Enhancing Student Performance Prediction In CS1 Via In-Class CodingOnlineCC
This program is tentative and subject to change.
Computer science’s increased recognition as a prominent field of study has attracted students with diverse academic backgrounds. This has significantly increased the already high failure rates in introductory courses. To address this challenge, it is essential to identify struggling students early on. Incorporating in-class coding exercises in these courses not only offers additional practice opportunities to students but may also reveal their abilities and help teachers identify those in need of assistance. In this work, we seek to determine the extent to which the practice of using in-class coding exercises enhances the ability to predict student performance, especially early in the semester. Based on data obtained in a CS1 course taught at a mid-size American university, we found that in-class exercises could improve the prediction of students’ eventual performance. In particular, we found relatively accurately predictions as early as academic weeks 3 through 5, making it possible to devise early intervention strategies. This work can benefit future studies on the impact of in-class exercises as well as intervention strategies throughout the semester.
This program is tentative and subject to change.
Thu 27 FebDisplayed time zone: Eastern Time (US & Canada) change
15:45 - 17:00 | |||
15:45 18mTalk | Enhancing Student Performance Prediction In CS1 Via In-Class CodingOnlineCC Papers Eric Hicks University Of Memphis, Vinhthuy Phan The University of Memphis, Kriangsiri Malasri University of Memphis | ||
16:03 18mTalk | In-class Coding Exercises as a Mechanism to Inform Early Intervention in Programming CoursesOnlineCC Papers | ||
16:22 18mTalk | Needs-Supportive Teaching Interventions in an Intro Computer Science Course: Exploring Impacts on Student Motivation and AchievementOnlineGlobal Papers Jessica Hunter McGill University, Elena Bai McGill University, Giulia Alberini McGill University, Kristy Robinson McGill University | ||
16:41 18mTalk | Programming Self-Efficacy in CS: Adding Four Areas of Validity to the Steinhorst InstrumentOnline Papers Gozde Cetin Uzun Georgia State University, Lauren Margulieux Georgia State University, Yin-Chan Liao Georgia State University |