Detecting changes in student behavior from clickstream data

Park, Jihyun; Denaro, Kameryn; Rodrı́guez, Fernando; Smyth, Padhraic; Warschauer, Mark

doi:10.1145/3027385.3027430

Cited by 45 publications

(38 citation statements)

References 7 publications

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“…The first two example studies aggregated raw clickstream data into daily activity counts for each student and applied two different statistical models to summarize the temporal patterns of students' engagement to examine scheduling behavior. The first example study, Park et al (2017), focused on how student engagement changes over the length of a course. The authors used Poisson regression models to examine how active each student was at a daily level, relative to the class mean.…”

Section: Complications In Constructing Valid Measurement Using Clicksmentioning

confidence: 99%

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The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

Baker

Park

et al. 2020

Int J Educ Technol High Educ

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Student clickstream data-time-stamped records of click events in online courses-can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these data often require advanced analytic techniques, as they only provide a partial and noisy record of students' actions. Consequently, these data are not always accessible or useful for course instructors and administrators. In this paper, we provide an overview of the use of clickstream data to define and identify behavioral patterns that are related to student learning outcomes. Through discussions of four studies, we provide examples of the complexities and particular considerations of using these data to examine student self-regulated learning.

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Section: Complications In Constructing Valid Measurement Using Clicksmentioning

confidence: 99%

“…Dates of exams and Mondays are shown in thick dashed lines and solid lines, respectively. Figure based on data and analyses inPark, Denaro, Rodriguez, Smyth, and Warschauer (2017) Baker et al International Journal of Educational Technology in Higher Education (2020) 17:13…”

mentioning

confidence: 99%

The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

Baker

Park

et al. 2020

Int J Educ Technol High Educ

Self Cite

View full text Add to dashboard Cite

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“…Moreover, clickstream data has been applied in E-learning to improve the video lectures (Sinha, Jermann, Li, & Dillenbourg, 2014), as well as to detect and predict students' activities and performance so as to help instructors manage courses more effectively (Brinton & Chiang, 2015;J. Park, Denaro, Rodriguez, Smyth, & Warschauer, 2017).…”

Section: Clickstream Data Analysismentioning

confidence: 99%

A Clickstream Data Analysis of the Differences between Visiting Behaviors of Desktop and Mobile Users

Jiang

Yang

et al. 2018

Data and Information Management

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Mobile devices are gaining popularity among online shoppers whose behavior has been reshaped by the changes in screen size, interface, functionality, and context of use. This study, based on a log file from a cross-border E-commerce platform, conducted a clickstream data analysis to compare desktop and mobile users’ visiting behavior. The original 2,827,449 clickstream records generated over a 4-day period were cleaned and analyzed according to an established analysis framework at the footprint level. Differences are found between desktop and mobile users in the distribution of footprints, core footprints, and footprint depth. As the results show, online shoppers preferred to explore various products on mobile devices and read product details on desktops. The E-commerce mobile application (app) presented higher interactivity than the desktop and mobile websites, thus increasing both user involvement and product visibility. It enabled users to engage in the intended activities more effectively on the corresponding pages. Mobile users were further divided into iOS and Android users whose visiting behaviors were basically similar to each other, though the latter might experience slower response speed.

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“…One example is Park et al (2017), understanding student use of online classroom resources using features drawn from student clickstreams. These features focus on generalizing beyond the course content, focusing on simple frequency measures (e.g., number of clicks per day) and abstractions of how the content accessed relates to the course schedule, determining whether the content being accessed is being "previewed" or "reviewed. "…”

Section: Learning Analytics Of Student Activitymentioning

confidence: 99%

Using Learning Analytics to Understand Scientific Modeling in the Classroom

et al. 2017

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Scientific models represent ideas, processes, and phenomena by describing important components, characteristics, and interactions. Models are constructed across various scientific disciplines, such as the food web in biology, the water cycle in Earth science, or the structure of the solar system in astronomy. Models are central for scientists to understand phenomena, construct explanations, and communicate theories. Constructing and using models to explain scientific phenomena is also an essential practice in contemporary science classrooms. Our research explores new techniques for understanding scientific modeling and engagement with modeling practices. We work with students in secondary biology classrooms as they use a web-based software tool-EcoSurvey-to characterize organisms and their interrelationships found in their local ecosystem. We use learning analytics and machine learning techniques to answer the following questions:(1) How can we automatically measure the extent to which students' scientific models support complete explanations of phenomena? (2) How does the design of student modeling tools influence the complexity and completeness of students' models? (3) How do clickstreams reflect and differentiate student engagement with modeling practices? We analyzed EcoSurvey usage data collected from two different deployments with over 1,000 secondary students across a large urban school district. We observe large variations in the completeness and complexity of student models, and large variations in their iterative refinement processes. These differences reveal that certain key model features are highly predictive of other aspects of the model. We also observe large differences in student modeling practices across different classrooms and teachers. We can predict a student's teacher based on the observed modeling practices with a high degree of accuracy without significant tuning of the predictive model. These results highlight the value of this approach for extending our understanding of student engagement with scientific modeling, an important contemporary science practice, as well as the potential value of analytics for identifying critical differences in classroom implementation.

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Detecting changes in student behavior from clickstream data

Cited by 45 publications

References 7 publications

The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

A Clickstream Data Analysis of the Differences between Visiting Behaviors of Desktop and Mobile Users

Using Learning Analytics to Understand Scientific Modeling in the Classroom

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