Use this URL to cite or link to this record in EThOS:
Title: Enhancing online course materials for self-revision in higher education
Author: Sajjacholapunt, Petch
ISNI:       0000 0004 6351 1414
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
Access from Institution:
Revision is an important process for learning in higher education. At present, many universities provide online course materials including guidelines to support ubiquitous revision. Most of the traditional course websites, however, simply provide online materials for students to download. The main aim of this thesis is, therefore, to enhance these materials on a course website to facilitate student self-revision. We firstly present a brief review of some aspects of exam revision. Subsequently, we conduct a questionnaire survey to identify patterns of students' revision, difficulties during revision, and potential approaches to address those difficulties. From the survey, many students have concerns about the amount of learning materials to be reviewed in a short period of time. We thus designed a novel software framework (“SRECMATs") that aims to reduce students' workload by enabling them to have direct access to learning materials, gaining quick overviews and related material recommendations. In the second part of the thesis, we develop, launch, and evaluate the first prototype of the SRECMATs software framework. The prototype system was introduced to students on a level 1 Data Structures and Algorithms module in the summer term of 2014/2015. Many of them were willing to use the system and engaged with it constantly during their revision. The usability evaluation of each feature is positive, and students reported that all provided features are simple to use and some are effective for them. The first prototype used TF-IDF as a term weighting scheme to calculate cosine similarity between learning materials. To improve retrieval accuracy, we have proposed a new technique to adjust the weight of the TF-IDF scheme with term important (TI) and term location (TL) components. The results illustrated that using the TI component with the TF-IDF scheme yields the best result for all datasets while the TL technique can improve accuracy on some datasets. Finally, our results contribute to an understanding of students' revision difficulties and how to improve the existing online course materials to maximise the benefits for students.
Supervisor: Not available Sponsor: Mahāwitthayālai Mahidon
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: LB Theory and practice of education