Table of Contents

• Self-Efficacy, Cognitive Load, and Emotional Reactions in Collaborative Algorithms Labs - A Case StudyLaura Toma, Jan Vahrenhold. 1-10 [doi]
• Fixed versus Growth Mindset Does not Seem to Matter Much: A Prospective Observational Study in Two Late Bachelor level Computer Science CoursesAntti-Juhani Kaijanaho, Ville Tirronen. 11-20 [doi]
• Towards an Analysis of Program Complexity From a Cognitive PerspectiveRodrigo Duran, Juha Sorva, Sofia Leite. 21-30 [doi]
• On Use of Theory in Computing Education ResearchGreg L. Nelson, Andrew J. Ko. 31-39 [doi]
• Metacognitive Difficulties Faced by Novice Programmers in Automated Assessment ToolsJames Prather, Raymond Pettit, Kayla Holcomb McMurry, Alani Peters, John Homer, Maxine Cohen. 41-50 [doi]
• Who Tests the Testers?John Wrenn, Shriram Krishnamurthi, Kathi Fisler. 51-59 [doi]
• Evaluating the Efficiency and Effectiveness of Adaptive Parsons ProblemsBarbara J. Ericson, James D. Foley, Jochen Rick. 60-68 [doi]
• Pedagogical Content Knowledge for Teaching Inclusive DesignAlannah Oleson, Christopher J. Mendez, Zoe Steine-Hanson, Claudia Hilderbrand, Christopher Perdriau, Margaret M. Burnett, Andrew J. Ko. 69-77 [doi]
• Digital Competence, Teacher Self-Efficacy and Training NeedsLinda Mannila, Lars-Åke Norden, Arnold Pears. 78-85 [doi]
• Professional Learning in the Midst of Teaching Computer ScienceAleata Hubbard, Katie D'Silva. 86-94 [doi]
• Growing Minds - 21st Century Competences and Digitalisation among Finnish Youth?Kirsti Lonka. 95-96 [doi]
• Socioeconomic Status and Computer Science Achievement: Spatial Ability as a Mediating Variable in a Novel Model of UnderstandingMiranda C. Parker, Amber Solomon, Brianna Pritchett, David A. Illingworth, Lauren E. Margulieux, Mark Guzdial. 97-105 [doi]
• Investigating the Relationship Between Spatial Skills and Computer ScienceJack Parkinson, Quintin I. Cutts. 106-114 [doi]
• Experiences of Computer Science Transfer StudentsHarrison Kwik, Benjamin Xie, Andrew J. Ko. 115-123 [doi]
• Decomposition: A K-8 Computational Thinking Learning TrajectoryKathryn M. Rich, T. Andrew Binkowski, Carla Strickland, Diana Franklin. 124-132 [doi]
• Unravelling the Cognition of Coding in 3-to-6-year Olds: The development of an assessment tool and the relation between coding ability and cognitive compiling of syntax in natural languageEva Marinus, Zoe Powell, Rosalind Thornton, Genevieve McArthur, Stephen Crain. 133-141 [doi]
• Starting from Scratch: Outcomes of Early Computer Science Learning Experiences and Implications for What Comes NextDavid Weintrop, Alexandria K. Hansen, Danielle B. Harlow, Diana Franklin. 142-150 [doi]
• Programming Misconceptions for School StudentsAlaaeddin Swidan, Felienne Hermans, Marileen Smit. 151-159 [doi]
• Misconception-Driven Feedback: Results from an Experimental StudyLuke Gusukuma, Austin Cory Bart, Dennis G. Kafura, Jeremy Ernst. 160-168 [doi]
• Identifying Student Difficulties with Basic Data StructuresDaniel Zingaro, Cynthia Taylor, Leo Porter, Michael Clancy, Cynthia Bailey Lee, Soohyun Nam Liao, Kevin C. Webb. 169-177 [doi]
• Automated Plagiarism Detection for Computer Programming Exercises Based on Patterns of ResubmissionNarjes Tahaei, David C. Noelle. 178-186 [doi]
• Objects Count so Count Objects!Ewan D. Tempero, Paul Denny, Andrew Luxton-Reilly, Paul Ralph. 187-195 [doi]
• Blackbox, Five Years On: An Evaluation of a Large-scale Programming Data Collection ProjectNeil C. C. Brown, Amjad AlTadmri, Sue Sentance, Michael Kölling. 196-204 [doi]
• Using Social Cognitive Career Theory to Understand Why Students Choose to Study Computer ScienceAmnah Alshahrani, Isla Ross, Murray I. Wood. 205-214 [doi]
• How Mother and Father Support Affect Youths' Interest in Computer ScienceJody Clarke-Midura, Frederick J. Poole, Katarina Pantic, Chongning Sun, Vicki Allan. 215-222 [doi]
• "I told you this last time, right?": Re-visiting narratives of STEM educationSebastian Dziallas, Sally Fincher. 223-231 [doi]
• TA Marking Parties: Worth the Price of Pizza?Brian Harrington, Marzieh Ahmadzadeh, Nick Cheng, Eric Heqi Wang, Vladimir Efimov. 232-240 [doi]
• Exploring how Students Perform in a Theory of Computation Course using Final Exam and Homework Assignments DataChristiane Frede, Maria Knobelsdorf. 241-249 [doi]
• Applying a Gesture Taxonomy to Introductory Computing ConceptsAmber Solomon, Mark Guzdial, Betsy DiSalvo, Ben Rydal Shapiro. 250-257 [doi]
• Emotions Experienced by First-Year Engineering Students During Programming TasksZahra Atiq. 258-259 [doi]
• Towards a Theory of HtDP-based Program-Design LearningFrancisco Enrique Vicente G. Castro. 260-261 [doi]
• Towards an Instructional Design of Complex Learning in Introductory Programming CoursesRodrigo Duran. 262-263 [doi]
• A Study on the Assessment of Introductory Computational Thinking via Scratch Programming in Primary SchoolsJanne Fagerlund. 264-265 [doi]
• A Misconception Driven Student Model to Author FeedbackLuke Gusukuma. 266-267 [doi]
• Practical Thinking while Programming: A Deweyan Approach to Knowledge in Computer ScienceKristina Von Hausswolff. 268-269 [doi]
• Data Mining and Machine Learning in Education with Focus in Undergraduate CS Student SuccessWilliam Gregory Johnson. 270-271 [doi]
• What are they thinking?Cazembe Kennedy. 272-273 [doi]
• The Collegiate Hackathon ExperienceBrittany Ann Kos. 274-275 [doi]
• Fostering Computational Thinking through Problem-Solving at SchoolAmelie Labusch. 276-277 [doi]
• Developing Students' Conceptual Design Skills for Software EngineeringT. G. Lakshmi. 278-279 [doi]
• Progression of Student Reasoning about ConcurrencyAubrey Lawson. 280-281 [doi]
• The <1%: Black Women Obtaining PhDs in ComputingAmari Lewis. 282-283 [doi]
• Developing Students' Cognitive Processes Required for Software Design VerificationPrajish Prasad. 284-285 [doi]
• Using Computational Thinking to Transform Elementary Mathematics InstructionKathryn M. Rich. 286-287 [doi]
• Massive Open Online Courses in Programming: the Case of EstoniaMerilin Säde. 288-289 [doi]
• Easing Learners into Data Science via Visualization of Concepts and ComputationsLovisa Sundin. 290-291 [doi]
• Using Program Analysis to Improve API LearnabilityKyle Thayer. 292-293 [doi]
• Exploring Data-driven Worked Examples for Block-based ProgrammingRui Zhi. 294-295 [doi]