This study reports on an in-depth research into student-learning using a "thinking-first" framework combined with stepwise heuristics, to provide students structure throughout the entire programming process.

The study targetted secondary education students in an elective computer science course. There was one class with 11 Dutch high school students, of which 2 females and 9 males. The group was heterogeneous, with students from different academic levels and age-groups. Each student’s level and previous experience with CS was determined a priori using a pretest.

For this study we developed sets of quizes, tasks and tests comprised of code comprehension, code composition questions (including reading and creating flowchart designs). The student responses to each were anaylzed.

This repository contains the following data: - taxonomyPerQ.pdf: indicates taxonomy level of each (quiz, task, test) question answered by students - assessments_unanswered: all questions (quizes, tasks, tests) administered to students - pretask (responses): anonymized student responses to pretask questions - midtask (responses): anonymized student responses to midtask questions - finaltask (responses): anonymized student responses to finaltask questions - quiz 1 (responses): anonymized student responses to quiz 1 questions - quiz 2 (responses): anonymized student responses to quiz 2 questions - final test (responses): anonymized student responses to final test questions

The student's handwritten work was scanned, saved as pdf and coded in atlas.ti. These coded pdf's cannot be anonymized anymore, and thus not openly distributed or published.

This data collection contains information from the first wave of High School and Beyond (HSB), a longitudinal study of American youth conducted by the National Opinion Research Center on behalf of the National Center for Education Statistics (NCES). Data were collected from 58,270 high school students (28,240 seniors and 30,030 sophomores) and 1,015 secondary schools in the spring of 1980. Many items overlap with the NCES's NATIONAL LONGITUDINAL STUDY OF THE CLASS OF 1972 (ICPSR 8085). The HSB study's data are contained in eight files. Part 1 (School Data) contains data from questionnaires completed by high school principals about various school attributes and programs. Part 2 (Student Data) contains data from surveys administered to students. Included are questionnaire responses on family and religious background, perceptions of self and others, personal values, extracurricular activities, type of high school program, and educational expectations and aspirations. Also supplied are scores on a battery of cognitive tests including vocabulary, reading, mathematics, science, writing, civics, spatial orientation, and visualization. To gather the data in Part 3 (Parent Data), a subsample of the seniors and sophomores surveyed in HSB was drawn, and questionnaires were administered to one parent of each of 3,367 sophomores and of 3,197 seniors. The questionnaires contain a number of items in common with the student questionnaires, and there are a number of items in common between the parent-of-sophomore and the parent-of-senior questionnaires. This is a revised file from the one originally released in Autumn 1981, and it includes 22 new analytically constructed variables imputed by NCES from the original survey data gathered from parents. The new data are concerned primarily with the areas of family income, liabilities, and assets. Other data in the file concentrate on financing of post-secondary education, including numerous parent opinions and projections concerning the educational future of the student, anticipated financial aid, student's plans after high school, expected ages for student's marriage and childbearing, estimated costs of post-secondary education, and government financial aid policies. Also supplied are data on family size, value of property and other assets, home financing, family income and debts, and the age, sex, marital, and employment status of parents, plus current income and expenses for the student. Part 4 (Language Data) provides information on each student who reported some non-English language experience, with data on past and current exposure to and use of languages. In Parts 5-6, there are responses from 14,103 teachers about 18,291 senior and sophomore students from 616 schools. Students were evaluated by an average of four different teachers who had the opportunity to express knowledge or opinions of HSB students whom they had taught during the 1979-1980 school year. Part 5 (Teacher Comment Data: Seniors) contains 67,053 records, and Part 6 (Teacher Comment Data: Sophomores) contains 76,560 records. Questions were asked regarding the teacher's opinions of their student's likelihood of attending college, popularity, and physical or emotional handicaps affecting school work. The sophomore file also contains questions on teacher characteristics, e.g., sex, ethnic origin, subjects taught, and time devoted to maintaining order. The data in Part 7 (Twins and Siblings Data) are from students in the HSB sample identified as twins, triplets, or other siblings. Of the 1,348 families included, 524 had twins or triplets only, 810 contained non-twin siblings only, and the remaining 14 contained both types of siblings. Finally, Part 8 (Friends Data) contained the first-, second-, and third-choice friends listed by each of the students in Part 2, along with identifying information allowing links between friendship pairs. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR as four separate datasets here:

1980: https://doi.org/10.3886/ICPSR07896.v2

1982: https://doi.org/10.3886/ICPSR08297.v3

1984: https://doi.org/10.3886/ICPSR08443.v1

1986: https://doi.org/10.3886/ICPSR08896.v3

We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The open science movement produces vast quantities of openly published data connected to journal articles, creating an enormous resource for educators to engage students in current topics and analyses. However, educators face challenges using these materials to meet course objectives. I present a case study using open science (published articles and their corresponding datasets) and open educational practices in a capstone course. While engaging in current topics of conservation, students trace connections in the research process, learn statistical analyses, and recreate analyses using the programming language R. I assessed the presence of best practices in open articles and datasets, examined student selection in the open grading policy, surveyed students on their perceived learning gains, and conducted a thematic analysis on student reflections. First, articles and datasets met just over half of the assessed fairness practices, but this increased with the publication date. There was a marginal difference in how assessment categories were weighted by students, with reflections highlighting appreciation for student agency. In course content, students reported the greatest learning gains in describing variables, while collaborative activities (e.g., interacting with peers and instructor) were the most effective support. The most effective tasks to facilitate these learning gains included coding exercises and team-led assignments. Autocoding of student reflections identified 16 themes, and positive sentiments were written nearly 4x more often than negative sentiments. Students positively reflected on their growth in statistical analyses, and negative sentiments focused on how limited prior experience with statistics and coding made them feel nervous. As a group, we encountered several challenges and opportunities in using open science materials. I present key recommendations, based on student experiences, for scientists to consider when publishing open data to provide additional educational benefits to the open science community. Methods Article and dataset fairness To assess the utility of open articles and their datasets as an educational tool in an undergraduate academic setting, I measured the congruence of each pair to a set of best practices and guiding principles. I assessed ten guiding principles and best practices (Table 1), where each category was scored ‘1’ or ‘0’ based on whether it met that criteria, with a total possible score of ten. Open grading policies Students were allowed to specify the percentage weight for each assessment category in the course, including 1) six coding exercises (Exercises), 2) one lead exercise (Lead Exercise), 3) fourteen annotation assignments of readings (Annotations), 4) one final project (Final Project), 5) five discussion board posts and a statement of learning reflection (Discussion), and 6) attendance and participation (Participation). I examined if assessment categories (independent variable) were weighted (dependent variable) differently by students using an analysis of variance (ANOVA) and examined pairwise differences with Tukey HSD. Assessment of perceived learning gains I used a student assessment of learning gains (SALG) survey to measure students’ perceptions of learning gains related to course objectives (Seymour et al. 2000). This Likert-scale survey provided five response categories ranging from ‘no gains’ to ‘great gains’ in learning and the option of open responses in each category. A summary report that converted Likert responses to numbers and calculated descriptive statistics was produced from the SALG instrument website. Student reflections In student reflections, I examined the frequency of the 100 most frequent words, with stop words excluded and a minimum length of four (letters), both “with synonyms” and “with generalizations”. Due to this paper's explorative nature, I used autocoding to identify students' broad themes and sentiments in their reflections. Autocoding examines the sentiment of each word and scores it as positive, neutral, mixed, or negative. In this process, I compared how students felt about each theme, focusing on positive (i.e., satisfaction) and negative (i.e., dissatisfaction) sentiments. The relationship of how sentiment was coded to themes was visualized in a treemap, where the size of a block is relative to the number of references for that code. All reflection processing and analyses were performed in NVivo 14 (Windows). All data were collected with institutional IRB approval (IRB-24–0314). All statistical analyses were performed in R (ver. 4.3.1; R Core Team 2023).

Abstract

Opportunity-focused, high-growth entrepreneurship and science-led innovation are crucial for continued economic growth and productivity. Working in these fields offers the opportunity for rewarding and high-paying careers. However, the majority of youth in developing countries do not consider either as job options, affecting their choices of what to study. Youth may not select these educational and career paths due to lack of knowledge, lack of appropriate skills, and lack of role models. We provide a scalable approach to overcoming these constraints through an online education course for secondary school students that covers entrepreneurial soft skills, scientific methods, and interviews with role models.

The study comprises three experimental trials provided Before and during COVID-19 pandemic in different regions of Ecuador. This catalog entry includes data from Experiment 1: Educational Zone 2/Municipality of Quito 2019-2020. The data from the other two experiments are also available in the catalog.

Experiment 1: Educational Zone 2/Municipality of Quito 2019-2020 In course of Showing Life Opportunities project we conducted a randomized control trial in high schools in Educational Zone 2, Ecuador and Municipality of Quito, Ecuador in 2019-2020; Students finish the program in July 2020. The intervention is an online education course that covers entrepreneurial soft skills, scientific methods, and interviews with role models. This course is taken by students at school (some students finish the program at school during COVID-19 outbreak). We work with mostly 14-19 year-old students (16,570 students). The experimental program covers 126 schools in Educational Zone 2 and 11 schools in Municipality of Quito. We randomly assign schools either to treatment (and receiving the entrepreneurship courses online), or placebo-control (receiving a placebo treatment of online courses from standard curricula) groups. We also cross-randomize the role models and evaluate set of nimble interventions to increase take-up.

The details of intervention can be found in AEA registry: Asanov, Igor and David McKenzie. 2020. Showing Life Opportunities: Increasing opportunity-driven entrepreneurship and STEM careers through online courses in schools. AEA RCT Registry. July 19.

Geographic coverage

Experiment 1: Municipality of Quito and Educational Zone 2 Educational Zone 2 has its administrative headquarters in the city of Tena, Napo province. Its covers provinces of Napo, Orellana and Pichincha, 8 districts (15D01, 22D01, 17D10, 17D11, 15D02, 17D12, 22D02, 22D03), its 16 cantons and 68 parishes. It has an area of 39,542.58 km². The educational zone 2 spread from east to the western border of the Ecuador. We cover students of age 14-18 in schools that has sufficient access to the internet and classes of the K10, K11, or K12. We included the municipality of Quito in the study to enrich the coverage of program by having large (capital) city in the sample.

Analysis unit

Student

Kind of data

Sample survey data [ssd]

Sampling procedure

All students in selected schools who were present in classes filled out the baseline questionnaire

Mode of data collection

Internet [int]

Research instrument

Questionnaires We execute three main sets of questioners. A. Internet (Online Based survey)

The survey consists of a multi-topic questionnaire administered to the students through online learning platform in school during normal educational hours before COVID-19 pandemic or at home during the COVID-19 pandemic. We collect next information: 1. Subject specific knowledge tests. Spanish, English, Statistics, Personal Initiative (only endline), Negotiations (only endline). 2. Career intentions, preferences, beliefs, expectations, and attitudes. STEM and entrepreneurial intentions, preferences, beliefs, expectations, and attitudes. 3. Psychological characteristics. Personal Initiative, Negotiations, General Cognitions (General Self-Efficacy, Youth Self-Efficacy, Perceived Subsidiary Self-Efficacy Scale, Self-Regulatory Focus, Short Grit Scale), Entrepreneurial Cognitions (Business Self-Efficacy, Identifying Opportunities, Business Attitudes, Social Entrepreneurship Standards). 4. Behavior in (incentivized) games: Other-regarding preferences (dictator game), tendency to cooperate (Prisoners Dilemma), Perseverance (triangle game), preference for honesty, creativity (unscramble game). 5. Other background information. Socioeconomic level, language spoken, risk and time preferences, trust level, parents background, big-five personality traits of student, cognitive abilities. Background information (5) collected only at the baseline. B. First follow-up Phone-based Survey Zone 2, Summer (Phone Based). The survey replicates by phone shorter version of the internet-based survey above. We collect next information: 1. Subject specific knowledge tests.
2. Career intentions, preferences, beliefs, expectations, and attitudes. 3. Psychological characteristics

C. (Second) Follow-up Phone-Based Survey, Winter, Zone 2, Highlands Educational Regime.

We execute multi-topic questionnaire by phone to capture the first life-outcomes of students who finished the school. We collect next information:

1. Life Outcome 1- Education. The set of questions that aims to measure the learning success, career/study intentions, propensity to plan and approach others with studying tasks, entrepreneurial intentions.
2. Life Outcome 2- Labor. The set of questions that aims to measure employment status and income, job searching behavior, time devoted for working/business, salary expectations and knowledge about the careers, self-initiated contribution to the family.
3. Personal Initiative/Negotiations related and other measures. The set of questions that aim to measure level of personal initiative, negotiation strategies, pregnancy rate, gender stereotypes, math/STEM self-efficacy, gender attitudes, parent-student communication effects.

Cleaning operations

Data Editing A. Internet, Online-based surveys. We extracted the raw data generated on online platform from each experiment and prepared it for research purposes. We made several pre-processing steps of data: 1. We transform the raw data generated on platform in standard statistical software (R/STATA) readable format. 2. We extracted the answer for each item for each student for each survey (Baseline, Midline, Endline). 3. We cleaned duplicated students and duplicated answers for each item in each survey based on administrative data, performance and information given by students on platform. 4. In case of baseline survey, we standardized items/scales but also kept the raw items.

B. Phone-based surveys. The phone-based surveys are collected with help of advanced CATI kit. It contains all cases (attempts to call) and indication if the survey was effective. The data is cleaned to be ready for analysis. The data is anonymized but contains unique anonymous student id for merging across datasets.

Data for this study were collected at the University of California – Irvine (UCI) as part of the UCI-MUST (Measuring Undergraduate Success Trajectories) Project, a larger longitudinal measurement project aimed at improving understanding of undergraduate experience, trajectories and outcomes, while supporting campus efforts to improve institutional performance and enhance educational equity (Arum et. al. 2021). The project is focused on student educational experience at a selective large, research-oriented public university on the quarter system with half of its students first-generation and 85 percent Hispanic, Asian, African-American, Pacific Islander or Native American. Since Fall 2019, the project has tracked annually new cohorts of freshmen and juniors with longitudinal surveys administered at the end of every academic quarter. Data from the Winter 2023 end of term assessment, administered in the first week of April, was pooled for four longitudinal study cohorts for this study (i.e., Fall 2019-2022 cohorts). There was an overall response rate of 42.5 percent for the Winter 2023 end of term assessment. This allowed us to consider student responses from freshmen through senior years enrolled in courses throughout the university. Students completed questionnaire items about their knowledge and use of ChatGPT in and out of the classroom during the winter 2023 academic term. In total 1,129 students completed the questionnaire, which asked questions about: knowledge of ChatGPT (“Do you know what ChatGPT is?”); general use (“Have you used ChatGPT before?”); and instructor attitude (“What was the attitude of the instructor for [a specific course students enrolled in] regarding the use of ChatGPT?”). Of those 1,129 students, 191 had missing data for at least one variable of interest and were subsequently dropped from analysis, resulting in a final sample of 938 students. In addition, for this study we merged our survey data with administrative data from campus that encompasses details on student background, including gender, race, first-generation college-going, and international student status. Campus administrative data also provides course-level characteristics, including whether a particular class is a lower- or upper-division course as well as the academic unit on campus offering the course. In addition, we used administrative data on all students enrolled at the university to generate classroom composition measures for every individual course taken by students in our sample – specifically the proportion of underrepresented minority students in the class, the proportion of international students in the class and the proportion of female students in the class. For our student-level analysis [R1], we used binary logistic regressions to examine the association between individual characteristics and (1) individual awareness and (2) individual academic use of ChatGPT utilizing the student-level data of 938 students. Individual characteristics include gender, underrepresented minority student status, international student status, first generation college-going student status, student standing (i.e. lower or upper classmen), cumulative grade point average and field of study. Field of study was based on student major assigned to the broad categories of physical sciences (i.e. physical sciences, engineering, and information and computer science), health sciences (i.e. pharmacy, biological sciences, public health, and nursing), humanities, social sciences (i.e. business, education, and social sciences), the arts, or undeclared. We defined awareness of ChatGPT as an affirmative response to the question “Do you know what ChatGPT is?” Regarding ChatGPT use, we focused on academic use which was defined as an affirmative response of either “Yes, for academic use” or “Yes, for academic and personal use” to the question “Have you used ChatGPT before?” For our course-level analysis [R2], we constructed a measure – course-level instructor encouragement for ChatGPT use – based on student responses to the end of the term survey conducted at the completion of the Winter 2023 term. In the survey, students were asked to indicate the extent to which their instructors encouraged them to use ChatGPT in each of their enrolled courses. The response

This document describes data collected from dental students on their perception about a teaching proposal of Evidence-Based Dentistry in undergraduate level. The component was proposed in the end of the dental course in a Southern Brazilian university. One of the questionnaires applied to the students verified their perception of the component, while the other, based on the Questionnaire to Evaluate the Competency in Evidence-Based Practice (EBP-COQ Prof©) assessed questions pertaining to attitudes, knowledge and skills towards Evidence-Based Dentistry. The datasets present the original data based on the answers of the students.

The purpose of the study: to provide impartial information for the school, its students, and their parents (caregivers, foster parents) about the achievements to make decisions on the further improvements of teaching and studying on student, teacher, class, school, municipality, and national level. The objectives of National Survey of Student Achievement (NASA): to collect the information for monitoring the national students’ achievements, planning the novelties, and implementing the novelties for monitoring the success; to evaluate the educational content, and substantiating students’ achievement criteria based on collected data; to prepare the necessary tools (i.e., standardized tests, etc.) for students and teachers for the impartial evaluation of their work results; to prepare the necessary tools (i.e., standardized tests, etc.) for the municipality’s education subdivisions and school principals for collecting the required data of work result assessments and planning of activities. National Survey of Student Achievement, first implemented in 2002, became the responsibility of the Education Supply Centre. Due to economic reasons, the assessments were not provided from 2009 to 2011. In 2012, the renewed assessment implementation was consigned to the National Examination Centre. Since the 2nd of September, 2019, the National Agency of Education took over the activities of the National Examination Centre and continues to carry them on to this day. In 2014, 5 NASA surveys were carried out. One line in SPSS Statistics from the 2014 National Survey of Student Achievement coincides with the achievements or questionnaire answers of one particular student or a teacher. The information provided in databases is impersonal - a student or a teacher is identified based on code, without providing the class or school’s name. Each school that has participated in the 2014 National Survey of Student Achievement received a unique five-number school code. The code used for identifying the schools of both grade 4 and grade 8 students and teachers consists of a school code and the numbers identifying a class and a student. The class code in the student’s database coincides with the code in the teacher’s database. To connect these databases, the variable named “ID_klase” would have to be used as an identifier. This dataset contains data from a survey of 8th grade teachers of the mathematics study. All the provided questionnaire answers from teachers appear in teacher databases from the 2014 National Survey of Student Achievement. The same questionnaire was given to all the teachers. The teacher questionnaire consisted of general questions (to analyse the educational context), as well as personal questions or questions about the objective field. Dataset "NSSA 2014: 8th Grade Teachers of the Mathematics Study, 2014" metadata and data were prepared implementing project "Disparities in School Achievement from a Person and Variable-Oriented Perspective: A Prototype of a Learning Analytics Tool NO-GAP" from 2020 to 2023. Project leader is chief research fellow Rasa Erentaitė. Project is funded by the European Regional Development Fund according to the 2014–2020 Operational Programme for the European Union Funds’ Investments, under measure’s No. 01.2.2-LMT-K-718 activity “Research Projects Implemented by World-class Researcher Groups to develop R&D activities relevant to economic sectors, which could later be commercialized” under a grant agreement with the Lithuanian Research Council (LMTLT).

The PHQ-9 Student Depression Dataset contains responses from 500 students to the PHQ-9 questionnaire, a well-established tool for diagnosing depression. This dataset is designed to support the development of machine learning models aimed at automated depression detection by analyzing text responses to common depression-related questions.

The PHQ-9 questionnaire includes 9 questions that assess symptoms of depression over the past two weeks, covering areas like mood, energy levels, sleep, appetite, and thoughts of self-harm. The responses are scored on a scale from 0 (Not at all) to 3 (Nearly every day), with the total score ranging from 0 to 27. Based on this score, the depression severity is classified into one of the following categories: Minimal (0-4) Mild (5-9) Moderate (10-14) Moderately Severe (15-19) Severe (20-27)

This dataset is primarily designed for building models that can assist in automated depression detection. Some potential use cases include: Sentiment Analysis: Analyzing emotional tones in text responses to assess depression. Text Classification: Classifying responses into different depression severity levels. Predictive Modeling: Predicting depression severity based on textual responses. Feature Engineering: Extracting linguistic features (e.g., sentiment, keywords) to predict depression. The dataset is diverse, with synthetic responses across different levels of depression, providing a versatile foundation for machine learning applications. While the dataset does not contain personally identifiable information (PII), real-world applications should follow ethical guidelines regarding privacy, consent, and mental health resources. When working with real data or applying this dataset in clinical research, it is essential to adhere to ethical standards, including:

Data Privacy: Anonymizing personal information. Informed Consent: Ensuring participants give consent before data collection. Support Resources: Providing support for individuals who may exhibit serious mental health concerns.

Applications: Clinical Research: This dataset is valuable for studying depression detection using natural language processing and machine learning techniques. AI in Healthcare: It can be used in the development of tools for automated mental health assessment. Education: Training students or professionals in recognizing depression symptoms and analyzing responses.

Data base Database that collects data on gender, age, grade at the beginning of the course, grades of the activity and satisfaction with the activity.

An experimental study using randomisation of team work groups was developed. Some student groups developed CBL activities in combination with Escape Room activities, and other student groups developed CBL activities alone. The latter can be considered a control group.

This innovative teaching project was performed by social work students at the University of Zaragoza (Spain). This degree comprises 240 ECTS credits spread out over four years. Specifically, this experimental study was created for "Social Work with Groups" , a compulsory subject taught during the second semester of the second academic year of the Social Work degree programme. It is divided into two parts: the first one is presented from a social psychology perspective, and it is made up of five course curriculum topics. The second one is taught from a social work/social services perspective, which focuses more on the specifics of the profession (four course curriculum topics). This experiment was conducted in February and March 2023, during the delivery of the social psychology part of the course. There are taught five course curriculum topics that fall within the domain of social psychology (psychology of groups). These topics are: 1) group meaning and types; 2) group growth processes, cohesion, conflict, obedience and group violence, group decision-making; 3) group structure: definition, status, roles, norms, group culture; 4) leadership and 5) group characteristics such as communication and empathy.

The participants were students enrolled in the “Social Work with Groups” course at the University of Zaragoza (Spain) during the 2022-2023 academic year. The sample size was 111 students: 56 performed CBL activities with Escape Room activities, and 55 performed CBL activities without Escape Room activities.

The variable outcome of this experimental study was academic performance, assessed by the grade obtained in the mark for CBL activities with a rating from 0 to 10, where the higher score indicated a better performance. This mark showed the number of correct concepts that were identified and extracted from the case. This score was translated to a categorical assessment going from fail (between 0 and 4.9), to pass (between 5.0 and 6.9), to merit (between 7.0 and 8.9), to outstanding (between 9.0 and 10).

Secondary outcomes

The secondary variables were: 1) quantitative and qualitative exam score (on the psychology of groups´ contents) 2) students´ satisfaction with the activity, and 3) time needed for performing the activities.

The academic performance data were collected using the exam score for the subject (psychology of groups´ contents). This exam consisted of 40 multiple-choice questions with three response options, taking the chance factor into account (so marks were deducted for wrong answers). The quantitative rating of each academic score can range between 0 and 10, with a higher score denoting a higher percentage of correct answers. The categorical holistic assessment of achievement goes from fail (between 0 and 4.9), to pass (between 5.0 and 6.9), to merit (between 7.0 and 8.9), to outstanding (between 9.0 and 10).

The data on students´ satisfaction with the activity performed were collected using a self-reporting questionnaire made up of seven statements on the course and teaching methodology used (Gómez-Poyato et al. 2020; Oliván-Blázquez et al. 2022; Olivan-Blázquez et al. 2019), which were answered on a Likert scale from 0 to 4, with 0 meaning not at all and 4 meaning to a great extent. The statements to be evaluated were as follows: the teaching methodology used has encouraged new knowledge acquisition; it has favoured deep learning; it has helped me to think more critically; it has helped me to apply theoretical content to practice; it has helped me to apply theoretical content to assessments; it has helped me to understand concepts better; I believe it is an appropriate teaching methodology. A free response section was also included so that students could express themselves openly.

The data for the time used to carry out the activities were also collected, measured in minutes used for finishing the activities.

Age, gender and university admittance mark data were also obtained in order to to determine if the student groups were in the same conditions regarding these values at the start of the analysis.

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Book publishers Colleges Universities Religious Organizations Education Supply Companies Office Supply Companies Fundraising Product Companies

The global education data security market is experiencing robust growth, driven by the increasing digitization of educational institutions and the rising concerns around student data privacy and compliance with regulations like FERPA and GDPR. The market's value in 2025 is estimated at $10 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, projecting a market value exceeding $30 billion by 2033. Key growth drivers include the expanding adoption of cloud-based learning platforms, the proliferation of connected devices within educational settings, and the increasing sophistication of cyber threats targeting sensitive student and institutional data. Market segmentation reveals a strong demand for cloud-based solutions across both K-12 and higher education sectors, reflecting a broader industry trend towards flexible and scalable IT infrastructure. However, factors like budget constraints within educational institutions, particularly in developing regions, and the complexities associated with integrating data security solutions into existing IT systems present significant restraints to market expansion. Leading vendors such as Cisco, Citrix, McAfee, and others are actively developing and marketing advanced security solutions tailored to the specific needs of the education sector, further fueling market competition and innovation. The North American market currently dominates the education data security landscape, fueled by high technology adoption rates and stringent data privacy regulations. However, significant growth opportunities exist in the Asia-Pacific region, driven by rapid digital transformation in education systems and increasing internet penetration across developing economies like India and China. The European market is also expected to witness substantial growth, influenced by the implementation of GDPR and a focus on digital learning initiatives. Further growth will be fueled by the development of AI-powered security solutions that can proactively identify and mitigate threats in real-time, combined with robust data loss prevention (DLP) mechanisms to protect sensitive information. The increasing adoption of multi-factor authentication (MFA) and zero-trust security models are also expected to significantly impact market growth in the coming years.

The Higher Education Data Management Service market is experiencing robust growth, driven by the increasing need for efficient data management within educational institutions. The rising adoption of cloud-based solutions, coupled with the expanding use of data analytics for improved decision-making, is fueling this expansion. Institutions are increasingly recognizing the value of centralized data management systems to streamline administrative processes, enhance student experience, and improve operational efficiency. The market is segmented by application (schools and educational institutions) and deployment type (cloud-based and on-premises), with cloud-based solutions gaining significant traction due to their scalability, cost-effectiveness, and accessibility. Key players like Ellucian, Oracle, Workday, Campus Management, Blackboard, PowerSchool, and Jenzabar are actively competing in this space, offering a range of solutions to cater to diverse institutional needs and sizes. The North American market currently holds a significant share, owing to higher technology adoption rates and substantial investments in educational infrastructure. However, the Asia-Pacific region is projected to witness substantial growth in the coming years, driven by increasing government initiatives to digitize education and a growing number of higher education institutions. The market is expected to maintain a steady growth trajectory throughout the forecast period (2025-2033), although challenges such as data security concerns and the need for robust integration with existing systems may present some constraints. The competitive landscape is characterized by both established players and emerging vendors, leading to continuous innovation and the development of more sophisticated data management solutions. These solutions are increasingly incorporating advanced features such as predictive analytics, AI-powered insights, and robust data security measures. Further market growth is anticipated from the expanding adoption of learning analytics to personalize student learning experiences and improve retention rates. The ongoing shift towards digital learning and the growing demand for seamless data integration across different platforms will continue to shape the market's trajectory. Strategic partnerships, mergers and acquisitions, and product enhancements will likely remain key strategies for market players seeking to maintain a competitive edge. The market's future hinges on the continued evolution of technology and the willingness of educational institutions to embrace innovative data management strategies for improved outcomes.

Student Management Software Market Outlook

The global student management software market size was valued at approximately USD 9.8 billion in 2023 and is projected to reach USD 20.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.6% during the forecast period. This significant growth can be attributed to the increasing adoption of digital technologies in educational institutions and the rising demand for efficient management systems that streamline various administrative and academic processes.

The burgeoning demand for enhanced student engagement and personalized learning experiences is a key growth factor driving the student management software market. Educational institutions are increasingly investing in digital tools that facilitate better communication between students, teachers, and parents. These systems offer functionalities such as attendance tracking, grade management, and course scheduling, which collectively contribute to a more organized and efficient educational environment. Furthermore, the integration of artificial intelligence and machine learning in these software solutions is enabling more adaptive and personalized learning pathways for students.

Another significant driver for market growth is the increasing need for compliance and data security in educational institutions. As schools and universities handle a vast amount of sensitive information, including student records and financial data, there is a growing emphasis on adopting software solutions that ensure data integrity and privacy. Regulatory requirements, such as the Family Educational Rights and Privacy Act (FERPA) in the United States, are compelling institutions to implement robust student management systems that comply with data protection standards.

The rising trend of online and remote learning is also contributing to the market's expansion. The COVID-19 pandemic accelerated the shift towards digital education, prompting institutions to adopt cloud-based student management software to facilitate seamless distance learning experiences. These solutions offer the flexibility to access information from any location, thereby supporting continuous learning even during disruptions. The ongoing digital transformation in education is expected to sustain the demand for such software solutions in the coming years.

Regionally, North America is expected to hold the largest share of the student management software market, driven by the high adoption rate of advanced technologies in educational institutions and the presence of key market players. Europe and the Asia Pacific are also projected to witness significant growth, with increasing investments in educational infrastructure and digital learning initiatives. The Asia Pacific region, in particular, is anticipated to exhibit the highest CAGR during the forecast period, fueled by the rapid expansion of the education sector in countries like China and India.

Loan Management Software is becoming increasingly relevant in the educational sector, particularly for institutions that offer financial aid and student loans. As universities and colleges manage large volumes of financial transactions, having a robust loan management system can streamline processes, reduce errors, and ensure compliance with financial regulations. These software solutions provide tools for tracking loan disbursements, managing repayment schedules, and generating detailed financial reports. By integrating loan management capabilities with existing student management systems, educational institutions can offer a more comprehensive financial support service to students, enhancing their overall educational experience.

Component Analysis

The student management software market can be segmented by component into software and services. The software component dominates the market, accounting for the majority share, as it constitutes the core functionality required by educational institutions. This segment includes various modules such as enrollment management, attendance tracking, grade book management, and learning management systems. The continuous advancements in software technologies and the integration of AI and ML capabilities are enhancing the overall efficiency and effectiveness of these systems.

Within the software segment, cloud-based solutions are gaining significant traction due to their scalability, flexibility, and cost-effectiveness. These solutions allow educational inst

The data is the result from the test of computational thinking tasks based on Riau Malay culture to elementary school students in a public school in Pekanbaru

This dataset contains student responses to each item on the Views of Climate and Learning (VOCAL) survey since 2018. These responses are aggregated at the state level by grade and student group to protect student privacy.

The VOCAL survey is designed to provide information on student perceptions of school climate. There are two reports with different types of data: responses to individual items and aggregate index scaled scores that combine item responses. For more information about the VOCAL survey, please visit the VOCAL home page.

This dataset is one of two containing the same data that is also published in the VOCAL state dashboard: VOCAL Index Scaled Scores and Favorability VOCAL Item Response Scores

List of Items by Index and Topic

Engagement - Cultural Competence

• ENGCLC1: Adults working at this school treat all students respectfully, regardless of a student's race, culture, family income, religion, sex, or sexual orientation.
• ENGCLC2: Teachers at this school accept me for who I am.
• ENGCLC3: My textbooks or class materials include people and examples that reflect my race, cultural background and/or identity.
• ENGCLC4: Within school, I am encouraged to take upper level courses (honors, AP).
• ENGCLC5: Students from different backgrounds respect each other in our school, regardless of their race, culture, family income, religion, sex, or sexual orientation.
• ENGCLC6: Students like to have friends who are different from themselves (for example, boys and girls, rich and poor, or classmates of different color).
• ENGCLC7: Students are open to having friends who come from different backgrounds (for example, friends from different races, cultures, family incomes, or religions, or friends of a different sex, or sexual orientation).
• ENGCLC8: I read books in class that include people who are similar to me (for example, we look the same, speak the same, or live in similar neighborhoods).
• ENGCLC9: Adults working at this school treat all students with respect.
• ENGCLC10: In my academic classes, I work with groups of students who are from different backgrounds (for example, students from different races, cultures, family incomes, or religions, or students of a different sex or sexual orientation).

• ENGPAR1: I get the chance to take part in school events (for example, science fairs, art or music shows).
• ENGPAR2: My parents feel respected when they participate at our school (e.g., at parent-teacher conferences, open houses).
• ENGPAR3: I feel welcome to participate in extra-curricular activities offered through my school, such as, school clubs or organizations, musical groups, sports teams, student council, or any other extra-curricular activities.
• ENGPAR4: My teachers use my ideas to help my classmates learn.
• ENGPAR5: I have a choice in how I show my learning (e.g., write a paper, prepare a presentation, make a video).
• ENGPAR6: My teachers will explain things in different ways until I understand.
• ENGPAR7: When I need help, my teachers use my interests to help me learn.
• ENGPAR8: My teachers ask me to share what I have learned in a lesson.
• ENGPAR9: When I am stuck, my teachers want me to try again before they help me.
• ENGPAR10: In my classes, my teachers use students' interests to plan class activities.
• ENGPAR11: In at least two of my academic classes, I can work on assignments that interest me personally.
• ENGPAR12: If I finish my work early, I have a opportunity to do more challenging work.
• ENGPAR13: My classmates behave the way my teachers want them to.
• ENGPAR14: In at least two of my academic classes, students are asked to teach a lesson or part of a lesson.
• ENGPAR15: In my classes, students teach each other how they solved a problem.
• ENGPAR16: Students plan and work on group projects that solve real-world (everyday) problems.
• ENGPAR17: In at least two of my academic classes, students plan and work on projects that solve real-world problems.
• ENGPAR18: In my academic classes, students review each other's work and provided advice on how to improve it.
• ENGPAR19: In my classes, teachers use open-ended questions that make students think of many possible answers.
• ENGPAR20: I can connect what I learn in on class to what I learn in other classes.
• ENGPAR21: In my academic classes, students wrestle with problems that don't have an obvious answer.
• ENGPAR22: In my academic classes, I am asked to apply what I know to new types of complex tasks or problems.
• ENGPAR24: In my academic classes, students work on long-term group projects (more than one month in length) that they independently carry out.
• ENGPAR25: Students plan and work on group projects that solve real problems.

• ENGREL1: Students respect one another.
• ENGREL2: Students respect each other in my school.
• ENGREL3: My teachers care about me as a person.
• ENGREL4: Students at my school get along well with each other.
• ENGREL6: Teachers are available when I need to talk with them.
• ENGREL13: Adults at our school are respectful of student ideas even if the ideas expressed are different from their own.
• ENGREL14: My teachers promote respect among students.
• ENGREL15: In my classes, students work well together in groups.

• ENVDIS1: Students have a voice in deciding school rules.
• ENVDIS2: School rules are fair for all students.
• ENVDIS3: Students help decide school rules.
• ENVDIS4: School staff are consistent when enforcing rules in school.
• ENVDIS5: Teachers give students a chance to explain when they do something wrong.
• ENVDIS6: The consequences for the same inappropriate behavior (e.g., disrupting the class) are the same, no matter who the student is.
• ENVDIS7: Teachers give students a chance to explain their behavior when they do something wrong.
• ENVDIS8: My teachers will first try to help (guide) students who break class rules, instead of punishing them.
• ENVDIS9: My teachers will first try to help students who break class rules, instead of punishing them.

• ENVINS1: Students help each other learn without having to be asked by the teacher.
• ENVINS2: My teachers are proud of me when I work hard in school.
• ENVINS3: My teachers help me succeed with my schoolwork when I need help.
• ENVINS4: In class, students help each other learn.
• ENVINS5: My teachers set high expectations for my work.
• ENVINS8: My teachers believe that all students can do well in their learning.
• ENVINS9: My school work is challenging (hard) but not too difficult.
• ENVINS10: My classwork is hard but not too hard.
• ENVINS11: My teachers support me even when my work is not my best.
• ENVINS12: The things I am learning in school are relevant (important) to me.
• ENVINS13: Teachers ask students for feedback on their classroom instruction.
• ENVINS14: When I am home, I like to learn more about the things we are learning in school.
• ENVINS15: My teachers inspire confidence in my ability to be ready for college or career.
• ENVINS16: In this class, other students take the time to listen to my ideas.
• ENVINS17: In my classes, it is OK for me to suggest other ways to do my work.
• ENVINS18: Teachers go over my work with me so I can improve it before it is graded.
• ENVINS19: In my school, teachers focus on my understanding of the material and not on my grades.
• ENVINS20: In my academic classes, there is a good balance between students having to master subject content and being able to explore topics that interest them.
• ENVINS21: In my classes, mistakes or even failure on an assignment are viewed as an important part of our learning.
• ENVINS22: Students are given multiple opportunities to show that they have mastered their classwork.
• ENVINS23: Teachers go over my work with me so I can improve it.

• ENVMEN1: In school, I learn how to manage (control) my feelings when I am angry or upset.
• ENVMEN2: In school, I learn how to manage (control) my feelings when I am upset.
• ENVMEN3: Our school offers guidance to students on how to mediate (settle) conflicts (e.g., arguments, fights) by themselves.
• ENVMEN4: If I need help with my emotions (feelings), effective help is available at my school.
• ENVMEN6: I have access to effective help at school if I am struggling emotionally or mentally.
• ENVMEN7: At our school, students learn to care about other students' feelings.
• ENVMEN9: The level of pressure I feel at school to perform well is unhealthy.

• SAFBUL1: If I tell a teacher or other adult that someone is being bullied, the teacher/adult will do something to help.
• SAFBUL2: I have been punched or shoved by other students more than once in the school or on the

Background: The concept of health has undergone profound changes. Lifestyle Medicine (LSM) consists of therapeutic approaches that focus on the prevention and treatment of diseases. It follows that the quality of life of university students directly affects their health and educational progress. Experimental Methodology: Socioeconomic, lifestyle (LS), and sense of coherence (SOC) questionnaires were administered to college students from three different areas. The results were analyzed for normality and homogeneity, followed by ANOVA variance analysis and Dunn and Tukey post hoc test for multiple comparisons. Spearman's correlation coefficient evaluated the correlation between lifestyle and sense of coherence; p values < 0.05 were considered statistically significant. Results: The correlation between LS and SOC was higher among males and higher among Medical and Human sciences students compared to Exact sciences. Medical students' scores were higher than Applied sciences and Human sciences students on the LS questionnaire. Exact science students' scores on the SOC questionnaire were higher than Human sciences students. In the LS areas related to alcohol intake, sleeping quality, and behavior, there were no differences between the areas. However, women scored better in the nutrition domain and alcohol intake. The SOC was also higher in men compared to women. Conclusion: The results obtained demonstrate in an unprecedented way in the literature that the correlation between the LS and SOC of college students varies according to gender and areas of knowledge, reflecting the importance of actions on improving students' quality of life and enabling better academic performance. Methods Data gathering The researchers invited the students to answer an online form - through Google Forms virtual platform - containing the questionnaires: sociodemographic information, FANTASTIC questionnaire on Lifestyle, and a questionnaire on Sense of Coherence. The researchers clearly explained the research objectives and collection procedures on the home page, and the participants were given the Free and Informed Consent Form. The data gathered in the online form were transferred to a spreadsheet in Microsoft Excel. The results were filtered, classified, and treated in order to be in line with the desired statistical analysis and could feed the statistical programs used. Statistical analysis The statistical analyses were performed by the JASP statistical software, and part of the graphics by the SPSS software. First, the researchers submitted the results to normality (Shapiro Wilk) and homogeneity (Levene test) analysis. Next, the normal homogeneous data were submitted to the ANOVA analysis of variance and Kruskal Wallis non-parametric test, followed by Dunn's post hoc test of multiple comparisons and Tukey's correction. Spearman's correlation coefficient evaluated the correlation between Lifestyle and Sense of Coherence by determining the value of R. Values of p < 0.05 were considered statistically significant. The normality of data was checked by the Shapiro-Wilk test, and since the distribution was not normal, analyses were performed as described below:

Descriptive results are presented by the median and interquartile ranges.

The comparisons between the study variables in HA, ESA and HM (age, BMI, lifestyle, sense of coherence and domains of the questionnaires) and by gender (Lifestyle and Sense of Coherence) were performed by the Mann-Whitney test.

The domains of the questionnaires in each group (HA, ESA, and HM) were compared by analyzing repeated measures, Friedman test, and the Post Hoc by Dunn's multiple comparisons test.

The comparisons between lifestyle and Sense of Coherence among students in each of the selected courses were performed by analysis of variance, Kruskal-Wallis non-parametric test and Post Hoc by Dunn's multiple comparisons test.

The correlations between the profile of lifestyle and sense of coherence of students in each area of knowledge and by gender were performed by Spearman's correlation coefficient.

The significance index adopted in all analyses was 5% (p ≤ 0.05).

Data in this digital opportunity dashboard comes from students' and families' answers to the Internet Access at Home Survey, which school districts use to gather data on home internet and learning device access for students in their districts. While this is an optional data collection, DPI encouraged districts to collect this information and push it to WISEdata to help drive statewide initiatives to improve digital learning opportunity in Wisconsin. Data is given in percentages to protect student privacy. View statewide digital opportunity data on the WISEdash Public Portal.The digital opportunity questions are the result of a coordinated effort with the Council of Chief State School Officers (CCSSO), Education SuperHighway, and the Ed-Fi Alliance (affiliated with the Dell Foundation). In May 2021, the US Department of Education added these questions as data elements to the Common Educational Data Standard (CEDS). CEDS is the federal government’s framework for all education data, adding significant validation to the questions and items. See the questions DPI provided to districts to use in their surveys here.

This dataset contains information about pupils in primary education. The data attributes include student gender, race/ethnicity, parental education, lunch type, information about whether students have completed a test preparation course, and students scores for maths, reading and writing. Data has 8 columns and 1001 row. Data is formatted into a table. By analysing this data set we could answer the following questions: • How effective is the test preparation course? • Which major factors contribute to test outcomes? • Is there a correlation between race/ethnicity, parental education and pupils test score? • What patterns and interactions in the data can you find?

Snapshot img

Student Information System Market Size 2024-2028

The student information system market size is forecast to increase by USD 13.05 billion at a CAGR of 20.56% between 2023 and 2028.

The increase in replacement activities is a key driver of the student information system (SIS) market. As educational institutions upgrade or replace outdated systems, there is a growing demand for more advanced, integrated solutions that improve the management of student data, enhance communication, and streamline administrative tasks. These newer systems offer improved functionality, better user interfaces, and more robust features that align with the evolving needs of educational institutions, including test preparation.
In the North American market, substantial growth is expected due to the increasing adoption of software by higher education institutions to streamline administrative processes. As colleges and universities seek to improve efficiency and provide better services to students and faculty, the demand for advanced SIS solutions is rising. This trend is driving market expansion in the region.

What will be the Size of the Student Information System Market During the Forecast Period?

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How is this Student Information System Industry segmented and which is the largest segment?

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

End-user

  Higher education
  K-12


Deployment

  On-premises
  Cloud based


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By End-user Insights

The higher education segment is estimated to witness significant growth during the forecast period.

The higher education sector's the market is experiencing notable growth due to the increasing adoption of cloud-based systems for streamlining administrative processes. These systems enable institutions to efficiently gather, analyze, and utilize student data for academic and financial management. With investments in data warehousing, analytics, and business intelligence, educational institutions can enhance student assessments using features like dashboards and analytics tools. companies are prioritizing integration with learning analytics and visualization software, reducing the need for IT expertise. This empowers administrative staff and teachers to manage and update data, improving communication and resource allocation. Additionally, student information systems offer features for financial aid management, curriculum management, marketing to prospective students, and class size management.

Security is a significant concern, with measures taken against potential security breaches, identity theft, fraud, and cyber threats. The market is also influenced by digitalization, internet penetration, and the use of e-learning, edge computing, 5G telecommunication, mobile analytics, and AI.

Get a glance at the Student Information System Industry report of share of various segments. Request Free Sample

The Higher education segment was valued at USD 2.71 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 30% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market is poised for substantial growth due to the increasing adoption of software solutions by higher education institutions to streamline administrative processes. The integration of student information systems with other administration software is gaining popularity, enabling institutions to enhance operational efficiency. Major market players have a significant presence in North America, providing educational institutions with numerous options for advanced solutions. Companies are also introducing software with built-in analytics tools to support data-driven decision-making. Cloud-based systems enable easy data gathering, analysis, and access to student information from different departments, including enrollments, academics, billing, and student behavior. Additionally, these systems offer features like student attendance tracking, class performance monitoring, and examination result analysis.

Security concerns, such as identity theft, fraud, and data breaches, are addressed through advanced security measures and compliance management. The digitalization of higher education, driven by internet penetration and the use of eLearning technologies, is furthe

Description. This project contains the dataset relative to the Galatanet survey, conducted in 2009 and 2010 at the Galatasaray University in Istanbul (Turkey). The goal of this survey was to retrieve information regarding the social relationships between students, their feeling regarding the university in general, and their purchase behavior. The survey was conducted during two phases: the first one in 2009 and the second in 2010.

The dataset includes two kinds of data. First, the answers to most of the questions are contained in a large table, available under both CSV and MS Excel formats. An description file allows understanding the meaning of each field appearing in the table. Note thesurvey form is also contained in the archive, for reference (it is in French and Turkish only, though). Second, the social network of students is available under both Pajek and Graphml formats. Having both individual (nodal attributes) and relational (links) information in the same dataset is, to our knowledge, rare and difficult to find in public sources, and this makes (to our opinion) this dataset interesting and valuable.

All data are completely anonymous: students' names have been replaced by random numbers. Note that the survey is not exactly the same between the two phases: some small adjustments were applied thanks to the feedback from the first phase (but the datasets have been normalized since then). Also, the electronic form was very much improved for the second phase, which explains why the answers are much more complete than in the first phase.

The data were used in our following publications:

Labatut, V. & Balasque, J.-M. (2010). Business-oriented Analysis of a Social Network of University Students. In: International Conference on Advances in Social Network Analysis and Mining, 25-32. Odense, DK : IEEE. ⟨hal-00633643⟩ - DOI: 10.1109/ASONAM.2010.15

An extended version of the original article: Labatut, V. & Balasque, J.-M. (2013). Informative Value of Individual and Relational Data Compared Through Business-Oriented Community Detection. Özyer, T.; Rokne, J.; Wagner, G. & Reuser, A. H. (Eds.), The Influence of Technology on Social Network Analysis and Mining, Springer, 2013, chap.6, 303-330. ⟨hal-00633650⟩ - DOI: 10.1007/978-3-7091-1346-2_13

A more didactic article using some of these data just for illustration purposes: Labatut, V. & Balasque, J.-M. (2012). Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application. Abraham, A. & Hassanien, A.-E. (Eds.), Computational Social Networks: Tools, Perspectives and Applications, Springer, chap.4, 81-113. ⟨hal-00633653⟩ - DOI: 10.1007/978-1-4471-4048-1_4

Citation. If you use this data, please cite article [1] above:

@InProceedings{Labatut2010, author = {Labatut, Vincent and Balasque, Jean-Michel}, title = {Business-oriented Analysis of a Social Network of University Students}, booktitle = {International Conference on Advances in Social Networks Analysis and Mining}, year = {2010}, pages = {25-32}, address = {Odense, DK}, publisher = {IEEE Publishing}, doi = {10.1109/ASONAM.2010.15},}

Contact. 2009-2010 by Jean-Michel Balasque (jmbalasque@gsu.edu.tr) & Vincent Labatut (vlabatut@gsu.edu.tr)

License. This dataset is open data: you can redistribute it and/or use it under the terms of the Creative Commons Zero license (see license.txt).