Identifying Job Categories and Required Competencies for Instructional Technologist: A Text Mining and Content Analysis
| dc.contributor.author | Chen, Le | en |
| dc.contributor.committeechair | Potter, Kenneth R. | en |
| dc.contributor.committeechair | Lockee, Barbara B. | en |
| dc.contributor.committeemember | Cennamo, Katherine S. | en |
| dc.contributor.committeemember | Bond, Mark Aaron | en |
| dc.contributor.department | Education, Vocational-Technical | en |
| dc.date.accessioned | 2020-07-07T08:00:37Z | en |
| dc.date.available | 2020-07-07T08:00:37Z | en |
| dc.date.issued | 2020-07-06 | en |
| dc.description.abstract | This study applied both human-based and computer-based techniques to conduct a job analysis in the field of instructional technology. The primary research focus of the job analysis was to examine the efficacy of text mining by comparing text mining results with content analysis results. This agenda was fulfilled by using job announcement data as an example to determine essential job categories and required competencies. In phase one, a job title analysis was conducted. Different categorizing strategies were explored, and primary job categories were reported. In phase two, the human-based content analysis was conducted, which identified 20 competencies in the knowledge domain, 22 in the ability domain, 23 in the skill domain, and 13 other competencies. In phase three, text mining (topic modeling) was applied to the entire data set, resulting in 50 themes. From these 50 themes, the researcher selected 20 themes that were most relevant to instructional technology competencies. The findings of the two research techniques differ in terms of granularity, comprehensibility, and objectivity. Based on evidence revealed in the current study, the author recommends that future studies explore ways to combine the two techniques to complement one another. | en |
| dc.description.abstractgeneral | According to Kimmons and Veletsianos (2018), text mining has not been widely applied in the field of instructional technology. This study provides an example of using text mining techniques to discover a set of required job competencies. It can be helpful to researchers unfamiliar with text mining methodology, allowing them to understand its potentials and limitations better. The primary research focus was to examine the efficacy of text mining by comparing text mining results with content analysis results. Both content analysis and text mining procedures were applied to the same data set to extract job competencies. Similarities and differences between the results were compared, and the pros and cons of each methodology were discussed. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:26033 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/99279 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | text mining | en |
| dc.subject | content analysis | en |
| dc.subject | job analysis | en |
| dc.subject | competency | en |
| dc.subject | T-LAB | en |
| dc.subject | topic modeling | en |
| dc.title | Identifying Job Categories and Required Competencies for Instructional Technologist: A Text Mining and Content Analysis | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Curriculum and Instruction | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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