Can we identify the similarity of courses in computer science?

被引:0
|
作者
Karadag, Tugay [1 ]
Parim, Coskun [1 ]
Buyuklu, Ali Hakan [1 ]
机构
[1] Yildiz Tech Univ, Dept Stat, TR-34349 Istanbul, Turkiye
来源
SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI | 2023年 / 41卷 / 04期
关键词
Computer Science; Curriculum; Data Processing and Interpretation; Higher Education; Science; Knowledge; Technology; LATENT DIRICHLET ALLOCATION; BIG DATA; ARTIFICIAL-INTELLIGENCE;
D O I
10.14744/sigma.2023.00089
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Especially on the Internet, popular topics in computer sciences which are artificial intelligence, big data, business analytics, data mining, data science, deep learning, and machine learning have been compared or classified using confusing Venn diagrams without any scientific proof. Relationships among the topics have been visualized in this study with the help of Venn diagrams to add scientificity to visualizations. Therefore, this study aims to determine the interactions among the seven popular topics in computer sciences. Five books for each topic (35 books) were included in the analysis. To illustrate the interactions among these topics, the Latent Dirichlet Allocation (LDA) analysis, a topic modeling analysis method, was applied. Further, the pairwise correlation was applied to determine the relationships among the chosen topics. The LDA analysis produced expected results in differentiating the topics, and pairwise correlation results revealed that all the topics are related to each other and that it is challenging to differentiate between them.
引用
收藏
页码:812 / 823
页数:12
相关论文
共 50 条
  • [31] Can science literacy help individuals identify misinformation in everyday life?
    Sharon, Aviv J.
    Baram-Tsabari, Ayelet
    SCIENCE EDUCATION, 2020, 104 (05) : 873 - 894
  • [32] What we can measure, we can manage: The importance of using robust welfare indicators in Equitation Science
    Waran, Natalie
    Randle, Hayley
    APPLIED ANIMAL BEHAVIOUR SCIENCE, 2017, 190 : 74 - 81
  • [33] Student participation in computer science courses via the Network Peer Assessment System (NetPeas)
    Liu, EZF
    Chiu, CH
    Lin, SSJ
    Yuan, SM
    ADVANCED RESEARCH IN COMPUTERS AND COMMUNICATIONS IN EDUCATION, VOL 2: NEW HUMAN ABILITIES FOR THE NETWORKED SOCIETY, 1999, 55 : 744 - 747
  • [34] Student-Collaboration in Online Computer Science Courses-An Explorative Case Study
    Standl, Bernhard
    Kuehn, Thomas
    Schlomske-Bodenstein, Nadine
    INTERNATIONAL JOURNAL OF ENGINEERING PEDAGOGY, 2021, 11 (05): : 87 - 104
  • [35] Coordinate: A Virtual Classroom Management Tool For Large Computer Science Courses Using Discord
    Brown, Cameron
    Castro, Laura Cruz
    PROCEEDINGS OF THE 56TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE TS 2025, VOL 1, 2025, : 165 - 171
  • [36] Exploring students' and lecturers' views on collaboration and cooperation in computer science courses - a qualitative analysis
    Schulz, Sandra
    Berndt, Sarah
    Hawlitschek, Anja
    COMPUTER SCIENCE EDUCATION, 2023, 33 (03) : 318 - 341
  • [37] Coordinate: A Virtual Classroom Management Tool For Large Computer Science Courses Using Discord
    Brown, Cameron
    Castro, Laura Cruz
    PROCEEDINGS OF THE 56TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE TS 2025, VOL 2, 2025, : 165 - 171
  • [38] An Augmented Reality Based Mobile Software to Support Learning Experiences in Computer Science Courses
    Kose, Utku
    Koc, Durmus
    Yucesoy, Suleyman Anil
    2013 INTERNATIONAL CONFERENCE ON VIRTUAL AND AUGMENTED REALITY IN EDUCATION, 2013, 25 : 370 - 374
  • [39] When we can trust computers (and when we can't)
    Coveney, Peter, V
    Highfield, Roger R.
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2021, 379 (2197):
  • [40] Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science
    Bin Qushem, Umar
    Oyelere, Solomon Sunday
    Akcapinar, Gokhan
    Kaliisa, Rogers
    Laakso, Mikko-Jussi
    TECHNOLOGY KNOWLEDGE AND LEARNING, 2024, 29 (03) : 1385 - 1400