Analysing features of lecture slides and past exam paper materials towards automatic associating E-materials for self-revision

Digital materials not only provide opportunities as enablers of e-learning development, but also create a new challenge. The current e-materials provided on a course website are individually designed for learning in classrooms rather than for revision. In order to enable the capability of e-materials to support a students revision, we need an efficient system to associate related pieces of different e-materials. In this case, the features of each item of e-material, including the structure and the technical terms they contain, need to be studied and applied in order to calculate the similarity between relevant e-materials. Even though difficulties regarding technical term extraction and the similarities between two text documents have been widely discussed, empirical experiments for particular types of e-learning materials (for instance, lecture slides and past exam papers) are still rare. In this paper, we propose a framework and relatedness model for associating lecture slides and past exam paper materials to support revision based on Natural Language Processing (NLP) techniques. We compare and evaluate the efficiency of different combinations of three weighted schemes, term frequency (TF), inverse document frequency (IDF), and term location (TL), for calculating the relatedness score. The experiments were conducted on 30 lectures (~900 slides) and 3 past exam papers (12 pages) of a data structures course at the authors’ institution. The findings indicate the appropriate features for calculating the relatedness score between lecture slides and past exam papers

SRECMATs - an intelligent tutoring system to deliver online materials for student revision

The use of online course material is the approach adopted by most universities to support students’ revision, and teachers usually have the responsibility for designing or uploading online materials on their own course websites. However, some teachers might lack programming skills or motivation, and most current online materials are just uploaded in a static format (such as PDF) which is not suitable for all students. Moreover, during revision periods students may be faced with a lot of unorganised materials to be revised in a short period of time, and this can lead to an ineffective revision process. In order to address these issues, this paper proposes a software framework that aims to maximise the benefit of current online materials when used to support student revision. This framework is called SRECMATs (Self-Revision E-Course MATerials) and has been deployed as a tool that allows teachers to automatically create an intelligent tutoring system to manage online materials without any programming knowledge, and to support students to navigate easily through these online materials during their revision. This paper evaluates the proposed framework in order to understand students’ perceptions with regard to the use of the system prototype, and the results indicate which features are suitable for providing online revision materials as well as confirming the benefit of the revision framework

Exploring patterns of using learning resources as a guideline to improve self-revision

An examination is a tool to measure a student’s performance and his or her level of understanding. Many universities provide general revision guides for students to prepare them for examinations, and this guidance aims to increase the students’ awareness of strategies for revision and for understanding material before an examination. In fact, each individual student is more likely to have their own form of understanding and to operate differently in terms of organising materials and seeking information. In order to succeed in preparing materials before examinations, students need to understand the course materials that are provided by lecturers in the form of lecture slides, lecture notes and references, as well as their own notes. However, students sometimes suffer from a short period of time for revision, excessive amounts of learning resources provided, or poor quality of learning resources. These can lead to ineffective revision processes, some of which are time-consuming or lead to a low level of understanding. In order to provide technologies to address these issues, we need to understand how an individual student uses learning resources and which resources or strategies work well for them. The result of a student’s engagement in learning resources is of interest from both educational and technological perspectives. From an educational perspective, different ways of using of learning resources may affect the performance of a student – for example, students who spend more time on past exam paper may have better results than students who spent more time on textbook. This paper, therefore, explores the use of learning resources of postgraduate students at the authors' university in the UK. A questionnaire survey was used to identify patterns of students’ participation in the use of learning resources, their revision strategies, and difficulties students may have during revision. From a technological perspective, we have gained an insight into what kind of tools students need to support their use of learning resources – for example, organisation tools are available to support students during revision, and students may gain benefits in term of rapid comprehension from having different point of views on learning content. This research also reveals strategies for using learning resources for revision as well as potential issues that need to be addressed. Additionally we propose potential ideas for designing presentation and organisation tools that may support a student’s revision

Enhancing online course materials for self-revision in higher education.

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