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..., 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 ..., , # Data for: Integrating open education practices with data analysis of open science in an undergraduate course

Author: Marja H Bakermans Affiliation: Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609 USA ORCID: https://orcid.org/0000-0002-4879-7771 Institutional IRB approval: IRB-24–0314

Data and file overview

The full dataset file called OEPandOSdata (.xlsx extension) contains 8 files. Below are descriptions of the name and contents of each file. NA = not applicable or no data available

1. BestPracticesData.csv
   • Description: Data to assess the adherence of articles and datasets to open science best practices.
   • Column headers and descriptions:
     • Article: articles used in the study, numbered randomly
     • F1: Findable, Data are assigned a unique and persistent doi
     • F2: Findable, Metadata includes an identifier of data
     • F3: Findable, Data are registered in a searchable database
     • A1: ...

In this research, we have generated student retention alerts. The alerts are classified into two types: preventive and corrective. This classification varies according to the level of maturity of the data systematization process. Therefore, to systematize the data, data mining techniques have been applied. The experimental analytical method has been used, with a population of 13,715 students with 62 sociological, academic, family, personal, economic, psychological, and institutional variables, and factors such as academic follow-up and performance, financial situation, and personal information. In particular, information is collected on each of the problems or a combination of problems that could affect dropout rates. Following the methodology, the information has been generated through an abstract data model to reflect the profile of the dropout student. As advancement from previous research, this proposal will create preventive and corrective alternatives to avoid dropout higher education. Also, in contrast to previous work, we generated corrective warnings with the application of data mining techniques such as neural networks until reaching a precision of 97% and losses of 0.1052. In conclusion, this study pretends to analyze the behavior of students who drop out the university through the evaluation of predictive patterns. The overall objective is to predict the profile of student dropout, considering reasons such as admission to higher education and career changes. Consequently, using a data systematization process promotes the permanence of students in higher education. Once the profile of the dropout has been identified, student retention strategies have been approached, according to the time of its appearance and the point of view of the institution.

This synthetic Student Performance Dataset is designed as an educational resource for data science, machine learning, and education analytics applications. The dataset provides detailed information on various factors influencing students’ academic performance, including demographics, family background, extracurricular activities, and study habits. It aims to help users analyze relationships between these factors and students’ grades, providing insights into student success and well-being.

Dataset Features:

• Gender: Gender of the student (e.g., "Male," "Female").
• Age: Age of the student (in years).
• Family Size: Size of the student’s family.
• Parental Status (Together/Apart): Whether the parents are living together or apart.
• Mother's Education Level: Education level of the student’s mother.
• Father's Education Level: Education level of the student’s father.
• Mother's Job: Occupation of the student’s mother.
• Father's Job: Occupation of the student’s father.
• Reason for Choosing School: Primary reason for selecting the school (e.g., proximity, reputation).
• Legal Guardian: Legal guardian of the student (e.g., "Mother," "Father," "Other").
• Travel Time to School (in hours): Daily travel time between home and school.
• Weekly Study Time (in hours): Hours spent studying outside school per week.
• Number of Past Failures: Number of previously failed subjects.
• Extra Educational Support: Whether the student receives additional educational support (e.g., "Yes," "No").
• Family Educational Support: Whether the family provides educational support (e.g., "Yes," "No").
• Paid Extra Classes: Whether the student takes extra paid classes (e.g., "Yes," "No").
• Extracurricular Activities: Participation in extracurricular activities (e.g., "Yes," "No").
• Attended Nursery School: Whether the student attended nursery school (e.g., "Yes," "No").
• Aspiration for Higher Education: Whether the student aspires to pursue higher education (e.g., "Yes," "No").
• Internet Access at Home: Availability of internet access at home (e.g., "Yes," "No").
• In a Romantic Relationship: Whether the student is in a romantic relationship (e.g., "Yes," "No").
• Quality of Family Relationships: Rated quality of relationships within the family.
• Free Time After School: Amount of free time available after school hours.
• Going Out with Friends: Frequency of going out with friends.
• Workday Alcohol Consumption: Level of alcohol consumption during workdays.
• Weekend Alcohol Consumption: Level of alcohol consumption during weekends.
• Current Health Status: Self-reported health status of the student.
• Number of School Absences: Total number of school days missed.
• First Period Grade: Grade received during the first grading period.
• Second Period Grade: Grade received during the second grading period.
• Final Grade: Final grade achieved by the student.

Distribution:

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https://storage.googleapis.com/opendatabay_public/images/image_55f1fa29-442d-49ea-89a1-e90b85d8c95f.png" alt="Student Performance Data">

Usage:

This dataset is useful for a variety of applications, including:

• Student Performance Analysis: To explore relationships between family background, study habits, and academic outcomes.
• Educational Research: To identify key factors influencing student success and well-being.
• Predictive Modeling: To build models that predict student grades or identify students at risk of underperforming.
• Policy Making: To analyze how socioeconomic factors and family structure impact education outcomes.

Coverage:

This dataset is synthetic and anonymized, ensuring that it is safe for experimentation and learning without compromising any real student data.

License:

CCO (Public Domain)

Who can use it:

Data science learners: For practising data manipulation, visualization, and predictive modelling. Educators and researchers: For academic studies or teaching purposes in student analytics and education research. Education professionals: For analyzing factors that influence student success and tailoring interventions to improve outcomes.

Analysis of ‘School Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/smeilisa07/number of school teacher student class on 13 February 2022.

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

Context

This is my first analyst data. This dataset i got from open data Jakarta website (http://data.jakarta.go.id/), so mostly the dataset is in Indonesian. But i have try describe it that you can find it on VARIABLE DESCRIPTION.txt file.

Content

The title of this dataset is jumlah-sekolah-guru-murid-dan-ruang-kelas-menurut-jenis-sekolah-2011-2016, with type is CSV, so you can easily access it. If you not understand, the title means the number of school, teacher, student, and classroom according to the type of school 2011 - 2016. I think, if you just read from the title, you can imagine the contents. So this dataset have 50 observations and 8 variables, taken from 2011 until 2016.

In general, this dataset is about the quality of education in Jakarta, which each year some of school level always decreasing and some is increase, but not significant.

Acknowledgements

This dataset comes from Indonesian education authorities, which is already established in the CSV file by Open Data Jakarta.

Inspiration

Althought this data given from Open Data Jakarta publicly, i want always continue to improve my Data Scientist skill, especially in R programming, because i think R programming is easy to learn and really help me to be always curious about Data Scientist. So, this dataset that I am still struggle with below problem, and i need solution.

Question :

1. How can i cleaning this dataset ? I have try cleaning this dataset, but i still not sure. You can check on
   my_hypothesis.txt file, when i try cleaning and visualize this dataset.

2. How can i specify the model for machine learning ? What recommended steps i should take ?

3. How should i cluster my dataset, if i want the label is not number but tingkat_sekolah for every tahun and
   jenis_sekolah ? You can check on my_hypothesis.txt file.

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

2022

Analysis of ‘ Student Performance Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/larsen0966/student-performance-data-set on 28 January 2022.

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

If this Data Set is useful, and upvote is appreciated. 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).

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

This is a simulated dataset exploring how lifestyle habits affect academic performance in students. With 1,000 synthetic student records and 15+ features including study hours, sleep patterns, social media usage, diet quality, mental health, and final exam scores, it’s perfect for ML projects, regression analysis, clustering, and data viz. Created using realistic patterns for educational practice.

Ever wondered how much Netflix, sleep, or TikTok scrolling affects your grades? 👀 This dataset simulates 1,000 students' daily habits—from study time to mental health—and compares them to final exam scores. It's like spying on your GPA through the lens of lifestyle. Perfect for EDA, ML practice, or just vibing with data while pretending to be productive.

The purpose of the study: to investigate the opinions of school leaders (principals or vice-principals) in Lithuanian general education schools regarding the analysis of student achievement and its significance in school work. Major investigated questions: first, respondents were asked how often there is a need to analyse aggregated data on students’ achievements and who in the school is responsible for aggregating and analysing such data. They were asked about the purposes for which the school management analyses and aggregates student achievement data and which data are used to analyse student achievement. It was further asked to indicate which databases or systems and data analysis tools are used to analyse students’ achievement and what types of data analysis would be relevant for the school. The first two blocks of questions assessed how leaders view their own competences (i.e. self-efficacy). In the first block, respondents assessed their own capacity by finding aggregated results of national assessments for their school and by using national achievement data systems (I know where and how to get the results of the national assessments for my school; I know what data and reports I need to get to understand the situation of my school's students achievements; total 7). The second is to understand students’ achievement reports and to use achievement data to make data-driven decisions (I can understand the student’s achievement reports produced or generated for my school; I can understand the level of achievement of my school's students from the scores given in the national assessments; 9 in total). The third block of questions assessed the favourability of school leaders' attitudes towards students’ achievement analytics (Students achievement analytics is very useful for school leaders; Students achievement analytics is very useful for teachers; 11 in total). Socio-demographic characteristics: age, main position in the school, seniority in management, seniority in teaching, education, pedagogical qualifications, type of educational institution the school belongs to, general education curriculum provided by the school, subordination of the school, number of students, location of the school, number of socially disadvantaged students, achievement of students in the school in mathematics. Survey partners: Lithuanian School Leaders Association Lithuanian Association of Gymnasiums Lithuanian Association of Progymnasiums Lithuanian Association of Basic Schools. The survey was conducted as part of the joint project between Sonaro Ltd and Kaunas University of Technology "Innovative system for students’ data analysis, progress evaluation and achievement forecasting" (No 02-019-K-0027).

School ❤️❤️

Student Performance

DESCRIPTION❤️❤️

This DataSet from course Data Analysis from Google

About the columns 😃

• school
• sex
• age
• address
• famsize
• P status
• Medu
• reason
• guardian ... ect

Note😃😃😃😃 This data is for training how using data analysis 🤝🎉

Please appreciate the effort with an upvote 👍 😃😃

Thank You ❤️❤️❤️

Analysis of ‘Student Performance Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/impapan/student-performance-data-set on 13 February 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.

Attribute Information:

# Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:
1 school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
2 sex - student's sex (binary: 'F' - female or 'M' - male)
3 age - student's age (numeric: from 15 to 22)
4 address - student's home address type (binary: 'U' - urban or 'R' - rural)
5 famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)
6 Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)
7 Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
8 Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
9 Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
10 Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
11 reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')
12 guardian - student's guardian (nominal: 'mother', 'father' or 'other')
13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16 schoolsup - extra educational support (binary: yes or no)
17 famsup - family educational support (binary: yes or no)
18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19 activities - extra-curricular activities (binary: yes or no)
20 nursery - attended nursery school (binary: yes or no)
21 higher - wants to take higher education (binary: yes or no)
22 internet - Internet access at home (binary: yes or no)
23 romantic - with a romantic relationship (binary: yes or no)
24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)
26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)
27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29 health - current health status (numeric: from 1 - very bad to 5 - very good)
30 absences - number of school absences (numeric: from 0 to 93)

# these grades are related with the course subject, Math or Portuguese:
31 G1 - first period grade (numeric: from 0 to 20)
31 G2 - second period grade (numeric: from 0 to 20)
32 G3 - final grade (numeric: from 0 to 20, output target)

Acknowledgements

If you use this dataset in your research, please credit the authors

Citations

P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

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

The OECD Programme for International Student Assessment (PISA) surveys collected data on students’ performance in reading, mathematics and science, as well as contextual information on students’ background, home characteristics and school factors which could influence performance. This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses. In addition to the inclusion of the necessary techniques, the manual also includes a detailed account of the PISA 2006 database. It also includes worked examples providing full syntax in SAS

Data is becoming increasingly ubiquitous today, and data literacy has emerged an essential skill in the workplace. Therefore, it is necessary to equip high school students with data literacy skills in order to prepare them for further learning and future employment. In Indonesia, there is a growing shift towards integrating data literacy in the high school curriculum. As part of a pilot intervention project, academics from two leading Universities organised data literacy boot camps for high school students across various cities in Indonesia. The boot camps aimed at increasing participants’ awareness of the power of analytical and exploration skills, which in turn, would contribute to creating independent and data-literate students. This paper explores student participants’ self-perception of their data literacy as a result of the skills acquired from the boot camps. Qualitative and quantitative data were collected through student surveys and a focus group discussion, and were used to analyse student perception post-intervention. The findings indicate that students became more aware of the usefulness of data literacy and its application in future studies and work after participating in the boot camp. Of the materials delivered at the boot camps, students found the greatest benefit in learning basic statistical concepts and applying them through the use of Microsoft Excel as a tool for basic data analysis. These findings provide valuable policy recommendations that educators and policymakers can use as guidelines for effective data literacy teaching in high schools.

Dataset Name: Fictional Student Performance Dataset

Description: The "Fictional Student Performance Dataset" is a comprehensive collection of fictional student records designed for educational and analytical purposes. This dataset comprises 500 student profiles and their associated attributes, making it a valuable resource for exploring various aspects of student performance and data analysis.

Attributes:

StudentID: A unique identifier for each student, facilitating individual tracking and analysis. Name: The name of each student, ensuring the dataset's personalization. Age: The age of each student, providing demographic information. Gender: The gender of each student, offering insights into gender-based performance trends. Grade: A continuous variable representing the academic performance of students, which can be used for regression and prediction tasks. Attendance: A percentage value denoting the attendance rate of each student, enabling attendance-related analyses. FinalExamScore: A continuous variable indicating the final exam score achieved by each student, making it suitable for evaluation and prediction tasks. Use Cases:

Educational Research: This dataset is ideal for educational institutions and researchers to analyze student performance and identify factors that influence academic outcomes. Machine Learning Practice: It is an excellent resource for data science enthusiasts and students looking to practice various machine learning techniques, such as regression, classification, and clustering. Predictive Modeling: The "Grade" and "FinalExamScore" attributes can be used to develop predictive models to forecast student performance. Gender-Based Analysis: Explore gender-based trends in student performance and attendance. Attendance Impact: Investigate the correlation between attendance and academic success. Disclaimer: Please note that this dataset is entirely fictional and created for educational and practice purposes. Any resemblance to real individuals or institutions is purely coincidental.

Citation: If you use this dataset in your research or projects, kindly acknowledge its source as the "Fictional Student Performance Dataset"

Data Generation: The dataset was generated using a combination of randomization and scripting to ensure that it does not contain any real or personally identifiable information.

Feel free to explore and utilize this dataset for educational purposes, data analysis, or machine learning exercises. It is intended to foster learning and experimentation in data science.

Analysis of ‘2019 Public Data File - Students’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f7cc1ebe-7bdc-453a-959d-10c6147e27e9 on 26 January 2022.

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

To collect feedback on their learning environment from families, students and teachers. Aids in facilitating the understanding of families perceptions, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Each year all parents, teachers and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

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

Analysis of ‘2014 - 2015 Student School Survey Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4955fbbc-5e28-493b-aec3-066a626b3902 on 26 January 2022.

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

2015 NYC School Survey data for all schools.
To understand the perceptions of families, students, and teachers regarding their school. School leaders use feedback from the survey to reflect and make improvements to schools and programs. Also, results from the survey used to help measure school quality. Each year, all parents, teachers, and students in grades 6-12 take the NYC School Survey. The survey is aligned to the DOE's Framework for Great Schools. It is designed to collect important information about each school's ability to support student success.

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

Market Analysis for Student Management Software The global student management software market is projected to reach a valuation of USD XXX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. Key market drivers include the increasing demand for digitalization in education, the rising adoption of cloud-based solutions, and government initiatives to enhance student data management. The market is segmented by type (cloud-based and on-premises) and application (schools, training institutions, and others). Prominent players in the industry include Blackbaud, Hero, PowerSchool, Infinite Campus, and Skyward. Regional analysis indicates that North America holds the largest market share due to the high penetration of digital technologies in the education sector. However, Asia-Pacific is expected to witness significant growth over the forecast period, driven by the increasing government investments in education and the growing number of students. The market is also influenced by trends such as the integration of artificial intelligence (AI) and machine learning (ML) in student management software, which enhances data analysis and automates tasks. Restraints include security concerns and the resistance to change among some educational institutions.

The K-12 Student Information System (SIS) market is experiencing robust growth, driven by increasing demand for efficient student management, improved data analytics capabilities, and the need for enhanced school-parent communication. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based SIS solutions offering scalability and cost-effectiveness, increasing government initiatives promoting digitalization in education, and the growing emphasis on data-driven decision-making in educational institutions. The shift towards personalized learning experiences and the need for better data security are further bolstering market expansion. Segmentation reveals a strong preference for cloud-based solutions across all school levels (primary, junior middle, and high school), indicating a clear trend toward flexible and accessible technology. Competition is fierce among established players like Skyward, PowerSchool, and Illuminate Education, leading to continuous innovation in features and functionalities. Geographic expansion, particularly in developing economies with increasing digital literacy, presents significant opportunities for market players. While factors such as the initial investment costs associated with SIS implementation and data migration challenges can act as restraints, the long-term benefits in terms of operational efficiency and improved student outcomes are driving widespread adoption. The market's success hinges on the ability of vendors to provide intuitive, user-friendly interfaces, seamless integration with other educational platforms, and robust data security measures. Future growth will likely be influenced by advancements in artificial intelligence (AI) and machine learning (ML) that could lead to more personalized learning pathways and predictive analytics for improved student success. The ongoing development of sophisticated reporting and analytics tools will further enhance the value proposition of SIS solutions, driving market penetration and contributing to the overall growth trajectory. Regional variations in technology adoption rates and digital infrastructure will continue to shape the market landscape, with North America and Europe remaining key regions, but considerable growth potential existing in Asia-Pacific and other emerging markets.

The Synthetic Student Performance Dataset is designed to support research, analytics, and educational projects focused on academic performance, family background, and behavioral factors affecting students. It mirrors real-world educational data and offers diverse features to explore student success patterns.

Dataset Features

• student_id: Unique identifier for each student.
• school: Attended school (e.g., GP or MS).
• sex: Gender of the student (F/M).
• age: Student's age in years.
• address_type: Urban or Rural home location.
• family_size: Family size (Less than or equal to 3 / Greater than 3).
• parent_status: Parental cohabitation status (Living together / Apart).
• mother_education / father_education: Highest education level completed (e.g., Primary, Secondary, Higher).
• mother_job / father_job: Occupation of the student's parents.
• school_choice_reason: Reason for choosing the school (e.g., Reputation, Proximity).
• guardian: Primary caregiver (e.g., Mother, Father, Other).
• travel_time: Daily travel time to school.
• study_time: Weekly study time outside school.
• class_failures: Number of past class failures.
• school_support / family_support: Extra academic support received at school and from family (Yes/No).
• extra_paid_classes: Attending paid private tutoring (Yes/No).
• activities: Participation in extracurricular activities (Yes/No).
• nursery_school: Attended preschool (Yes/No).
• higher_ed: Desire to pursue higher education (Yes/No).
• internet_access: Access to the internet at home (Yes/No).
• romantic_relationship: Currently in a romantic relationship (Yes/No).
• family_relationship: Quality of family relationships (numeric scale).
• free_time: Amount of free time after school (numeric scale).
• social: Frequency of social activities with peers (numeric scale).
• weekday_alcohol / weekend_alcohol: Alcohol consumption levels on weekdays and weekends.
• health: Current health status (1–5 scale).
• absences: Number of school absences.
• grade_1 / grade_2 / final_grade: First and second period grades and final academic performance.

Distribution

https://storage.googleapis.com/opendatabay_public/41933042-6ec7-49c4-b151-508fc8f5592b/7537d999da0b_student_performance_visuals.png" alt="Synthetic student performance data visuals and distribution.png">

Usage

This dataset is ideal for:

• Academic Performance Prediction: Predict final grades based on behavioral and background features.
• Feature Importance Analysis: Identify key influences on student success.
• Sociological Insights: Understand the impact of family, relationship, and lifestyle factors on education.
• Model Training: Suitable for classification, regression, and clustering tasks in educational data mining.

Coverage

Captures a comprehensive view of student life, including family background, academic history, health, and lifestyle. The dataset supports multi-disciplinary research across education, sociology, and data science.

License

CC0 (Public Domain)

Who Can Use It

• Educational Researchers: For testing interventions and identifying risk factors.
• Data Scientists and ML Practitioners: For building predictive models in education.
• Instructors and Students: For coursework in data analysis, machine learning, and statistics.

BackgroundAn understanding of the resources which engineering students use to write their academic papers provides information about student behaviour as well as the effectiveness of information literacy programs designed for engineering students. One of the most informative sources of information which can be used to determine the nature of the material that students use is the bibliography at the end of the students’ papers. While reference list analysis has been utilised in other disciplines, few studies have focussed on engineering students or used the results to improve the effectiveness of information literacy programs. Gadd, Baldwin and Norris (2010) found that civil engineering students undertaking a finalyear research project cited journal articles more than other types of material, followed by books and reports, with web sites ranked fourth. Several studies, however, have shown that in their first year at least, most students prefer to use Internet search engines (Ellis & Salisbury, 2004; Wilkes & Gurney, 2009).PURPOSEThe aim of this study was to find out exactly what resources undergraduate students studying civil engineering at La Trobe University were using, and in particular, the extent to which students were utilising the scholarly resources paid for by the library. A secondary purpose of the research was to ascertain whether information literacy sessions delivered to those students had any influence on the resources used, and to investigate ways in which the information literacy component of the unit can be improved to encourage students to make better use of the resources purchased by the Library to support their research.DESIGN/METHODThe study examined student bibliographies for three civil engineering group projects at the Bendigo Campus of La Trobe University over a two-year period, including two first-year units (CIV1EP – Engineering Practice) and one-second year unit (CIV2GR – Engineering Group Research). All units included a mandatory library session at the start of the project where student groups were required to meet with the relevant faculty librarian for guidance. In each case, the Faculty Librarian highlighted specific resources relevant to the topic, including books, e-books, video recordings, websites and internet documents. The students were also shown tips for searching the Library catalogue, Google Scholar, LibSearch (the LTU Library’s research and discovery tool) and ProQuest Central. Subject-specific databases for civil engineering and science were also referred to. After the final reports for each project had been submitted and assessed, the Faculty Librarian contacted the lecturer responsible for the unit, requesting copies of the student bibliographies for each group. References for each bibliography were then entered into EndNote. The Faculty Librarian grouped them according to various facets, including the name of the unit and the group within the unit; the material type of the item being referenced; and whether the item required a Library subscription to access it. A total of 58 references were collated for the 2010 CIV1EP unit; 237 references for the 2010 CIV2GR unit; and 225 references for the 2011 CIV1EP unit.INTERIM FINDINGSThe initial findings showed that student bibliographies for the three group projects were primarily made up of freely available internet resources which required no library subscription. For the 2010 CIV1EP unit, all 58 resources used were freely available on the Internet. For the 2011 CIV1EP unit, 28 of the 225 resources used (12.44%) required a Library subscription or purchase for access, while the second-year students (CIV2GR) used a greater variety of resources, with 71 of the 237 resources used (29.96%) requiring a Library subscription or purchase for access. The results suggest that the library sessions had little or no influence on the 2010 CIV1EP group, but the sessions may have assisted students in the 2011 CIV1EP and 2010 CIV2GR groups to find books, journal articles and conference papers, which were all represented in their bibliographiesFURTHER RESEARCHThe next step in the research is to investigate ways to increase the representation of scholarly references (found by resources other than Google) in student bibliographies. It is anticipated that such a change would lead to an overall improvement in the quality of the student papers. One way of achieving this would be to make it mandatory for students to include a specified number of journal articles, conference papers, or scholarly books in their bibliographies. It is also anticipated that embedding La Trobe University’s Inquiry/Research Quiz (IRQ) using a constructively aligned approach will further enhance the students’ research skills and increase their ability to find suitable scholarly material which relates to their topic. This has already been done successfully (Salisbury, Yager, & Kirkman, 2012)CONCLUSIONS & CHALLENGESThe study shows that most students rely heavily on the free Internet for information. Students don’t naturally use Library databases or scholarly resources such as Google Scholar to find information, without encouragement from their teachers, tutors and/or librarians. It is acknowledged that the use of scholarly resources doesn’t automatically lead to a high quality paper. Resources must be used appropriately and students also need to have the skills to identify and synthesise key findings in the existing literature and relate these to their own paper. Ideally, students should be able to see the benefit of using scholarly resources in their papers, and continue to seek these out even when it’s not a specific assessment requirement, though it can’t be assumed that this will be the outcome.REFERENCESEllis, J., & Salisbury, F. (2004). Information literacy milestones: building upon the prior knowledge of first-year students. Australian Library Journal, 53(4), 383-396.Gadd, E., Baldwin, A., & Norris, M. (2010). The citation behaviour of civil engineering students. Journal of Information Literacy, 4(2), 37-49.Salisbury, F., Yager, Z., & Kirkman, L. (2012). Embedding Inquiry/Research: Moving from a minimalist model to constructive alignment. Paper presented at the 15th International First Year in Higher Education Conference, Brisbane. Retrieved from http://www.fyhe.com.au/past_papers/papers12/Papers/11A.pdfWilkes, J., & Gurney, L. J. (2009). Perceptions and applications of information literacy by first year applied science students. Australian Academic & Research Libraries, 40(3), 159-171.

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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 market is experiencing significant shifts, driven by the increasing prevalence of replacement activities as institutions seek to modernize their legacy systems. This trend is fueled by technological advancements, particularly in the area of Artificial Intelligence (AI), which offers enhanced capabilities for data analysis and personalized learning experiences. 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. However, the implementation of these advanced systems poses a challenge: a lack of adequately trained users. Institutions must invest in employee education and development to effectively leverage these new tools and ensure a smooth transition.
This dual dynamic of replacement activities and the need for user training presents both opportunities and challenges for market participants. Companies that can effectively address these issues, offering comprehensive implementation support and ongoing training programs, will be well-positioned to capitalize on the market's potential for growth.

What will be the Size of the Student Information System Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, with dynamic market activities shaping its applications across various sectors. Seamless integration of features such as attendance tracking, security audits, data visualization, technical support, exam management, customizable dashboards, curriculum management, communication modules, data migration, user experience (UX), and student support services, among others, is paramount. These systems are increasingly adopting cloud-based platforms for mobility and accessibility, while maintaining data encryption for security. Automated workflows streamline student enrollment and financial aid management, with real-time data and reporting and analytics providing valuable insights. Integration APIs enable seamless third-party integrations, while single sign-on (SSO) and notification systems enhance user experience.

Access control, fee management, and system maintenance ensure efficient operations. Performance monitoring and system updates keep the software current, with faculty portals and API documentation catering to the unique needs of educators. UX and accessibility compliance adhere to evolving industry standards. Continuous system enhancements address the ever-changing needs of educational institutions, ensuring a comprehensive solution for managing student information.

How is this Student Information System Industry segmented?

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

    Australia
    China


  Rest of World (ROW)

By End-user Insights

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

The higher education market is experiencing notable growth due to the rising demand for advanced data management and analytical tools. Institutions are investing heavily in data warehousing, analytics, and business intelligence, enabling the integration of student information systems with learning analytics and visualization software. This empowers administrative staff and faculty with the ability to manage and update data more efficiently. Key features of student information systems, such as parent portals, student enrollment, automated workflow, financial aid management, performance monitoring, reporting and analytics, and student portals, are increasingly essential for educational institutions. Integration APIs, data encryption, single sign-on, notification systems, access control, fee management, mobile accessibility, progress tracking, and customizable dashboards are critical components that enhance user experience and streamline processes.

Course management, scheduling systems, real-time data, attendance tracking, security audits, data visualization, technical support, exam management, and curriculum management are additional features that cater to the evolving needs of educational institutions. Compliance with accessibility standards and data security regulations is also a priority for companies, ensuring the protection o