Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107776
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Type: Conference paper
Title: Applying validated pedagogy to MOOCs: an introductory programming course with media computation
Author: Falkner, K.
Falkner, N.
Szabo, C.
Vivian, R.
Citation: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, 2016 / vol.11-13-July-2016, pp.326-331
Publisher: ACM New York
Issue Date: 2016
ISBN: 9781450342315
ISSN: 1942-647X
Conference Name: 2016 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '16) (11 Jul 2016 - 13 Jul 2016 : Arequipa, Peru)
Statement of
Responsibility: 
Katrina Falkner, Nickolas Falkner, Claudia Szabo, Rebecca Vivian
Abstract: Significant advances have been made in the learning and teaching of Introductory Programming, including the integration of active and contextualised learning pedagogy. However, Massively Open Online Courses (MOOCs), where Computer Science and, more specifically, introductory programming courses dominate, do not typically adopt such pedagogies or lessons learned from more traditional learning environments. Moreover, the improvement of learning within the MOOC context in terms of discipline-specific pedagogy, and the improvement of student learning outcomes and processes have not been studied in depth. This paper reports findings from a foundation programming skills MOOC that supports the learning of fundamental Computer Science concepts and the development of programming skills through a media computation approach, based upon digital artworks and animations. In this paper, we explore the course activity data as well as a sample of students' source code submissions to investigate their engagement with the course and the quality and development of their programming skill over the six weeks of the course duration.
Keywords: Online learning, Massively Open Online Course, MOOC, introductory programming, CS101
Rights: Copyright is held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.
RMID: 0030051668
DOI: 10.1145/2899415.2899429
Appears in Collections:Computer Science publications

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