Visualising Virtual Learning Environments: Case Studies of the Website Exploration Tool

Pascual-Cid, Victor; Vigentini, Lorenzo; Quixal, Martí

doi:10.1109/iv.2010.31

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…To evaluate the level of student's learning, the decision tree technique was used. The authors in [94] developed a system to explore and visualize generated data in virtual learning environments and analyzed these data using web-mining and statistical techniques to extract behavior patterns of the student. The authors in [95] grouped and analyzed access data in order to recognize behavior patterns (e.g.…”

Section: ) Behaviour Learningmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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Section: ) Behaviour Learningmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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“…Learning curves are also frequently used in HCI for off-line comparison and evaluation of alternative interfaces, including information visualization systems, e.g., [35,39,45]. In contrast, in this paper we use the concept of a learning curve for building predictive models that can identify in real-time a user's evolving proficiency with an information visualization system.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Users' Learning Curves for Adaptation while Using an Information Visualization

Lallé

Toker

Conati

et al. 2015

Proceedings of the 20th International Conference on Intelligent User Interfaces

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User performance and satisfaction when working with an interface is influenced by how quickly the user can acquire the skills necessary to work with the interface through practice. Learning curves are mathematical models that can represent a user's skill acquisition ability through parameters that describe the user's initial expertise as well as her learning rate. This information could be used by an interface to provide adaptive support to users who may otherwise be slow in learning the necessary skills. In this paper, we investigate the feasibility of predicting in real time a user's learning curve when working with ValueChart, an interactive visualization for decision making. Our models leverage various data sources (a user's gaze behavior, pupil dilation, cognitive abilities), and we show that they outperform a baseline that leverages only knowledge on user task performance so far. We also show that the best performing model achieves good accuracies in predicting users' learning curves even after observing users' performance only on a few tasks. These results are promising toward the design of user-adaptive visualizations that can dynamically support a user in acquiring the necessary skills to complete visual tasks.

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“…While it is not within the scope of this paper to argue for the correctness or fit of any of these theories, we focus on the fact that a typical method used in psychology for tracking how user performance improves with practice is by using a learning curve [23]. Learning curves are also frequently used in HCI to compare and evaluate the effectiveness of various systems, including information visualization systems (e.g., [28][22] [20]). In this paper, we leverage the concept of learning curve as a way to identify two broad stages of a user's skill acquisition, which we then use to evaluate the detection of a user's skill using eye-gaze data.…”

Section: System Expertise In Adaptive Systems and Skill Acquisitionmentioning

confidence: 99%

Towards facilitating user skill acquisition

Toker

Steichen

Gingerich

et al. 2014

Proceedings of the 19th International Conference on Intelligent User Interfaces

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A key challenge for information visualization designers lies in developing systems that best support users in terms of their individual abilities, needs, and preferences. However, most visualizations require users to first gather a certain set of skills before they can efficiently process the displayed information. This paper presents a first step towards designing visualizations that provide personalized support in order to ease the so-called 'learning curve' during a user's skill acquisition phase. We present prediction models, trained on users' gaze data, that can identify if users are still in the skill acquisition phase or if they have gained the necessary abilities. The paper first reveals that users exhibit the learning curve even during the usage of simple information visualizations, and then shows that we can generate reasonably accurate predictions about a user's skill acquisition using solely their eye gaze behavior.

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Visualising Virtual Learning Environments: Case Studies of the Website Exploration Tool

Cited by 6 publications

References 16 publications

Big Educational Data & Analytics: Survey, Architecture and Challenges

Big Educational Data & Analytics: Survey, Architecture and Challenges

Prediction of Users' Learning Curves for Adaptation while Using an Information Visualization

Towards facilitating user skill acquisition

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