Abstract: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.

No abstract

Sourness is one of the basic yet essential tastes of coffee that is chemically composed of acids and quantitatively represented in the pH scale. Current tools for measuring the acidity level in roasted coffee beans, including traditional methods, require brewing sample coffee and probing the chemical components, limiting the applicability to end customers seeking to estimate the acidity level before choosing the right coffee beans to purchase. This paper proposes a novel approach to directly estimate the acidity levels from roasted coffee beans images by framing the problem into an image classification task, where a picture of roasted coffee beans is categorized into its appropriate pH range. As a result, end customers could simply estimate coffee beans' acidity levels by taking photos with conventional cameras. Multiple traditional machine learning and deep learning algorithms are validated for their ability to predict the correct acidity levels. The experiment results reveal that EfficientNet yields the best performance with an average F1 of 0.71 when trained with images from separate portable devices. Practical Applications The research's findings could also be extended to applications in the coffee‐industrial settings, such as automatically monitoring roasted coffee beans' quality from image and video streams. For end customers, the trade‐off between efficacy and efficiency of the EfficientNet algorithm is also investigated, which sheds light on the implementation aspects of state‐of‐the‐art deep learning models in portable devices such as smartphones or cameras. Such applications could prove to be a cost‐effective and convenient solution for customers to quickly measure roasted coffee beans' sourness before deciding to purchase.

Abstract: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.

No abstract