Collaborative assessments in computer science education: A survey

Yuan, Hans; Cao, Paul

doi:10.26599/tst.2018.9010108

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…Assessment in interdisciplinary courses requires to balance the experiences of different groups of learners as well as the targeted outcome with respect to their interaction with AI. In addition to traditional assessment methods such as exams, tests oral presentations or reports, the applied nature of domain-specific AI teaching can also benefit from project-or problem-based assessments that are connected to real-world applications (see [58] as an example). Moreover, research in interdisciplinary education indicates that using assessment through reflection can help students to bridge the disciplinary silos [9].…”

Section: Assessmentmentioning

confidence: 99%

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

Schleiss,

Laupichler,

Raupach

et al. 2023

Education Sciences

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The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into the classroom and create so-called domain-specific AI courses. In this paper, we introduce the “AI Course Design Planning Framework” as a course planning framework to structure the development of domain-specific AI courses at the university level. The tool evolves non-specific course planning frameworks to address the context of domain-specific AI education. Following a design-based research approach, we evaluated a first prototype of the tool with instructors in the field of AI education who are developing domain-specific courses in this area. The results of our evaluation indicate that the tool allows instructors to create domain-specific AI courses in an efficient and comprehensible way. In general, instructors rated the tool as useful and user-friendly and made recommendations to improve its usability. Future research will focus on testing the application of the tool for domain-specific AI course developments in different domain contexts and examine the influence of using the tool on AI course quality and learning outcomes.

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Section: Assessmentmentioning

confidence: 99%

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

Schleiss,

Laupichler,

Raupach

et al. 2023

Education Sciences

View full text Add to dashboard Cite

show abstract

“…Besides course topics, students are exposed to some state-of-the-art teaching and learning paradigms, such as flip-classroom [4] , collaborative assessments [5] , and so on.…”

Section: Introductionmentioning

confidence: 99%

Towards connecting discrete mathematics and software engineering

Liu

Chen

et al. 2020

Tinshhua Sci. Technol.

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To enhance training in software development, we argue that students of software engineering should be exposed to software development activities early in the curriculum. This entails meeting the challenge of engaging students in software development before they take the software engineering course. In this paper, we propose a method to connect courses in the software engineering curriculum by setting comprehensive development projects to students in prerequisite courses for software development. Using the Discrete Mathematics (DM) course as an example, we describe the implementation of the proposed method and teaching practices using several practical and comprehensive projects derived from topics in discrete mathematics. Detailed descriptions of the sample projects, their application, and training results are given. Results and lessons learned from applying these practices show that it is a promising way to connect courses in the software engineering curriculum.

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Maximizing user type diversity for task assignment in crowdsourcing

Wang

Ren

et al. 2020

J Comb Optim

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Collaborative assessments in computer science education: A survey

Cited by 4 publications

References 51 publications

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

Towards connecting discrete mathematics and software engineering

Maximizing user type diversity for task assignment in crowdsourcing

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