Active Learning Design Patterns for CS Education

Dehbozorgi, Nasrin

doi:10.1145/3105726.3105741

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…Design patterns and pattern languages originated from Alexander et al in the field of architecture in 1977 (Alexander, 1977). Alexander et al's intention was to democratize town planning by proposing a set of conceptual frameworks that could be applied by ordinary people in buildings and architecture (Dehbozorgi, 2017).…”

Section: Design Patterns In Software Engineering Educationmentioning

confidence: 99%

“…In a more recent study, the authors proposed an object-based pattern model which has different attributes to address problems specifically in collaborative student-centered learning. The multiple attributes of this pattern model allow capturing diverse dimensions of teamwork in software engineering classes (Dehbozorgi, 2018(Dehbozorgi, -2017. This model has four main constructs of 'pattern name', 'meta-data, 'pattern core', and 'implementation' (Dehbozorgi, 2018).…”

Section: Design Patterns In Software Engineering Educationmentioning

confidence: 99%

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Teaching Software Engineering using Abstraction through Modeling

Dorodchi¹,

Dehbozorgi²,

Fallahian³

et al. 2021

Informatics in Education

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Teaching software engineering (SWE) as a core computer science course (ACM, 2013) is a challenging task. The challenge lies in the emphasis on what a large-scale software means, implementing teamwork, and teaching abstraction in software design while simultaneously engaging students into reasonable coding tasks. The abstraction of the system design is perhaps the most critical and theoretical part of the course and requires early engagement of the students with the necessary topics followed by implementation of the abstract model consistently. Normally, students would take such courses in the undergraduate curriculum sequence after data structures and/or object-oriented design/programming. Therefore, they would be able to learn about systematic modeling of software as a system. In this work, we address how to facilitate the teaching of SWE by introducing abstract modeling. Furthermore, functional decomposition is reviewed as a critical component which in turn, requires understanding of how different tasks are accomplished by enterprise software. Combining such pieces with concepts of architecture and design patterns of software provides foundational knowledge for students to be able to navigate around enterprise software in the real world.

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Section: Design Patterns In Software Engineering Educationmentioning

confidence: 99%

Section: Design Patterns In Software Engineering Educationmentioning

confidence: 99%

Teaching Software Engineering using Abstraction through Modeling

Dorodchi¹,

Dehbozorgi²,

Fallahian³

et al. 2021

Informatics in Education

Self Cite

View full text Add to dashboard Cite

show abstract

“…As noted earlier, active learning methods put emphasis on student learning and gives them greater motivation in the process of learning [24], [32], [42]. In [43], the authors described a Project-based Learning (PBL) program for UAVbased remote sensing.…”

Section: Related Workmentioning

confidence: 99%

Preparing Students for Drone Careers Using Active Learning Instruction

et al. 2021

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The exponential growth in the adoption of drones in various industrial applications has created a demand for professionals experienced in designing, programming, operating, and piloting drones. This paper reports on an active learning-based instructional approach that prepares students for careers in the drone industry. The approach focuses on three foundational pillars of drone careers: 1) drone programming using Python, 2) designing and fabricating drones using Computer-Aided Design (CAD) and rapid prototyping, and 3) the US Federal Aviation Administration (FAA) Part 107 Commercial small Unmanned Aerial Vehicles (sUAVs) pilot test. The approach was implemented and refined over four semesters in an undergraduate class attended by engineering and computer science students at a research institution located in the United States. A case study approach using multiple assessment methods was implemented to examine students' gains in skills and knowledge and attitudes towards an active learning-based approach for drone education. Assessment methods included direct formative (FAA quizzes and programming assignments), direct summative (capstone project), indirect quantitative (survey of learning gains), and indirect qualitative (focus group interviews and capstone project process videos) tools. Assessment results noted in this paper indicate that students were actively engaged during the course and made positive gains in their learning outcomes.

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“…Most forms of active learning emphasize collaboration and the social construction of knowledge. During the past decade, active learning has been applied more often in higher education, and it highlights the importance of having structured protocols for team formation, team size, and assessment [10], [11]. The inherently complex nature of teamwork in active learning calls for methods to assess students' performance at both individual and team levels [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Emotion Mining from Speech in Collaborative Learning

Dehbozorgi¹,

Maher²,

Dorodchi³

2021

Adv. sci. technol. eng. syst. j.

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Affective states, a dimension of attitude, have a critical role in the learning process. In the educational setting, affective states are commonly captured by self-report tools or based on sentiment analysis on asynchronous textual chats, discussions, or students' journals. Drawbacks of such tools include: distracting the learning process, demanding time and commitment from students to provide answers, and lack of emotional self-awareness which reduces the reliability. Research suggests speech is one of the most reliable modalities to capture emotion and affective states in real-time since it captures sentiments directly. This research, which is an extension of the work originally presented in FIE conference'20 [1], analyses students' emotions during teamwork and explores the correlation of emotional states with students' overall performance. The novelty of this research is using speech as the source of emotion mining in a learning context. We record students' conversations as they work in low-stake teams in an introductory programming course (CS1) taught in active learning format and apply natural language processing algorithms on the speech transcription to extract different emotions from conversations. The result of our data analysis shows a strong positive correlation between students' positive emotions as they work in teams and their overall performance in the course. We conduct aspect-based sentiment analysis to explore the themes of the positive emotions and conclude that the student's positive feelings were mostly centered around course-related topics. The result of this analysis contributes to future development of predictive models to identify lowperforming students based on the emotions they express in teams at earlier stages of the semester in order to provide timely feedback or pedagogical interventions to improve their learning experience.

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Active Learning Design Patterns for CS Education

Cited by 13 publications

References 4 publications

Teaching Software Engineering using Abstraction through Modeling

Teaching Software Engineering using Abstraction through Modeling

Preparing Students for Drone Careers Using Active Learning Instruction

Emotion Mining from Speech in Collaborative Learning

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