Knowledge Creation Analytics for Online Engineering Learning
| dc.contributor.author | Teo, Hon Jie | en |
| dc.contributor.committeechair | Johri, Aditya | en |
| dc.contributor.committeechair | Lohani, Vinod K. | en |
| dc.contributor.committeemember | Evans, Michael A. | en |
| dc.contributor.committeemember | Williams, Christopher B. | en |
| dc.contributor.department | Engineering Education | en |
| dc.date.accessioned | 2016-01-17T07:00:11Z | en |
| dc.date.available | 2016-01-17T07:00:11Z | en |
| dc.date.issued | 2014-07-25 | en |
| dc.description.abstract | The ubiquitous use of computers and greater accessibility of the Internet have triggered widespread use of educational innovations such as online discussion forums, Wikis, Open Educational Resources, MOOCs, to name a few. These advances have led to the creation of a wide range of instructional videos, written documents and discussion archives by engineering learners seeking to expand their learning and advance their knowledge beyond the engineering classroom. However, it remains a challenging task to assess the quality of knowledge advancement on these learning platforms particularly due to the informal nature of engagement as a whole and the massive amount of learner-generated data. This research addresses this broad challenge through a research approach based on the examination of the state of knowledge advancement, analysis of relationships between variables indicative of knowledge creation and participation in knowledge creation, and identification of groups of learners. The study site is an online engineering community, All About Circuits, that serves 31,219 electrical and electronics engineering learners who contributed 503,908 messages in 65,209 topics. The knowledge creation metaphor provides the guiding theoretical framework for this research. This metaphor is based on a set of related theories that conceptualizes learning as a collaborative process of developing shared knowledge artifacts for the collective benefit of a community of learners. In a knowledge-creating community, the quality of learning and participation can be evaluated by examining the degree of collaboration and the advancement of knowledge artifacts over an extended period of time. Software routines were written in Python programming language to collect and process more than half a million messages, and to extract user-produced data from 87,263 web pages to examine the use of engineering terms, social networks and engineering artifacts. Descriptive analysis found that state of knowledge advancement varies across discussion topics and the level of engagement in knowledge creating activities varies across individuals. Non-parametric correlation analysis uncovered strong associations between topic length and knowledge creating activities, and between the total interactions experienced by individuals and individual engagement in knowledge creating activities. On the other hand, the variable of individual total membership period has week associations with individual engagement in knowledge creating activities. K-means clustering analysis identified the presence of eight clusters of individuals with varying lengths of participation and membership, and Kruskal-Wallis tests confirmed that significant differences between the clusters. Based on a comparative analysis of Kruskal-Wallis Score Means and the examination of descriptive statistics for each cluster, three groups of learners were identified: Disengaged (88% of all individuals), Transient (10%) and Engaged (2%). A comparison of Spearman Correlations between pairs of variables suggests that variable of individual active membership period exhibits stronger association with knowledge creation activities for the group of Disengaged, whereas the variable of individual total interactions exhibits stronger association with knowledge creation activities for the group of Engaged. Limitations of the study are discussed and recommendations for future work are made. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:3334 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/64465 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | knowledge creation | en |
| dc.subject | engineering education | en |
| dc.subject | online engineering community | en |
| dc.subject | analytics | en |
| dc.title | Knowledge Creation Analytics for Online Engineering Learning | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Engineering Education | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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