Selected Presentations

Abstract: As people solve problems together that they cannot do alone, automatic analysis of conversations can inform and enhance their design to aid learning and teaching. Such analyses must traverse the obstacle course of voice transcription, complex categorization, and statistical analysis. Automated transcription feeds automatic categorization via computational linguistics to create a database (Big Data). Automated statistical analysis integrates statistical discourse analysis (SDA) and artificial intelligence. SDA models (a) pivotal actions that radically change subsequent processes and (b) explanatory variables at multiple levels (sequences of turns/messages, time periods, individuals, groups, organizations, etc.) on multiple target actions. The artificial intelligence expert system translates my theory into a statistical model, tests it on the data, interprets the results, (if needed, rewrites itself to execute revised analyses), and prints a table of results. I showcase automated SDA on 321,867 words in 1,330 messages by 17 student-teachers in 13 weekly online discussions of lesson designs.

Abstract: A high degree of individual variability has been observed in the learning success of a foreign language. In our research, we evaluate genetic and behavioral factors that are hypothesized to influence the success of foreign language learning by capitalizing on a large cohort of French, German, and Spanish learners in Hong Kong. All participants were native Cantonese speakers of Han Chinese descent who spoke English as the second language (L2) and learned one of the target languages as the third language (L3). With comprehensive data on their demographic information (e.g., age), parental socioeconomic status, music background, motivation, L1, L2, and L3 proficiency, and genetic variation, we tested two sets of hypotheses. The first hypothesis argues that the core language function is universal across languages and is independent of when and under what conditions learning occurs. The second hypothesis postulates that different languages and languages learned at different times and under different conditions have different genetic and behavioral underpinnings. For our genetic studies, a candidate gene approach was used. Our findings support the second hypothesis that additional language learning relies on the shared learning conditions between prior language learning experiences, and there was a decreasing influence of genetic contributions from L1 to L3.