Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance
Alireza Ahadi, Raymond Lister, Heikki Haapala, Arto Vihavainen
Zu finden in: ICER 2015 (Seite 121 bis 130), 2015
Methods for automatically identifying students in need of assistance have been studied for decades. Initially, the work was based on somewhat static factors such as students' educational background and results from various questionnaires, while more recently, constantly accumulating data such as progress with course assignments and behavior in lectures has gained attention. We contribute to this work with results on early detection of students in need of assistance, and provide a starting point for using machine learning techniques on naturally accumulating programming process data.
When combining source code snapshot data that is recorded from students' programming process with machine learning methods, we are able to detect high- and low-performing students with high accuracy already after the very first week of an introductory programming course. Comparison of our results to the prominent methods for predicting students' performance using source code snapshot data is also provided.
This early information on students' performance is beneficial from multiple viewpoints. Instructors can target their guidance to struggling students early on, and provide more challenging assignments for high-performing students. Moreover, students that perform poorly in the introductory programming course, but who nevertheless pass, can be monitored more closely in their future studies.
Dieses Konferenz-Paper erwähnt...
- ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research, ICER 2016, Melbourne, VIC, Australia, September 8-12, 2016 (Judy Sheard, Josh Tenenberg, Donald Chinn, Brian Dorn) (2016)
- With a Little Help From My Friends - An Empirical Study of the Interplay of Students' Social Activities, Programming Activities, and Course Success (Adam S. Carter, Christopher D. Hundhausen) (2016)
Volltext dieses Dokuments
|Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance: Fulltext at the ACM Digital Library (: , 738 kByte; : 2018-09-11)|
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