monitoring & Improvement

Established a feedback loop to refine algorithms and enhance personalization based on new data and evolving student needs.

Results

Enhanced Student Engagement

The platform increased student participation by providing interactive and tailored learning experiences.

Improved Learning Outcomes

Personalized learning paths helped students grasp concepts more effectively, resulting in better performance across assessments.

Tailored Educational Experiences

The adaptive system offered individualized support, catering to students with diverse needs and learning styles.

Proactive Support

Predictive insights enabled educators to identify struggling students early and provide necessary interventions.

Scalable Solution

The platform scaled efficiently to accommodate thousands of students, ensuring consistent quality and personalization.

Provides eligibility and compliance reports about the approximately 6,000 postsecondary institutions that participate in the Title IV programs.

Analysis of ‘ Predicting Student Performance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/student-performance on 28 January 2022.

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

About this dataset

• This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

How to use this dataset

• Predict Student's future performance
• Understand the root causes for low performance
• More datasets

Acknowledgements

If you use this dataset in your research, please credit ewenme

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

Analysis of ‘International Educational Attainment by Year & Age’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/international-comp-attainmente on 13 February 2022.

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

About this dataset

The National Center for Education Statistics (NCES) is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. NCES is located within the U.S. Department of Education and the Institute of Education Sciences. NCES fulfills a Congressional mandate to collect, collate, analyze, and report complete statistics on the condition of American education; conduct and publish reports; and review and report on education activities internationally.

• Table 603.10. Percentage of the population 25 to 64 years old who completed high school, by age group and country: Selected years, 2001 through 2012
• Table 603.20. Percentage of the population 25 to 64 years old who attained selected levels of postsecondary education, by age group and country: 2001 and 2012
• Table 603.30. Percentage of the population 25 to 64 years old who attained a bachelor's or higher degree, by age group and country: Selected years, 1999 through 2012
• Table 603.40 Percentage of the population 25 to 64 years old who attained a postsecondary vocational degree, by age group and country: Selected years, 1999 through 2012
• Table 603.50 Number of bachelor's degree recipients per 100 persons at the typical minimum age of graduation, by sex and country: Selected years, 2005 through 2012
• Table 603.60. Percentage of postsecondary degrees awarded to women, by field of study and country: 2013
• Table 603.70. Percentage of bachelor's or equivalent degrees awarded in mathematics, science, and engineering, by field of study and country: 2013
• Table 603.80. Percentage of master's or equivalent degrees and of doctoral or equivalent degrees awarded in mathematics, science, and engineering, by field of study and country: 2013
• Table 603.90. Employment to population ratios of -25 to 64-year-olds, by sex, highest level of educational attainment, and country: 2014

Source: https://nces.ed.gov/programs/digest/current_tables.asp

This dataset was created by National Center for Education Statistics and contains around 100 samples along with Unnamed: 20, Unnamed: 24, technical information and other features such as: - Unnamed: 11 - Unnamed: 16 - and more.

How to use this dataset

• Analyze Unnamed: 15 in relation to Unnamed: 6
• Study the influence of Unnamed: 1 on Unnamed: 10
• More datasets

Acknowledgements

If you use this dataset in your research, please credit National Center for Education Statistics

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