Abstract:

Word lists are essential in this age of data science to analyze texts for language teaching as well as for examining classroom discourse. Because the accuracy of word lists can affect such analyses, we suggest the need to reconsider the classification of academic words in these lists. We have been examining lectures in chemistry, physics, and biology to elucidate how knowledge is constructed in the classroom in the United States and Japan. We have used the Legitimation Code Theory (LCT), which offers a system for revealing the aspects of knowledge building by analyzing the semantic gravity, or the degree of abstraction of concepts, together with the semantic density, or the degree of vocabulary complexity. This can reveal how classroom discourse is employed to describe principles to capture a phenomenon, use diagrams, models and simulations to express the phenomenon, and finally describe how it can be embodied in the real world. Our previous study revealed differences in the usage of technical terminology between the American and Japanese lectures examined. To automatize the analysis, we developed a Semantic Density Evaluation (SDE) program using Python to visualize the variation in vocabulary usage in order to enable examination of the movement in semantic density; i.e., the more technical and specific the vocabulary is, the higher the semantic density would be. We tested the program using the New Academic Word List (NAWL), which has been based on much work with English vocabulary. We hoped to capture the differences in the use of general, academic, and off-list (technical) vocabulary. However, we found that the words listed as ‘academic’ included many that could be classified as technical terms with specific definitions for the disciplinary fields of chemistry, biology, and physics. In order to apply LCT, we needed to create new lists that would discriminate between academic and technical vocabulary. Here, we defined ‘academic’ as words or collocations that could be used in various disciplines to guide the audience rhetorically through scholarly texts, both written and spoken. With respect to the ‘technical’ vocabulary, we considered this to include words or collocations that had specific disciplinary connotations. To identify these words, we used the SDE program, loaded with NAWL, to create lists of academic vocabulary for the 430 American lectures in our OnCAL corpus (http://www.oncal.sci.waseda.ac.jp/). The words classified as academic were then reclassified as academic, technical or general by the three researchers with discussion to reach agreement on contested items. As a result, we developed new academic and technical words lists that enabled finer examination of the semantic density used in the American lectures. Work is now in progress on the Japanese lectures. Thus far, our findings using LCT have supported our earlier findings of cultural differences between American and Japanese lectures in science and engineering. By automatizing the investigation process, we hope to be able to analyze more texts and identify how to contribute to effective science and technology education at the tertiary level.

Keywords:

Academic word lists, knowledge building in science and technology, Legitimation Code Theory.