An empirical study of teaching qualities of popular computer science and software engineering instructors using RateMyProfessor.com data

Kavalchuk, Aliaksei; Goldenberg, Alec; Hussain, Ishtiaque

doi:10.1145/3377814.3381700

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…We extend the state of the art by connecting findings in SET to the previous research of student bias by Ivins et al, Hewner, and Gold-Veerkamp [3]- [6]. In this manner, we extend the scope of investigation from evaluating teacher characteristics [38] to systematic evaluation of course characteristics.…”

Section: Co N C L U S Io Nmentioning

confidence: 76%

“…There are some recent works that deal with SET in software engineering and computing education. Kavalchuk et al [38] analyzed data from RateMyProfessor.com to distinguish the qualities of popular CS and SWE instructors. In a similar vein Carbone and Ceddia mined student evaluations for improvement areas in the ICT field [39].…”

Section: B Set In Computer Science and Software Engineering Educationmentioning

confidence: 99%

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To Get Good Student Ratings should you only Teach Programming Courses? Investigation and Implications of Student Evaluations of Teaching in a Software Engineering Context

Knutas

Hynninen

Hujala

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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Student evaluations of teaching (SET) are commonly used in universities for assessing teaching quality. However, previous literature shows that in software engineering students tend to rate certain topics higher than others: In particular students tend to value programming and software construction over software design, software engineering models and methods, or soft skills. We hypothesize that these biases also play a role in SET responses collected from students. The objective of this study is to investigate how the topic of a software engineering course affects the SET metrics. We accomplish this by performing multilevel regression analysis on SET data collected in a software engineering programme. We analyzed a total of 1295 student evaluations from 46 university courses in a Finnish university. The results of the analysis verifies that the student course evaluations exhibit similar biases as distinguished by previous software engineering education research. The type of the course can predict a higher SET rating. In our dataset, software construction and programming courses received higher SET ratings compared to courses on software engineering processes, models, and methods.

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Section: Co N C L U S Io Nmentioning

confidence: 76%

Section: B Set In Computer Science and Software Engineering Educationmentioning

confidence: 99%

To Get Good Student Ratings should you only Teach Programming Courses? Investigation and Implications of Student Evaluations of Teaching in a Software Engineering Context

Knutas

Hynninen

Hujala

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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“…Although most work has emphasized the role of instructor reputation and meeting time (Brown & Kosovich, 2015;Kavalchuk, Goldenberg and Hussain 2020), recent studies on elective course selection have revealed that the decision-making process is more complex than previous theoretical explanations have proposed (Chaturapruek et al, 2021). The present study offered an opportunity to reveal underlying complexity in the decision-making process previously obscured by assumptions that instructor reputation or course content account for the majority of variation in student behavior.…”

Section: Patterns By Spacementioning

confidence: 76%

Student enrollment decisions and academic success: evaluating the impact of classroom space design

Ralph

Schneider

Benson

et al. 2021

Learning Environ Res

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Instruction based on active learning is being promoted in higher education by the creation of new collaborative teaching and learning spaces, but capacity does not yet exist for all courses to be held in these new spaces. In this study, we examined how students choose between sections of the same course, with the same materials and techniques used by the same instructor, delivered in a collaborative teaching and learning space compared with a lecture-theater space. We also measured how the factors used in enrollment decisionmaking, and the subsequent learning experiences in the chosen environment, are associated with student attitudes related to learning chemistry. We collected student survey data from undergraduate students enrolled in Organic Chemistry II using items from the Colorado Learning Attitudes toward Science Survey for Chemistry and questions about the enrollment process. Students also responded to in-class prompts at the end of the semester about several different factors that could have influenced their decision to enroll in their section of the course. We combined these student responses with other data to create structural equation models for how the design of the teaching and learning space is associated with decision-making factors or attitudes about learning chemistry. Our models showed correlations among space enrollment, aspects of student identity, decision-making factors, and attitudes toward learning chemistry. These results suggest that students are self-sorting between the various learning spaces, which demonstrates a need for further research into the drivers of these sorting patterns over time.

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“…With various characters and competencies, computer science has produced many experts [6], [7]. The professionals are at diverse levels for working in different occupations [8]. During the disclosure of computer science simultaneously, various problems arise from computer technology and produce scientific units systematically, each specifically referred to as a unit of lecture material from a curriculum [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Computer Science

Nasution¹,

Hidayat²,

Syah³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Possible for science itself, conceptually, to have and will understand differently, let alone science also seen as technology, such as computer science. After all, science and technology are viewpoints diverse by either individual, community, or social. Generally, it depends on socioeconomic capabilities. So it is with computer science has become a phenomenon and fashionable, where based on the stream of documents, various issues arise in either its theory or implementation, adapting different communities, or designing curriculum holds in the education system. Generally, there are gaps between rich and poor, masculine and feminine, and basic sciences, engineering, and social sciences in accessing and using computer science. In particular, there have been various developments in hardware, software, and brain-ware from computer science in the application. There are recurring problems shortly, in the same or different places, both in developed and technologically lagging countries. Although computer science is still new, it has provided various innovations through research, which is proven by many documents. Therefore, following the growth of computer science -documents in the reputable indexed database, Scopus -there are clues for showing the trends. That is, dividing documents into three-time sessions: sloping, increasing and decreasing. That is by using a growth graphic. Then equip it with some parameters: topics and the weights from hit counts of databases. It gives the meaning as an interpretation to support the reviews. Therefore, this paper will provide a commentary related to that growth. It is for showing that despite the spread of studies, at the last time-session, the issues were always present simultaneously at the same time in accumulation. That is for describing a study title of computer science about what the world already has, going, and will understand about computer science. It is a view regarding its philosophy, basic science, applications, and technology as concepts and solutions that make computer science the primary foundation for skills and professions in the 21 st century through the education system. Therefore, other scientific fields such as informatics, computer engineering, information science, information technology, and data science, which are at the crossroads of computer science, are also scientific fields that raise different issues.

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An empirical study of teaching qualities of popular computer science and software engineering instructors using RateMyProfessor.com data

Cited by 4 publications

References 12 publications

To Get Good Student Ratings should you only Teach Programming Courses? Investigation and Implications of Student Evaluations of Teaching in a Software Engineering Context

To Get Good Student Ratings should you only Teach Programming Courses? Investigation and Implications of Student Evaluations of Teaching in a Software Engineering Context

Student enrollment decisions and academic success: evaluating the impact of classroom space design

Computer Science

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