NDLI: Comparison of Time Metrics in Programming

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Comparison of Time Metrics in Programming

Content Provider	ACM Digital Library
Author	Hellas, Arto Leinonen, Juho Ihantola, Petri Leppänen, Leo
Abstract	Research on the indicators of student performance in introductory programming courses has traditionally focused on individual metrics and specific behaviors. These metrics include the amount of time and the quantity of steps such as code compilations, the number of completed assignments, and metrics that one cannot acquire from a programming environment. However, the differences in the predictive powers of different metrics and the cross-metric correlations are unclear, and thus there is no generally preferred metric of choice for examining time on task or effort in programming. In this work, we contribute to the stream of research on student time on task indicators through the analysis of a multi-source dataset that contains information about students' use of a programming environment, their use of the learning material as well as self-reported data on the amount of time that the students invested in the course and per-assignment perceptions on workload, educational value and difficulty. We compare and contrast metrics from the dataset with course performance. Our results indicate that traditionally used metrics from the same data source tend to form clusters that are highly correlated with each other, but correlate poorly with metrics from other data sources. Thus, researchers should utilize multiple data sources to gain a more accurate picture of students' learning.
Starting Page	200
Ending Page	208
Page Count	9
File Format	PDF
ISBN	9781450349680
DOI	10.1145/3105726.3106181
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Educational data mining Time on task Multi-source data analysis Academic success prediction Time metrics
Content Type	Text
Resource Type	Article

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