4,896 research outputs found
Recognizing Multidimensional Engagement of E-learners Based on Multi-channel Data in E-learning Environment
Despite recent advances in MOOC, the current e-learning systems have
advantages of alleviating barriers by time differences, and geographically
spatial separation between teachers and students. However, there has been a
'lack of supervision' problem that e-learner's learning unit state(LUS) can't
be supervised automatically. In this paper, we present a fusion framework
considering three channel data sources: 1) videos/images from a camera, 2) eye
movement information tracked by a low solution eye tracker and 3) mouse
movement. Based on these data modalities, we propose a novel approach of
multi-channel data fusion to explore the learning unit state recognition. We
also propose a method to build a learning state recognition model to avoid
manually labeling image data. The experiments were carried on our designed
online learning prototype system, and we choose CART, Random Forest and GBDT
regression model to predict e-learner's learning state. The results show that
multi-channel data fusion model have a better recognition performance in
comparison with single channel model. In addition, a best recognition
performance can be reached when image, eye movement and mouse movement features
are fused.Comment: 4 pages, 4 figures, 2 table
Exploring study profiles of Computer Science students with Social Network Analysis
Information technology is widely adapted in all levels of education. The extensive information resources facilitate enhanced human capacity and the social environment to support learning. In particular, Social Network Analysis (SNA) has been broadly used in teaching and learning practices. In this paper, we perform community detection analysis to identify the learning behavior profiles of undergraduate computer science students in a Nordic university. The social network was created using 273 responses to an online survey. The students themselves provided their social connections at the university, and node attributes were created based on responses to questions regarding Educational Values, Goals Orientation, Self-efficacy, and the university teaching methods. We analyze the biggest communities to identify the factors that characterize the learning strategy and preferences of undergraduate computer science students
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
Educational Data Mining (EDM) has emerged as a vital field of research, which
harnesses the power of computational techniques to analyze educational data.
With the increasing complexity and diversity of educational data, Deep Learning
techniques have shown significant advantages in addressing the challenges
associated with analyzing and modeling this data. This survey aims to
systematically review the state-of-the-art in EDM with Deep Learning. We begin
by providing a brief introduction to EDM and Deep Learning, highlighting their
relevance in the context of modern education. Next, we present a detailed
review of Deep Learning techniques applied in four typical educational
scenarios, including knowledge tracing, undesirable student detecting,
performance prediction, and personalized recommendation. Furthermore, a
comprehensive overview of public datasets and processing tools for EDM is
provided. Finally, we point out emerging trends and future directions in this
research area.Comment: 21 pages, 5 figure
The role of learning theory in multimodal learning analytics
This study presents the outcomes of a semi-systematic
literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on
previous systematic literature reviews in MMLA and
an additional new search, 35MMLA works were identified that use theory. The results show that MMLA
studies do not always discuss their findings within
an established theoretical framework. Most of the
theory-driven MMLA studies are positioned in the
cognitive and affective domains, and the three most
frequently used theories are embodied cognition,
cognitive load theory and controlâvalue theory of
achievement emotions. Often, the theories are only
used to inform the study design, but there is a relationship between the most frequently used theories
and the data modalities used to operationalize those
theories. Although studies such as these are rare, the
findings indicate that MMLA affordances can, indeed,
lead to theoretical contributions to learning sciences.
In this work, we discuss methods of accelerating
theory-driven MMLA research and how this acceleration can extend or even create new theoretical
knowledge
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
Data Visualization in Online Educational Research
This chapter presents a general and practical guideline that is intended to introduce the traditional visualization methods (word clouds), and the advanced visualization methods including interactive visualization (heatmap matrix) and dynamic visualization (dashboard), which can be applied in quantitative, qualitative, and mixed-methods research. This chapter also presents the potentials of each visualization method for assisting researchers in choosing the most appropriate one in the web-based research study. Graduate students, educational researchers, and practitioners can contribute to take strengths from each visual analytical method to enhance the reach of significant research findings into the public sphere. By leveraging the novel visualization techniques used in the web-based research study, while staying true to the analytical methods of research design, graduate students, educational researchers, and practitioners will gain a broader understanding of big data and analytics for data use and representation in the field of education
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