Wencan Luo

ShadowStory: creative and collaborative digital storytelling inspired by cultural heritage

With the fast economic growth and urbanization of many developing countries come concerns that their children now have fewer opportunities to express creativity and develop collaboration skills, or to experience their local cultural heritage. We propose to address these concerns by creating technologies inspired by traditional arts, and allowing children to create and collaborate through playing with them. ShadowStory is our first attempt in this direction, a digital storytelling system inspired by traditional Chinese shadow puppetry. We present the design and implementation of ShadowStory and a 7-day field trial in a primary school. Findings illustrated that ShadowStory promoted creativity, collaboration, and intimacy with traditional culture among children, as well as interleaved childrens digital and physical playing experience.

Let's play chinese characters: mobile learning approaches via culturally inspired group games

In many developing countries such as India and China, low educational levels often hinder economic empowerment. In this paper, we argue that mobile learning games can play an important role in the Chinese literacy acquisition process. We report on the unique challenges in the learning Chinese language, especially its logographic writing system. Based on an analysis of 25 traditional Chinese games currently played by children in China, we present the design and implementation of two culturally inspired mobile group learning games, Multimedia Word and Drumming Strokes. These two mobile games are designed to match Chinese childrens understanding of everyday games. An informal evaluation reveals that these two games have the potential to enhance the intuitiveness and engagement of traditional games, and children may improve their knowledge of Chinese characters through group learning activities such as controversy, judgments and self-correction during the game play.

Automatic Summarization of Student Course Feedback.

Student course feedback is generated daily in both classrooms and online course discussion forums. Traditionally, instructors manually analyze these responses in a costly manner. In this work, we propose a new approach to summarizing student course feedback based on the integer linear programming (ILP) framework. Our approach allows different student responses to share co-occurrence statistics and alleviates sparsity issues. Experimental results on a student feedback corpus show that our approach outperforms a range of baselines in terms of both ROUGE scores and human evaluation.

Enhancing Instructor-Student and Student-Student Interactions with Mobile Interfaces and Summarization

Educational research has demonstrated that asking students to respond to reflection prompts can increase interaction between instructors and students, which in turn can improve both teaching and learning especially in large classrooms. However, administering an instructor’s prompts, collecting the students’ responses, and summarizing these responses for both instructors and students is challenging and expensive. To address these challenges, we have developed an application called CourseMIRROR (Mobile Insitu Reflections and Review with Optimized Rubrics). CourseMIRROR uses a mobile interface to administer prompts and collect reflective responses for a set of instructorassigned course lectures. After collection, CourseMIRROR automatically summarizes the reflections with an extractive phrase summarization method, using a clustering algorithm to rank extracted phrases by student coverage. Finally, CourseMIRROR presents the phrase summary to both instructors and students to help them understand the difficulties and misunderstandings encountered.

Summarizing Student Responses to Reflection Prompts

We propose to automatically summarize student responses to reflection prompts and introduce a novel summarization algorithm that differs from traditional methods in several ways. First, since the linguistic units of student inputs range from single words to multiple sentences, our summaries are created from extracted phrases rather than from sentences. Second, the phrase summarization algorithm ranks the phrases by the number of students who semantically mention a phrase in a summary. Experimental results show that the proposed phrase summarization approach achieves significantly better summarization performance on an engineering course corpus in terms of ROUGE scores when compared to other summarization methods, including MEAD, LexRank and MMR.

Mastery Learning of Second Language through Asynchronous Modeling of Native Speakers in a Collaborative Mobile Game

Acquiring Chinese tones is often considered as the most difficult task in learning Chinese as a Second Language (CSL). Recently, ToneWars, a collaborative mobile learning game, demonstrated the feasibility and efficacy of connecting CSL learners with native speakers for tone learning. However, the synchronous gameplay nature in ToneWars can be hard to scale due to the time constraint and limited availability of native speakers. We present principled research to make ToneWars scalable and sustainable. First, we address the scalability issue via asynchronous modeling of native speakers. Second, we quantify whether a CSL learner achieves native level mastery for a specific phrase, and explore the use of fine-grained feedback on language mastery as a sustainable motivator for language learning. The insights in this research are generalizable to designing second language learning technologies beyond Chinese. In a longitudinal study with 18 CSL learners, we found that asynchronous gameplay significantly improved learning with an average gain of 29.7 tones and 16.4 syllables, and helped participants achieve native level mastery on 58.2 out of 69 phrases.

An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition

Word segmentation is usually recognized as the first step for many Chinese natural language processing tasks, yet its impact on these subsequent tasks is relatively under-studied. For example, how to solve the mismatch problem when applying an existing word segmenter to new data? Does a better word segmenter yield a better subsequent NLP task performance? In this work, we conduct an initial attempt to answer these questions on two related subsequent tasks: semantic slot filling in spoken language understanding and named entity recognition. We propose three techniques to solve the mismatch problem: using word segmentation outputs as additional features, adaptation with partial-learning and taking advantage of n-best word segmentation list. Experimental results demonstrate the effectiveness of these techniques for both tasks and we achieve an error reduction of about 11% for spoken language understanding and 24% for named entity recognition over the baseline systems.