What is Card-it?

Card-it is a web application for learning Italian verb morphology, in other words, Italian verb conjugations. Unlike other flashcard applications (i.e., Anki), Card-it’s offers (1) the semi-automatic creation of cards using a Finite-State Morphological (FSM) analyzer, reducing repetitive labour and human error inputting the morphological data, and (2) the possibility of classroom integration with student analytics supporting students, teachers and autonomous learners of Italian as a second language.

How was Card-it created?

Card-it was born from a collaboration between two Ph.D. students: Mariana Shimabukuro (Ontario Tech University) and Jessica Zipf (University of Konstanz). As a computational linguist, Jessica focuses on rule-based morphological tools to support second language acquisition and computer-assisted language learning (CALL). As a computer scientist, Mariana is trained in human-computer interaction (HCI); her work focuses on data-driven and learner-centred design for language learning applications to empower second language learners towards autonomy. Combining the interests and expertise of these two, Card-it features a learner-centred design providing an NLP-based approach to creating the study content and flexibility for curating and studying flashcards with informative learner feedback. Shawn Yama is also a valuable member of this project; Shawn was a research assistant who was responsible for most of the implementation of Card-it during his undergraduate studies.

Is Card-it available in other languages?

Unfortunately, Card-it only supports learners of Italian at the moment. However, Card-it features a modularized architecture which makes it easily expandable to other languages as long as we can provide it with the FSM or an extensive schema of verb forms in a different target language. Other modules in Card-it, such as its user interface, classroom, and interaction features, are applicable to any other language.

If you have the resources or interest in adapting Card-it to a different language, please contact us, and we will be happy to work with you to make it happen.

Video Presentation from EUROCALL 2021

Other language learning projects:

• Hierarchical Matrix for Visual Analysis of Cross-Linguistic Features

See more about Card-it in its demo and related publications:

Try Card-it Yourself: DEMO

Although Card-IT is still in the latter stages of development, you can try out the demo at cardit.vialab.ca by logging in using demo@email.com with the password livecardit.

Card-it at the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

M. Shimabukuro, J. Zipf, S. Yama, and C. Collins. 2023. Evaluating Classroom Potential for Card-it: Digital Flashcards for Studying and Learning Italian Morphology. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 130–136, Toronto, Canada. Association for Computational Linguistics.

• Link to the poster:  Card-it Poster – BEA 2023
• Link to the demo video: Card-it DEMO

Card-it Versus: Bachelors Theses 2022

S. Yama, “Card-IT Versus: A Competitive Multiplayer Game for Testing Italian Verb Morphology,” Bachelors Thesis, 2022.

Card-it Versus is a gamified multiplayer version of Card-it. Using Card-it as the underlying system, Shawn added a module, “Card-it Versus”. In this extension, multiple players can compete for points while answering flashcard quizzes on Italian conjugations synchronously. While they compete to finish their quizzes the fastest, players are rewarded with items designed to boost their own performance or to sabotage their opponents. For example, an item can be used to erase or scramble your opponent’s letter, which may lead to them losing accuracy and points! This extension is not available in the live version of Card-it, but it exemplifies how Card-it can be expanded into adjacent projects.

Online presented talk at EuroCALL 2021

J. Zipf, M. Shimabukuro, and C. Collins, Card-IT: a Dynamic FSM-based Flashcard Generator for Learning
Italian Verb Morphology, Abstract presented at EuroCALL (online), 2021.

Extended Abstract

We report on a novel approach to training and testing Italian verb morphology by developing a flashcard application. Instead of manually curated content, this application integrates a large-scale finite-state morphological (FSM) analyzer which both analyzes a user’s input and dynamically generates specific verb forms (flashcards). FSMs are widely used in natural language processing as part of a system’s text preprocessing pipeline. Our main contribution is to leverage the FSM as the core component to implement a dynamic verb generator based on defined morphological features or return a form’s morphological analysis. Therefore, we developed Card-IT, a web-based application powered by the FSM that uses flashcards as a way for learners to utilize the analyzer in a user-friendly manner. The two-sided cards represent both functions of the FSM: analysis and generation.

Card-IT can be used to quickly analyze a form or to look up entire verb paradigms where the users (teachers or learners) can freely define morphological features, such as tense, mood, etc. Optionally, they can choose to leave any feature unspecified. Depending on the user’s selection, the application returns the corresponding flashcards, which can be saved and organized into a new or existing deck for testing and training. The organization and sorting of decks and cards allow learners to study verbs based on their individual study interests/needs e.g., one might choose to focus on subjunctive forms or past tense only. Additionally, teachers can create decks to provide their students with specific learning content and exercises.

As studies have shown, knowledge of the underlying linguistic concepts benefits the acquisition of a new language (e.g., Heift, 2004). Therefore Card-IT embeds explanations of linguistic terms (e.g., mood, conditional) using visual components, to allow learners to identify linguistic patterns and raise their metalinguistic awareness over time. Moreover, in Card-IT all linguistic terms are provided in the target language.

We plan on evaluating Card-IT with experts, Italian teachers, and implementing their feedback before evaluating it with students. At its current version, Card-IT offers three functions: (1) form analysis and look-up as mentioned above; (2) training, and (3) testing. In training using the self or teacher-curated decks generated with the help of the FSM, learners can study and learn verbs along with their inflectional forms. The testing mode consists of two different exercises: a conjugation quiz that prompts the user to type a form based on provided linguistic specification; and a tense quiz that offers a form asking the user to pick the corresponding tense out of three. Optimally, the learner may also select a mixed-mode that combines both testing exercises.

Feedback plays a crucial role in learning in that it must be both informative and motivating, yet not discouraging (Livingstone, 2012). Whenever the learner enters an incorrect verb form, the FSM the system checks whether it corresponds to any other tense/mood configurations. If so, the system reports it to the user to provide targeted feedback on errors with indications of how to improve rather than just an (in)correct message.

Twitter feeds are a potential source of useful information regarding the state of mind of persons who are the subject of legal or medical assessment. These may include persons suspected of committing crimes or patients that arrive at a hospital for a mental health emergency, for example, attempted suicide. Messages called “tweets” can expose the state of mind of a Twitter user.  Analysts are challenged with creating reports of the online presence of users quickly and efficiently. We present a web-based visualization tool called SentimentState that performs sentiment analysis on tweets from a user’s Twitter account.

SentimentState analyses tweets based on ten emotions (positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise and trust) and creates an interactive timeline graph of the emotional state of the user. It uses a collection of emotion 24,200 word-sense pairs collected from the National Research Council of Canada (NRC). We anticipate that this interactive visualization can have applications throughout, and even beyond, legal and medical assessments, and will provide analysts with timely and relevant information regarding the mood state of clients, patients and other persons under assessment.

Check out our Online Demo and our GitHub Repository for source code related to this project.

Acknowledgements

Thanks to Saif Mohammed for providing the NRC Emotion Lexicon for this project.

Drawing boards offer a self-stable work surface that is continuously adjustable. On digital displays, such as the Microsoft Surface Studio, these properties open up a class of techniques that sense and respond to tilt adjustments. Each display posture—whether angled high, low, or somewhere in-between—affords some activities, but not others. Because what is appropriate also depends on the application and task, we explore a range of app-specific transitions between reading vs. writing (annotation), public vs. personal, shared person-space vs. task-space, and other nuances of input and feedback, contingent on display angle. Continuous responses provide interactive transitions tailored to each use-case. We show how a variety of knowledge work scenarios can use sensed display adjustments to drive context-appropriate transitions, as well as technical software details of how to best realize these concepts. A preliminary remote user study suggests that techniques must balance the effort required to adjust the tilt, versus the potential benefits of a sensed transition.

Based on word-colour associations from a comprehensive, crowdsourced lexicon, we present Lexichrome: a web application that explores the popular perception of relationships between English words and eleven basic colour terms using interactive visualization. Lexichrome provides three complementary visualizations: “Palette” presents the diversity of word-colour associations across the colour palette; “Words” reveals the colour associations of individual words using a dictionary-like interface; “Roget’s Thesaurus” uncovers colour association patterns in different semantic categories found in the thesaurus. Finally, our text editor allows users to compose their own texts and examine the resultant chromatic fingerprints throughout the process. We studied the utility of Lexichrome in a two-part qualitative user study with nine participants from various writing-intensive professions. We find that the presence of word-colour associations promotes awareness surrounding word choice, editorial decision, and audience reception, and introduces a variety of use cases, features, and future opportunities applicable to creative writing, corporate communication, and journalism.

Lexichrome is available for public access at http://lexichrome.com.

Acknowledgements

Thanks to Jason Boyd and Laurie Petrou. This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Bubble Sets is a visualization technique for dynamically creating ‘bubble’ borders around sets of visual objects, while avoiding inclusion of items not in the set.…

Title: Data-driven Storytelling: Transforming Data into Visually Shared Stories Abstract: In this talk, I will present my most recent research efforts in the field of information…

Vialab members had several contributions to the IEEE VIS conference in Phoenix this month. Our contributions also represented the extent of the lab’s collaborations, from…

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  Zachary Hills

  Software Developer

Lattice graphs are used as underlying data structures in many statistical processing systems, including natural language processing. Lattices compactly represent multiple possible outputs and are usually hidden from users. We present a novel visualization intended to reveal the uncertainty and variability inherent in statistically-derived outputs of language technologies. Applications such as machine translation and automated speech recognition typically present users with a best guess about the appropriate output, with apparent complete confidence.

Through case studies in cross-lingual instant messaging chat and speech recognition, we show how our visualization uses a hybrid layout along with varying transparency, colour, and size to reveal the various hypotheses considered by the algorithms and help people make better-informed decisions about statistically derived outputs.

Acknowledgements

Do court cases differ from place to place? What kind of picture do we get by looking at a country’s collection of law cases? We introduce Parallel Tag Clouds: a new way to visualize differences amongst facets of very large metadata-rich text corpora. We have pointed Parallel Tag Clouds at a collection of over 600,000 US Circuit Court decisions spanning a period of 50 years and have discovered regional as well as linguistic differences between courts. The visualization technique combines graphical elements from parallel coordinates and traditional tag clouds to provide rich overviews of a document collection while acting as an entry point for the exploration of individual texts. We augment basic parallel tag clouds with a details-in-context display and an option to visualize changes over a second facet of the data, such as time. We also address text mining challenges such as selecting the best words to visualize, and how to do so in reasonable time periods to maintain interactivity.

This research was given the VAST Test of Time Award at the IEEE Conference in 2019.