CCVis: Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Data

Goulden, Maggie Celeste; Gronda, Eric; Yang, Yurou; Zhang, Zihang; Tao, Jun; Wang, Chaoli; Duan, Xiaojing; Ambrose, Gavin; Abbott, Kevin; Miller, Patrick

doi:10.2352/issn.2470-1173.2019.1.vda-681

Cited by 21 publications

(13 citation statements)

References 18 publications

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“…Some learning analytics dashboards, for example SAM, are based on the CCV is tool to provide visualization of the course progress to both teachers and students [43]. It allows teachers to easily study student behavior patterns and identify the course resources that are most or least often clicked.…”

Section: Discussionmentioning

confidence: 99%

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

Zotov

Ibrahim

Petunina

et al. 2021

Int. J. Emerg. Technol. Learn.

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The study describes an approach to e-learning based on the Moodle platform that is used to visualize participation in the learning community and is proposed to be used to inform students and teachers about their involvement in the social learn-ing environment. The experiment involved 5 teachers and 3 experts who deter-mined the most significant visualization indicators for the virtual learning envi-ronment dashboards. There were 42 students aged 21 to 23. The virtual learning environment is based on the Moodle and Blackboard platforms that are common-ly used in universities. SocialWall allowed participants to perform actions in the social environment that are visualized in graphs under the specified criteria. A Wiki repository plugin was also added in order to accumulate student knowledge in shared structured documents stored in a shared repository. The relational data-base management system MySQL allows creation of additional relations, data-base design and administration. The visualization activities described in the study are based on modified state transition networks to analyze and visualize the stu-dent learning path. Student trajectory networks show the interaction of individual learners or groups with the course structure and material.

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Section: Discussionmentioning

confidence: 99%

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

Zotov

Ibrahim

Petunina

et al. 2021

Int. J. Emerg. Technol. Learn.

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“…The increasing availability of such event sequence data permits analysts to extract valuable insights in website design and commercial activities such as advertising. Existing visual techniques have been introduced to explore frequent visiting traces [60], [61], [124] and user behavior patterns [10], [26], [32], [39], [68].…”

Section: E-commercementioning

confidence: 99%

“…Analyzing clickstream data can help e-commerce companies explore users behavior and optimize their business plans. This idea has been extended to online education platforms with the goal to explore student learning behaviors [10], [32], [39], [68]. For instance, PeakVizor [10] analyzes students' interaction activities to understand how students respond to video material.…”

Section: E-commercementioning

confidence: 99%

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Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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“…The literature has shown significant interest in specific learner behavior patterns through the click patterns produced by the learner during online learning sessions. Within such studies, the click patterns have been examined in terms of video streaming [5], [6], or position of clicks [7] [8]. However, numerous patterns could be exploited from the clicks.…”

Section: Introductionmentioning

confidence: 99%

Clickstream Data Schema for Learning Analytics to Understand Learner Behaviour

2020

IJATCSE

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Learning analytics has caught numerous researchers' attention in the last decade due to its ability to analyze, report, and process information produced by learners. Several research studies have examined learning analytics in terms of learner behavior to provide quality and efficient reporting for educators and higher learning institutions. In particular, the click pattern of the learner has been examined in terms of its significant impact on determining learner performance. Yet, the clickstream data schema that has been addressed in the literature was limited to click patterns focusing on event-based click patterns and time in the logging history. However, learner interaction logging history available in clickstream data schema for major online learning management systems holds great potential to reveal diverse and complex learner behavior patterns based on various significant learner behavior elements that are waiting to be discovered. The literature has shown that great interest has been given in understanding specific behavior produced by the learner through an online learning management system. Thus, this study aims to review previous related studies that could reveal possible learner click patterns, which could be considered as an indicator to predict the learner's performance and behavior. Potential learner interaction data that could be applied to produce a clickstream data stream has been outlined. As an implication, the contribution of the study could open up new frontiers that trigger the development of a learning analytics dashboard for various online learning platforms.

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CCVis: Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Data

Cited by 21 publications

References 18 publications

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

Visual Progression Analysis of Event Sequence Data

Clickstream Data Schema for Learning Analytics to Understand Learner Behaviour

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