Is Invisible XML Ready for College Students?

Is Invisible XML ready for teaching university undergraduates? Is it a good idea to try this? This paper will attempt to address these questions. University students in the Digital Media, Arts, and Technology program at Penn State Behrend are offered a course in “Large-Scale Text Analysis”. Going into this course, students have experience in encoding text with XML, transforming XML with XSLT, and web development with HTML and CSS. In the past, the Text Analysis course has been a procedural “Regex-and-Python course”: preparing text corpora by generating simple XML from regularly-patterned files using regular expression search-and-replace operations, using XQuery to extract the portions of the texts to analyze, and producing plain-text inputs to provide to Python. Python has dominated the experience of the pipeline. This year’s course tried a different approach. Students were taught ixml grammars as a way to prepare XML for analysis and XProc for pipelining. Regular expression matching involved working with XSLT, and the entire XML stack was used before approaching Python. Students learned how to install software in alpha stages, and they tested how well it works across platforms. From this exploratory start, one student project team found a very practical use-case for applying Invisible XML in a project pipeline for analyzing chord chart musical notation. In this paper, we discuss the potential we discovered for Invisible XML in music analysis. We also share our recommendations for guiding people to prepare processing pipelines that incorporate Invisible XML, and we reflect on what aspects of this risky teaching experiment were most worthwhile.