A curated list of assessments, classifications and papers regarding programming asssessments
Visit http://www.barkmin.eu/programming-assessments/ for more details.
- Ahadi et.al (2013): Geek Genes, Prior Knowledge, Stumbling Points and Learning Edge Momentum: Parts of the One Elephant?
- Ahadi et.al (2019): ArAl: An Online Tool for Source Code Snapshot Metadata Analysis
- Alvarado et.al (2018): The Persistent Effect of Pre-College Computing Experience on College CS Course Grades
- Bari et.al (2019): {{EvoParsons}}: Design, Implementation and Preliminary Evaluation of Evolutionary {{Parsons}} Puzzle
- Barkmin (2020): Competency Structure Model for Programming for the Transition from School to University
- Blanchard et.al (2019): Effects of Code Representation on Student Perceptions and Attitudes Toward Programming
- Bockmon et.al (2019): (Re)Validating Cognitive Introductory Computing Instruments
- Bockmon et.al (2020): A CS1 Spatial Skills Intervention and the Impact on Introductory Programming Abilities
- Caceffo et.al (2016): Developing a Computer Science Concept Inventory for Introductory Programming
- Clifton (1995): Self-Assessment Procedure XXIII: Programming Languages
- Dierbach et.al (2005): Experiences with a CS0 Course Targeted for CS1 Success
- Fronza et.al (2019): An Exploration of Cognitive Shifting in Writing Code
- Izu et.al (2019): Fostering Program Comprehension in Novice Programmers - Learning Activities and Learning Trajectories
- Izu et.al (2020): Assessing CS1 Design Skills with a String Manipulation Task
- Jacková et.al (2019): Introductory Programming Exams and Their Benchmarking in Slovakia
- Joyner (2018): Toward CS1 at Scale: Building and Testing a MOOC-for-Credit Candidate
- Joyner (2018): Intelligent Evaluation and Feedback in Support of a Credit-Bearing MOOC
- Joyner et.al (2019): Replicating and Unraveling Performance and Behavioral Differences between an Online and a Traditional CS Course
- Liao et.al (2019): A Robust Machine Learning Technique to Predict Low-Performing Students
- Lister (2005): One Small Step toward a Culture of Peer Review and Multi-Institutional Sharing of Educational Resources: A Multiple Choice Exam for First Semester Programming Students
- Lodi (2019): Does Studying CS Automatically Foster a Growth Mindset?
- Luxton-Reilly et.al (2018): Developing Assessments to Determine Mastery of Programming Fundamentals
- Morrison (2017): Dual Modality Code Explanations for Novices: Unexpected Results
- Parker et.al (2016): Replication, Validation, and Use of a Language Independent CS1 Knowledge Assessment
- Parker et.al (2018): Socioeconomic Status and Computer Science Achievement: Spatial Ability as a Mediating Variable in a Novel Model of Understanding
- Patitsas et.al (2019): Evidence That Computer Science Grades Are Not Bimodal
- Porter et.al (2019): BDSI: A Validated Concept Inventory for Basic Data Structures
- Rosenstein et.al (2020): Identifying the Prevalence of the Impostor Phenomenon Among Computer Science Students
- Sheard et.al (2014): Benchmarking a Set of Exam Questions for Introductory Programming
- Simon et.al (2016): Benchmarking Introductory Programming Exams: Some Preliminary Results
- Somerville et.al (2020): Addressing Mixed Levels of Prior Knowledge by Individualising Learning Pathways in a Degree Apprenticeship Summer School
- Timmermann et.al (2016): Evidence-based re-design of an introductory course “programming in C”
- Xhakaj et.al (2018): Towards {{Improving Introductory Computer Programming}} with an {{ITS}} for {{Conceptual Learning}}
- Xie et.al (2019): An Item Response Theory Evaluation of a Language-Independent CS1 Knowledge Assessment
- Xie et.al (2020): The Effect of Informing Agency in Self-Directed Online Learning Environments
 |
|---|
| Mike Barkmin |
Contributions welcome! Read the contribution guidelines first.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
The licensor cannot revoke these freedoms as long as you follow the license terms.