TechExplorations
Friday, Sept. 22, 2017 | 3pm-5pm | Gallery Room
New to TSLL this year, TechExplorations allows participants to interact with cutting edge technology for applied linguistics & language learning through hands on demos and explorations.
Tables 1-3| CyWrite: Biometric Tracking of Cognitive Processes during Writing
Evgeny Chukharev-Hudilainen, Brody Dingel, and Rosie Zbaracki, Iowa State University
Producing text requires a complex orchestration of various cognitive activities, but little is known about what is actually happening in the writer’s mind as the process of composition unfolds. We developed a deployable biometric technology that offers direct and instant insights into cognitive processing that underlies writing. Visit our session to get first-hand experience in using this technology and see if you can find out something new about your own writing process. This project was supported by the National Science Foundation under Grant No. 1550122.
3 Takeaways from this session: 1) Writing is a complex cognitive activity, and writers themselves might not know exactly how they produce text; 2) Our deployable biometric technology can unobtrusively capture the writing process in real-life settings; and 3) Instant visualizations and automatically calculated statistics offer direct insights into what underlies writing.
Tables 4-5 | Golden Speaker
John Levis, Evgeny Chukharev-Hudilainen, Sinem Sonsaat, Iows State University
The best model voice for a learner to practice pronunciation of a language is their own voice (Probst, Ke, & Eskenazi, 2002). Our Golden Speaker – a combination of the learner’s voice with a native speaker’s segmental and prosodic features – may provide effective practice and motivate learners. In this session, we show how the Golden Speaker Builder, produced by the collaboration of Texas A&M University and Iowa State University is used to (1) collect input from a non-native English speaker, (2) build a learner’s individual golden speaker, and (3) practice with it.
3 Takeaways from this session: 1) You will see how a hybrid speaker model is produced; 2) You will hear your Golden Speaker, a combination of your voice with that of a native speaker; and 3) You will experience practicing with your own Golden Speaker.
Table 6 | Linguatorium: Research-Grounded Tools for Language Learning
Evgeny Chukharev-Hudilainen, Linguatorium; Monica Richards, Iowa State University
ESL teachers: are your students satisfied with their vocabulary acquisition via your classes? Do you feel equipped to help students who want to develop more Standard English pronunciation? Visit our TechExploration session to see how science can help. We will demonstrate two tools grounded in SLA and CALL research: Lexis – a supplementary vocabulary learning system that can complement any curriculum or textbook, and Auris – an effective tool that will improve your students’ listening comprehension and pronunciation skills.
3 Takeaways from this session: 1) Just 10 minutes of practice a day can increase vocabulary learning gains by 195%; 2) High-variability pronunciation training improves both comprehension and pronunciation skills; and 3) There are research-grounded, highly effective tools that accelerate language learning AND save teachers’ time.”
Table 7 | Extracting Textual Features for Corpus Analysis and Text Mining
Sowmya Vajjala and Sagnik Banerjee, Iowa State University
We are developing a small tool (command line interface, not graphical) to extract different kinds of textual features such as word n-grams, POS n-grams, frequencies of various dependency relations and phrasal relations etc. The goal of the tool is two-fold: 1) support more linguistic corpus analysis; and 2) support text mining researchers by providing a suite of text features they can use to benchmark text classification problems. The tool is still under development, and is currently seen as a single large python file with some documentation. Our goal is to release the code for public use by December.
3 Takeaways from this session: 1) learning about how to extract different kinds of textual features beyond words and word sequences; 2) walking through the process of how to extract such features; and 3) sharing the python code so that enthusiastic people can try to use it and give feedback for future improvement of the tool.
Tables 8-9 | Analyzing Talk with LENA
Constance Beecher, Craig Van Pay, Iowa State University
The LENA Digital Language Processor is optimized to capture utterances between a parent or caregiver (or even other children) and the child in the natural language environment. The software uses algorithmic-based speech recognition technology that incorporates advanced speech modeling techniques to analyze conversations and noise. The computer in the recorder analyzes distance to only record language directed to the target child. It does not record extraneous conversation around the child. Once this data is downloaded to a computer, the LENA software analyzes the data, providing percentile rank comparisons and quantified speech language environment data.
3 Takeaways from this session: 1) Analyzing talk is very complex; 2) There is more than one way to approach analyzing talk; and 3) Quantitative language feedback is useful to a variety of stakeholders.
Tables 10-12 | Teaching, Learning, and Revising with the Research Writing Tutor (RWT)
Elena Cotos, Sarah Huffman, Monica Richards, Iowa State University
RWT was developed for novice scholars who need to learn, practice, and internalize a style of scientific writing that conforms to disciplinary conventions. The current version of RWT contains three interactive modules for learning, demonstration, and automated feedback. Three concurrent sessions will provide a detailed explanation and rationale for RWT’s affordances while demonstrating the features of each module. The presenters will also explain how this tool is used to complement instruction in graduate writing courses as well as to support self-paced revision in other writing development environments.
3 Takeaways from this session: The participants will gain an understanding of: 1) how theory and research were applied to the development of multimodal materials that describe the rhetorical conventions of the research article genre, 2) how teachers can use corpus-based affordances for data-driven learning, and 3) how rhetorical feedback and scaffolding may enable students to progress toward autonomous use of research genre conventions.