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ERIC Number: EJ1334536
Record Type: Journal
Publication Date: 2021
Pages: 16
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1947-1017
EISSN: N/A
Available Date: N/A
Understanding Student Learning Pathways in Traditional Online History Courses: Utilizing Process Mining Analysis on Clickstream Data
Crosslin, Matt; Breuer, Kimberly; Milikic, Nikola; Dellinger, Justin T.
Journal of Research in Innovative Teaching & Learning, v14 n3 p399-414 2021
Purpose: This study explores ongoing research into self-mapped learning pathways that students utilize to move through a course when given two modalities to choose from: one that is instructor-led and one that is student-directed. Design/methodology/approach: Process mining analysis was utilized to examine and cluster clickstream data from an online college-level History course designed with dual modality choices. This paper examines some of the results from different approaches to clustering the available data. Findings: By examining how often students interacted with others, whether they were more internal or external facing with their pathway choices, and whether or not they completed a learning pathway, this study identified five general tactics from the data: Individualistic Internal; Non-completing Internal; Completing, Interactive Internal; Completing, Interactive, and Reflective and Completing External. Further analysis of when students used each tactic led to the identification of four different strategies that learners utilized during class sessions. Practical implications: The results of this analysis could potentially lead to the creation of customizable design models that can assist learners as they navigate modality choices in learner-centered or less-structured learning design methodologies. Originality/value: Few courses are designed to give the learners the options to follow the instructor or create their own learning pathway. Knowing how to identify what choices a learner might take in these scenarios is even less explored. Preliminary data for this paper was originally presented as a poster session at the Learning Analytics and Knowledge conference in 2019.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A