Date of Award
© 2017 Joel Lowsky
Doctor of Education (EdD)
This single-site, instrumental case study created and tested a methodological road map by which academic institutions can use text data mining techniques to derive technology skillset weaknesses and professional development topics from the site’s technical support helpdesk database. The methods employed were described in detail and applied to the helpdesk database of an independent, co-educational boarding high school in the northeastern United States. Standard text data mining procedures, including the formation of a wordlist (frequently occurring terms), and the creation and application of clustering (automated data grouping) and classification (automated data labeling) models generated meaningful and revealing themes from the helpdesk database. The results of the text mining procedures were bolstered and analyzed using human interpretation and spreadsheet-based summaries. Major findings included the discovery of four prominent technologies that warranted professional development at the site and a universally-applicable approach to undertaking successful helpdesk data mining endeavors. The case study’s conclusions included a call to action for researchers to leverage the methodology at other locations. Future data mining studies may yield practical and applicable knowledge at research sites. Shared methods, approaches, and findings from such studies will advance the field of helpdesk data mining used to glean professional development topics for the very people who have submitted technological support requests to helpdesk providers.
Lowsky, Joel T., "Mining Helpdesk Databases For Professional Development Topic Discovery" (2017). All Theses And Dissertations. 113.