Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior

AbstractThere is a lack of clarity in information systems research on which factors lead people to use or not use technologies of varying degrees of perceived legality. To address this gap, we use arguments from the information systems and political ideology literatures to theorize on the influence of individuals’ political ideologies on online media piracy. Specifically, we hypothesize that individuals with a more conservative ideology, and thus lower openness to experience and higher conscientiousness, generally engage in less online media piracy. We further hypothesize that this effect is stronger for online piracy technology that is legally ambiguous. Using clickstream data from 3873 individuals in the U.S., we find that this effect in fact exists only for online media piracy technologies that are perceived as legally ambiguous. Specifically, more conservative individuals, who typically have lower ambiguity intolerance, use (legal but ambiguously perceived) pirated streaming websites less, while there is no difference for the (clearly illegal) use of pirated file sharing websites.

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Understanding Learning Analytics Indicators for Predicting Study Success

10.14742/ascilite2020.0101 ◽

2020 ◽

Author(s):

Dirk Ifenthaler ◽

Jane Yin-Kim Yau

Keyword(s):

Learning Analytics ◽

Education Institution ◽

Sociodemographic Factors ◽

Cognitive Capacity ◽

Study Success ◽

Related Factors ◽

Clickstream Data ◽

Learning History ◽

Learning Journey ◽

Key Indicators

Common factors, which are related to study success include students’ sociodemographic factors, cognitive capacity, or prior academic performance, and individual attributes as well as course related factors such as active learning and attention or environmental factors related to supportive academic and social embeddedness. The aim of this research is to gain a deeper understanding of not only if learning analytics can support study success, but which aspects of a learner’s learning journey can benefit from the utilisation of learning analytics. We, therefore, examined different learning analytics indicators to show which aspect of the learning journey they were successfully supporting. Key indicators may include GPA, learning history, and clickstream data. Depending on the type of higher education institution, and the mode of education (face-to-face and/or distance), the chosen indicators may be different due to them having different importance in predicting the learning out-comes and study success.

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Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data

Journal of Research in Innovative Teaching & Learning ◽

10.1108/jrit-03-2021-0024 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Matt Crosslin ◽

Kimberly Breuer ◽

Nikola Milikić ◽

Justin T. Dellinger

Keyword(s):

Poster Session ◽

Process Mining ◽

Structured Learning ◽

College Level ◽

Ongoing Research ◽

Clickstream Data ◽

Content Type ◽

Learner Centered ◽

Learning Pathways ◽

Practical Implications

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.

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