Modeling Programming Competency

Dr. Natalie Kiesler is currently a senior researcher at the Educational Computer Science group at the DIPF Leibniz Institute for Research and Information in Education, and a lecturer at the Computer Science department at Goethe University Frankfurt, Germany. Her research focuses on competency, educational technologies, and feedback in the context of computing education. She has received several awards for her work, including the Best Demo Award at the Educational Technologies Conference (DELFI), and the Hessian University Award for Excellence in University Teaching. She is also involved in various committees and reviews for several conferences and journals within the computing education research and education technology community. She also initiated and led a working group of the German Informatics Society to promote and investigate Open Data Practices in Educational Technology Research.

1 Introduction 1.1 Background and Motivation1.2 Goal and Research Questions 1.3 Contextualization of this Research 1.4 Structure of the BookReferences 2 Approaching the Concept of Competency2.1 Competency Definition2.1.1 Psychological Perspective on Competency/2.1.2 Historical Perspective on Competency/2.1.3 Recent Perspectives and Discussions22 2.2 Taxonomies and Competency Models for Computing2.2.1 Bloom's and Anderson-Krathwohl's Taxonomy 2.2.2 Competency Model of the German Informatics Society2.3 Competency-Based Curricula Recommendations in Computing2.3.1 Information Technology 20172.3.2 Computing Curricula 20202.3.3 National Curricula Recommendations 2.4 Related Research in Computing EducationReferences3 Research Design3.1 Summary of Research Desiderata3.2 Research Goals3.3 Research Questions3.4 Study DesignReferences
Part II Data Gathering and Analysis of University Curricula 4 Data Gathering of University Curricula4.1 Goals of Gathering and Analyzing University Curricula 4.2 Relevance of Gathering and Analyzing University Curricula4.3 Expectations and Limitations4.4 Sampling and Data Gathering4.4.1 Selection of Bachelor Degree Programs 4.4.2 Selection of Content Area4.4.3 Selection of Institutions and Study Programs4.4.4 Selection of ModulesReferences5 Data Analysis of University Curricula5.1 Methodology of the Data Analysis5.2 Pre-processing of Data5.2.1 Linguistic Smoothing of Competency Goals5.2.2 Basic Coding Guidelines5.2.3 Computer-Assisted Analysis5.3 Data Analysis5.3.1 Deductive Category Development5.3.2 Inductive Category Development 5.3.3 Deductive-Inductive Category Development5.4 Application of Quality CriteriaReferencesPart III Data Gathering and Analysis of Expert InterviewsData Gathering of Guided Expert Interviews6.1 Goals of Conducting and Analyzing Guided Expert Interviews6.2 Relevance of Conducting and Analyzing Guided Expert Interviews 6.3 Expectations and Limitations6.4 Developing an Interview Guide and Questions6.5 Data Gathering and Sampling6.5.1 Selecting and Contacting Experts6.5.2 Conducting the Interviews6.5.3 Recording the InterviewsReferences7 Data Analysis of Guided Expert Interviews7.1 Pre-processing of Data7.1.1 Transcription Guidelines7.1.2 Transcription System7.1.3 Transcription Process7.2 Data Analysis7.3 Application of Quality CriteriaReferencesPart IV Results 8 Results of University Curricula Analysis 8.1 Cognitive Competencies8.1.1 Cognitive Process Dimension Remembering 8.1.2 Cognitive Process Dimension Understanding8.1.3 Cognitive Process Dimension Applying8.1.4 Cognitive Process Dimension Analyzing8.1.5 Cognitive Process Dimension Evaluating8.1.6 Cognitive Process Dimension Creating8.1.7 Knowledge Dimensions8.2 Other Competencies8.3 Reliability8.4 Discussion of ResultsReferences9 Results of Guided Expert Interviews9.1 Cognitive Competencies9.2 Other Competencies9.3 Factors Preventing Programming Competency9.4 Factors Contributing to Programming Competency9.5 Reliability9.6 Discussion of ResultsReferences10 Summarizing and Reviewing the Components of Programming Competency10.1 Summary of Cognitive Programming Competencies10.2 Summary of Other Programming Competency Components10.3 Review of the Anderson Krathwohl TaxonomyReferences Part V Wrap Up11 Conclusion11.1 Brief Summary of Results11.1.1 Competencies Expected from Novice Programmers11.1.2 Adequacy of the Anderson Krathwohl Taxonomy 11.1.3 Factors Influencing Students' Competency Development11.2 Conclusions11.3 Future WorkReferences12 Complete List of References