Applying educational data mining to explore individual experiences in digital games

Research involving digital games and language learning is rapidly growing. One advantage of using digital games to support language learning is the ability to collect data on students learning in real time. In this study, we use educational data mining methods to explore the relationship between in-game data and elementary students’ Chinese language learning. Thirty-six students in the sixth grade played a digital game for eight 25-minute sessions as part of their Chinese Dual Language Immersion classroom instruction. We used classification and regression tree analyses and cluster analyses to explore how in- game indicators, such as battles, time spent reading a text, and the use of an in-game glossing tool are associated with language learning and change in affect. The results indicate that time on task and use of the glossing tool were the most important variables in determining language learning gains. We also identified four subgroups of gameplay styles. While there were no significant differences in learning or affective factors based on the subgroups, these gameplay styles allow for a more individualized approach to analyzing learning within digital environment

Evidence-Based Assessment of Student Performance in Virtual Worlds

Virtual Worlds (VWs) are popular tools for teaching/learning in the twenty-first century classroom. The challenge remains however, to provide the means by which teachers could sustainably analyse and assess the performance of large groups of students in such environments. Unfortunately, external game features such as game scores and play duration have turned out to be unfair in some assessments. In this context, a case study was carried out in a foreign language course, illustrating how teachers could easily retrieve a number of performance indicators from VW-interaction logs and harness them to conduct a fine-grained analysis of students' performance, while facilitating at the same time valuable tools for their assessment. Objective performance indicators in a server database were made accessible using an end-user development programming language. This way, a range of data visualisation methods could be employed to contrast different assumptions regarding learner performance when playing a VW-based game, which was designed to help CEFR A1 level students to learn German. This way, factors such as randomisation of game tasks, which could negatively affect learner performance, were alleviated

Visual Analytics for the Exploratory Analysis and Labeling of Cultural Data

Cultural data can come in various forms and modalities, such as text traditions, artworks, music, crafted objects, or even as intangible heritage such as biographies of people, performing arts, cultural customs and rites. The assignment of metadata to such cultural heritage objects is an important task that people working in galleries, libraries, archives, and museums (GLAM) do on a daily basis. These rich metadata collections are used to categorize, structure, and study collections, but can also be used to apply computational methods. Such computational methods are in the focus of Computational and Digital Humanities projects and research. For the longest time, the digital humanities community has focused on textual corpora, including text mining, and other natural language processing techniques. Although some disciplines of the humanities, such as art history and archaeology have a long history of using visualizations. In recent years, the digital humanities community has started to shift the focus to include other modalities, such as audio-visual data. In turn, methods in machine learning and computer vision have been proposed for the specificities of such corpora. Over the last decade, the visualization community has engaged in several collaborations with the digital humanities, often with a focus on exploratory or comparative analysis of the data at hand. This includes both methods and systems that support classical Close Reading of the material and Distant Reading methods that give an overview of larger collections, as well as methods in between, such as Meso Reading. Furthermore, a wider application of machine learning methods can be observed on cultural heritage collections. But they are rarely applied together with visualizations to allow for further perspectives on the collections in a visual analytics or human-in-the-loop setting. Visual analytics can help in the decision-making process by guiding domain experts through the collection of interest. However, state-of-the-art supervised machine learning methods are often not applicable to the collection of interest due to missing ground truth. One form of ground truth are class labels, e.g., of entities depicted in an image collection, assigned to the individual images. Labeling all objects in a collection is an arduous task when performed manually, because cultural heritage collections contain a wide variety of different objects with plenty of details. A problem that arises with these collections curated in different institutions is that not always a specific standard is followed, so the vocabulary used can drift apart from another, making it difficult to combine the data from these institutions for large-scale analysis. This thesis presents a series of projects that combine machine learning methods with interactive visualizations for the exploratory analysis and labeling of cultural data. First, we define cultural data with regard to heritage and contemporary data, then we look at the state-of-the-art of existing visualization, computer vision, and visual analytics methods and projects focusing on cultural data collections. After this, we present the problems addressed in this thesis and their solutions, starting with a series of visualizations to explore different facets of rap lyrics and rap artists with a focus on text reuse. Next, we engage in a more complex case of text reuse, the collation of medieval vernacular text editions. For this, a human-in-the-loop process is presented that applies word embeddings and interactive visualizations to perform textual alignments on under-resourced languages supported by labeling of the relations between lines and the relations between words. We then switch the focus from textual data to another modality of cultural data by presenting a Virtual Museum that combines interactive visualizations and computer vision in order to explore a collection of artworks. With the lessons learned from the previous projects, we engage in the labeling and analysis of medieval illuminated manuscripts and so combine some of the machine learning methods and visualizations that were used for textual data with computer vision methods. Finally, we give reflections on the interdisciplinary projects and the lessons learned, before we discuss existing challenges when working with cultural heritage data from the computer science perspective to outline potential research directions for machine learning and visual analytics of cultural heritage data

Information Outlook, May/June 2013

Volume 17, Issue 3https://scholarworks.sjsu.edu/sla_io_2013/1002/thumbnail.jp

Teaching Sciences in Virtual Worlds with Mastery Learning: A Case of Study in Elementary School

Virtual worlds are 3D environments that provide a feeling of immersion and a high degree of interaction, collaboration, communication between users. Its applicability can be focused on the educational scope, in which theories can be integrated as the basis to didactic activities carried out in the 3D environment, being its area of interdisciplinary comprehension. In this context, this article presents the use of a Virtual World built to assist in the teaching of Science for students of the middle school, whose articulation of the activities performed in the course are based on the precepts of the educational theory Mastery Learning. Tests were carried out in the subject of science, being divided into two periods with different groups for comparative purposes and realized evaluations during the period of the experiments. Kruskal-Wallis and Wilcoxon-Mann-Whitney non-parametric test were applied to the results of the assessments to ascertain the performance of each group. It was verified in the general analyzis that the participants who used the Virtual World had a growing performance, with high medians and adequate distribution of the results, being predominant of a smaller variability and amplitude. Thus, was possible to conclude that the results obtained with the approach were positive, which led to the validation of this research and presented a clear contribution to the academic environment

Workshop, Long and Short Paper, and Poster Proceedings from the Fourth Immersive Learning Research Network Conference (iLRN 2018 Montana), 2018.

ILRN 2018 - Conferência internacional realizada em Montana de 24-29 de june de 2018.Workshop, short paper, and long paper proceedingsinfo:eu-repo/semantics/publishedVersio

Multiple Views: different meanings and collocated words

We report on an in‐depth corpus linguistic study on ‘multiple views’ terminology and word collocation. We take a broad interpretation of these terms, and explore the meaning and diversity of their use in visualisation literature. First we explore senses of the term ‘multiple views’ (e.g., ‘multiple views’ can mean juxtaposition, many viewport projections or several alternative opinions). Second, we investigate term popularity and frequency of occurrences, investigating usage of ‘multiple’ and ‘view’ (e.g., multiple views, multiple visualisations, multiple sets). Third, we investigate word collocations and terms that have a similar sense (e.g., multiple views, side‐by‐side, small multiples). We built and used several corpora, including a 6‐million‐word corpus of all IEEE Visualisation conference articles published in IEEE Transactions on Visualisation and Computer Graphics 2012 to 2017. We draw on our substantial experience from early work in coordinated and multiple views, and with collocation analysis develop several lists of terms. This research provides insight into term use, a reference for novice and expert authors in visualisation, and contributes a taxonomy of ‘multiple view’ terms

VRIA: A Web-based Framework for Creating Immersive Analytics Experiences

We present, a Web-based framework for creating Immersive Analytics (IA) experiences in Virtual Reality.is built upon WebVR, A-Frame, React and D3.js, and offers a visualization creation workflow which enables users, of different levels of expertise, to rapidly develop Immersive Analytics experiences for the Web. The use of these open-standards Web-based technologies allows us to implement VR experiences in a browser and offers strong synergies with popular visualization libraries, through the HTMLDocument Object Model (DOM). This makesubiquitous and platform-independent. Moreover, by using WebVR’s progressive enhancement, the experiencescreates are accessible on a plethora of devices. We elaborate on our motivation for focusing on open-standards Web technologies, present thecreation workflow and detail the underlying mechanics of our framework. We also report on techniques and optimizations necessary for implementing Immersive Analytics experiences on the Web, discuss scalability implications of our framework, and present a series of use case applications to demonstrate the various features of . Finally, we discuss current limitations of our framework, the lessons learned from its development, and outline further extensions