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.

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

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

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

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

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

It's no secret that US university students often graduate with debt repayment obligations that far outstrip their employment and income prospects. While it's understood that students from elite colleges tend to earn more than graduates from less prestigious universities, the finer relationships between future income and university attendance are quite murky. In an effort to make educational investments less speculative, the US Department of Education has matched information from the student financial aid system with federal tax returns to create the College Scorecard dataset.

Kaggle is hosting the College Scorecard dataset in order to facilitate shared learning and collaboration. Insights from this dataset can help make the returns on higher education more transparent and, in turn, more fair.

Data Description

Here's a script showing an exploratory overview of some of the data.

college-scorecard-release-*.zip contains a compressed version of the same data available through Kaggle Scripts.

It consists of three components:

• All the raw data files released in version 1.40 of the college scorecard data
• Scorecard.csv, a single CSV file with all the years data combined. In it, we've converted categorical variables represented by integer keys in the original data to their labels and added a Year column
• database.sqlite, a SQLite database containing a single Scorecard table that contains the same information as Scorecard.csv

New to data exploration in R? Take the free, interactive DataCamp course, "Data Exploration With Kaggle Scripts," to learn the basics of visualizing data with ggplot. You'll also create your first Kaggle Scripts along the way.

The dataset comprises various attributes related to individuals' skills, experiences, academic achievements, and career goals in the field of computer programming and software development. Each row in the dataset represents a unique individual, while the columns contain specific information about their profile. Below is a brief description of each column:

Python: Indicates the proficiency level or experience with the Python programming language. Java: Indicates the proficiency level or experience with the Java programming language. C++: Indicates the proficiency level or experience with the C++ programming language. JavaScript: Indicates the proficiency level or experience with the JavaScript programming language. C#: Indicates the proficiency level or experience with the C# programming language. PHP: Indicates the proficiency level or experience with the PHP programming language. Ruby: Indicates the proficiency level or experience with the Ruby programming language. Swift: Indicates the proficiency level or experience with the Swift programming language. Go: Indicates the proficiency level or experience with the Go programming language. Rust: Indicates the proficiency level or experience with the Rust programming language. Others: Indicates any other programming languages or technologies not listed specifically. Software_Development_Experience: Reflects the overall experience in software development, typically measured in years. Database_Management: Indicates proficiency or experience in managing databases, such as SQL or NoSQL databases. Networking_Skills: Reflects proficiency or experience in computer networking concepts and protocols. Web_Development_Experience: Indicates experience or proficiency in web development technologies, frameworks, and languages. Communication_Skills: Assesses the individual's communication abilities, including written and verbal communication. Problem_Solving_Abilities: Reflects the individual's aptitude for solving complex problems and challenges. Teamwork_Collaboration: Indicates the ability to work effectively in teams and collaborate with others. Time_Management: Reflects the individual's ability to manage time effectively and prioritize tasks. Adaptability: Assesses the individual's capacity to adapt to new environments, technologies, or situations. GPA: Indicates the individual's Grade Point Average, typically from academic institutions. Coursework_Completion_Status: Reflects the completion status of relevant coursework or educational programs. Academic_Achievements: Indicates any notable academic achievements or awards received. Personal_Interests: Reflects the individual's personal interests or hobbies outside of academia or work. Internship_Experience: Indicates any relevant internship or work experience in the field. Certifications_Training: Reflects any certifications or training programs completed by the individual. Leadership_Experience: Indicates any leadership roles or experiences held by the individual. Career_Goals: Reflects the individual's aspirations and objectives in terms of their career path and professional development. This dataset can be utilized for various analytical purposes, such as talent acquisition, skill gap analysis, career counseling, and educational program development within the field of computer science and software engineering.

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

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)

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

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 ---

The dataset contains Academic-year- and Field-of-study-wise compiled data on the number of Indian students enrolled in United States of America (USA). The different fields of study covered in the dataset include Business/ Management, Engineering, Fine/ Applied Arts, Health Professions, Humanities, Intensive English, Math/ Computer Science, Physical/ Life Sciences, Social Sciences, etc.

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 total students amount from 2009 to 2023 for Academy Of Computer Science And Engineering

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 taught, degrees and certifications, institutional support received for integrated computing, gender, race, self-efficacy), and the school (e.g., socioeconomic status of students, racial composition, number of CS courses offered, number of CS teachers, years CS courses have been taught, number of students, school location (urban, suburban, rural)). Methods 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).

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 but included a few qualitative questions (e.g., "Please describe in 1-2 sentences the computing learning objective of this activity") and requested teachers to submit their lesson plans. The research team used these qualitative elements to verify data quality, such as by ensuring the lesson included computing and comparing elements of the lesson plans to the quantitative data provided by the teachers. Overall, we found, and excluded, very few instances of low-quality data.

Survey Questions and Descriptive Statistics Qualitative Questions: 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 butterflies and life cycles with their partners. Then they will modify the program and use conditionals to create the model.)

Quantitative Question Topic: Response Options (descriptive statistics in parentheses)

Programming and CT Concepts Programming paradigm: Select one: No Programming (80), Unplugged (87), Block-based (69), Text-based (26) Programming language: Open-ended Programming concepts: Select all that apply: Operator-arithmetic, Operator-Boolean, Operator-relational, Conditional-if-else, Conditional-if-then, Loop-for loop, Loop-while loop, Loop-loop index variable, Function-define/call, Function-parameter, Variable, Data types (string, integer, etc.), List, Multimedia component (sprite, sound, button, etc.), Multimedia properties (color, location, etc.), Multimedia movement (forward, back, turn), Output-string, Output-variable, User input, Event (M = 3.2, SD = 2.7) CT concepts: Select all that apply: Algorithms–sequences (158), Algorithms–parallelism (10), Pattern recognition (142), Abstraction (84), Decomposition (89), Debugging (40), Automation (40) (M = 2.1, SD = 1.1)

Classroom Context Integrated discipline: Select one: Art (5), Language arts (37), Foreign language (2), Math (67), Music (3), Science (61), Social Studies (13) Grades taught in lesson: Select all that apply: Kindergarten through 12th grade (activities that spanned K-5 = 107, 6-8 = 53, 9-12 = 93, K-12 = 9) Minimum amount of time the lesson takes: Select one: < 1 hour (90), 1-3 hours (126), 3-8 hours (32), 8+ hours (14) Source of the lesson plan: Select all that apply: Colleague (16), Online search (18), Professional development (20), Professional organization (23), Created based on an external source by myself or with colleagues (28), Modified from an external source (33), Created by myself or with colleagues (124)

Teacher Information Number of years teaching: Open-ended, M = 14.11, SD = 7.6 Current role: Select one: Teacher (220), STEM/Tech specialist (24), Librarian (9), Computer lab director (1), Other (8) Grade levels taught: Select all that apply: K-2, 3-5, 6-8, 9-10, 11-12 (grade levels that spanned K-5 = 79, 6-8 = 45, 9-12 = 93, K-12 = 45) Disciplines taught: Select all that apply: Art (13), Language arts (71), Foreign language (5), Math (134), Music (4), Science (100), Social Studies (54), Computer science (80), Technology (78), Other (8) Degrees, Certs, endorsements, etc. attained: Select all that apply: Teaching certificate in primary discipline(s) (164), Teaching certificate in CS (17), Bachelor’s degree in primary discipline education (129), Bachelor’s degree in CS or CS education (4), Master’s degree in primary discipline education (163), Master’s degree in CS or CS education (0), Endorsement in computer science education (47), EdD or PhD in education (17), Other (86) Support for integrated CS/CT development and implementation: Select all that apply: Professional development through my school/district/LEA/RESA (157), Professional development through external organizations (117), Peer/colleague/department collaboration in my school/district/LEA/RESA (130), Peer/colleague collaboration in external organizations (73), Funding for software licensing, hardware, or curricula (69) Self-efficacy: Views of CT and self-efficacy scale from Yadav, Caeli, Ocak, and Macann, 2022 (M = 4.23 out of 5, SD = 0.60) Gender: Select one: Man (60), Woman (198), Non-binary/third gender (2), Prefer not to say (2) Race: Select one: African American or Black (31), American Indian or Indigenous (1), Asian (13), Caucasian or White (193), Latino/a/x or Hispanic (10), Middle Eastern (0), Pacific Islander (0), Other (14)

School Context Number of students: Open-ended (M = 1179, SD = 741) Number of CS teachers: Open-ended (M = 1.6, SD = 1.4) Number of CS courses: Open-ended (M = 2.1, SD = 2.0) Number of years CS courses taught: Open-ended (M = 3.0, SD = 2.1) Racial composition: Give % of each race: American Indian or Native American (M = 1.8%), Asian (M = 4.5%), Black or African American (M = 23.3%), Hispanic or Latino (M = 17.2%), White or Caucasian (M = 47.5%), Other (M = 2.4%) % of students eligible for free or reduced lunch: Open-ended (M = 56%, SD = 34%) Type of area: Select one: Rural (90), Suburban (122), Urban (50)

Characteristics and median employment income of postsecondary graduates ten years after graduation, by educational qualification (Classification of programs and credentials - professional degree variant), field of study (Classification of Instructional Programs (CIP) Canada 2016 - Cannabis 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).

In Dublin City University, students learn how to code by taking a variety of programming modules. Students develop code algorithms for problems proposed by Faculty. Many of these courses or modules are delivered through a custom Virtual Learning Environment (VLE) built for the purpose of teaching and learning computer programming. This custom VLE enables students to access course information, material and slides for each module. In addition, our system integrates an automatic grading platform where students can verify their code submissions for programming exercises. Students typically develop solutions locally for laboratory sheets for the computer programming courses. Then, they submit their programs online to the automatic grading platform which runs a number of testcases specified by the lecturer on each exercise. This provides instant feedback to students based on the suite of testcases run and ultimately tells the student whether the program is considered correct or incorrect if any of the testcases fail. This information is invaluable to their learning and such a platform is needed to verify their programs work as expected. The computer programming grading system has been used for several years on a variety of programming courses at our University. This allowed researchers and Faculty to gather a fine-grained digital footprint of students learning programming at our University. Recently, research in Learning Analytics has focused on Predictive Modelling and identifying those students having difficulties with course material, also in programming courses, and offering remediation, personalized feedback and interventions to students using Machine Learning techniques. Prior work has reported that customized notifications sent to students regarding their performance and offering resources such as further learning material, code solutions from peers in their class and university support services helped students to increase their differential performance and engagement on these programming courses. However, there is a limit to this prior work where most of the models use little or no programming work as features for the learning algorithms or feedback sent to students. In this work we explore different mechanisms to represent students’ code to predict its correctness and to better analyze students’ progress using their interactions which can be exploited to provide effective feedback and support better recommendations. Every time a student submits a code solution for verification, the system stores the code submission, the student identifier, the IP used on the network for the upload, the results of the testcases run with inputs and outputs, the course the submission belongs to, the exercise and the task name the student is attempting by using the submission’s filename. In total, we collected more than half a million programming submissions (591,707) for 666 students from 5 Python programming courses over 3 academic years.