This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 2deep learning
- 1LSTM
- 1Long Short Term Memory Model
- 1automated essay scoring
- 1cluster analysis
- 1coh-metrix
-
A Neural Network approach to Automated Essay Scoring: A Comparison with the Method of Integrating Deep Language Features using Coh-Metrix
DownloadFall 2018
Automated essay scoring (AES) has emerged as a secondary or as a sole marker for many high-stakes educational assessments due to remarkable advances in feature engineering using natural language processing, machine learning, and deep learning algorithms. The purpose of the study was to compare...
-
LSTM Cluster: An Integrated Approach to Cluster Students' Problem Solving Sequences in Log Files
DownloadFall 2018
Modern technology-based assessments have the capacity to record every student-computer interaction in log files. Cluster analysis of log files could yield insights about students’ problem solving strategies and their misconceptions. However, current cluster analysis algorithms often rely on...