Année : 2014
Lieu de publication de l'article :

Résumé de l'article

Informed by cognitive theories of learning, this work examinedhow students’ self-reported study patterns (spacing vs. cramming)corresponded to their engagement with the Learning ManagementSystem (LMS) across two years in a large biology course. We specif-ically focused on how students accessed non-mandatory resources(lecture videos, lecture slides) and considered whether this patterndiffered by underrepresented minority (URM) status. Overall, stu-dents who self-reported utilizing spacing strategies throughout thecourse had higher grades than students who reported crammingthroughout the course. When examining LMS engagement, only asmall percentage of students accessed the lecture videos and lec-ture slides. Applying a negative binomial regression model to dailycounts of click activities, we also found that students who utilizedspacing strategies accessed LMS resources more often but not ear-lier before major deadlines. Moreover, this finding was not differentfor underrepresented students. Our results provide some initialevidence showing how spacing behaviors correspond to accessinglearning resources. However, given the lack of general engagementwith LMS resources, our results underscore the value of encour-aging students to utilize these resources when studying coursematerial.CCS CONCEPTS• Applied computing → Learning management systems; E-learning;KEYWORDSStudy Skills; Spacing Effect; Learning Analytics; Higher Education;STEM Education; Underrepresented StudentsACM Reference Format:Fernando Rodriguez, Renzhe Yu, Jihyun Park, Mariela Janet Rivas, MarkWarschauer, and Brian K. Sato. 2019. Utilizing Learning Analytics to MapStudents’ Self-Reported Study Strategies to Click Behaviors in STEMCourses.In The 9th International Learning Analytics & Knowledge Conference (LAK19),Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.LAK19, March 4–8, 2019, Tempe, AZ, USA© 2019 Association for Computing Machinery.ACM ISBN 978-1-4503-6256-6/19/03.https://doi.org/10.1145/3303772.3303841March 4–8, 2019, Tempe, AZ, USA. ACM, New York, NY, USA, 5 pages.https://doi.org/10.1145/3303772.3303841

Mots-clés

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