A Comparison of Lecture-based and Active Learning Design Patterns in CS Education

Dehbozorgi, Nasrin; MacNeil, Stephen; Maher, Mary Lou; Dorodchi, Mohsen

doi:10.1109/fie.2018.8659339

Cited by 12 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the concept of design patterns in SWE that offer generic repeatable solutions to the known problems, pedagogical design patterns formalize successful practices that can address recurring problems that educators face in the educational setting (Dehbozorgi, 2018). Pedagogical design patterns do not provide absolute and finalized solution, however, they present tested and proven paradigms that can be implemented in educational settings in different ways (Dehbozorgi, 2018).…”

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

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.

show abstract

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

“…The concept of a pattern language was introduced by Alexander et al (1977) in the architectural design domain and has since been applied in various other domains including software design (Gamma et al 1995), process design (Lerner et al 2010) and educational design (Goodyear 2005;Dehbozorgi et al 2018). Patterns are descriptions of solutions to recurring problems, including information about context and rationale.…”

Section: Pattern-language-based Designmentioning

confidence: 99%

“…2010) and educational design (Goodyear 2005; Dehbozorgi et al. 2018). Patterns are descriptions of solutions to recurring problems, including information about context and rationale.…”

Section: Instances Of Fast and Slow Design Thinkingmentioning

confidence: 99%

Design thinking, fast and slow: A framework for Kahneman’s dual-system theory in design

Kannengiesser¹,

Gero

2019

Des. Sci.

View full text Add to dashboard Cite

In his book Thinking, Fast and Slow, Daniel Kahneman presented a model of human cognition based on two modes or ‘systems’ of thinking: system 1 thinking that is fast and intuitive and system 2 thinking that is slow and tedious. This paper proposes a framework for applying Kahneman’s model to designing based on the function–behaviour–structure ontology. It casts four instances of designing in this framework: design fixation, case-based design, pattern-language-based design and brainstorming.

show abstract

“…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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A Comparison of Lecture-based and Active Learning Design Patterns in CS Education

Cited by 12 publications

References 9 publications

Teaching Software Engineering using Abstraction through Modeling

Teaching Software Engineering using Abstraction through Modeling

Design thinking, fast and slow: A framework for Kahneman’s dual-system theory in design

Emotion Mining from Speech in Collaborative Learning

Contact Info

Product

Resources

About