A Web-based Platform for Online Programming Education
Guest Speaker: Alice Oh, KAIST School of Computer (Korea)
Host: Center for Networked Systems
In this seminar from the CNS Lecture Series, Korean computer scientist Alice Oh (KAIST) explores a web-based platform for online programming education.
In this talk, I present Elice, an online CS (computer science) education platform, and two sub-systems Elivate and Eliph. Elivate is a system for taking student learning data from Elice and inferring their progress through an educational taxonomy tailored for programming education. Elice captures detailed student learning activities, such as the intermediate revisions of code as students make progress toward completing their programming exercises. With those data, Elivate recognizes each student’s progression through an education taxonomy which organizes intermediate stages of learning such that the taxonomy can be used to evaluate student progress as well as to design and improve course materials and structure. With more than 240,000 intermediate source codes generated by 1,000 students, we demonstrate the practicality of the Elice and Elivate. With Eliph, we investigate the effectiveness of visualization of code history on peer assessment of code.
Peer assessment is found to be an effective learning tool for programming education. While many systems are proposed to support peer assessment in programming education, little effort has been devoted to finding ways to improve the peer assessment by assisting the students to under- stand the programs they are assessing. Eliph is a web-based peer assessment system for programming education with code history visualization. Eliph incorporates the visualization of character-level code history, selection-based history tracking and the integration of execution events to assist students in understanding programs written by peers, thereby leading to more effective peer assessment. We evaluate Eliph with an experiment in an undergraduate CS course. We show that visualization of code history has positive effects on promoting higher quality of peer feedback by understanding the intention and thought process.
Faculty Host: Christine Alvarado (email@example.com)
Alice Hae Yun Oh is an assistant professor in Computer Science with joint appointments in Graduate School of Culture Technology and Graduate School of Information Security. She was a Visiting Scholar at Harvard Center for Research on Computation and Society during the 2013-14 academic year. She conducts research in computational social science, broadly defined as studying individual and group social behaviors using computational tools applied to large-scale social media data. She is also interested in machine learning research and has published papers on novel topic modeling techniques for text, image, and Web data. Her academic services include Program Co-Chair for ICWSM 2014, Co-Organizer for ACL 2014 Workshop on Social Dynamics and Personal Attributes, Co-Chair of Publicity for WWW 2014, and (Senior) PC for ACL, WSDM, COLING, EMNLP, NIPS, and AAAI. Alice completed her M.S. in Language and Information Technologies at CMU and her Ph.D. in Computer Science at MIT.