Visual Analysis of MOOC Forums with iForum

Artificial Intelligence-Based Medical Data Mining

Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

Magnetic Field Visualization Teaching Based on Fusion Method of Finite Element and Neural Network

We developed a visual teaching platform that can calculate the magnetic field of magnetic core inductance in real time. The platform adopts the combination of two theories of finite element calculation and neural network technology. It can enhance students’ understanding and application of the basic knowledge of electromagnetic fields. First, the finite element method was used to calculate the magnetic field of the magnetic core inductance, and the magnetic field data set under different input parameters was obtained. On this basis, the neural network method was used to learn the data set and train the corresponding model. Then the trained neural network model was used to calculate the magnetic core inductance magnetic field. After comparing with the finite element calculation results, we found that the calculation results of the neural network model combined with the finite element were in good agreement with the finite element calculation results. Compared with the finite element calculation method, the calculation speed of the magnetic field distribution calculated by the neural network was faster. Taking the calculation of the magnetic core inductance magnetic field as an example, the calculation time was shortened by about 170 times. Finally, we built a magnetic field visualization teaching platform based on MATLAB. The example magnetic field was quickly predicted by the neural network, saving computing time and effectively improving the teaching of electromagnetic field theory.

Role-Aware Information Spread in Online Social Networks

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

Passing the Data Baton : A Retrospective Analysis on Data Science Work and Workers

Data science is a rapidly growing discipline and organizations increasingly depend on data science work. Yet the ambiguity around data science, what it is, and who data scientists are can make it difficult for visualization researchers to identify impactful research trajectories. We have conducted a retrospective analysis of data science work and workers as described within the data visualization, human computer interaction, and data science literature. From this analysis we synthesis a comprehensive model that describes data science work and breakdown to data scientists into nine distinct roles. We summarise and reflect on the role that visualization has throughout data science work and the varied needs of data scientists themselves for tooling support. Our findings are intended to arm visualization researchers with a more concrete framing of data science with the hope that it will help them surface innovative opportunities for impacting data science work. Data availability: https://osf.io/z2xpd/?view_only=87fa24be486a473884adb9ffbe8db4ec.

An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion Forum

MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners’ grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.

Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Malaysia and many other developing countries progressively adopting massively open online course (MOOC) in their national higher education approach. We have observed an increasing need for facilitating MOOC monitoring that is associated with the rising adoption of MOOCs. Our observation suggests that recent adoption cases led analyst and instructors to focus on monitoring enrolment and learning activities. Visual analytics in MOOC support education analysts in analyzing MOOC data via interactive visualization. Existing literature on MOOC visualization focuses on enabling visual analysis on MOOC data from forum and course material. We found limited studies that investigate and characterize domain problems or design requirements of visual analytics for MOOC. This paper aims to present the empirical problem characterization and abstraction for visual analytics in MOOC learner's support monitoring. Detailed characterization and abstraction of the domain problem help visualization designer to derive design requirements in generating appropriate visualization solution. We examined the literature and conducted a case study to elicit a problem abstraction based on data, users, and tasks. We interviewed five Malaysian MOOC experts from three higher education institutes using semi-structured questions. Our case study reveals the priority of enabling MOOC analysis on learner's progression and course completion. There is an association between design and analysis priority with the pedagogical type of implemented MOOC and users. The characterized domain problems and requirements offer a design foundation for visual analytics in MOOC monitoring analysis.

Bridging Text Visualization and Mining: A Task-Driven Survey

Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.

VisForum

User grouping in asynchronous online forums is a common phenomenon nowadays. People with similar backgrounds or shared interests like to get together in group discussions. As tens of thousands of archived conversational posts accumulate, challenges emerge for forum administrators and analysts to effectively explore user groups in large-volume threads and gain meaningful insights into the hierarchical discussions. Identifying and comparing groups in discussion threads are nontrivial, since the number of users and posts increases with time and noises may hamper the detection of user groups. Researchers in data mining fields have proposed a large body of algorithms to explore user grouping. However, the mining result is not intuitive to understand and difficult for users to explore the details. To address these issues, we present VisForum, a visual analytic system allowing people to interactively explore user groups in a forum. We work closely with two educators who have released courses in Massive Open Online Courses (MOOC) platforms to compile a list of design goals to guide our design. Then, we design and implement a multi-coordinated interface as well as several novel glyphs, i.e., group glyph, user glyph, and set glyph, with different granularities. Accordingly, we propose the group Detecting 8 Sorting Algorithm to reduce noises in a collection of posts, and employ the concept of “forum-index” for users to identify high-impact forum members. Two case studies using real-world datasets demonstrate the usefulness of the system and the effectiveness of novel glyph designs. Furthermore, we conduct an in-lab user study to present the usability of VisForum.