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PhD Final Exam – Beatrice Moissinac

Computing Resource Usage as Factors of Student Success

Learning Analytics and other branches of Educational Research such as Computing Education Research (CER) implicitly assumes that students, especially college students, have no barrier to access a learning platform or software package. This assumption creates beliefs such as “everyone has a device”, or “everyone can access internet”. However, if students don’t use a laptop or other computing resources (i.e., phones, tablets, computer labs, loaner laptops, printers) as we expect them to, studies are missing a critical element to validate their findings. Consequently, Learning Analytics often overlook the usage of computing resources as a factor of student success. Explicitly investigating whether undergraduate student usage of laptops and computer labs are factors of academic success closes a crucial feedback loop for Learning Analytics.

In this thesis, we develop a method using operational data sets from a university to provide observable intensity of student usage. Leveraging unsupervised clustering, we extract student behaviors. This method also addresses oversights from traditional Learning Analytics studies, which lack systematic and continuous observation of the students.

The observed behaviors are triangulated by two studies: (1) we administer a survey to collect students perceptions and motivations for using computing resources, and compare observed vs. reported behaviors; and (2) we introduce a taxonomy of extrinsic parameters to differentiate behavioral patterns caused by external environmental factors vs. students inner motivation.

Finally, we use Structural Equation Model to model the intensity of usage of computing resources as a factor of student success. Results indicate that observed intensity of usage significantly impacts academic success for CS students.

Co Advisor: Robin Pappas
Co Advisor: Jennifer Parham-Mocello
Committee: Glencora Borradaile
Committee: Margaret Burnett
Committee: Prasad Tadepalli
GCR: Katy Schilke

Friday, October 11, 2019 at 1:00pm to 3:00pm

Bexell Hall, 321
2251 SW Campus Way, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

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