Abstract
PurposeThis 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/approachProcess 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.FindingsBy 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 implicationsThe 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/valueFew 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.
Highlights
Paper type Research paper© Matt Crosslin, Kimberly Breuer, Nikola Milikic and Justin T
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
Research question Based on the need to understand how students engage with SMLP, this study investigated one primary research question: (1) What patterns, clusters or characteristics of students’ pathway choices can be determined from process mining analysis of available clickstream data?
Summary
© Matt Crosslin, Kimberly Breuer, Nikola Milikic and Justin T. Other researchers noted the need to focus on self-regulated learning (Dawson et al, 2015) and understanding how learners moved through the course modality options Due to these results, along with feedback from course participants, the dual-layer concept was dropped in favor of focusing on how learners decide to map their own way through the learning choices. Some examples of this include: providing customized content and activities by clustering and using the K-Means algorithm to predict learners’ cognitive states (Troussas et al, 2020), personalizing learning pathways through data clustering (Iatrellis et al, 2020), examining temporal or sequential relationships of processes involved in self-regulated learning choices (Saint et al, 2021) and utilizing a sequence clustering algorithm to group students by similar learning pathway choices (Patel et al, 2017) These studies and others often focus on classifying, recommending and personalizing an instructor-led pathway that contains pre-selected learning resources (Ramirez-Arellano, 2019). This paper seeks to expand tinkering to include complete pathway control while following the work of others in the learning analytics field to classify and begin to understand these choices
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