TY - GEN
T1 - Using Association Rule Mining Algorithm to Improve the Order of Content Delivery in CS1 Course
AU - Kaur, Rupinder
AU - Brown, Tamaike
AU - Walia, Gursimran
AU - Singh, Maninder
AU - Reddy, Mourya
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported in part by the National Science Foundation under grant DUE-1525414. Any opinions, finding, and conclusions or recommendations expressed in this material are those of author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - This work in progress research paper discusses importance of appropriate order of content delivery in an introductory programming course (CS1). A majority of students face problems when programming concepts are introduced to them in disorderly fashion, reducing their retention. It has been observed that the instructors use their intrinsic judgement to order course contents without considering effective student learning outcome. This traditional approach of teaching a course in an unstructured way leads to high failure and dropout rate in CS1 course. In this study, an association rule mining (ARM) based approach is being used to understand the order of information that students should be exposed to in CS1 courses. Our proposed approach follows an empirical research methodology and generates strong ARM rules. These rules can assist instructors to structure CS1 topics and reflect on required prerequisites for problematic topics to improve students' learning. Our initial results and observations yielded promising and logical inferences and uncovered few anomalies with traditional pedagogy during course offering.
AB - This work in progress research paper discusses importance of appropriate order of content delivery in an introductory programming course (CS1). A majority of students face problems when programming concepts are introduced to them in disorderly fashion, reducing their retention. It has been observed that the instructors use their intrinsic judgement to order course contents without considering effective student learning outcome. This traditional approach of teaching a course in an unstructured way leads to high failure and dropout rate in CS1 course. In this study, an association rule mining (ARM) based approach is being used to understand the order of information that students should be exposed to in CS1 courses. Our proposed approach follows an empirical research methodology and generates strong ARM rules. These rules can assist instructors to structure CS1 topics and reflect on required prerequisites for problematic topics to improve students' learning. Our initial results and observations yielded promising and logical inferences and uncovered few anomalies with traditional pedagogy during course offering.
KW - Association rule mining
KW - computer education
KW - introductory programming
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85082443580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082443580&partnerID=8YFLogxK
U2 - 10.1109/FIE43999.2019.9028452
DO - 10.1109/FIE43999.2019.9028452
M3 - Conference contribution
AN - SCOPUS:85082443580
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2019 IEEE Frontiers in Education Conference, FIE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 49th IEEE Frontiers in Education Conference, FIE 2019
Y2 - 16 October 2019 through 19 October 2019
ER -