Computational Thinking (CT), particularly abstraction, is essential in engineering education, enabling students to break down complex systems into manageable parts. Abstraction helps learners focus on key elements of a problem, ignoring extraneous details. The PGK framework, suggested by Per-renet, Groote, and Kaasenbrood, defines abstraction across four cognitive levels: problem, algorithm, program, and execution. At a higher education institution that focuses on engineering education, we assessed students' abstraction skills using sorting algorithms, chosen for their foundational role and suitability for testing such skills. Our study focused on two areas: (1) the performance of computer science (CS) and non-CS students on algorithmic abstraction tasks, and (2) how factors like demographics, training, programming proficiency, and self-assessed abstraction mastery correlate with task performance. Results showed that all students, especially non-CS majors (including Engineering), need stronger skills at the algorithm, program (coding algorithms), and execution (code functionality) levels. Many non-CS students overestimated their abilities, highlighting a gap in mastery. Students with programming experience performed better, underscoring the importance of hands-on training. These findings suggest interventions for non-CS students are needed to gain experience in programming and to bridge the gap between perceived and actual skills. Future research should focus on discipline-specific curricula and long-term studies to ensure that all students develop the essential CT skills for the digital era.

The recent emergence of LLM-based code generation models can potentially transform programming education. To pinpoint the current state of research on using LLM-based code generators to support the teaching and learning of programming, we conducted a systematic literature review of 21 papers published since 2018. The review focuses on (1) the teaching and learning practices in programming education that utilized LLM-based code generation models, (2) characteristics and (3) performance indicators of the models, and (4) aspects to consider when utilizing the models in programming education, including the risks and challenges. We found that the most commonly reported uses of LLM-based code generation models for teachers are generating assignments and evaluating student work, while for students, the models function as virtual tutors. We identified that the models exhibit accuracy limitations; generated content often contains minor errors that are manageable by instructors but pose risks for novice learners. Moreover, risks such as academic misconduct and over-reliance on the models are critical when considering integrating these models into education. Overall, LLM-based code generation models can be an assistive tool for both learners and instructors if the risks are mitigated.

Computational Thinking (CT) is considered a core 21st century digital skill. The aspect of assessment is crucial and knowing what, who, when, how, and where to assess is important for assessment design. In this study, we conducted an umbrella review to gain insights regarding CT assessment in higher education. In total, we analyzed 11 reviews, focusing on: (1) bibliographical and methodological characteristics of the reviews; (2) aspects relevant of assessment design, including a) assessed constructs, b) applied assessment methodologies, and c) assessment contexts. Our findings suggest an increased attention on this topic. However, hardly any reviews reasoned the selection of their review methodology, and most of the reviews did not thoroughly examine existing reviews. Regarding assessment design aspects, most reviews did not confine their scope to higher education; however, findings on interventions and educational settings show commonalities. We identified 120 unique assessed constructs and around 10 types of assessment methods. Though a combined use of distinct assessment methods is suggested in reviews, guidelines for appropriate assessment design are yet to be constructed. Based on the findings, we argue that it is necessary to explore different combinations of assessment design in various contexts to construct assessment guidelines.

With the vision to promote CT to a wider group of audiences, this PhD project explores the formative assessment of CT skills in Programming Education to support students to learn CT skills in Higher Education. In this project, we plan to investigate the importance of CT in the context of Higher Education, explore the relationship between CT skills and programming skills, build a model to assess learners' CT skills and develop a computer-assisted assessment system with automated components to enhance students' CT competences in Higher Education. Mixed-method research methodologies will be employed in distinct phases of the project accordingly. A system which allows formative assessment of CT skills will be iteratively designed and constructed throughout the project. The outcome of the project should support the CT learning process, make CT more visible for people from diverse backgrounds and empower them with a CT mindset to embrace the digitalization of society.