Tampere University of Technology

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Comparison of time metrics in programming

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationICER 2017 - Proceedings of the 2017 ACM Conference on International Computing Education Research
PublisherACM
Pages200-208
Number of pages9
ISBN (Electronic)9781450349680
DOIs
Publication statusPublished - 14 Aug 2017
Publication typeA4 Article in a conference publication
EventINTERNATIONAL COMPUTING EDUCATION RESEARCH CONFERENCE -
Duration: 1 Jan 1900 → …

Conference

ConferenceINTERNATIONAL COMPUTING EDUCATION RESEARCH CONFERENCE
Period1/01/00 → …

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.