Supporting learning analytics in computing education

Abstract

As is the case for many undergraduate STEM degree programs, computing degree programs are plagued by high attrition rates. This is especially true in early computing courses, in which failure and drop-out rates in the 35 to 50 percent range are common. By collecting learning process data as students engage in computer programming assignments, computing educators can place themselves in a position not only to better understand students' struggles, but also to better tailor instructional interventions to students' needs. We have developed OSBLE+, a learning management and analytics environment that interfaces with a computer programming environment to support the automatic collection of learners' programming process and social data as they work on programming assignments, while also providing an interactive environment for the analysis and visualization of those data. In ongoing work, we are using OSBLE+ to explore two possibilities: (a) leveraging learning and social data to strategically deliver automated learning interventions, and (b) presenting learners with visual representations of their learning data in order to prompt them to reflect on and discuss their learning processes.

DOI: 10.1145/3027385.3029472

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Cite this paper

@inproceedings{Olivares2017SupportingLA, title={Supporting learning analytics in computing education}, author={Daniel M. Olivares and Christopher D. Hundhausen}, booktitle={LAK}, year={2017} }