Local Law 177 enacted in 2016 requires the Department of Education of the New York City School District to submit to the Council an annual report concerning computer science education for the prior school year. This report provides information about the number of computer science programs offered and the number of students enrolled in those programs as defined in Local Law 177 as reported through the 2017-2018 STARS database. It is important to note that schools self-report their computer science course information in STARS. This report also includes information regarding the number and ratio of certified STEM instructors, the department's STEM Institute, the nature of the district's computer science initiatives and the total available bandwidth in each school.

Context

Pakistan has a large number of public and private universities offering degrees in multiple disciplines. There are 162 universities out of which 64 are in private sector and 98 are public sector/government universities recognized by the Higher Education Commission of Pakistan (HEC).

According to HEC, Pakistani universities are producing over half a million graduates per year, which include over more than 10,000 Computer Science/IT graduates.

From year 2001 to 2015 there is a mass increase in number of enrollment in universities. The recent statistics shows that in 2015, 1,298,600 students enrolled in different levels of degree, 869,378 in Bachelors (16 years), 63,412 in Bachelors (17 years), 219,280 in Masters (16 years), 124,107 in M.Phil/MS, 14,373 in Ph.D, and 8,319 in P.G.D. However, in 2014 the number of doctoral degree awarded were 1,351 only.

Moreover, according to HEC report, in 2014-2015 there are over 10,125 fulltime Ph.D. faculty teaching in Pakistan in all disciplines. Computer Science and related disciplines are widely taught in Pakistan with over 90 universities offering this discipline with qualified faculty. According to our dataset, there are 504 PhD faculty members in Computer Science in Pakistan for 10,000 students. So we have a PhD faculty member for every 20 students on average in computer science program.

Current Student to PhD Professor Ratio in Pakistan is 130:1 (while India is going towards 10:1 in Post-Graduate and 25:1 in Undergrad education).

Here is world's Top 100 universities with Student to Staff Ratio.

Content

Dataset: The dataset contains list of computer science/IT professors from 89 different universities of Pakistan.

Variables: The dataset contains Serial No, Teacher’s Name, University Currently Teaching, Department, Province University Located, Designation, Terminal Degree, Graduated from (university for professor), Country of graduation, Year, Area of Specialization/Research Interests, and some Other Information

Acknowledgements

Data has been collected from respective university websites. Some of the universities did not mention about their faculty profiles or were unavailable (hence the limitation of this dataset). The statistics mentioned above are gathered by Higher Education Commission of Pakistan (HEC) website and other web resources.

Inspiration

Here is what I like you to do:

1. Which area of interest/expertise is in abundance in Pakistan and where we need more people?
2. How many professors we have in Data Sciences, Artificial Intelligence, or Machine Learning?
3. Which country and university hosted majority of our teachers?
4. Which research areas were most common in Pakistan?
5. How does Pakistan Student to PhD Professor Ratio compare against rest of the world, especially with USA, India and China?
6. Any visualization and patterns you can generate from this data

Let me know how I can improve this dataset and best of luck with your work

Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an introductory computing course by introducing computing concepts and practices in required courses. This dataset analyzed integrated computing curricula to determine which CS practices and concepts they teach and how extensively and, thus, how they prepare students for later computing courses. The authors conducted a content analysis to examine primary and lower secondary (i.e., K-8) curricula that are taught in non-CS classrooms, have explicit CS learning objectives (i.e., CS+X), and that took 5+ hours to complete. Lesson plans, descriptions, and resources were scored based on frameworks developed from the K-12 CS Framework, including programming conc..., Search and Inclusion Criteria While the current dataset used many of the same tools as a systematic literature review to find curricula, it is not a systematic review. Unlike in literature reviews, there are no databases of integrated computing curricula to search systematically. Instead, we searched the literature for evidence-based curricula. We first searched the ACM Digital Library for papers with "(integration OR integrated) AND (computing OR 'computer science' OR CS) AND curriculum" to find curricula that had been studied. We repeated the search with Google Scholar in journals that include "(computing OR 'computer science' OR computers) AND (education OR research)" in their titles, such as Computer Science Education, Computers & Education, and Journal of Educational Computing Research. Last, we examined each entry in CSforAll's curriculum directory for curricula that matched our inclusion criteria. We used four inclusion criteria to select curricula for analysis. Our first cri..., , # Extended computing integrated curricula scored for K-12 CS standards

https://doi.org/10.5061/dryad.j6q573nnt

Framework Development and Scoring Training

Full details about the framework development and training for the scorers can be found at Margulieux, L. E., Liao, Y-C., Anderson, E., Parker, M. C., & Calandra, B. D. (2024). Intent and extent: Computer science concepts and practices in integrated computing. ACM’s Transactions on Computing Education. doi: 10.1145/3664825

Description of the data and file structure

The listed computing integrated extended curricula were scored for which concepts and practices they included. The concepts and practices are based on the K-12 CS framework.

Computer Science Practices & Non-Programming Concepts

1 = Present, Blank = Not present

Programming Concept Codes

Mutually exclusive codes

• Nothing - students do not use the concept, or they use a program ...

Brief Description of Dataset

The dataset contains information about students in a 5-year Bachelor of Technology Degree in Computer Science from a North Eastern Nigerian University of Technology. The year of enrolment of the students ranges from 2005 to 2015. In the dataset, “NA” means that the student did not attempt the course.

Data Cleaning

First steps: the student marks that are less than 40 are excluded, as the course has to be retaken to be passed with a minimum of 50 marks. In addition, courses that are taken outside of graduation audit by students are eliminated.

There were 430 students screened for enrolment in the study with 95 being excluded because they did not take the course within the period of degree program for their early exemption. The exact ages of the participants are unknown other than all students enrolled were aged above 18 years of age.

The number of postsecondary graduates, by Classification of Instructional Programs, Primary groupings (CIP_PG), International Standard Classification of Education (ISCED), age group and gender.

Characteristics and median employment income of postsecondary graduates two years after graduation, by educational qualification (Classification of programs and credentials - professional degree variant), field of study (Classification of Instructional Programs (CIP) Canada 2016 - STEM (science, technology, engineering and mathematics and computer sciences) and BHASE (business, humanities, health, arts, social science and education) groupings), gender, age group and status of student in Canada (cross-sectional analysis).

This dataset tracks annual graduation rate from 2014 to 2022 for Academy Of Computer Science And Engineering vs. Connecticut and Capitol Region Education Council School District

This dataset file reports information regarding offering or receipt of STEM-H postsecondary degrees within the state of Virginia, as reported by the State Council of Higher Education for Virginia (SCHEV). SCHEV public files were combined and reconfigured to assist researchers using the Virginia Longitudinal Data System (VLDS) as part of the Building Research Infrastructure and Community Project (BRIC).

Datafiles contained in this dataset include: • STEM-H, Engineering and Computer/Information Science Degrees Awarded 2013 – 2017 • Two-Year Virginia Institutions of Higher Education Offering Engineering and Computer Science Degrees

This dataset tracks annual total students amount from 2009 to 2023 for Academy Of Computer Science And Engineering

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be nontraditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students to a variety of techniques to analyze small, neat, and clean datasets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that are considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms. Supplementary materials for this article are available online. [Received June 2014. Revised July 2015.]

The dataset comprises novel aspects specifically, in terms of student grading in diverse educational cultures within the multiple countries – Researchers and other education sectors will be able to see the impact of having varied curriculums in a country. Dataset compares different levelling cases when student transfer from curriculum to curriculum and the unreliable levelling criteria set by schools currently in an international school. The collected data can be used within the intelligent algorithms specifically machine learning and pattern analysis methods, to develop an intelligent framework applicable in multi-cultural educational systems to aid in a smooth transition “levelling, hereafter” of students who relocate from a particular education curriculum to another; and minimize the impact of switching on the students’ educational performance. The preliminary variables taken into consideration when deciding which data to collect depended on the variables. UAE is a multicultural country with many expats relocating from regions such as Asia, Europe and America. In order to meet expats needs, UAE has established many international private schools, therefore UAE was chosen to be the location of study based on many cases and struggles in levelling declared by the Ministry of Education and schools. For the first time, we present this dataset comprising students’ records for two academic years that included math, English, and science for 3 terms. Selection of subject areas and number of terms was based on influence from other researchers in similar subject matters.

This dataset tracks annual distribution of students across grade levels in Academy Of Computer Science And Engineering

The six data sets were created for an undergraduate course at the Babes-Bolyai University, Faculty of Mathematics and Computer Science, held for second year students in the autumn semester. The course is taught both in Romanian and English with the same content and evaluation rules in both languages. The six data sets are the following: - FirstCaseStudy_RO_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the Romanian language - FirstCaseStudy_RO_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the Romanian language - SecondCaseStudy_EN_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the English language - SecondCaseStudy_EN_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the English language - ThirdCaseStudy_Both_traditional_2019-2020.txt - the concatenation of the two data sets for the 2019-2020 academic year (so all instances from FirstCaseStudy_RO_traditional_2019-2020 and SecondCaseStudy_EN_traditional_2019-2020 together) - ThirdCaseStudy_Both_online_2020-2021.txt - the concatenation of the two data sets for the 2020-2021 academic year (so all instances from FirstCaseStudy_RO_online_2020-2021 and SecondCaseStudy_EN_online_2020-2021 together)Instances from the data sets for the 2019-2020 academic year contain 12 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - the grades received by the student for 2 practical exams. If a student did not participate in a practical exam, de grade was 0. Possible values are between 0 and 10. - the number of seminar activities that the student had. Possible values are between 0 and 7. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4Instances from the data sets for the 2020-2021 academic year contain 10 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - a seminar bonus computed based on the number of seminar activities the student had during the semester, which was added to the final grade. Possible values are between 0 and 0.5. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This dataset examined integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom (i.e., primary discipline, grade level, programming paradigm, programming language, minimum amount of time the lesson takes, source of the lesson plan), the teacher (i.e., years teaching, current role (classroom teacher, tech specialist, STEM specialist, etc.), grade levels taught, disciplines taugh..., Procedure Data about integrated computing lessons in non-CS classrooms were collected from in-service K-12 teachers in the United States via an online survey, and 262 surveys were completed. Participants were recruited first through teacher networks and districts to include diverse populations and then through LinkedIn. Teachers received a $100 gift card upon completion of the survey, which took approximately 30 minutes. Due to the incentive, submissions were screened during data collection to ensure eligibility (i.e., having a valid school district email) and quality (described below). Â Instrument The survey asked about the programming and CT concepts taught in the activities and 20 factors related to classroom, teacher, and school context. The programming concepts included were based on a framework developed by Margulieux et al., 2023. A full list of concepts and contextual factors can be found below. Due to the large sample size, the survey was designed to be primarily quantitative ..., , # Programming and Computational Thinking Concepts and Contextual Factors in Integrated Computing Activities in U.S. Schools

https://doi.org/10.5061/dryad.ttdz08m6v

Some of the early submissions were missing questions related to school context. These missing data are marked as "null".

Survey Questions Code Book

Survey Questions and Descriptive Statistics

Qualitative Questions (all open-ended):

Title of lesson plan One sentence describing the activity topic (e.g., In this activity, students apply their computational thinking skills to explore the life cycle of a butterfly.) One sentence describing the disciplinary learning objective (e.g., The primary learning goal is to model the life cycle of a butterfly.) One sentence describing the computing learning objective (e.g., Students will conditionals to match body features to life stages.) 1-3 sentences describing the instructional paradigm (e.g., Students will discuss butterfli...

Analysis of ‘Engineering Graduate Salary Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/manishkc06/engineering-graduate-salary-prediction on 28 January 2022.

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

Context

Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings. The discipline of engineering encompasses a broad range of more specialized fields of engineering, each with a more specific emphasis on particular areas of applied mathematics, applied science, and types of application. https://images.pexels.com/photos/414579/pexels-photo-414579.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500" alt="eng"> Engineering is a broad discipline that is often broken down into several sub-disciplines. Although an engineer will usually be trained in a specific discipline, he or she may become multi-disciplined through experience. Engineering is often characterized as having four main branches: chemical engineering, civil engineering, electrical engineering, and mechanical engineering. [Reference: Wikipedia]

Engineering Graduates in India India has a total 6,214 Engineering and Technology Institutions in which around 2.9 million students are enrolled. Every year on an average 1.5 million students get their degree in engineering, but due to lack of skill required to perform technical jobs less than 20 percent get employment in their core domain. [source of information: BWEDUCATION]

Objective

A relevant question is what determines the salary and the jobs these engineers are offered right after graduation. Various factors such as college grades, candidate skills, the proximity of the college to industrial hubs, the specialization one have, market conditions for specific industries determine this. On the basis of these various factors, your objective is to determine the salary of an engineering graduate in India.

Data Description

• ID: A unique ID to identify a candidate
• Salary: Annual CTC offered to the candidate (in INR)
• Gender: Candidate's gender
• DOB: Date of birth of the candidate
• 10percentage: Overall marks obtained in grade 10 examinations
• 10board: The school board whose curriculum the candidate followed in grade 10
• 12graduation: Year of graduation - senior year high school
• 12percentage: Overall marks obtained in grade 12 examinations
• 12board: The school board whose curriculum the candidate followed
• CollegeID: Unique ID identifying the university/college which the candidate attended for her/his undergraduate
• CollegeTier: Each college has been annotated as 1 or 2. The annotations have been computed from the average AMCAT scores obtained by the students in the college/university. Colleges with an average score above a threshold are tagged as 1 and others as 2.
• Degree: Degree obtained/pursued by the candidate
• Specialization: Specialization pursued by the candidate
• CollegeGPA: Aggregate GPA at graduation
• CollegeCityID: A unique ID to identify the city in which the college is located in.
• CollegeCityTier: The tier of the city in which the college is located in. This is annotated based on the population of the cities.
• CollegeState: Name of the state in which the college is located
• GraduationYear: Year of graduation (Bachelor's degree)
• English: Scores in AMCAT English section
• Logical: Score in AMCAT Logical ability section
• Quant: Score in AMCAT's Quantitative ability section
• Domain: Scores in AMCAT's domain module
• ComputerProgramming: Score in AMCAT's Computer programming section
• ElectronicsAndSemicon: Score in AMCAT's Electronics & Semiconductor Engineering section
• ComputerScience: Score in AMCAT's Computer Science section
• MechanicalEngg: Score in AMCAT's Mechanical Engineering section
• ElectricalEngg: Score in AMCAT's Electrical Engineering section
• TelecomEngg: Score in AMCAT's Telecommunication Engineering section
• CivilEngg: Score in AMCAT's Civil Engineering section
• conscientiousness: Scores in one of the sections of AMCAT's personality test
• agreeableness: Scores in one of the sections of AMCAT's personality test
• extraversion: Scores in one of the sections of AMCAT's personality test
• nueroticism: Scores in one of the sections of AMCAT's personality test
• openess_to_experience: Scores in one of the sections of AMCAT's personality test

**Note: **To give you more context AMCAT is a job portal.

Acknowledgemet

I would like to thank ‘Aspiring Minds Research’ for making this dataset available publicly.

Inspiration

The data can be used not only to make an accurate salary predictor but also to understand what influences salary and job titles in the labour market. It’s up to you to explore things.

This Dataset is also available at DPhi

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

Historical Dataset of Computer Science Virtual Academy is provided by PublicSchoolReview and contain statistics on metrics:Distribution of Students By Grade Trends

Dataset Overview

This dataset captures detailed interaction logs from 781 students in the Department of Computer Science at VNU-HCM University of Technology (Vietnam) over the 2023 and 2024 academic years. It covers two courses:

Programming Fundamentals (PF) – first-year course; prerequisite: Introduction to Computing
Data Structures & Algorithms (DSA) – second-year course; prerequisite: PF

Both courses run for six weeks, each week concluding with an exam on the previous week’s… See the full description on the dataset page: https://huggingface.co/datasets/CodeInsightTeam/code_insights_csv.

This dataset tracks annual distribution of students across grade levels in Computer Science Virtual Academy

Some research has indicated that the relationship between students' study behavior and their academic performance is as strong as the relationship to more common predictors such as past performance and test scores. However, knowledge about students' study behavior, how behavior develops and is influenced by program and course design, and consequently, the effect various design parameters have on learning is limited. This data is part of a PhD project and relates to Study 2. This mixed-method study followed a population of computing students through their first year. Results from in-depth interviews with students throughout their first year found that the educational structure and organization of a study program conditions the students' study behavior. In order to further investigate these tendencies, two surveys (N=215) were conducted within the whole first-year student population at the beginning and end of the year. The dataset for this analysis is included in this repository. A significant difference found was in the use of surface and deep strategies at the beginning and end for the first year, indicating that students shift from deep to surface learning during the year. Even if students initially seek a deep content-driven approach to learning, the structure of the education and other organizational factors may be the cause of a more surface and task-focused approach towards the end of the first year. Students' study behavior is constrained by the educational design, which furthermore may lead to different learning outcomes than desired. Researching and developing learning goals, course content, lectures and assignments is one way to improve computing education; however, this research suggests that taking a comprehensive and integrated approach to educational design might also lead to improvements.

Historical Dataset of Academy Of Computer Science And Engineering is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2009-2023),Total Classroom Teachers Trends Over Years (2009-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2009-2023),Asian Student Percentage Comparison Over Years (2012-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (2009-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2009-2023),Free Lunch Eligibility Comparison Over Years (2009-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2009-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2010-2022),Graduation Rate Comparison Over Years (2014-2022)