Proceedings of the 8th Computer Science Education Research Conference on ZZZ最新文献

筛选

英文中文

DigitalJS

Proceedings of the 8th Computer Science Education Research Conference on ZZZ Pub Date : 2019-11-18 DOI: 10.1145/3375258.3375272

Marek Materzok

引用次数: 2

DaST DaST

Proceedings of the 8th Computer Science Education Research Conference on ZZZ Pub Date : 2019-11-18 DOI: 10.1145/3375258.3375271

Charis Kotsiopoulos, Ioannis Doudoumis, Paraskevi Raftopoulou, Christos Tryfonopoulos

{"title":"DaST","authors":"Charis Kotsiopoulos, Ioannis Doudoumis, Paraskevi Raftopoulou, Christos Tryfonopoulos","doi":"10.1145/3375258.3375271","DOIUrl":"https://doi.org/10.1145/3375258.3375271","url":null,"abstract":"Over the last few years data science has emerged both as a new research field and as an educational domain that attracted a large number of researchers and data practitioners. Although data science research is developing at a high pace, the educational process in the field has been left behind in terms of educational tools and practices, despite the high number of data science courses offered and the number of involved stakeholders (professors, tutors, and students). The present work aims to cover the gap of educational data science tools by proposing a novel platform; the platform, coined Data Science Tutor (DaST), is a free online tool that offers automated step-by-step exercise solving in a variety of data science algorithms/techniques aiming at giving insight to the particularities of each algorithm. The solutions of the exercises are accompanied with in-context explanations that refer to the operation of the respective algorithm/technique, and are compatible with the terminology and the methodology in popular textbooks. The tool aims at students, lecturers, and data practitioners in many diverse fields (ranging from data analysts to transport engineers to logistics managers) that want to learn the particularities of data science algorithms in a stepwise, interactive manner. Through the proposed platform (a) students in the data science field or in related courses (e.g., machine learning, information retrieval) may get solutions for different types of exercises and focus on the details of each algorithm, (b) tutors (lecturers, lab assistants) may easily produce a wide variety of exercises with the accompanying solutions and use them in classroom or as an auxiliary tool for test correction, and (c) data practitioners may get valuable insights on popular data science algorithms. To the best of our knowledge, the proposed platform is the first educational tool that aims at the data science field, and has so far been warmly accepted by departments worldwide.","PeriodicalId":202521,"journal":{"name":"Proceedings of the 8th Computer Science Education Research Conference on ZZZ","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123040374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1