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020 ▼a 9780438017993
035 ▼a (MiAaPQ)AAI10791835
035 ▼a (MiAaPQ)purdue:22513
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 371
1001 ▼a Qian, Yizhou.
24510 ▼a Understanding and Addressing Misconceptions in Introductory Programming: A Data-Driven Approach.
260 ▼a [S.l.] : ▼b Purdue University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 116 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: A.
500 ▼a Adviser: James D. Lehman.
5021 ▼a Thesis (Ph.D.)--Purdue University, 2018.
520 ▼a With the expansion of computer science (CS) education, CS teachers in K-12 schools should be cognizant of student misconceptions and be prepared to help students establish accurate understanding of computer science and programming. This explorat
520 ▼a Using students' erroneous solutions, 55 distinct compilation errors were identified, and 15 of them were categorized as common ones. The 15 common compilation errors accounted for 92% of all compilation errors. Based on the 15 common compilation
520 ▼a Both quantitative and qualitative data analysis were conducted to see whether and how the targeted feedback affected students' solutions. Quantitative analysis indicated that targeted feedback messages enhanced students' rates of improving erron
520 ▼a The results of this study suggest that a data-driven approach to understanding and addressing student misconceptions, which is using student data in automated assessment systems, has the potential to improve students' learning of programming and
590 ▼a School code: 0183.
650 4 ▼a Educational technology.
650 4 ▼a Computer science.
690 ▼a 0710
690 ▼a 0984
71020 ▼a Purdue University. ▼b Curriculum and Instruction.
7730 ▼t Dissertation Abstracts International ▼g 79-10A(E).
773 ▼t Dissertation Abstract International
790 ▼a 0183
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997664 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자